challenges on wireless heterogeneous networks for mobile cloud computing in a smart city scenario
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Challenges on wireless Heterogeneous Networks for Mobile Cloud Computing in a Smart City scenarioTRANSCRIPT
Challenges on Wireless HetNet for Mobile Cloud Computing
in a Smart City scenario Bologna, November 7th 2014
Daniela Mazza, PhD Student - 28th Cycle
Department of Electronics Engineering, Telecommunications and Information Technology University of Bologna, Italy Supervisor: Prof. Giovanni Emanuele CorazzaCo-advisor: Prof. Daniele Tarchi
Outline◆ Urbanization and ICT trends. The Smart City concept ◆ Urban Mobile Cloud Computing
◆ HetNets: Macro and small cells ◆ Cloud Topologies
◆ Offloading in UMCC: Throughput, Energy and Time spent for computation
◆ Cost Function ◆ Numerical results
Urbanization: where are we?
Source: United Nations World Urbanization Prospects 2014 Revision
Urbanization: where are we?
2014: 28 mega-cities (>10M inhabitants) 54% of population resides in urban area
Source: United Nations World Urbanization Prospects 2014 Revision
Urbanization: where are we going?
2030: 41 mega-cities (>10M inhabitants) 60% of population resides in urban area
Source: United Nations World Urbanization Prospects 2014 Revision
Urbanization: Where are we going?
Societal Challenges
Energy supply, waste management, natural disasters, energy consumption, traffic, pollution, …….
Global Mobile vs Desktop Internet User Projection (Morgan Stanley Research)
Connections: Where are we?
Connections: where are we going?
Cisco VNI Forecast• 2018: almost 4 billion Internet users, 52% of the world’s projected
population. • the average fixed broadband speed will grow from 16 to 42 Mbps from
2013 to 2018
287M → 317M
235M → 371M
323M → 346M 224M → 431M
213M → 431M
1.2B → 2.1B
Smart CityA city that promotes the use of ICT to make better use of infrastructure, reduces the use of environmental capital and supports smart growth, to achieve a better urban way of life..
• Environment-friendly design buildings • Regional Emergency Medical Service • Smart Buildings • MegaSolar • Biomass Fuels • Electric Vehicle Car Sharing • Smart House • Electric Bus • Multi-energy Station • Off-shore wind farm • Solar panel • Intelligent transportation System (ITS) • Next Generation vehicle center • Battery Storage System • WindFarm
this image: 197 results on Google
“Smart City”: 250.000.000 results on
Smart City and data exchange
System of systems (main functional areas interconnected)
Data exchanged (Users devices as data input / output )
Wireless Communication – data are exchanged between the citizens' devices and the Smart City system both uploading and downloading
Smart City and data exchange
• Sensors: acquisition of data regarding the users and the environment
• Nodes: organization of a distributed mobile cloud, VCN (Vehicular Cloud Network)
• Outputs: providing results for users and for machines (M2M)
Urban Mobile Cloud Computing Framework
Urban area with a pervasive wireless coverage, where several mobile devices are interacting with:
• a traditional centralized cloud service
• roadside units (cloudlets)
• a distributed mobile cloud consisting of many SMD
Access nodes of the HetNet (macro and microcells) connecting SMD to the Centralized Cloud
Cloud TopologiesCentralized Cloud (remote infrastructure) • big storage capacity • high computing power • elasticity of resource provisioning • drawbacks: latency, congestion
Distributed Mobile Cloud (neighboring SMD sharing resouces) • small storage capacity (each SMD) • small computing power (each SMD) • useful when neighbors need the same
resources
Cloudlets (proximity infrastructures) • medium storage capacity • medium computing power • address latency drawbacks • drawbacks: limited area
HetNet: Macro and small cellsMacrocells (3G, LTE):
• coverage > 500 m • total coverage of the area • minimal handover frequency • channel fading and traffic congestion
Small cells Picocells (malls, airports, stadium):
• coverage > 200m • High number of connected devices
Femtocells (home or small business): • coverage < 200m • Only for selected devices
WiFi access (home or small business): • Coverage < 100 m • Only for selected devices
Application Requirements
APPLICATIONS latency energy throughput
computing
exchanged data
storage users
Mobility restrictive variable restrictive high high variable high
Healthcare restrictive non-restrictive
non-restrictive high high high low
Disaster Recovery restrictive restrictive non-restrictive high high high variable
Energy non-restrictive
non-restrictive
non-restrictive high high high high
Waste Management non-restrictive restrictive non-
restrictive low low low low
Tourism non-restrictive restrictive non-
restrictive high high high variable
System Interactions
The utility function acts for distributing and performing the application in different parts of the Urban MCC
Devices and clouds
Processing speed
Storage Capacity
Communication equipments
Channel capacity
Priority QoS management
Communication Interfaces
QoS Requirements
Latency
Energy consumption
Throughput
Computing
Exchanged data
Storage
Users
Smart City Applications
Mobility
Healthcare
Disaster recovery
Energy
Waste Management
Tourism
Utility or Cost
Function
Partition of the
application and node and cloud
association
System Interactions
Offloading Distribution among the different topologies of clouds
Througput
BW bandwidth n no. of the devices connected to the node
SNR Signal to Noise Ratio
d (distance from the device to the node)
d
n
Energy and time for computationUser's point of view • The mobile device consumes energy
to transfer data to the cloud • The mobile device consumes (little)
energy waiting for the computation while the task is performed in the cloud
• The mobile device consumes energy to transfer results from the cloud
● The mobile device consumes energy for the computation of the task
● The time is related to the trasfer of data from the mobile device and transfer of results from the cloud
● The computation is faster due to the high computing capacity of the cloud servers
● The time is related to the poor computing capacity of the mobile device
Local computation
C number of instructions of the task
Smd calculation speed Pl power for local computing
Energy for local computation: Time for local computation:
Total data offloading
Cloud server computation
D exchanged data Ptr power for sending and receiving data Str transmission speed
C instructions (no.)
Pid power while being idle
Scs cloud server’s calculation speed
Energy for total offloading computing: Time for total offloading computing:
Partial offloadingLocal
computationOffloading data Cloud server
computation
C instructions (no.)D exchanged data (bit)
C instructions (no.)
weight coefficients - percentage of the computational task and of the exchanged data for offloading
Cost Function
Network centric approach bounded discretionary chosen (= 0.5)
Numerical results
LTE eNodeB – channel capacity 100 mHz
WiFi acces points – channel capacity 22 mHz
Pid = 0.3 W Power while being idle
Smd = 400 MHz Computation Speed
Pl = 0.9 W Power for local computing
Ptr = 1.3 W Power for sending and receiving data
Numerical results
Application 1: Real time traffic analysis
Application 2: mobile video and audio communication
Application 3: mobile social networking
When the network is overloaded,, with both a large amount of computation to execute and data to exchange, tasks are better performed for a specific value of gamma
Application 3 – Cost function's results
Energy and time consumption for the application with high computation and high amount of data to be transferred
A User-Satisfaction Based Utility Function
U1(x) =1
1+ e−α (x−β )U2 (x) = 1−
11+ e−α (x−β )
f2 (Epart _od ,ijk ) = 1−1
1+ e−α2 (Epart _od ,ijk−Eo ,k )f1(Str ,ij ) =
11+ e−α1(Str ,ij−Stro ,k )
f3(Tpart _od ,ijk ) = 1−1
1+ e−α3 (Tpart _od ,ijk−To ,k )
Uij = c1 ⋅ f1(Str ,ij )+ c2 ⋅ f2 (Epart _od ,ijk )+ c3 ⋅ f3(Tpart _od ,ijk )
Reference Values
Numerical Results
Performance results in terms of average energy consumption with a variable number of SMDs
Numerical Results
Performance results in terms of average computation time with a variable number of SMDs
Numerical Results
Performance results in terms of average throughput time with a variable number of SMDs
Complexity• M available HetNet nodes Nod[i] for offloading towards the centralized
cloud,
• N cloudlets Ccl[j]
• K SMDs MD[k], to share the computation in the distributed cloud
• total of 1 + M + N + K entities, including the local node RSMD
Aim: to distribute, by means of all these entities, different percentages αi of operations O, βi of data D, and γi of memory S, to all the available nodes, cloudlets and SMDs.
A real application: realtime navigation
Cloudlets only
Cloudlets and near vehicle
Numerical Results
Numerical Results
Numerical Results
Numerical Results
PapersD. Mazza, D. Tarchi, and G. E. Corazza, “A partial offloading technique for wireless mobile cloud computing in smart cities,” in Proc. of 2014 European Conference on Networks and Communications (EuCNC), Bologna, Italy, Jun. 2014.
D. Mazza, D. Tarchi, and G. E. Corazza, “A user-satisfaction based offloading technique for smart city applications,” in Proc. of IEEE Globecom 2014, Austin, TX, USA, Dec 2014, accepted for publication.
D. Mazza, D. Tarchi, and G. E. Corazza,, “Urban mobile cloud computing: a framework at the service of smart cities,” IEEE Commun. Mag., submitted.
D. Mazza, D. Tarchi, and G. E. Corazza, “Improving Execution of Smart City Applications Through Heterogeneous Networks and Clouds,” IEEE ICC International Conference on Communication 2015, London, UK, submitted.
