ABSTRACT
The past few years have seen a growing number of mobile and sensor applications that rely on Cloud support. The role of the Cloud is to allow these resource-limited devices to offload and execute some of their compute-intensive tasks in the Cloud for energy saving and/or faster processing. However, such offloading to the Cloud may result in high network overhead which is not suitable for many mobile/sensor applications that require low latency. So, people have looked at an alternative Cloud design whose resources are located at the edge of the Internet, called Edge Cloud. Although the use of Edge Cloud can mitigate the offloading overhead, the computational power and network bandwidth of Edge Cloud's resources are typically much more limited compared to the centralized Cloud and hence are more sensitive to workload variation (e.g., due to CPU or I/O contention). In this paper, we propose a locality-aware load sharing technique that allows edge resources to share their workload in order to maintain the low latency requirement of Mobile-Cloud applications. Specifically, we study how to determine which edge nodes should be used to share the workload with and how much of the workload should be shared to each node. Our experiments show that our locality-aware load sharing technique is able to maintain low average end-to-end latency of mobile applications with low latency variation, while achieving good utilization of resources in the presence of a dynamic workload.
- Suman Banerjee and Dapeng Oliver Wu. 2013. Final report from the NSF Workshop on Future Directions in Wireless Networking. (2013).Google Scholar
- Marco V. Barbera, Sokol Kosta, Alessandro Mei, and Julinda Stefa. 2013. To offload or not to offload? the bandwidth and energy costs of mobile cloud computing INFOCOM, 2013 Proceedings IEEE. IEEE, 1285--1293.Google Scholar
- David Barrett. 2013. One surveillance camera for every 11 people in Britain, says CCTV survey. The Telegraph Vol. 10 (2013).Google Scholar
- Flavio Bonomi, Rodolfo Milito, Jiang Zhu, and Sateesh Addepalli. 2012. Fog computing and its role in the internet of things Proceedings of the first edition of the MCC workshop on Mobile cloud computing. ACM, 13--16. Google ScholarDigital Library
- Valeria Cardellini, Michele Colajanni, and Philip S. Yu. 1999. Dynamic load balancing on web-server systems. IEEE Internet computing Vol. 3, 3 (1999), 28--39. Google ScholarDigital Library
- Kyungmin Lee David Chu, Eduardo Cuervo, Johannes Kopf, Sergey Grizan, Alec Wolman, and Jason Flinn. {n. d.}. Outatime: Using Speculation to Enable Low-Latency Continuous Interaction for Cloud Gaming. (. {n. d.}).Google Scholar
- Brent Chun, David Culler, Timothy Roscoe, Andy Bavier, Larry Peterson, Mike Wawrzoniak, and Mic Bowman. 2003. Planetlab: an overlay testbed for broad-coverage services. ACM SIGCOMM Computer Communication Review Vol. 33, 3 (2003), 3--12. Google ScholarDigital Library
- Eduardo Cuervo, Aruna Balasubramanian, Dae-ki Cho, Alec Wolman, Stefan Saroiu, Ranveer Chandra, and Paramvir Bahl. 2010. MAUI: making smartphones last longer with code offload Proceedings of the 8th international conference on Mobile systems, applications, and services. ACM, 49--62. Google ScholarDigital Library
- Stephen R. Ellis, Katerina Mania, Bernard D. Adelstein, and Michael I. Hill. 2004. Generalizeability of latency detection in a variety of virtual environments Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Vol. Vol. 48. SAGE Publications Sage CA: Los Angeles, CA, 2632--2636.Google Scholar
- Dave Evans. 2011. The internet of things: How the next evolution of the internet is changing everything. CISCO white paper Vol. 1 (2011), 1--11.Google Scholar
- Chaima Ghribi, Makhlouf Hadji, and Djamal Zeghlache. 2013. Energy efficient vm scheduling for cloud data centers: Exact allocation and migration algorithms. In Cluster, Cloud and Grid Computing (CCGrid), 2013 13th IEEE/ACM International Symposium on. IEEE, 671--678.Google ScholarDigital Library
- Mark S. Gordon, Davoud Anoushe Jamshidi, Scott A. Mahlke, Zhuoqing Morley Mao, and Xu Chen. 2012. COMET: Code Offload by Migrating Execution Transparently. OSDI, Vol. Vol. 12. 93--106. Google ScholarDigital Library
- Trinabh Gupta, Rayman Preet Singh, Amar Phanishayee, Jaeyeon Jung, and Ratul Mahajan. 2014. Bolt: Data Management for Connected Homes. In NSDI. 243--256. Google ScholarDigital Library
- Kiryong Ha, Yoshihisa Abe, Zhuo Chen, Wenlu Hu, Brandon Amos, Padmanabhan Pillai, and Mahadev Satyanarayanan. 2015. Adaptive vm handoff across cloudlets. Technical Report. Technical Report Carnegie Mellon University-CS-15-113, Carnegie Mellon University School of Computer Science.Google Scholar
- Kiryong Ha, Zhuo Chen, Wenlu Hu, Wolfgang Richter, Padmanabhan Pillai, and Mahadev Satyanarayanan. 2014. Towards wearable cognitive assistance. In Proceedings of the 12th annual international conference on Mobile systems, applications, and services. ACM, 68--81. Google ScholarDigital Library
- Karim Habak, Mostafa Ammar, Khaled A. Harras, and Ellen Zegura. 2015. Femto clouds: Leveraging mobile devices to provide cloud service at the edge Cloud Computing (CLOUD), 2015 IEEE 8th International Conference on. IEEE, 9--16. Google ScholarDigital Library
- Wassim Itani, Ayman Kayssi, and Ali Chehab. 2010. Energy-efficient incremental integrity for securing storage in mobile cloud computing Energy Aware Computing (ICEAC), 2010 International Conference on. IEEE, 1--2.Google Scholar
- Albert Jonathan, Mathew Ryden, Kwangsung Oh, Abhishek Chandra, and Jon Weissman. 2017. Nebula: Distributed Edge Cloud for Data Intensive Computing. IEEE Transactions on Parallel and Distributed Systems (2017).Google Scholar
- Sudarsun Kannan, Ada Gavrilovska, and Karsten Schwan. 2011. Cloud4Home-Enhancing Data Services with@ Home Clouds Distributed Computing Systems (ICDCS), 2011 31st International Conference on. IEEE, 539--548. Google ScholarDigital Library
- Johannes Kolb, William Myott, Thao Nguyen, Aniruddha Chandra, and Jon Weissman. 2014. Exploiting User Interest in Data-Driven Cloud-Based Mobile Optimization Mobile Cloud Computing, Services, and Engineering (MobileCloud), 2014 2nd IEEE International Conference on. IEEE, 228--235. Google ScholarDigital Library
- Kyungmin Lee, David Chu, Eduardo Cuervo, Johannes Kopf, Alec Wolman, Yury Degtyarev, Sergey Grizan, and Jason Flinn. 2015. Outatime: Using speculation to enable low-latency continuous interaction for mobile cloud gaming. GetMobile: Mobile Computing and Communications, Vol. 19, 3 (2015), 14--17. Google ScholarDigital Library
- Min-Joong Lee and Chin-Wan Chung. 2011. A user similarity calculation based on the location for social network services Database Systems for Advanced Applications. Springer, 38--52. Google ScholarDigital Library
- Lei Lei, Zhangdui Zhong, Kan Zheng, Jiadi Chen, and Hanlin Meng. 2013. Challenges on wireless heterogeneous networks for mobile cloud computing. IEEE Wireless Communications Vol. 20, 3 (2013), 34--44.Google ScholarCross Ref
- Dawei Li, Theodoros Salonidis, Nirmit V. Desai, and Mooi Choo Chuah. 2016. DeepCham: Collaborative Edge-Mediated Adaptive Deep Learning for Mobile Object Recognition Edge Computing (SEC), IEEE/ACM Symposium on. IEEE, 64--76.Google Scholar
- Christian Licoppe and Yoriko Inada. 2006. Emergent uses of a multiplayer location-aware mobile game: The interactional consequences of mediated encounters. Mobilities, Vol. 1, 1 (2006), 39--61.Google ScholarCross Ref
- Peng Liu, Dale Willis, and Suman Banerjee. 2016. ParaDrop: Enabling Lightweight Multi-tenancy at the Network's Extreme Edge Edge Computing (SEC), IEEE/ACM Symposium on. IEEE, 1--13.Google Scholar
- Emiliano Miluzzo, Ramón Cáceres, and Yih-Farn Chen. 2012. Vision: mClouds-computing on clouds of mobile devices Proceedings of the third ACM workshop on Mobile cloud computing and services. ACM, 9--14. Google ScholarDigital Library
- Venkata N. Padmanabhan and Lakshminarayanan Subramanian. 2001. An investigation of geographic mapping techniques for Internet hosts ACM SIGCOMM Computer Communication Review, Vol. Vol. 31. ACM, 173--185. Google ScholarDigital Library
- Ananth Rao, Karthik Lakshminarayanan, Sonesh Surana, Richard Karp, and Ion Stoica. 2003. Load balancing in structured P2P systems. Peer-to-Peer Systems II (2003), 68--79.Google Scholar
- Mahadev Satyanarayanan. 1996. Fundamental challenges in mobile computing. In Proceedings of the fifteenth annual ACM symposium on Principles of distributed computing. ACM, 1--7. Google ScholarDigital Library
- Mahadev Satyanarayanan, Paramvir Bahl, Ramón Caceres, and Nigel Davies. 2009. The case for vm-based cloudlets in mobile computing. Pervasive Computing, IEEE Vol. 8, 4 (2009), 14--23. Google ScholarDigital Library
- Lakshminarayanan Subramanian, Venkata N. Padmanabhan, and Randy H. Katz. 2002. Geographic Properties of Internet Routing. In USENIX Annual Technical Conference, General Track. 243--259. Google ScholarDigital Library
- Pratap Tokekar, Deepak Bhadauria, Andrew Studenski, and Volkan Isler. 2010. A robotic system for monitoring carp in Minnesota lakes. Journal of Field Robotics Vol. 27, 6 (2010), 779--789.Google ScholarCross Ref
- Franco Travostino, Paul Daspit, Leon Gommans, Chetan Jog, Cees De Laat, Joe Mambretti, Inder Monga, Bas Van Oudenaarde, Satish Raghunath, and Phil Yonghui Wang. 2006. Seamless live migration of virtual machines over the MAN/WAN. Future Generation Computer Systems Vol. 22, 8 (2006), 901--907. Google ScholarDigital Library
- Luis M. Vaquero and Luis Rodero-Merino. 2014. Finding your way in the fog: Towards a comprehensive definition of fog computing. ACM SIGCOMM Computer Communication Review Vol. 44, 5 (2014), 27--32. Google ScholarDigital Library
- George Vellidis, Michael Tucker, Calvin Perry, Craig Kvien, and C. Bednarz. 2008. A real-time wireless smart sensor array for scheduling irrigation. Computers and electronics in agriculture Vol. 61, 1 (2008), 44--50. Google ScholarDigital Library
- Shiqiang Wang, Rahul Urgaonkar, Murtaza Zafer, Ting He, Kevin Chan, and Kin K. Leung. 2015. Dynamic service migration in mobile edge-clouds. IFIP Networking Conference (IFIP Networking), 2015. IEEE, 1--9.Google ScholarCross Ref
- Xianglin Wei, Jianhua Fan, Ziyi Lu, and Ke Ding. 2013. Application scheduling in mobile cloud computing with load balancing. Journal of Applied Mathematics Vol. 2013 (2013).Google ScholarCross Ref
- W. Wen. 2008. A dynamic and automatic traffic light control expert system for solving the road congestion problem. Expert Systems with Applications Vol. 34, 4 (2008), 2370--2381. Google ScholarDigital Library
- Ben Zhang, Nitesh Mor, John Kolb, Douglas S. Chan, Ken Lutz, Eric Allman, John Wawrzynek, Edward Lee, and John Kubiatowicz. 2015 b. The cloud is not enough: saving iot from the cloud 7th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 15). Google ScholarDigital Library
- Irene Zhang, Niel Lebeck, Pedro Fonseca, Brandon Holt, Raymond Cheng, Ariadna Norberg, Arvind Krishnamurthy, and Henry M. Levy. 2016. Diamond: Automating Data Management and Storage for Wide-Area, Reactive Applications. OSDI. 723--738. Google ScholarDigital Library
- Tan Zhang, Aakanksha Chowdhery, Paramvir Victor Bahl, Kyle Jamieson, and Suman Banerjee. 2015 a. The design and implementation of a wireless video surveillance system Proceedings of the 21st Annual International Conference on Mobile Computing and Networking. ACM, 426--438. Google ScholarDigital Library
- Jiang Zhu, Douglas S. Chan, Mythili Suryanarayana Prabhu, Prem Natarajan, Hao Hu, and Flavio Bonomi. 2013. Improving web sites performance using edge servers in fog computing architecture Service Oriented System Engineering (SOSE), 2013 IEEE 7th International Symposium on. IEEE, 320--323. Google ScholarDigital Library
Index Terms
- Locality-Aware Load Sharing in Mobile Cloud Computing
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