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Mobile Cloud Computing MCC

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  1. Mira Vrbaski mvrba094@uottawa.ca 7526787 Mobile Cloud Computing MCC CSI 5619 Wireless Networks and Mobile Computing

  2. Where mobile computation is going?Information on your finger tips any where any time Click View then Header and Footer to change this footer

  3. Mobile Cloud Computing • Mobile devices • Wireless network • Cloud Computing Mobile Devices Mobile Network Mobile Computing Cloud Computing Image take from and modified: http://www.tutorialspoint.com/cloud_computing/cloud_computing_mobile.htm

  4. Definition: Mobile Cloud Computing • Mobile cloud computing as an integration of cloud computing technology with mobile devices to make the mobile devices resource-full in terms of computational power, memory, storage, energy, and context awareness. • Two perspective: infrastructure (hardware remains static and provides services) or ad-hoc cloud (group of smartphones that act as an cloud) Click View then Header and Footer to change this footer

  5. Mobile Devices • All kinds of devices that have mobility • Limitations – resource constrained: battery life, CPU power, speed, memory • Future: Internet of things (sensors on internet) Click View then Header and Footer to change this footer

  6. Wireless network • heterogeneous and different radio access technologies: GPRS, 3G, WLAN, WiMax. • “always-on”, “on-demand”, requires a network selection and use that takes energy-efficiency and costs into account. • WIFI less latency and use less power that 3G • Latency • Connectivity • Coverage Click View then Header and Footer to change this footer

  7. Cloud Computing (IaaS, PaaS, SaaS) Elasticity: illusion of never ended resources Security: identities with digital credentials, passwords and digital certificates. MCC even greater since low CPU. Agility, Cost, Performance, Maintenance, Reliability http://felixbodmer.wordpress.com/tag/iaas/

  8. Network as a Service (NaaS) Cloud based Network Architecture • business model for delivering network services virtually over the Internet on a pay-per-use or monthly subscription basis. • Telco opening up network APIs to expose network capabilities (e.g. presence, location, billing and charging). • These network resources are exposed in a standard and flexible manner to 3rd Party ASPs where they can mash-up Telco services with IT services and vise versa. Tao Feng, Jun Bi, Hongyu Hu and Hui Cao, “Networking as a Service: a Cloud-based Network Architecture”, JOURNAL OF NETWORKS, VOL. 6, NO. 7, JULY 2011

  9. Mobile Cloud Computing architecture directions Collaborated architecture User collaborated device resources for computation. Mobile devices part of the cloud. Agent Client Architecture offloading the mobile computational process to the cloud Click View then Header and Footer to change this footer

  10. Mobile device computational offload / affecting entities • Computation offloading migrates resource-intensive computations from a mobile device to the cloud or server. • Cloud computation offloading enhances the applications performance, reduces battery power consumption, and execute applications that are unable to execute due to insufficient smartphone resources. Click View then Header and Footer to change this footer

  11. Clone Cloud: Elastic Execution between Mobile Device and Cloud Execution conditions: Network, CPU speed, Energy consumption end etc. (low and high end mobile devices, connectivity) Motivation: as long as execution on the cloud is significantly faster (more reliable, secure, etc.) then on mobile device, paying the cost for sending the relevant data and code from device to the cloud and back is worth it. Byung-Gon Chun, SunghwanIhm, PetrosManiatis, “CloneCloud: Elastic Execution between Mobile Device and Cloud”, EuroSys’11, April 10–13, 2011, Salzburg, Austria.

  12. Clone Cloud: Elastic Execution between Mobile Device and Cloud cont. • Partitioning pick which parts of an application’s execution to retain on the mobile device and which to migrate to the cloud. • The partitioneruses static analysis to identify legal choices for placing migration and re-integration points in the code. Byung-Gon Chun, SunghwanIhm, PetrosManiatis, “CloneCloud: Elastic Execution between Mobile Device and Cloud”, EuroSys’11, April 10–13, 2011, Salzburg, Austria.

  13. Mobile devices as resources – Markov chain for false tolerance • Monitoring technic based on Marko chain, analyze and predicts state. • Usage pattern collected from a Mobile devices at University campus JiSu Park, HeonChang Yu, KwangSik Chung, EunYoung Lee ,”Markov Chain based Monitoring Service for Fault Tolerance in Mobile Cloud Computing”,

  14. Mobile devices as resources – Markov chain for false tolerance 2 • Resource information definition: • CPU power • Memory • Bandwidth • Location • Util- utilization rate • User –utilization rate of user • Sys – utilization rate of system - • Total – maximum available utilization • Cache –utilization rate of cache memory • Lper -rate of distance between the center of AP area • Llimcommunication coverage of distance of AP • Lcencenter of AP of current • Llcurthe current location of resource JiSu Park, HeonChang Yu, KwangSik Chung, EunYoung Lee ,”Markov Chain based Monitoring Service for Fault Tolerance in Mobile Cloud Computing”,

  15. Mobile devices as resources – Markov chain for false tolerance 3 Markov Chain matrix • Model for predicting fault: • 3 possible states: Stable Unstable Disable SstI present Stable state at time I Possibility of each state time can be written as PSstI, PUstI and ODstI JiSu Park, HeonChang Yu, KwangSik Chung, EunYoung Lee ,”Markov Chain based Monitoring Service for Fault Tolerance in Mobile Cloud Computing”,

  16. Secure Data Processing Framework for Mobile Cloud Computing MobiCloud • amobile device is treated as a Service Node (SN), and it is mirrored to one or more Extended Semi- Shadow Images (ESSIs) • A mobile device and its ESSI can act like a service provider or a service broker. • the cloud’s boundary is extended to the customer device domain • ESSI can be an exact, partial clone, or an image. Dijiang Huang, Zhibin Zhou, Le Xu, Tianyi Xing, Yunji Zhong, “Secure Data Processing Framework for Mobile Cloud Computing:

  17. Geographic-based Mobile Cloud Computing HLH –home location host VM VLH – visiting location host VM Idea: move the subscriber VM to the closed geographical host when the mobile user moves from Home location to Visiting. Users can access their VMs on the VLH with the same domain name but different IP address in another domain. Solution isolate the data plane and control plane of the network. Tianyi Xing, Hongbin Liang, Dijiang Huang, Lin X. Cai, „Geographic-based Service Request Scheduling Model for Mobile Cloud Computing”

  18. Geographic-based Mobile Cloud Computing 2 • The schedulerallocates system resources based on not only user’s preference and available system resources locally, but also available resource in all other Cloud clusters. • In distributed resource allocation scheme achieves better user experience and system utilization compared to centralized resource allocation scheme. Tianyi Xing, Hongbin Liang, Dijiang Huang, Lin X. Cai, „Geographic-based Service Request Scheduling Model for Mobile Cloud Computing”

  19. Geographic-based Mobile Cloud Computing 4 n – number of provisioning domains K- number of virtual machines in the whole system Ki - is number of virtual machines per domaini, K is Sum of Ki C- is number of VMs allocated for one service where c ∈ {1, 2, .., C}, C ≤ K R- available resources in the whole system Ri- available resources in the domaini Nreqi – number of VMs requested in the domain i Scheduling(Nreqi, Ri) service request claiming Nreqi VMs arrives at or is transferred to the ith domain with Ri available resource in term of Vms. Click View then Header and Footer to change this footer

  20. Distributed Service Request Scheduling Algorithm Click View then Header and Footer to change this footer

  21. Distributed Service Request Scheduling Algorithm Example • n=4, K=20, K1=4, k2=5, K3=4, K4=7, c=5, R = 5, R1=0, R2=2, R3=2, R4=1 • Request c=5 is directed to domain 3, show how algorithm with be executed. • Nreq3 =5 > R3(2) • New Nreq3 = 5-2=3, R3 =0 and request is sent to domain 4. • Nreq4=3-1=2, R4=0 and new request is sent to domain 1. • Nreq1=2-0, R1-0, request is sent to 2 • Nreq2=2-2, R2=0 Click View then Header and Footer to change this footer

  22. Access Network Discovery and Service Function (ANDSF) – WiFi offload3GPP TS 24.312 Click View then Header and Footer to change this footer

  23. Alcatel-Lucent WiFi Control Module 2 http://www2.alcatel-lucent.com/techzine/policy-empowered-carrier-wi-fi-control/

  24. Alcatel-Lucent WiFi Control Module 3 http://www2.alcatel-lucent.com/techzine/policy-empowered-carrier-wi-fi-control/

  25. Question 1 People very often get confused between with Cloud Computing and Mobile Cloud Computing terminology, a)what is the difference between this two? b)Which are three components on the Mobile Cloud Computing? a)Cloud computing is sub set of the mobile cloud computing that takes case based on its type about the managing resources (IaaS – infrastructure), platforms (PaaS –platform) or applications (SaaS – services) on the cloud. Even thought to access Cloud Computing resources we use internet, mobile networks, this is not a concern of Cloud computing paradigm. Click View then Header and Footer to change this footer

  26. Question 1 cont. People very often get confused between with Cloud Computing and Mobile Cloud Computing terminology, a)what is the difference between this two? b)Which are three components on the Mobile Cloud Computing? b) From other side Mobile Cloud Computing is consisted of the three overlapping areas: Mobile devices, Mobile Network and Cloud Computing, where all three areas are bringing its own limitation to the mobile cloud computing paradigm. Click View then Header and Footer to change this footer

  27. Question 2 • Explain what is the computational offload? Why we should consider computational offload? • Explain Elastic Execution between Mobile Devices and Cloud solution. a) Mobile Devices have limited CPU power, speed and memory. Computation offloading migrates resource-intensive computations from a mobile device to the cloud or the server. Cloud computation offloading enhances the applications performance, reduces battery power consumption, and execute applications that are unable to execute due to insufficient smartphone resources. Click View then Header and Footer to change this footer

  28. Question 2 cont. • Explain what is the computational offload? Why we should consider computational offload? • Explain Elastic Execution between Mobile Devices and Cloud solution. • b) Create a device clone on the cloud, do partitioning of the application. Partitioning pick which parts of an application’s execution to retain on the mobile device and which to migrate to the cloud. The partitioner uses static analysis to identify legal choices for placing migration and re-integration points in the code. Once the offloaded part of the code is completed the code is result of the process is returned and integrate into the application flow. Click View then Header and Footer to change this footer

  29. Question 3 Using the Distributed Service Request Scheduling Algorithm where the next values are defined and algorithm is shown: n – number of provisioning domains K- number of virtual machines in the whole system Ki - is number of virtual machines per domaini, K is Sum of Ki C- is number of VMs allocated for one service where c ∈ {1, 2, .., C}, C ≤ K R- available resources in the whole system Ri- available resources in the domaini Nreqi – number of VMs requested in the domain i Scheduling(Nreqi, Ri) service request claiming Nreqi VMs arrives at or is transferred to the ith domain with Ri available resource in term of Vms. Click View then Header and Footer to change this footer

  30. Question 3 Click View then Header and Footer to change this footer

  31. Question 3 The system values are: n =4, K =22, K1=5, K2=5, K3=7, K4=5, R=10, R1=0, R2=2, R3=5, R4=3 a)If C the number of required VMs is 11, do you think that the system will be able to handle request and why? b) What can be the biggest C (required VMs for the service) number in other that system can handle the request if nothing else in the system is changed. c) If C=8 and request comes to domain 3, show the algorithm execution. Click View then Header and Footer to change this footer

  32. Question 3 A) R=10 and present available resource which is smaller that C=11 required VMs. That means that system will not be able to handle requires for C=11. Request will be rejected. B) C must be equal or less that R. So C max is 10. C)n =4, K =22, K1=5, K2=5, K3=7, K4=5, R=10, R1=0, R2=2, R3=5, R4=3, Request c=8 is directed to domain 3, show how algorithm with be executed. • Nreq3 =8 > R3(5) • New Nreq3 = 8-5=3, R3 =0 and request is sent to domain 4. • Nreq4=3-3=0, R4=0 -> completed Click View then Header and Footer to change this footer