A hierarchical edge cloud architecture for mobile computing

The performance of mobile computing would be significantly improved by leveraging cloud computing and migrating mobile workloads for remote execution at the cloud. In this paper, to efficiently handle the peak load and satisfy the requirements of remote program execution, we propose to deploy cloud servers at the network edge and design the edge cloud as a tree hierarchy of geo-distributed servers, so as to efficiently utilize the cloud resources to serve the peak loads from mobile users. The hierarchical architecture of edge cloud enables aggregation of the peak loads across different tiers of cloud servers to maximize the amount of mobile workloads being served. To ensure efficient utilization of cloud resources, we further propose a workload placement algorithm that decides which edge cloud servers mobile programs are placed on and how much computational capacity is provisioned to execute each program. The performance of our proposed hierarchical edge cloud architecture on serving mobile workloads is evaluated by formal analysis, small-scale system experimentation, and large-scale trace-based simulations.

[1]  Xu Chen,et al.  Decentralized Computation Offloading Game for Mobile Cloud Computing , 2014, IEEE Transactions on Parallel and Distributed Systems.

[2]  David Breitgand,et al.  Improving consolidation of virtual machines with risk-aware bandwidth oversubscription in compute clouds , 2012, 2012 Proceedings IEEE INFOCOM.

[3]  T. V. Lakshman,et al.  Online Allocation of Virtual Machines in a Distributed Cloud , 2017, IEEE/ACM Transactions on Networking.

[4]  R. Srikant,et al.  Stochastic models of load balancing and scheduling in cloud computing clusters , 2012, 2012 Proceedings IEEE INFOCOM.

[5]  Akshat Verma,et al.  Virtual machine consolidation in the wild , 2014, Middleware.

[6]  Guillaume Pierre,et al.  Wikipedia workload analysis for decentralized hosting , 2009, Comput. Networks.

[7]  Mahadev Satyanarayanan,et al.  Just-in-time provisioning for cyber foraging , 2013, MobiSys '13.

[8]  Pan Hui,et al.  ThinkAir: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading , 2012, 2012 Proceedings IEEE INFOCOM.

[9]  Xu Chen,et al.  COMET: Code Offload by Migrating Execution Transparently , 2012, OSDI.

[10]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[11]  Meng Wang,et al.  Consolidating virtual machines with dynamic bandwidth demand in data centers , 2011, 2011 Proceedings IEEE INFOCOM.

[12]  Alex X. Liu,et al.  Secure unlocking of mobile touch screen devices by simple gestures: you can see it but you can not do it , 2013, MobiCom.

[13]  Jerome A. Rolia,et al.  Workload Analysis and Demand Prediction of Enterprise Data Center Applications , 2007, 2007 IEEE 10th International Symposium on Workload Characterization.

[14]  Mahadev Satyanarayanan,et al.  Towards wearable cognitive assistance , 2014, MobiSys.

[15]  Alec Wolman,et al.  MAUI: making smartphones last longer with code offload , 2010, MobiSys '10.

[16]  Tim Verbelen,et al.  Cloudlets: bringing the cloud to the mobile user , 2012, MCS '12.

[17]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[18]  Haiying Shen.  RIAL: Resource Intensity Aware Load Balancing in Clouds , 2017 .

[19]  Alec Wolman,et al.  Demo: DeLorean: using speculation to enable low-latency continuous interaction for mobile cloud gaming , 2014, MobiSys.

[20]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[21]  Paramvir Bahl,et al.  The Case for VM-Based Cloudlets in Mobile Computing , 2009, IEEE Pervasive Computing.

[22]  Chuang Lin,et al.  Delay guaranteed live migration of Virtual Machines , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[23]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[24]  Liang Hu,et al.  A Heuristic Clustering-Based Task Deployment Approach for Load Balancing Using Bayes Theorem in Cloud Environment , 2016, IEEE Transactions on Parallel and Distributed Systems.