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Computing Applications International Journal of High Performance

DOI: 10.1177/1094342007083773 2007; 21; 388 International Journal of High Performance Computing Applications

H.S. Bhatt, H.J. Kotecha, B.K. Singh, K. Bandyopadhyay, V.H. Patel and A. Dasgupta Connecting Grids Using Communication Satellites

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388 COMPUTING APPLICATIONS

CONNECTING GRIDS USING COMMUNICATION SATELLITES

H. S. BhattH. J. KotechaB. K. SinghK. BandyopadhyayV. H. PatelA. DasguptaSPACE APPLICATIONS CENTRE, INDIAN SPACE RESEARCH ORGANISATION, AHMEDABAD, INDIA([email protected])

Abstract

Grid computing is viewed as a strong alternative to tradi-tional supercomputers and mainframes. Grid computing isexpanding its horizon to replace the current Internet as wellas to address issues related to data sharing, collaboration,security, economics, and so forth. We treat a grid as a sin-gle global computer for a given community connecting alltypes of resources. Most of the current grid research isbased on high-speed terrestrial networks. Emerging com-munications satellites are not restricting their applicationsonly to telecommunication, but also aim to provide an alter-native to terrestrial links in computing fields. This paperdescribes the supplementary features of both technologiesand explores the possibility of handshaking between thetwo. It also presents the architecture and discusses possi-ble experiments leading to live applications.

Key words: grid computing, communication satellites,satellite based grid computing, protocols

1 Introduction

The term “grid” was coined in the mid 1990s to denote aproposed distributed computing infrastructure for advancedscience and engineering (Foster and Kesselman 2003).Grid technology has emerged as an important new fieldthrough its focus on large-scale resource sharing, innova-tive applications and in some cases, high-performanceorientation (Foster, Kesselman and Tuecke 2001). It hasbecome efficient, effective and economically feasible(Buyya 2002). Various authors and researchers havedefined grid computing in different but related ways (Fos-ter, Kesselman and Tuecke 2001; Foster 2002; von Lasze-wski, Pieper and Wagstrom 2002; Foster and Kesselman2003). We visualize the grid as a global, ubiquitous andavailable-without-interrupt “large” computer (Baker, Buyyaand Laforenza 2000) having the capability of effectiveresource utilization to meet the users’ requirements. Wedefine each and everything connected with the grid as aresource. It can be a machine, a data set, an expert givinga set of services, a network, a telescope, software, and soforth. Its architecture is service oriented. Researchers,working groups and collaborative agencies are concen-trating on various aspects of quality of services andresource management. Current grid research is based onavailable terrestrial links.

Communication satellites are available and have beenproviding services in multiple domains for decades. Most ofthe satellites such as INSAT (Sengupta, Hanjura and Mathur1991), INTELSAT (Evans 1995; Lilly 1989), TELESAT(Miller 1992), and EUTELSAT (Amadesi 1994) are used intelecommunications focusing on data, audio and videocommunications. These sorts of satellites normally allo-cate their channels for dedicated purposes over long periods.People have started exploring communication satellites’capabilities in other domains. EDUSAT’s (Education Sat-ellite) (SAC & DECU /ISRO 2003) goal is to support dis-tance education. GSAT4 (SAC & DECU /ISRO 2004) andother advanced satellites are being launched for specificapplications. Satellites with multiple goals need effectiveutilization for different purposes. Coordination betweenthe available satellite resources and the demand for themneeds a focus.

Handshaking of grid technology and satellite commu-nication will be very useful for real life applications suchas mobile computing, telemedicine, tele-education, NationalSpatial Data Infrastructure (NSDI), Internet via satellite,audio and video distribution, financial data delivery,remote industrial control, managing the activities of pub-lic administration, remote locations, database distributionand so forth.

We have carried out an exhaustive study to explore thepossibility of handshaking between grid technology andcommunication satellites. Research carried out in gridtechnology for effective resource(s) utilization, security,

The International Journal of High Performance Computing Applications,Volume 21, No. 4, Winter 2007, pp. 388–404DOI: 10.1177/1094342007083773© 2007 SAGE Publications Los Angeles, London, New Delhi and SingaporeFigures 3–5, 7–10 appear in color online: http://hpc.sagepub.com



389CONNECTING GRIDS USING SATELLITES

and bandwidth optimization as well as its service-ori-ented architecture can be extended to communication sat-ellites as this is one of the most important resources.These aspects of the grid are explored in Section 3. Satel-lite technology is not only a strong alternative to the ter-restrial links, but has several advantages over terrestriallinks including broadcast, multicast, ease of relocatingand adding new sites, which may be useful in grid tech-nology at the connectivity layer. These features areexplored in Section 4. In Section 5, a framework is pro-posed for the handshaking of the two technologies andthe components that need further research to realize thisframework are explained.

2 Goals and Objectives

We write this paper to explore three main areas: (1) Howcan grid technology be used effectively to enrich thecapabilities of communication satellites? (2) How arecommunication satellites useful in grid technology? (3)What sort of advances are required in both the technolo-gies for effective handshaking?

3 Grid Technology for Communication Satellites

Satellites are some of the most costly resources, consistof many communication channels and should be usedoptimally. We shall discuss how grid technology can beuseful in managing these communication resources.

We concentrate on core research areas of grid comput-ing that can be useful to communication satellites. First,we propose to explore grid resource management aspectsto use (1) the complete satellite as one of the resources,(2) a payload as a resource, (3) a transponder as a resourceand (4) a specific channel of the satellite as a resource.Second, the grid emphasizes a high level of security, pro-viding authentication and authorization as required bythe owner, administrator and user. Third, grid technologyis carrying out aggressive research in increasing the datacommunication speed that can be extended for satellitecommunication. Fourth, grid computing focuses on beingservice oriented, a characteristic which can be easilyextended to communication satellites.

Grid layered architecture addresses the resource man-agement aspect of the individual channel at the resourcelayer whereas the collective layer addresses the effectiveutilization of transponders, payloads and complete satel-lites. Grid research focuses on effective resource manage-ment, addressing the following areas: resource registration,resource indexing, location of the resource, allocation,reservation, usage, monitoring, controlling and fault tol-erance. When there are a set of resources and a set ofapplications having different requirements, a good match-

making algorithm for effective resource utilization, aswell as satisfaction of the applications needs to a greatextent, is required. This is also being addressed by gridtechnology. Globus toolkit (Czajkowski et al. 2001a)provides various facilities to support the grid architec-ture. The Globus MDS architecture (Foster 2002) imple-ments a service that consists of Grid Resource InformationServers (GRIS) associated with resources and a Grid Infor-mation Index Server (GIIS) that aggregates informationfrom multiple GRISs. These services are linked by GridResource Information Protocol (GRIP) and Grid ResourceRegistration Protocol (GRRP). GRRP is used by resourceproviders to register information about resources and torefresh that information periodically. GRIP enables usersto get resource information. Grid-wide discovery canexploit a loose coherency model, further reducing theoverhead of collecting and searching for informationabout large, distributed resource sets (Fitzgerald et al.1997; Czajkowski et al. 2001b). The HTTP-based GridResource Access and Management (GRAM) protocol(Czajkowski et al. 1998) is used for allocation of compu-tational resources and for monitoring and control of com-putation on those resources (Grimshaw, White and Nguyen-Tuong n.d.). GRAM has extensible Resource Specifica-tion Language (RSL) (Globus n.d.a) to communicaterequests for resources between components: from appli-cations to resource brokers, resource co-allocators andresource managers. At each stage in this process, infor-mation about resource requirements is coded into anRSL expression by the application or refined by one ormore resource brokers and co-allocators. Informationabout resource availability and characteristics can beobtained from an information service. Resource brokersare responsible for taking high-level RSL specificationsand transforming them into more concrete specifications.Condor’s ClassAds (Litzkow, Livny and Mutka 1988)also provides a way of defining compute resources whichcan be extended for other resources. ClassAds has beenextended to support matching with multiple resources(Raman, Livny and Solomon 2003), sets of resources(Liu et al. 2002) and matching via constraint solving (Liuand Foster 2003). Raman et al. also have provided astandard mechanism for expressing resource and taskrequirements (Raman, Livny and Solomon 2003). The Gen-eral purpose Architecture for Reservation and Allocation(GARA) defines APIs that allow users and applicationsto manipulate both reservations, allocations and end-to-end management of the quality of service of differenttypes of resources, including networks, CPUs and disks,in uniform ways (Foster, Roy and Sender 2000; Foster etal. 2004). Grid applications often require concurrent allo-cation of multiple resources, necessitating a structure inwhich resource use can be coordinated across administra-tive domains (Czajkowski, Foster and Kesselman 1999).




Oceano (Appleby et al. 2001) works towards providingsophisticated management of pools or clusters of distrib-uted resources. The general case of on-demand access isoften referred to as creating an “advance reservation”(Degermark et al 1997; Ferrari, Gupta and Ventre 1997).GARA uses a special form of RSL for describing reser-vations. One experiment focuses on reserving and pro-viding access to network bandwidth using GARA (Foster,Roy and Sender 2000). The experiment has a networkbandwidth broker, called slot manager, which can setaside a fraction of channel capacity for a fixed duration.Reserving an end-to-end network path may require span-ning network links. Network bandwidth brokerage is alsoaddressed by Kim and Nahrstedt (2000) and Nahrstedtand Smith (1995). A Bandwidth Reservation System (BRS)is described by Hoo and Johnston (1999) to provide qual-ity of service (QoS) to scientists who are involved inremotely controlling experiments.

Communication satellites are precious resources andhence their security and administration is of utmostimportance. The grid emphasizes single sign-on, privi-lege delegation to programs, integration with local securitymechanisms and user based trust relationships (Foster,Kesselman and Tuecke 2001). The public-key based GridSecurity Infrastructure (GSI) (Foster et al. 1998; Butler etal. 2000) provides facilities for the mutual authenticationof services and their users, protection of data, and thedelegation of user credentials (Hinke n.d.). The GSI isuseful for all resource access and sharing. X.509-formatidentity certificates are used. Stakeholder control ofauthorization is supported via an authorization toolkitthat allows resource owners to integrate local policies viaa Generic Authorization and Access (GAA) control inter-face. Community authorization service enforces communitypolicies governing resource access, generating capabili-ties that community members can use to access commu-nity resources. It provides a global policy enforcementservice by building on resource information and resourcemanagement protocols (in the resource layer) and secu-rity protocols in the connectivity layer. Akenti (Thomp-son et al. 1999) addresses some of these issues. GARArequest-communication is secure, with each broker onthe path being able to enforce local policies and modifythe request with additional constraints. A delegationmodel addresses the lack of transitive trust relationshipbetween source and destination where each broker on thepath is able to identify all upstream partners by accessingthe credentials of the full delegation chain (Foster et al.2001).

Communication satellite channel data rates are nor-mally much lower compared with current high-speed ter-restrial links and hence should be optimally used. Thegrid has given special emphasis to data communicationperformance. The GridFTP transport service is based on

extensions to standard FTP protocol that create a univer-sal grid-wide transport protocol. GridFTP uses GSI toprovide secure data transport between all grid storageservers. It provides secure, high-throughput data transfereven on high-speed wide-area networks and third partytransfers which allow the source, destination, or both tobe striped, with arbitrary and potentially different topolo-gies or even file access mechanisms. Features that signif-icantly increase GridFTP’s speed are the ability to flexiblyset TCP tuning parameters, and to create multiple TCPdata channels between source and destination hosts.GridFTP supports 64-bit file sizes and offsets, and hasthe ability to transfer partial files in arbitrary non-contig-uous segments. Other advanced features include check-point/restart of transfers, data channel reuse for consecutivefile transfers, and user plug-ins that allow advanced appli-cations to perform memory-to-memory transfer, serverside computation without additional data copies, and pro-vide integrated instrumentation for the recording of log-ging and audit trails. The wide-area network performanceachieved by GridFTP is impressive and is a major focusof our ongoing research and development. It can cur-rently sustain 500 Mbps for hours and achieve 5 secondbursts over 1 Gbps, continuously transferring 2-GBytefiles striped between 8 servers at each end point over a2.5 Gaps NTON OC-48 link (Allcock et al. 2001). GridFTPis continuously being revised and refined (Allcock n.d.;Globus n.d.b; Mandrichenko n.d.). GridFTP uses GSI toprovide secure data transport between all grid storageservers. GASS (Bester et al. 1999) enables access to thefiles and remote data that is away from the execution sitevia standard I/O. High performance is achieved viaimplementing default data movement strategies that arespecialized for I/O patterns in wide-area distributed com-puting and by providing programmer support for datamovement control.

Communication satellite technology is quite matureand has well defined quality of services. Open Grid ServiceArchitecture (Foster et al. 2002) has provided the base onwhich remarkable progress has been made towards matu-ration and standardization of grid technology. Open GridService Infrastructure (Tuecke et al. n.d.) defines set ofinterfaces for visualization, standardization and interop-erability of important services that are required by OGSA.Globus Toolkit provides a complete implementation ofOGSI and defines OGSI compliant services for discoveryof services, submission and monitoring of program exe-cution jobs, and reliable file transfer. OGSA widely usesSOAP (SOAP n.d.) and WSDL (Christiansen et al. n.d.).OGSA and grid resource management defines differentservice-level agreements (SLAs) for tasks, resources andbinding. Grid defines a protocol called Service Negotia-tion and Acquisition Protocol (SNAP) for negotiatingthese SLAs (Czajkowski et al. 2002). GRAAP-WG1 of




Global Grid Forum is adapting the SNAP model for useas a standard grid service interface for negotiating resourcesmanagement. This research can be extended to addresssatellite services.

4 Communication Satellites for Grid Technology

Current state of the art grid technology relies on terres-trial networks for data communication. Satellites can beused as a communication resource in many grid applica-tions. The satellite payload incorporates two types oftransponders: bent pipe and regenerative transponders.Bent pipe transponders perform frequency conversionand amplification of the signal before retransmission,whereas regenerative transponders do on-board demodu-lation, data processing, switching, remodulation andretransmission. The former is useful as a high speed gate-way while the latter is useful in generating a dynamiccommunication network grid using a smaller antenna andless power.

The major advantages of satellite links2,3 are: ubiqui-tous availability, superior economics, reliability, broad-casting capability, relocating network sites, addingnetwork sites, and bandwidth-on-demand flexibility. Sat-ellites have ubiquitous availability as they are the onlytelephony and broadband wide-area network technologythat is available everywhere, in even the most remoteurban and rural areas, rain forests and concrete jungles.All that is needed is a clear view of the sky. In contrast,terrestrial broadband technologies, such as DSL, fiberlinks, ISDN and cable, reach only a small percentage ofhomes and businesses. Leased lines are used frequentlybut they are peak rate dimensioned and expensive forlimited bandwidth. DSL modem technology is relativelynew and successful in urban areas. However, there is arapid degradation of service with increasing distancebetween the customer and DSLAM (Digital SubscriberLine Access Multiplexer). Therefore this technology isnot suitable for rural and remote locations. T1 line is ahigh-speed digital phone line. It can support limitedbandwidth up to 1.544 Mbps. Fiber networks are pre-dominantly used in network backbones and for long dis-tances. Fiber links allow high bandwidth capacity withvery low bit error rates, but are cost effective only inareas where there is high revenue potential. Satellite net-works offer superior economics for large geographicalarea coverage, as they are less costly to deploy, maintainand operate than terrestrial network technologies. Terres-trial networks require heavy infrastructure, whether theyare telephony networks, or broadband data networks. Inremote areas where such infrastructure does not exist, theexpense of building such networks is often quite prohibi-tive. Satellite networks provide unmatched reliability,

with far fewer potential points of failure than terrestrialsolutions and built-in redundancy at almost every level tolimit service interruptions when problems do occur. Ter-restrial networks have multiple potential points of failurewhere outages can occur: construction projects diggingup streets; falling trees taking down telephone poles; andequipment failures at the local telephone central office, toname a few. Often, terrestrial network providers may notbe able to fix these outages without third-party carriers.Satellites’ inherent strengths as a broadcast medium makethem ideal for the distribution of bandwidth-intensiveinformation – data, video or audio – to large numbers ofremote locations. To send a message to N number ofrecipients over a terrestrial network requires sending Nseparate and identical messages, each of which consumesvaluable bandwidth and server resources. They are alsolikely to arrive at different locations at different times.Satellite IP multicasting, on the other hand, can simulta-neously deliver content to a virtually unlimited numberof end-user locations. Relocating and adding networksites is significantly less complicated and less expensivewith a satellite network than with most terrestrial tech-nologies. Communication satellites can provide band-width-on-demand flexibility as per the class of servicessubjected to security and e-commerce related issues.

Performance features of satellite technology pertainingto grid technology are QoS, security and throughput. QoSis the ability of a network element to have some level ofassurance that its traffic and service requirement can besatisfied. It manages bandwidth according to applicationdemand and network management settings. The band-width allocated to an application in a resource reservationis no longer available for use by best-effort applications.From a user perspective, end-to-end performance require-ments in satellite/terrestrial networks (Kota and Marchese2003) depend on the QoS achieved at each layer of thenetwork based on satellite dependent and independentfunction performed at the layer interfaces. At each layer,using efficient technologies and counteracting factorsresponsible for performance degradation achieve the userperformance requirements. Several scrambling and encryp-tion related coding techniques are available to ensure thesecurity of the data transmitted via satellite. Being prima-rily a communication resource, it does not put muchemphasis on security issues. Several coding techniquesare available to make data transmission throughput effi-cient, but they compromise security. Hence these issuesshould be addressed by grid technology.

5 Handshaking of Grid and Satellite Technology

Satellites can be useful as communication resources ingrid technology, whereas the grid is useful in effective sat-




ellite resource management. Grid protocols are normallywritten over TCP/IP. Communication satellites have wrap-pers that provide compatibility to TCP/IP but compromiseon bandwidth. Alternatively TCP/IP accelerator or per-formance enhancement proxies (PEP) can be used butthese also have limitations in terms of false acknowledg-ments during link unavailability among others. UDPphilosophy is quite useful in communication satellites,especially in broadcasting and multicasting. Grid protocolcan be extended not only to handle the satellite bandwidthissue but also to support UDP/IP. Current resource speci-fication languages such as RSL, Class Ads and JSDL(Savva et al. 2004) primarily address compute resources.Satellite resources have quite different characteristics. Weare studying current grid resource specifications to iden-tify a Satellite Resource Specification Language (SRSL).Similarly, current grid resource managers and brokersaddress compute-bound application requirements. Thesecan be extended to identify satellite resource managersand brokers.

There are several issues of satellite communicationaffecting grid technology that are not present in any ofthe terrestrial communication technologies. The majorissues are: latency, bandwidth asymmetry, bandwidthdelay product and link impairments (Kota and Marchese2003).

• Latency: Among the three components of latency, prop-agation delay, transmission delay and queuing delay,propagation delay is the dominant part in satellite links.Especially for GEO configurations, large variations ofround trip time (RTT) may lead to false time outs andtransmissions.

• Bandwidth asymmetry: In broadband satellite net-works bandwidth asymmetry in terms of forward toreturn channels exist anywhere in the order of 20:1 ormore. Network asymmetry affects the performance ofthe grid connectivity layer protocol because the proto-col relies on feedback in the form of cumulativeacknowledgments from the receivers to ensure reliabil-ity. Grid connectivity layer protocol uses arrival rate ofacknowledgments from the reverse direction to controlthe flow of packets in the forward direction. Thus con-gestion in the reverse direction can lead to poor chan-nel utilization in the forward direction.

• Bandwidth delay product: The bandwidth delay prod-uct defines the maximum amount of data that can be inflight (transmitted but unacknowledged). In a connec-tion with a large bandwidth delay product, such as GEOsatellite networks, grid users with limited congestion/receive windows will not be able to take advantage ofthe available bandwidth.

• Link impairments: Satellite systems are subjected tolink impairments that include multipath interference,

fading, rain attenuation and shadowing. Grid connec-tivity layer protocol may not distinguish between asegment loss due to congestion and loss due to biterrors. Consequently, the grid connectivity layer proto-col will reduce its transmission rate even when segmentsare lost because of bit errors not due to congestion.

Of the above issues, the low bandwidth channels,transmission errors and delay involved in communicationsatellites pose major challenges in extending grid com-puting to a remote location. Hence these are discussed indetail below.

Low speed Technologies such as CondorG (Frey et al.2001), Globus (Foster and Kesselman 1997) and GridBus(Buyya, Abramson and Giddy 2000) are capable of stag-ing input and output data before and after computationrespectively. Such data communication and computationis sequential. The communication overhead is considerednegligible due to the higher order of the data communica-tion speed. Communication satellites are almost 200times slower. Hence the data communication overheadcannot be ignored especially in applications such as dis-aster management where each and every minute is of theutmost importance. This can be resolved by parallelingcommunication and computation by splitting the datainto multiple small disjoint segments.

Completion time (T) is the most critical parameter forquality of service (QoS) in the compute grid. We knowthat:

For terrestrial networks T ≈ Tc * N, (1)

while for satellite networks it would be,

T = (Tdi + Tc + Tdo) * N (2)

where:

Tdi = input data loading time,Tc = compute time,Tdo = output data fetching time, andN = number of data segments.

Paralleling communication and computation is highlydependent on the available communication resourcesdepending on either luxurious or optimum (where samedata channel is shared for input and output) environ-ments.

T = Tdi + (Max (Tc, Tdo, Tdi)) * (N – 1)+Tc + Tdo (3)

for the luxurious environment, and




T = Tdi + (Max (Tc, Tdo+Tdi)) * (N – 1) + Tc + Tdo (4)

for the optimum environment.The above equations show that the slowest operation

among computation, data output and input will drive thecompletion time as well as initial input data loading andlast output data fetching. Using multiple grids can furtherreduce such application completion times. In the casethat all the compute grids are connected by terrestrial net-works, the completion time can be given by,

Tmg = T/Ng (5)

where Ng is number of grids.Access and connectivity mechanisms as well as the

type of application play major roles in the case of satel-lite-based multi-grid utilization. Satellite broadcast capa-bility can be useful to MASD (Multiple Algorithm SingleData) types of applications in which a single data seg-ment will be broadcast to all the grids and different algo-rithms will be simultaneously executed on the data. Othertypes of application can be executed by using data multi-plexing techniques, round robin data loading techniquesor first-come-first-serve data loading techniques. The Tmgwill be similar to that of the terrestrial network except forthe input data loading overhead for the first segment andoutput data fetching overhead of the last segment pro-vided Tc/Ng ≥ Max (Tdo, Tdi) for the luxurious environ-ment and Tc/Ng > Tdo + Tdi for the optimum environment.

Transmission error and delay Satellite-based datatransmission causes a delay of the order of 500 ms onaverage per packet in a round trip, which causes TCP/IPto restrict the communication bandwidth to 1 Mbps. It isoptimized to a certain extent by different TCP/IP acceler-ators. Satellite communication is prone to error, having aBit Error Rate (BER) of the order of 10–7 to 10–11. TCP/IPwill reduce its effective bandwidth to half on each error.Its recovery time is very slow compared with that of theabove BER.

We are conducting experiments to use GridFTP to seeits effectiveness in a satellite environment. Its multi-threading architecture will overcome the satellite delayby using multiple buffer spaces for multiple TCP connec-tion. Its multi-threading architecture also helps in han-dling the BER impact on TCP effective data transmissionspeed. Each error would affect only one of the threadsresulting in halving of a single thread speed rather thancomplete bandwidth.

The Sabul (Yunhong et al. n.d.) protocol provides avery high throughput of the order of 75% bandwidthunder the above conditions. We are exploring the possi-bility of developing FTP over Sabul. It would be an inter-

esting case to experiment GridFTP over SabulFTP andwe are working on this.

For effective handshaking of the two technologies, allthe above-stated factors need further research and con-sideration. Furthermore, grid protocols and satellite com-munication protocols should be seamlessly mapped.

5.1 Mapping the Grid–Satellite Interface

The grid has application, collective, resource, connectiv-ity and fabric layers (Foster, Kesselman and Tuecke 2001).The application layer talks to collective, resource andconnectivity layers, which is not the case with the OSImodel of the networks. Hence, management of resourcesand connectivity is done according to the type of theapplication, making it a perfect service-oriented architec-ture.

We propose a connectivity layer for the satellite sys-tem that talks to the connectivity layer of the grid asshown in Figure 1. The connectivity layer of the satelliteinteracts with a link layer that performs medium accesscontrol and satellite link control functions according tothe application requirement defined by the grid.

To achieve performance requirements at the physicallayer of the satellite, power performance (Eb/N0), band-width efficiency (bps/Hz) should be improved and rainattenuation and fade mitigation techniques should beimplemented. To achieve this, modulation, coding, powercontrol and site diversity factors should be chosen accord-ingly. In satellite systems, data rate, modulation and cod-ing choice drive link availability, required transmitterpower and bandwidth. For example, higher order modu-lation such as 8-QPSK to increase the bps/Hz comparedto most common QPSK or 16-QAM (Quadrature ampli-tude modulation) (Dankberg and Puetz 2002) should beused with linear channels to reduce link Eb/N0 comparedto saturated transponder. Turbo coding can be used as itreduces Eb/N0 close to the Shannon channel capacitylimit (within about 1–2 db) (Kota 1998; Valenti 2000)needed to close a link at a given code rate. Use of adap-tive coding allows a satellite system to be throughputefficient while conserving satellite electrical power. Cod-ing should be applied on a link–link basis, depending onlink condition, by automated sensing of degrading andimproving link condition. One way of adapting is to con-trol the coding rate (always less than one), defined as theratio of the number of input bits to the encoder to thenumber of output bits. Another way is to assume that thechannel symbol rate is constant, i.e. transmission band-width becomes constant and the coding rate wouldchange with the link condition to provide guaranteedbandwidth. Adaptive system coding schemes allocate aslittle system capacity as possible to link margins, whichis particularly critical at the edges of the beam. In order




to protect service availability from rain events, excessdownlink margin in an adaptive coding system allowseach terminal enough time to: (1) determine that a rainevent is going to cause, with high probability, perform-ance degradation with the present coding state, (2) requesta new coding state via grid protocol and (3) respond to acommand from the network control center to change thecoding state. In addition to this grid, by itself, can initiate thechange of coding state suited to that rain cell (5–10 km)based on the rain condition in nearby areas. Another wayof compensating for rain attenuation is through powercontrol. Uplink power control (Foster, Roy and Sender2000) can be achieved via a feedback loop through thegrid. The network control center monitors the level of allthe carriers and commands the affected earth station toadjust its uplink power accordingly.

At the link layer of the satellite, appropriate mediumaccess control (MAC) technology should be adapted toachieve performance requirements. Several TDMA schemes(Tobagi 1980; Kota 1981; Tasaka 1984; White and Kota1989; Kota, Huey and Lucantoni. 1997; Peyravi 1999;Connors 2000; Combes, Fouquet and Renat 2001; Hu andLi 2001) span from fully random access schemes such asALOHA to hybrid reservation DAMA schemes. In multi-frequency-time division multiple access (MF-TDMA)systems (Park et al. 2000), the dynamic bandwidth alloca-tion is done for different classes of service. An alternativeto MF-TDMA is code division multiple access (CDMA)technology based on spread ALOHA multiple accesseswith single code (SAOA). CDMA can be considered as aconnection-oriented multiple-access scheme in which the

assignment of the available set of codes is made on a peruser basis. For voice and continuous flow of information,this strategy can be efficient, but it may lack flexibilitywhen dealing with intermittent bursts of traffic. Variantsof this technology are spread ALOHA multiple access(SAMA), code reuse multiple access (CRMA) (Abram-son 1994; Zdunek, Ucci and LoCicero 1997), SAOA, andspread ALOHA one long code (SAOLA) (Kota, Vazquez-Castro and Carlin 2002). The other technology used forbandwidth allocation in response to user request on adynamic basis is Demand Assignment Multiple Access(DAMA). Variants of this access scheme are: combinedfree CFDAMA (Courville 2003; Luoras et al. 2003),weighted W_DAMA, round-robin R_CFDAMA (Le-Ngocand Jahangir 1998; Mobassery 2001) and PRDAMA (Jiangand Leung. 2003). To achieve performance requirements,issues which need further research are bandwidth ondemand (BoD) (Tobagi 1980; Kota 1981; Sooriyabandaraand Fairhurst 2003), dynamic bandwidth allocation inde-pendent of communication medium access technologyand bandwidth allocation with fading.

We are working on two layers above the link layer tohave effective handshaking with grid protocol layers. Theterrestrial communication network operates at the con-nectivity layer in the grid architecture. The same alsoappears true for communication satellites. We extend theconnectivity layer of grid architecture, as shown in Fig-ure 1, because the satellite is a set of different communica-tion channels rather than a single connection. Furthermorecombinations of different satellites can provide globalcoverage. Users can access a single fixed channel as with

Fig. 1 Mapping grid and satellite interface.




a terrestrial link and can use it optimally at the resourcelayer or can use it collectively with other links and satel-lites at the collective layer. The collective and resourcelayers of the satellite provide transparent and easy accessto satellite resources in similar ways to grid layers. Wepropose to have interoperability between satellite andgrid layers.

To satisfy different service level performance require-ments by the application, service classification, marking,queuing, and scheduling functions provide the servicesaccording to Service Level Agreement (SLAs). This isdone by satellite independent adaptation functions asshown in Figure 2. A Satellite Independent Service AccessPoint (SI-SAP) is defined for the air interface to separatethe satellite independent upper layers from the satellitedependent lower layers. Thus, it introduces an accesspoint independent of satellite technology. The servicesoffered by the lower layers provide the necessary per-formance requirements mapping to the upper layers. Therecent service oriented technologies for the terrestrialenvironment include: integrated services (IntServ) (Bradenet al. 1997; Shenker, Partridge and Guerin. 1997; Wro-clawski 1997), differentiated services (DiffServ) (Blake etal. 1998; Nicholas et al, 1998; Ronga et al. 2003) and mul-tiprotocol label switching (MPLS) traffic engineering(Awaduche 1999; Wang et al. 1999; Rosen, Vishwanathanand Callon 2001; Faucheur et al. 2002). IntServ uses anATM-like paradigm in which each source to destinationflow is distinguished. This means that the router through

the path keeps information about the status of each flowand refreshes it periodically. The DiffServ paradigm doesnot distinguish each traffic flow; instead traffic is dividedinto classes that deserve a common service in the network.MPLS attempts to set up the path along which packets thatcarry appropriate labels can be forwarded very efficiently,i.e. the forwarding engine would not look at the entirepacket header, rather only at the label and use that to for-ward the packet. MPLS attaches these labels on the basisof services needed by the packet. The applicability ofthese models for satellite networks requires research andexperimental demonstrations prior to broadband satellitedeployment.

5.2 Satellite–Grid Architecture

We extend the grid architecture for effective handshakingbetween grid technology and Communication Satellites,as shown in Figure 3. Different communication satelliteshave different capabilities in terms of various channels,channel capacities, subsystems and so forth. Each satel-lite has well defined QoS and commercial aspects. Eachsatellite provider will register his set of resources for eachsatellite with Satellite-Grid Resource Broker (SGRB),which is the part of the collective layer of the satellite sys-tem as shown in Figure 1. Different SGRBs will interactwith each other using grid protocols for informationexchange, request delegation, fault tolerance and so forth.An integrated satellite resource registry maintains infor-mation from a set of SGRBs. It provides high level serv-ices for satellite resource locating and match making touser to find the satellite of interest.

SRGB provides finer level services for resource regis-tration, allocation, deallocation, reservation, match mak-ing, and so forth. It will handshake with Satellite-GridResource Managers (SGRM) for monitoring the resources.The user will submit his request to a selected Satellite-Grid Resource Gatekeeper, which is responsible for allsecurity aspects. It will prevent unauthorized access to thesatellite resource. Furthermore, it will submit the author-ized request to one of the SGRM. We propose multipleSGRMs for a single satellite, as each satellite has manysubsystems and combinations of resources. Each SGRMwill carry out a resource management task very similar tothat of the grid resource manager. The current gridresource manager supports processing activities whileSGRM’s resource manager will support the data commu-nication activity. It may directly interact with the satelliteinterface for data communication that does not requireany protocol conversion. It will interact with SatelliteProtocol Manager in cases where protocol conversion orprotocol modulation is required. Satellite Resource Allo-cation and Reservation requires protocol conversion fromGARA to Satellite Protocols such as DAMA.

Fig. 2 Satellite–grid protocol architecture.




Some satellites provide authorized users a direct con-nectivity to the satellite through proprietary instruments.This mode of execution creates a headache for effectiveresource utilization. For example, two users have satelliteaccess from a region while the satellite has the capabilityfor providing access to one at a time. Normally, such sat-ellites use First Come First Serve (FCFS) policy. They arenot able to provide reservation capabilities. Hence, seri-ous users may be allocated resources well in advanced,even if they want it later, to avoid non-availability at thetime of actual requirement. Thus, satellite resources willbe wasted for a period. In our architecture, we proposethat each user should contact SGRB for resource alloca-tion and reservation. The satellite will not allow them tointeract directly without having a transmission code fromthe Satellite-Grid Resource Manager. Remote users willcontact SGRB using the Resource Management Channelfor resource allocation and reservation. Current satellitesdo not have a dedicated channel for resource manage-

ment. In that scenario, one of the data channels needs tobe shared for resource management.

5.3 Case Studies

5.3.1 Disaster Management Synthetic Aperture Radar(DMSAR) DMSAR is the project launched by ISRO(Indian Space Research Organization) for handling natu-ral disasters such as the floods which normally adverselyaffect eastern India every year during the monsoon sea-son. This is taken as a case study for the compute grid.The data collected by Airborne SAR while flying over thedisaster prone area needs to undergo preprocessing togenerate information that is useful for directing the actionsof the post-disaster rapid action force. This data needs tobe processed as early as possible to make rapid actionmore effective. The DMSAR team carries a portable sat-ellite communications terminal along with them to loadthe data to the target machines from the remote location.

Fig. 3 Satellite–grid system architecture.




Figure 4 shows the extension of a single compute grid toa remote location. The satellite network is configured asym-metrically where the forward channel is high speed whereasthe return channel is low speed. The high speed channel isused for sending or receiving the data while the low speedchannel is used for acknowledgment packets. The low speedchannel is used for grid command communication. Figure 5shows the extension to multiple compute grids.

DMSAR has to process about 50 datasets. Each datasetis of 7 GB data volume and requires 1 hour of processingtime on the tera scale super computing system, PARAMPadma4.

ISRO will be launching the GSAT-4 communicationsatellite which would be used for the DMSAR project. Ithas two types of transponders, bent pipe and regenera-tive. GSAT-4 has 8 beams with a likely 60 Mbps/beam

Fig. 4 Single compute grid.

Fig. 5 Multiple compute grids.




sustained data communication speed using the bent pipetransponder, 2 Mbps/beam using the regenerative trans-ponder and 8 links of 64 Kbps/beam using the regenera-tive transponder. The satellite is general purpose and theresources are not specifically dedicated to any applica-tions. All applications will share the resources “ondemand” from different remote locations as well asthrough portable devices. The satellite delay is 500 msfor bent pipe and 250 ms for regenerative transponders.The nominal bit error rate may reduce to 10–7. NetworkSimulator (NS-2)5 and NISTNET6 are useful and widelypopular to simulate such a satellite network over terres-trial connectivity.

Most developers (Foster and Kesselman 1997; Buyya,Abramson and Giddy 2000; Frey et al. 2001) ignore thedata-staging overhead due to the higher order of the datacommunication speed. In the case of DMSAR, men-tioned above, data transfer time over a 10 Gbps terrestrialnetwork for 7 GB is 7–10 seconds which is negligiblecompared to the 1 hour processing time. The data transfertime is quite critical when accessing the PARAM Padmafrom a remote location using the communication satel-lite, which has an effective data speed of 60 Mbps. Ittakes about 15 minutes, which will impact on the effi-ciency. This will cause a delay of about 12–13 hours inprocessing all the datasets needed to take the requireddecision. This is critical especially in the case of disastermanagement where each and every minute is of theutmost importance. This can be largely overcome by par-alleling the computation and communication. Only thefirst dataset needs to be sequential and all remaining datacommunication can be in parallel to the computationresulting in only 15 minutes overhead while processingthe 50 datasets.

There are number of parameters that affect the alloca-tion and utilization of the communication resource insynchronization of the compute resources. The computa-tion time is 1 hour for our case whereas communicationtime is only 15 minutes. If the remote compute resourceshave sufficient buffer disk space and communicationresources, all the input data can be transferred at oncethrough a data broadcast. This is also true for the outputdata. For pipeline processing, the compute resourceshould have input and output data space for a minimumof one additional segment. With such conditions the nextdata segment should be made available to the computeresource before it has completed the computation on thefirst one. In our case, a constraint was to make data avail-able in an hour. In the case of MASD applications wheresatellite-broadcasting capability is used, users have thefreedom to utilize as many compute resources as theywant for their parallel processing. The communicationresource will be idle for 45 minutes during which time itcan be allocated to another application. If another appli-

cation wants the communication resource for longer than45 minutes, but less then 90 minutes, it can be satisfiedunder certain conditions.

In the case of the DMSAR application, designers usingSAMD architecture are restricted to a maximum of 4compute resources. The communication resource utiliza-tion will become a close fit round robin. If the computeresource requirement is less than 4 then it will be a caseof first come first serve. If the DMSAR applicationdesigner were using MASD architecture, slow communi-cation would not impose any restriction on utilizing anumber of compute resources, as data transfer agentswould be using satellite broadcast capabilities.

According to the bandwidth delay product equation,we would need approximately 60 threads in GridFTP(Allcock et al. 2005) to handle the 550 ms delay for 60Mbps link. Each thread would be working with 64 KBbuffer space and hence each thread would provide 1Mbps speed resulting into a total 60 Mbps speed.

In a normal TCP environment, the 60 Mbps speed willnot sustain with a 10–7 bit error rate and will hang up infew seconds. On each error itself, its speed will becomehalf according to TCP characteristics and it will not beable to recover quickly. Its typical recovery time from 1Mbps to 2 Mbps is 1 minute. If we run 1000 threads inGridFTP to sustain the 60 Mbps speed with a 10–7 biterror rate to transmit 7 GB data, each thread would beeffectively be working with 60 Kbps and the impact ofeach error is reduced by a factor of 1000.

5.3.2 National Spatial Data Infrastructure (NSDI)NSDI is taken as case study for the information grid. TheNSDI project aims to provide integration and interopera-tion among the spatial information databases collected byall districts of all states in India. Such interoperabilityand integration helps in nationwide and statewide deci-sion-making. The NSDI servers cannot be connected on apublic network such as the Internet and at the same timemaking a nationwide private network for NSDI is notcost effective. Making dynamic networks using satellitesas and where required is a more suitable solution forNSDI.

NSDI servers are distributed across the states of India.Some states have local LAN and some of the servers areconnected on the local LAN, which can form NSDI data/information grids. We aim to use the satellite to connectthe grids on demand. We also aim to access all such gridsfrom remote locations. We need to integrate all individ-ual services through satellite connectivity. We make asmall change in the communication network when com-pared to the compute grid. We do not need 60 Mbps linksfor data transfer as the amount of data (rather, informa-tion) needed to be transmitted is very low. We normallyneed 64 Kbps full duplex connectivity and in some cases




we may need up to 2 Mbps, especially when topographicmaps need to be transmitted, forward or return link.

The satellite broadcast capabilities are extensivelyused in NSDI grids. The user fires a single NSDI query,which will be broadcast to all grids, which in turn broad-cast to all servers. Individual grids integrate results fromall servers by invoking integration services. All grids sendresults to the user machine, which will be integrated,either on the user machine or at the remote server byinvoking the integration service. This will result in theuser getting an integrated result to his query.

6 Results and Discussion

We have carried out experiments to evaluate differentprotocols in a simulated environment using the NISTNetsatellite link emulator shown in Figure 6 as well as via sat-ellite using GSAT-3 as shown in Figure 7. We have eval-uated different protocols based on TCP and UDP dependingon our application requirements. TCP-based protocols suchas GridTCP (Bhatt et al. 2005), GridFTP and UDP-basedprotocols such as SABUL and RBUDP were evaluated insimulated as well as satellite environments.

GridFTP performance (Bhansali et al. n.d.) was evaluatedby varying parameters such as delay, BER and file size.Since GridFTP works on the principle of parallel streams,we also observed the maximum number of sockets required

for peak performance. Table 1 describes the throughputperformance of GridFTP under different conditions.

Fig. 6 Experimental setup.

Fig. 7 Grid computing test set up using GSAT-3.

Table 1Peak performance at different delays and different BERs

Delay (ms) BERsGridFTP optimum number of streams

GridFTP aggregate performance at optimum streams (%)

0 (100mbps) 10–11–10–6 2–5 86.24–85.20

12 (LEO) (100mbps) 10–11–10–6 4–15 84.88–84.40

250 (MEO) (100mbps) 10–11–10–6 75–250 77.68–73.68

500 (GEO Mesh) (100mbps) 10–11–10–6 120–480 67.04–64.16

1000 (GEO Star) (100mbps) 10–11–10–6 220–790 63.08–52.24

GSAT-3 (6 mbps) > 10–7 8–9 83–87




GridTCP is an extension of TCP protocol, based onsplitting a single socket into multiple sockets to enhancewindow size to compensate for the large RTT in the satellitelink. It is independent of application layer protocol. Theperformance of GridTCP was evaluated in simulated as wellas satellite environments. The performance of GridTCP inthe simulated environment with 0.5% error and 1.0%error are given in Figure 8 and Figure 9 respectively.

We carried out satellite mode testing using the GSAT-3satellite with a BER of the order of 10–7. We used for-ward link and return link bandwidth of 7.5 Mbps and 384Kbps respectively. We used three different file sizes toobserve throughput performance and concluded the max-imum number of sockets required for different file sizetransfers. Figure 10 shows throughput in Mbps v/s number

of sockets for different file sizes. We observed that for an11 MB file the maximum performance is 37% achievedwith 400 sockets, for a 100 MB file 80% with 400 and700 sockets and for a 1.1 GB file the maximum through-put is 82% with 600 sockets.

We have also explored the UDP-based protocols suchas SABUL, TSUNAMI and RBUDP and evaluated themin a simulated environment. It was observed that theSABUL protocol gives 67% bandwidth utilization whereas TSUNAMI gives 22.4% with 10–6 BER and 500 msdelay. We have also evaluated our in-house developedscheduler

GANESH (Bhatt et al. 2004) was used for job andprocess scheduling for RADCOR (Bhatt et al. 1994) appli-cation processing. For 6 data sets we have observed thatRADCOR on a single machine takes 110 minutes to proc-ess the data whereas on three machines using the grid viasatellite takes only 46 minutes.

7 Conclusions

We conclude from our study that communication satel-lites are not only an alternative to terrestrial links but alsohave many advantages over terrestrial links in terms ofremote location connections, ease of relocating, broad-casting, multicasting, and so forth. Satellite links are aprecious resource. Grid technology can play a major rolein managing them effectively. Grid research is being car-ried out in areas of service oriented architecture focusingon resource optimization and meeting users’ securityrequirements. The possible handshake will be very usefulfor many applications such as disaster management,NSDI distributed data warehouse, EDUSAT distributed

Fig. 8 Performance with 0.5% loss.

Fig. 9 Performance with 1% loss.

Fig. 10 Performance with GSAT-3.




data warehouse, HealthSAT expert collaboration, and soforth. For effective handshaking, research in the area ofSatellite-Grid Resource Managers, Protocol mapper andbandwidth optimization needs to be carried out. Futuresatellites should have a separate Satellite-Grid ResourceManagement Channel. Alternatively, one of its datachannel can be used as Satellite-Grid Resource Manage-ment Channel. Satellites should be designed to supportthe service-oriented architecture of the grid, i.e. theyshould be able to support the service-class based Diff-Serv paradigm or labeled-service based on MPLS trafficengineering.

Author Biographies

Haresh S Bhatt is a senior scientist who has worked atthe Indian Space Research Organisation since 1984. Hewas actively involved in all the IRS (Indian Remote Sens-ing Satellite) programs till 1997. His remarkable contribu-tion in automatic cloud cover estimation for IRS and insolving IRS-1C on-board sensor calibration was interna-tionally acclaimed and was the first of its kind. He hasnumber of publications in international conferences andjournals. Currently, he is working in the field of hand-shaking of advanced communication satellites and state ofart computing technology. He has been a program com-mittee member and referee for many national and interna-tional conferences. He is a recipient of UN/ESA long-termfellowship award of the Office of Outer Space Affairs,United Nations. His fields of interest include image process-ing, parallel processing, distributed computing, grid com-puting, software engineering and data security.

Hitesh J. Kotecha received his B.E. in electronics andcommunication from L. D.College of Engineering, GujaratUniversity, India. He is currently working as scientist/engineer at the Networks Division of the Space Applica-tions Centre, Indian Space Research Organisation (ISRO),at Ahmedabad. He has worked on satellite-based net-works for Internet data dissemination, community infor-mation etc. He is currently involved in satellite-basedgrid technology and satellite-based networks. He is alsoinvolved in the development and evaluation of transportprotocols for satellite communication. He has co-authored5 papers at international conferences.

K. Bandyopadhyay obtained his Master Degree in elec-tronics and telecommunication engineering from JadavpurUniversity, Calcutta. He joined the Indian Space ResearchOrganisation (ISRO) in 1972. He worked in the GermanSpace Research Centre, DLR in 1978 as a visiting scien-tist. At present he is Group Director of two Groups: theSystems and Applications Group and the Satcom GroundSystem Technology Group of the Space Applications

Centre, ISRO, Ahmedabad. He was involved in many pio-neering activities of Indian satellite communication pro-gram. Some of them are,

• Project leader of India’s first satellite-based ComputerNetworking Experiment, COMNEX, using ATS-6 andSymphony satellites.

• Project Manager of India’s first indigenously devel-oped VSAT pilot project, Satellite Based Rural Teleg-raphy Network, SBRTN, for North-East Region of India.

• Chairman of System Definition Team for INSATMobile Satellite System.

• Project Director of GSAT-1 Applications Project tointroduce digital TV, digital sound and data broadcastin the Indian satellite system.

• Project Director of GSAT-4 Applications Projectinvolving use of onboard processing.

• Associated Project Director for Indian Satellite Navi-gation Project, GAGAN.

• Project Director of Communications Systems for Dis-aster Management in the Disaster Management Sys-tem Program.

He represented India and presented a paper at the GPSsummit conference in Munich, Germany during March2003. He has also presented papers at the UN workshop onBridging the Digital Divide – Space Technology Solu-tions, in 2000 at Kulalampur, Malaysia and the Interna-tional Computer Communication Conference in Sydney,Australia in 1984, and attended the International Astro-nautical Conference in Turin, Italy in 1997.

V. H. Patel was Head of Department of the NetworksDivision at Space Applications Centre, Indian SpaceResearch Organisation, and Ahmedabad, India. He has aB.Engg. in electrical communications engineering fromthe Indian Institute of Science, India. His experienceincludes development of video subsystems and imageprocessing systems. He has vast experience in computerengineering, computer architecture and networking, spacetechnology and remote sensing technology.

A. Dasgupta has an M.E. in electrical communicationsengineering and has been working in the Space Applica-tions Centre since November 1970. He worked in theSatellite Instructional Television Experiment (SITE).From 1976 till 2000 he was involved in the managementof applications programs for several remote-sensing sat-ellites including Bhaskara and IRS as well as develop-ment of image and information processing systems. Hewas the Deputy Director of SATCOM and IT Applica-tions Area. He is a recipient of the Astronautical Societyof India Award for Space Science and Applications forthe year 2000. He is a Senior Member of the Institute of




Electrical and Electronic Engineers, Inc, USA, was theChairman of the IEEE, Gujarat Section, 2003–04 and is aFellow of the Institution of Electronics and Telecommu-nications Engineers, India.

Notes

1 https://forge.gridforum.org/projects/graap-wg

2 www.gilat.com/Technology_SatelliteAdvantage.asp

3 www.Satlynx.com/html/Content/C1_techno.html

4 http://www.cdac.in/html/ctsf/resource.asp

5 http://www.isi.edu/nsnam/ns/

6 NIST Net Home page, http://snad.ncsl.nist.gov/itg/nistnet/ orNIST Net website http://www.antd.nist.gov/nistnet

References

Abramson, N. (1994). Multiple accesses in wireless digital net-works, Proceedings of the IEEE, 82(9): 1360–1370.

Allcock, W. (n.d.). GridFTP: Protocol extensions to FTP for thegrid, GGF Document (Recommendation).

Allcock, B., Bresnaham, J., Kettimuthu, R., Link, M., Dumitrescu,C., Raicu, I., Foster, I. (2001). Secure, efficient data trans-port and replica management for high-performance data-intensive computing, in Mass Storage Conference, HyattRegency Isandia, San Diego, CA.

Allcock, B., Bresnaham, J., Kettimuthu, R., Link, M., Dumitrescu,C., Raicu, I., Foster, I. (2005) The Globus Striped GridFTPFramework and Server, Proceedings of Super Computing2005 (SC05), November 2005, Washington.

Amadesi, P. (1994). The role of VSATs and other small satelliteterminals in the evolving telecommunications environ-ment, Satellite communications ICSC, 1: 59–66.

Appleby, K., Fakhouri, S., Fong, L., Goldszimdt, J., Kalanter,M., Krishnakumar, S., Pazel, D., Persing, J, and Rochw-erger, B, (2001). Oceano-SLA based management ofcomputing utility, 7th IFIP/IEEE International Sympo-sium on Integrated Network Management, Los Alamitos,CA: IEEE Computer Society Press.

Awaduche, D. O. (1999). Requirements for traffic engineeringover MPLS, IETF, RFC 2702.

Baker, M., Buyya, R., and Laforenza, D. (2000). The grid:international efforts in global computing. InternationalConference on Advances in Infrastructure for ElectronicBusiness, Science and Education on Internet (SSGRR2000), Rome, Italy, July 31 – Aug 6.

Bester, J., Foster, I., Kesselman, C., Tedesco, J., and Tuecke, S.(1999). GASS: A data movement and access service forwide area computing systems, Sixth Workshop on I/O inParallel and Distributed Systems, Atlanta, GA, May 5.

Bhansali, D., Bhatt, H. S., Bandyopadhyay, K. (n.d.) Perform-ance Analysis pf GridFTP for Satellite-based Grid Com-puting. Ready for transmission.

Bhatt, H., Shah, M., Daraji, N. P., Mritunjay, and Prakash, C.V. S. (1994). Optimization of image processing softwareon VAX/VMS system configuration, Proceedings of Sem-

inar on Supercomputing for Scientific Visualization, Bom-bay, India.

Bhatt, H., Kotecha, H., Patel, R.M., Patel, V.H., and Dasgupta,A.R. (2004). GANESH: Grid Application maNagementand Enhanced ScHeduling, Proceeding of ADCOM 2004,12th International Conference on Computing and Com-munications, Ahmedabad, India, 15–18 Dec, pp. 51–69.

Bhatt, H. S., Kotecha, H. J., Patel, V. H., and Bandyopadhyay,K. (2005). GridTCP: A transport layer data transfer proto-col for satellite based grid computing, Proceedings ofWoNGeN’05, International Workshop on Next GenerationWireless Networks, 18–21 Dec, Goa, India.

Blake, S., Blake, D., Carlson, M., Wang, Z., and Weiss, W.(1998). An architecture of differentiated services, IETF,RFC 2475.

Braden, R, Zhang, L., Berson, S., Herzog, S., and Kamin, S.(1997). Resource reservation protocol (RSVP) – ver-sion 1 functional specification, IETF, RFC 2205, Sep-tember.

Butler, R., Engert, D., Foster, I., Kesselman, C., Tuecke, S.,Volmer, J., and Welch, V. (2000). Design and deploymentof a national-scale authentication infrastructure, IEEEComputer, 33(12): 60–66.

Buyya, R. (2002). Economic based distributed resource man-agement and scheduling, Ph.D.Thesis submitted toMonash Univ., Melbourne, Australia. Online at: http://www.buyya.com/thesis/thesis.pdf

Buyya, R., Abramson, D., and Giddy, J. (2000). Nimrod/G: Anarchitecture for a resource management and schedulingsystem in a global computational grid, Proc. 4th Intl. Conf.on High Performance Computing in Asia-Pacific Region,Beijing, China, IEEE(CS).

Christiansen, E., Curbera, F., Meredith, G., and Weerawarana,S, Web Services Description Language (WSDL). Availa-ble at http://www.w3.org/TR/WSDL

Combes, S., Fouquet, C., and Renat, V. (2001). Packet basedDAMA protocols for new generation satellite networks,Proceedings of the 19th International Communication Sat-ellite Systems Conference and Exhibit, Vol. 3/3, (AIAApaper 083), Toulouse, France.

Connors, D. (2000). Medium access control protocols for satel-lite networks, Ph.D. thesis, University of California, LosAngeles, Department of Electrical Engineering.

Courville, N. (2003). QoS oriented traffic management in mul-timedia satellite systems, International Journal of Satel-lite Communication and Networking, 21(4–5): 367–399.

Czajkowski, K., Foster, I., and Kesselman, C. (1999). Coalloca-tion services for computational grids, 8th IEEE Interna-tional Symposium on High Performance DistributedComputing, San Francisco, Los Alamitos, CA: IEEEComputer Society Press.

Czajkowski, K., Foster, I., Karonis, N., Kesselman, C., Martin,S., Smith, W., and Tuecke, S. (1998). A resource manage-ment architecture for metacomputing systems, Proc.IPPS/SPDP ’98 Workshop on Job Scheduling Strategiesfor Parallel Processing, Orlando, FL.

Czajkowski, K., Fitzgerald, S., Foster, I., Kesselman, C. (2001a)Grid information services for distributed resource sharing,in IEEE International Symposium on High PerformanceDistributed Computing, Los Alamitos, CA: IEEE Press.




Czajkowski, K., Fitzgerald, S., Foster, I., and Kesselman, C.(2001b). Grid information services for distributedresource sharing, 10th IEEE International Symposium onHigh Performance Distributed Computing, San Francisco,Los Alamitos, CA: IEEE Computer Society Press, pp.181–184.

Czajkowski, K, Foster, I, Sander, V, Kesselman, C, andTuecke, S. (2002). SNAP: a protocol for negotiating serv-ice level agreement and coordinating resource manage-ment in distributed systems, 8th Workshop on JobScheduling Strategies for Parallel Processing, Edinburgh,Scotland.

Dankberg, M. and Puetz, J. (2002). Waveform advances for sat-ellite communication, PTC 2002 Conference, Whistler,Canada.

Degermark, M., Kohler, T., Pink, S., Schelen, O. (1997).Advanced reservation for predictive service in the Inter-net, Multimedia Systems Journal, 5(3): 177–186.

Evans, J.V. (1995). Twenty years of international satellite com-munication, International Conference on 100 Years ofRadio, London, UK, pp. 239–245.

Faucheur, F. L. (ed.), Wu, L., Davie, B., Davari, S., Vaananen,P., Krishnan, P., Cheval, P., and Heinanen, J. (2002).Multiprotocol level switching (MPLS), support of differ-entiated services, IETF, RFC 3270.

Ferrari, D., Gupta, A., and Ventre, G. (1997). Distributedadvance reservation of real time connection, MultimediaSystems Journal, 5(3): 187–198.

Fitzgerald, S., Foster, I., Kesselman, C., Laszewski, G., Smith,W., and Tucke, S. (1997). Directory service for configur-ing high performance distributed computations, 6th IEEEInternational Symposium on High Performance Distrib-uted Computing, Los Alamitos, CA: IEEE ComputerSociety Press, pp. 365–375.

Foster, I. (2002). What is the grid? A three point checklist,GRIDToday, July 20.

Foster, I. and Kesselman, C. (1997). Globus: A metacomputinginfrastructure toolkit, International Journal of Supercom-puter Applications, 11(2): 115–128.

Foster, I. and Kesselman, C. (eds.) (2003). The grid: Blueprintfor a New Computing Infrastructure, Morgan Kaufman.

Foster, I., Roy, A., and Sender, V. (2000). A quality of servicearchitecture that combines resource reservation and appli-cation adaptation, 8th International Workshop on Qualityof Service, Pitttsburgh, PA, June.

Foster, I., Kesselman, C., and Tuecke, S. (2001). The anatomyof the grid: enabling scalable virtual organizations, Inter-national Journal of Supercomputer Applications, 15(3):200–222.

Foster, I., Kesselman, C., Tsudik, G., and Tuecke, S. (1998).Security architecture for computational grids, in ACMConference on Computers and Security, San Francisco,CA, 83–91.

Foster, I., Roy, A., Sender, V., and Adamson, W. (2001). Endto end provision of policy information for network QoS,International Symposium on High Performance Distrib-uted Computing, Los Alamitos, CA: IEEE ComputerSociety Press, pp. 115–126.

Foster, I., Kesselman, C., Nick, J., and Tuecke, S. (2002). Thephysiology of the grid: An open grid services architecture

for distributed systems integration, Open Grid ServiceInfrastructure WG, Global Grid Forum, June 22. http://www. globus.org/research/papers/ogsa.pdf.

Foster, I., Fidler, M., Roy, A., Sender, V., and Winkler, L.(2004). End to end quality of service for high end applica-tions, Computer Communications, 27(14): 1375–1388.

Frey, J., Tannenbaum, T., Lynvy, M., Foster, I., Tuecke, S.(2001). A computation management agent for multi-insti-tutional grids, in 10th International Symposium on HighPerformance Distributed Computing, Los Alamitos, CA:IEEE Press, pp. 55–66.

Grimshaw, S., White, B. S., and Nguyen-Tuong (n.d.) A. grid-based file access: the Legion I/O model, 9th IEEE Interna-tional Symposium on High Performance Distributed Com-puting, August 1–4, Pittsburgh, PA.

Globus (n.d.a). The Globus Resource Specification LanguageRSL v1.0 http://www-fp.globus.org/gram/rsl_spec1.html

Globus (n.d.b). GRIDFTP: Universal Data Transfer for Thegrid, The Globus Project White Paper.

Hinke, T. H. (n.d.) Grids for Dummies Featuring Earth ScienceData Mining Application, NASA Ames Research Center.

Hoo, G., Johnston, W., Foster, I., and Roy, A. (1999). QoS asMiddleware: Bandwidth Reservation System Design, Pro-ceedings from High Performance Distributed Computing,1999, California, USA.

Hu, Y. and Li, V.O.K. (2001). Satellite based Internet: A tuto-rial, IEEE Communications Magazine, 39(3): 154–162.

Jiang, Z. and Leung, V. C. M. (2003). A predictive demandassignment multiple access protocol for Internet accessover broadband satellite networks, International Journalof Satellite Communication and Networking, 21(4–5):451–467.

Kim, K. and Nahrstedt, K. (2000). Resource broker model withintegrated reservation scheme, IEEE International Con-ference on Multimedia, Los Alamitos, CA: IEEE Compu-ter Society Press, pp. 859–862.

Kota, S. (1981). Demand Assign Multiple Access (DAMA)techniques for satellite communications, Proceedings ofthe IEEE, NTC81, New Orleans, LA, c8.5.1–c8.5.7.

Kota, S. (1998). Working document towards developing NGSOinterference protection requirement for Ka-band GSO sys-tems employing adaptive coding as a fade counter meas-ure, ITU-R 4b/40-E.

Kota, S. and Marchese, M. (2003). Quality of service for satel-lite IP networks: a survey, International Journal of Satel-lite Communication and Networking, 21(4–5): 303–349.

Kota, S., Huey, H., and Lucantoni, D. (1997). Demand AssignMultiple Access (DAMA) for multimedia services – ana-lytical result, Proceedings IEEE MILCOM, Monterey, CA.

Kota, S., Vazquez-Castro, M., and Carlin, J. (2002). SpreadALOHA multiple access for broadband satellite returnchannel. Proceedings of the 20th AIAA International Sat-ellite Systems Conference, Montreal, Canada, (AiAA-2002-1918).

Le-Ngoc, T. and Jahangir, I. M. (1998). Performance analysisof CFDAMA-PB protocol for satellite communication,IEEE Transaction on Communications, 46(9): 1206–1214.

Lilly, C. J. (1989). Services provided by international consortia,IEE Colloquium on Liberalization in the Provision of Sat-ellite Services.




Litzkow, M., Livny, M., and Mutka, M. (1988). Condor – Ahunter of idle workstations, Proc. 8th Intl Conf. on Dis-tributed Computing Systems, pp. 104–111.

Liu, C., Yang, L., Angulo, D., and Foster, I. (2002). Designand evaluation of a resource selection framework forgrid application, 11th IEEE International Symposium onHigh Performance Distributed Computing, Edinburgh,Scotland, Los Alamitos, CA: IEEE Computer SocietyPress.

Liu, C. and Foster, I. (2003). A constraint language approach togrid resource selection, University of Chicago, Depart-ment of Computer Science.

Luoras, N., Le-Ngoc, T., Ashour, M., and Elshabrawy, T.(2003). An IP-based satellite communication systemarchitecture for interactive multimedia services, Interna-tional Journal of Satellite Communication and Network-ing, 21(4–5): 401–426.

Mandrichenko, I. (n.d.). GridFTP Protocol Improvements, GGFDocument, GridFTP Working Group.

Miller, N. (1992). A shared hub VSAT services in Canada, IEEColloquium on VSAT Requirements for Future Applica-tions, November, pp. 1/1–1/4.

Mobassery, M. (2001). Performance evaluation of MAC proto-cols and buffer management strategies in ATM basedbroadband satellite networks, Master’s thesis, Universityof British Columbia.

Nahrstedt, K. and Smith, J. M. (1995). The QoS broker, IEEEMultimedia 2(1): 53–67.

Nicholas, K., Blake, S., Baker, F., and Black, D. (1998). Defini-tion of differentiated service field (DS field) in the Ipv4and Ipv6 headers, IETF, RFC 2474.

Park, J. M., Savagaonkar, U. R., Chong, E. K. P., Siegel, H. J.,and Jones, S. D. (2000). Efficient resource allocation forQoS channels in MF-TDMA satellite systems, MILCOM2000, 21st Century Military Communications ConferenceProceedings, 22–25 Oct., Vol. 2, pp. 645–649.

Peyravi, H. (1999). Medium access control protocol for spaceand satellite communications: A survey and assessment,IEEE Communications Magazine, 37(3): 62–71.

Raman, R., Livny, M., and Solomon, M. (2003). Policy drivenheterogeneous resource coallocation with gang matching,12th IEEE International Symposium on High PerformanceDistributed Computing, Seattle, WA, Los Alamitos, CA:IEEE Computer Society Press.

Ronga, L. S., Pecorella, T., Del Re, E., and Fantacci, R. (2003).A gateway architecture for IP satellite network withdynamic resource management and DiffServe QoS provi-sion, International Journal of Satellite Communicationand Networking, 21(4–5): 351–366.

Rosen, R., Vishwanathan, A., and Callon, R. (2001). Multipro-tocol level switching architecture, IETF, RFC 3031.

SAC & DECU /ISRO (2003). Project Report on GSAT-3(EDUSAT) Applications programme, March.

SAC & DECU /ISRO (2004). Project Report on GSAT-4Applications programme, April.

Savva, A., Anjomshoaa, A., Brisard, F., Cook, R. L., Fellows,D. K., Ly, A., McGough, S., and Pulsipher, D. (2004).Job submission description language (JSDL), specifica-tion, 17 May. http://www.ggf.org/Meetings/GGF11/Doc-uments/draft-ggf-jsdl-spec.pdf

Sengupta, A., Hanjura, A. K., and Mathur, B. S. (1991). Satel-lite broadcasting of time and frequency signals, Proceed-ings of the IEEE, July, 97: 973–982

Shenker, S., Partridge, C., and Guerin, R. (1997). Specification ofguaranteed quality of service, IETF, RFC 2212, September.

Silva, L, Pidroso, H., and Silva, J. (1997). The design of Jet: AJava library for embarrassingly parallel applications,WOTUGH 20 – Parallel Programming and Java Confer-ence, Amsterdam: IOS press, pp. 210–228.

SOAP (n.d.). Simple Object Access Protocol, W3C, Availableat http://www.w3.org/TR/SOAP

Sooriyabandara, M. and Fairhurst, G. (2003). Dynamics of TCPover BoD satellite networks, International Journal of Sat-ellite Communication and Networking, 21(4–5): 427–449.

Tasaka, S. (1984). Multiple-access protocols for satellite packetcommunication networks: A performance comparison,Proceedings of the IEEE, 72(11): 1573–1582.

Thompson, M., Johnston, W., Mudumbai, S., Hoo, G., Jackson,K., and Essiari, A., (1999). Certificate-based access con-trol for widely distributed resources, in Proc. 8th UsenixSecurity Symposium, Washington, DC.

Tobagi, F. A. (1980). Multi-access protocols in packet commu-nication systems, IEEE Transactions on Communication,28(4): 468–488.

Tuecke, S., Czakowski, K., Foster, I., Frey, J., Graham, S., Kes-selman, C., Maguire, T., Sandholm, T., Snelling, D., andVanderbilt, P. (n.d.). Open Grid Service Infrastructure,Global Grid Forum, Draft. Available at http://www.ggf.org/documents/drafts.

Valenti, M.C. (2000). Inserting turbo code technology intoDVB satellite broadcasting system, Proceedings IEEE Mil-itary Communication Conference (MILCOM), Los Angeles,CA, pp. 650–654.

von Laszewski, G., Pieper, G. W., and Wagstrom, P. (2002).Gestalt of the grid, May 12.

Wang, F., Mohapatra, P., Mukhrjee, S., and Bushmitch, D.(1999). MPLS and traffic engineering in IP networks,IEEE Communications Magazine, 37(12): 42–47.

White, B. E. and Kota, S. L. (1989). Two levels multiple accessscheme for packet satellite systems with low duty factorterminals. IEEE Transactions on Communications, 35(9):880–883.

Wroclawski, J. (1997). Specification of controlled-load net-work element service, IETF, RFC 2211, September.

Yunhong, G. and Grossman, R. (2003). SABUL: A transportprotocol for grid computing, Journal of grid Computing,1: 377–386.

Zdunek, K. J., Ucci, D. R., and LoCicero, J. L. (1997). Packetradio performance of inhibit sense multiple access with cap-ture, IEEE Transactions on Communications, 45: 164–167.



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