qos ranking prediction based on past service usage experience in cloud services

7/22/2019 QoS Ranking Prediction Based on Past Service Usage Experience in Cloud Services

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QoS Ranking Prediction based on past service usage experience in cloud

services

ABSTRACT

Cloud computing is becoming popular. Building high-quality cloud applications is a criticalresearch problem.Quality of Service (QoS) plays a critical role in the affective reservation ofresources within service oriented distributed systems and has been widely investigated in the

Cloud Computing. The aim of this paper is to address QoS specifically in the context of thenascent paradigm Cloud Computing and propose relevant research questions. QoS rankingsprovide valuable information for making optimal cloud service selection from a set of

functionally equivalent service candidates. To obtain QoS values, real-world invocations on theservice candidates are usually required. To avoid the time-consuming and expensive real-world

service invocations, this paper proposes a QoS ranking prediction framework for cloud services

by taking advantage of the past service usage experiences of other consumers. The QoS

Measures (Delay, Throughput, Loss, Cost) depend on offered traffic, and possibly other external

processes.

Keywords: Quality-of-service, cloud service, ranking prediction, personalization

Introduction:

Quality of Service (QoS) is a broad topic in Distributed Systems and is most often referred to as

the resource reservation control mechanisms in place to guarantee a certain level of performance

and availability of a service. The scope of this paper is primarily concerned with the managementand performance of resources such as processors memory, storage and networks in Cloud

Computing. A defined QoS is not just limited to guarantees of performance and availability and

can cover other aspects of service quality, which are outside the scope of this paper, such assecurity and dependability. The problems surrounding resource reservation are non-trivial for allbut the most basic best effort guarantees and the problems behind resource capacity planning are

non-deterministic polynomial-time hard to solve.

QoS provides a level of assurance that the resource requirements of an application are strictlysupported. QoS models are associated with End-Users and Providers (and often Brokers),

involve resource capacity planning via the use of schedulers and load balancers and utilize

Service Level Agreements (SLA). SLAs provide a facility to agree upon QoS between an End-User and Provider and define End-User resource requirements and Provider guarantees, thus

assuring an End-User that they are receiving the services they have payed for.

Objective of the project:

To predict the quality of service ranking prediction of the cloud services.

To ensure Quality of service to end users

For some application, availability of resources and isolation


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Literature Survey

M. Armbrust, A. Fox, R. Griffith, A.D. Joseph, R.H. Katz, A.Konwinski, G. Lee, D.A. Patterson,A. Rabkin, I. Stoica, and M.Zaharia, Above the Clouds: A Berkeley View of Cloud

Computing, Technical Report EECS-2009-28, Univ. California, Berkeley,2009.

Cloud Computing, the long-held dream of computing as a utility, has the potential to transform a

large part of the IT industry, making software even more attractive as a service and shaping theway IT hardware is designed and purchased. Developers with innovative ideas for new Internet

services no longer require the large capital outlays in hardware to deploy their service or the

human expense to operate it. They need not be concerned about over provisioning for a servicewhose popularity does not meet their predictions, thus wasting costly resources, or under

provisioning for one that becomes wildly popular, thus missing potential customers and revenue.

Moreover, companies with large batch-oriented tasks can get results as quickly as their programs

can scale, since using 1000 servers for one hour costs no more than using one server for 1000

hours. This elasticity of resources, without paying a premium for large scale, is unprecedented inthe history of IT.

Cloud Computing refers to both the applications delivered as services over the Internet and thehardware and systems software in the datacenters that provide those services. The services

themselves have long been referred to as Software as a Service (SaaS). The datacenter hardware

and software is what we will call a Cloud. When a Cloud is made available in a pay-as-you-gomanner to the general public, we call it a Public Cloud; the service being sold is Utility

Computing. We use the term Private Cloud to refer to internal datacenters of a business or other

organization, not made available to the general public. Thus, Cloud Computing is the sum of

SaaS and Utility Computing, but does not include Private Clouds. People can be users orproviders of SaaS, or users or providers of Utility Computing. We focus on SaaS Providers

(Cloud Users) and Cloud Providers, which have received less attention than SaaS Users.From a hardware point of view, three aspects are new in Cloud Computing.1. The illusion of infinite computing resources available on demand, thereby eliminating the

need for Cloud Computing users to plan far ahead for provisioning.

2. The elimination of an up-front commitment by Cloud users, thereby allowing companies tostart small and increase hardware resources only when there is an increase in their needs.

3. The ability to pay for use of computing resources on a short-term basis as needed (e.g.,

processors by the hour and storage by the day) and release them as needed, thereby rewardingconservation by letting machines and storage go when they are no longer useful.


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A Network and Device Aware QoS Approach for Cloud-Based Mobile

Streaming

Chin-FengLaiInst. of Comput. Sci. & Inf. Eng., Nat. ILan Univ., Ilan, Taiwan

Honggang Wang ; Han-Chieh Chao ; Guofang Nan

Cloud multimedia services provide an efficient, flexible, and scalable data processing method

and offer a solution for the user demands of high quality and diversified multimedia. As

intelligent mobile phones and wireless networks become more and more popular, networkservices for users are no longer limited to the home. Multimedia information can be obtained

easily using mobile devices, allowing users to enjoy ubiquitous network services. Considering

the limited bandwidth available for mobile streaming and different device requirements, thisstudy presented a network and device-aware Quality of Service (QoS) approach that provides

multimedia data suitable for a terminal unit environment via interactive mobile streamingservices, further considering the overall network environment and adjusting the interactive

transmission frequency and the dynamic multimedia transcoding, to avoid the waste ofbandwidth and terminal power. Finally, this study realized a prototype of this architecture to

validate the feasibility of the proposed method. According to the experiment, this method could

provide efficient self-adaptive multimedia streaming services for varying bandwidthenvironments.

W.W. Cohen, R.E. Schapire, and Y. Singer, Learning to order things, J.

Artificial Intelligent Research, vol. 10, no. 1, pp. 243-270,1999.

There are many applications in which it is desirable to order rather than classify instances. Herewe consider the problem of learning how to order instances given feedback in the form of

preference judgments, i.e., statements to the effect that one instance should be ranked ahead of

another. We outline a two-stage approach in which one first learns by conventional means abinary preference function indicating whether it is advisable to rank one instance before another.

Here we consider an on-line algorithm for learning preference functions that is based on Freund

and Schapire's 'Hedge' algorithm. In the second stage, new instances are ordered so as to

maximize agreement with the learned preference function. We show that the problem of findingthe ordering that agrees best with a learned preference function is NP-complete. Nevertheless,

we describe simple greedy algorithms that are guaranteed to find a good approximation. Finally,

we show how metasearch can be formulated as an ordering problem, and present experimental

results on learning a combination of 'search experts', each of which is a domain-specific queryexpansion strategy for a web search engine.


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M. Deshpande and G. Karypis, Item-Based Top-n Recommendation,

ACM Trans. Information System, vol. 22, no. 1, pp. 143-177,2004.

The explosive growth of the world-wide-web and the emergence of e-commerce has led to the

development ofrecommender systemsa personalized information filtering technology used to

identify a set of items that will be of interest to a certain user. User-based collaborative filteringis the most successful technology for building recommender systems to date and is extensively

used in many commercial recommender systems. Unfortunately, the computational complexity

of these methods grows linearly with the number of customers, which in typical commercialapplications can be several millions. To address these scalability concerns model-based

recommendation techniques have been developed. These techniques analyze the useritem

matrix to discover relations between the different items and use these relations to compute thelist of recommendations.

In this article, we present one such class of model-based recommendation algorithms that first

determines the similarities between the various items and then uses them to identify the set of

items to be recommended. The key steps in this class of algorithms are (i) the method used to

compute the similarity between the items, and (ii) the method used to combine these similaritiesin order to compute the similarity between a basketof items and a candidate recommender item.

Our experimental evaluation on eight real datasets shows that these item-basedalgorithms areup to two orders of magnitude faster than the traditional user-neighborhood based recommender

systems and provide recommendations with comparable or better quality.

A. Iosup, S. Ostermann, N. Yigitbasi, R. Prodan, T. Fahringer, and D. Epema,

Performance Analysis of Cloud Computing Services for Many-Tasks

Scientific Computing, IEEE Trans. Parallel Distributed System, vol. 22, no.

6, pp. 931-945, June 2011.Cloud computing is an emerging commercial infrastructure paradigm that promises to eliminate

the need for maintaining expensive computing facilities by companies and institutes alike.

Through the use of virtualization and resource time sharing, clouds serve with a single set ofphysical resources a large user base with different needs. Thus, clouds have the potential to

provide to their owners the benefits of an economy of scale and, at the same time, become an

alternative for scientists to clusters, grids, and parallel production environments. However, the

current commercial clouds have been built to support web and small database workloads, whichare very different from typical scientific computing workloads. Moreover, the use of

virtualization and resource time sharing may introduce significant performance penalties for the

demanding scientific computing workloads. In this work, we analyze the performance of cloudcomputing services for scientific computing workloads. We quantify the presence in real

scientific computing workloads of Many-Task Computing (MTC) users, that is, of users who

employ loosely coupled applications comprising many tasks to achieve their scientific goals.Then, we perform an empirical evaluation of the performance of four commercial cloud

computing services including Amazon EC2, which is currently the largest commercial cloud.

Last, we compare through trace-based simulation the performance characteristics and cost

models of clouds and other scientific computing platforms, for general and MTC-based scientific


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computing workloads. Our results indicate that the current clouds need an order of magnitude in

performance improvement to be useful to the scientific community, and show which

improvements should be considered first to address this discrepancy between offer and demand.

Architecture


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Expected Results:Our framework is mainly designed for cloud applications, because: 1) client-side QoS values of

different users can be easily obtained in the cloud environment; and 2) there are a lot of

redundant services abundantly available in the cloud, QoS ranking of candidate services becomesimportant when building cloud applications.

The framework can also be extended to other component-based applications, in case that the

components are used by a number of users, and the past usage experiences of different users can

be obtained.

qos ranking prediction based on past service usage experience in cloud services

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