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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 scenario

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Challenges on wireless Heterogeneous Networks for Mobile Cloud Computing in a Smart City scenario

  1. 1. 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 Corazza Co-advisor: Prof. Daniele Tarchi
  2. 2. 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
  3. 3. Urbanization: where are we? Source: United Nations World Urbanization Prospects 2014 Revision
  4. 4. Urbanization: where are we? Source: United Nations World Urbanization Prospects 2014 Revision 2014: 28 mega-cities (>10M inhabitants) 54% of population resides in urban area
  5. 5. Urbanization: where are we going? Source: United Nations World Urbanization Prospects 2014 Revision 2030: 41 mega-cities (>10M inhabitants) 60% of population resides in urban area
  6. 6. Urbanization: Where are we going?
  7. 7. Societal Challenges Energy supply, waste management, natural disasters, energy consumption, traffic, pollution, …….
  8. 8. Connections: Where are we? Global Mobile vs Desktop Internet User Projection (Morgan Stanley Research)
  9. 9. Connections: where are we going? 287M → 317M 323M → 346M 235M → 371M 224M → 431M 213M → 431M 1.2B → 2.1B 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
  10. 10. Smart City A 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 Google
  11. 11. 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
  12. 12. 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)
  13. 13. 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
  14. 14. Cloud Topologies Centralized Cloud (remote infrastructure) • big storage capacity • high computing power • elasticity of resource provisioning • drawbacks: latency, congestion Cloudlets (proximity infrastructures) • medium storage capacity • medium computing power • address latency drawbacks • drawbacks: limited area Distributed Mobile Cloud (neighboring SMD sharing resouces) • small storage capacity (each SMD) • small computing power (each SMD) • useful when neighbors need the same resources
  15. 15. HetNet: Macro and small cells Macrocells (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
  16. 16. Application Requirements APPLICATIONS latency energy through put computi ng exchang ed 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
  17. 17. 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
  18. 18. System Interactions
  19. 19. Offloading Distribution among the different topologies of clouds
  20. 20. 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
  21. 21. Energy and time for computation User'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 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 mobile device consumes energy for the computation of the task ● The time is related to the poor computing capacity of the mobile device
  22. 22. Local computation Energy for local computation: Time for local computation: C number of instructions of the task Smd calculation speed Pl power for local computing
  23. 23. Total data offloading Energy for total offloading computing: Time for total offloading computing: 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
  24. 24. Partial offloading Local computation Offloading 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
  25. 25. Cost Function Network centric approach bounded discretionary chosen (= 0.5)
  26. 26. 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
  27. 27. 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
  28. 28. Application 3 – Cost function's results Energy and time consumption for the application with high computation and high amount of data to be transferred
  29. 29. A User-Satisfaction Based Utility Function U1(x) = 1 1+ e−α (x−β ) U2 (x) = 1− 1 1+ e−α (x−β ) 1 1 f(S) = f(E) = 1− 1tr,ij 2 part _ od,ijk 1+ e−α 2 (Epart −_ od ,ijk Eo,k ) 1+ e−α1 (Str ,ij −Stro,k ) f3(Tpart _ od,ijk ) = 1− 1 1+ e−α 3 (Tpart _ od ,ijk −To,k ) Uij = c1 ⋅ f1(Str,ij )+ c2 ⋅ f2 (Epart _ od,ijk )+ c3 ⋅ f3(Tpart _ od,ijk )
  30. 30. Reference Values
  31. 31. Numerical Results Performance results in terms of average energy consumption with a variable number of SMDs
  32. 32. Numerical Results Performance results in terms of average computation time with a variable number of SMDs
  33. 33. Numerical Results Performance results in terms of average throughput time with a variable number of SMDs
  34. 34. 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.
  35. 35. A real application: realtime navigation Cloudlets only Cloudlets and near vehicle
  36. 36. Numerical Results
  37. 37. Numerical Results
  38. 38. Numerical Results
  39. 39. Numerical Results
  40. 40. Papers D. 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.
  41. 41. Thank you! Daniela Mazza University of Bologna daniela.mazza6@unibo.it www.unibo.it

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