Efficient service handoff across edge servers via docker container migration

Supporting smooth movement of mobile clients is important when offloading services on an edge computing platform. Interruption-free client mobility demands seamless migration of the offloading service to nearby edge servers. However, fast migration of offloading services across edge servers in a WAN environment poses significant challenges to the handoff service design. In this paper, we present a novel service handoff system which seamlessly migrates offloading services to the nearest edge server, while the mobile client is moving. Service handoff is achieved via container migration. We identify an important performance problem during Docker container migration. Based on our systematic study of container layer management and image stacking, we propose a migration method which leverages the layered storage system to reduce file system synchronization overhead, without dependence on the distributed file system. We implement a prototype system and conduct experiments using real world product applications. Evaluation results reveal that compared to state-of-the-art service handoff systems designed for edge computing platforms, our system reduces the total duration of service handoff time by 80%(56%) with network bandwidth 5Mbps(20Mbps).

[1]  Rami Rosen,et al.  Linux containers and the future cloud , 2014 .

[2]  Qun Li,et al.  Challenges and Software Architecture for Fog Computing , 2017, IEEE Internet Computing.

[3]  Weisong Shi,et al.  Edge Computing: Vision and Challenges , 2016, IEEE Internet of Things Journal.

[4]  Ken Thompson,et al.  The use of name spaces in Plan 9 , 1993, OPSR.

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

[6]  Jean-Pierre Auguste Banatre,et al.  Proceedings of the 5th workshop on ACM SIGOPS European workshop: Models and paradigms for distributed systems structuring , 1992 .

[7]  Avishai Ish-Shalom.  Containers from Scratch , 2018 .

[8]  Nicholas D. Lane,et al.  DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices , 2016, 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).

[9]  Tao Zhang,et al.  Fog and IoT: An Overview of Research Opportunities , 2016, IEEE Internet of Things Journal.

[10]  Qun Li,et al.  Poster Abstract: EdgeStore: Integrating Edge Computing into Cloud-Based Storage Systems , 2016, 2016 IEEE/ACM Symposium on Edge Computing (SEC).

[11]  Yuqing Qiu,et al.  Evaluating and Improving LXC Container Migration Between Cloudlets Using Multipath TCP , 2016 .

[12]  Mahadev Satyanarayanan,et al.  The Emergence of Edge Computing , 2017, Computer.

[13]  Peng Liu,et al.  ParaDrop: Enabling Lightweight Multi-tenancy at the Network’s Extreme Edge , 2016, 2016 IEEE/ACM Symposium on Edge Computing (SEC).

[14]  Mahadev Satyanarayanan,et al.  OpenFace: A general-purpose face recognition library with mobile applications , 2016 .

[15]  Qun Li,et al.  A Survey of Fog Computing: Concepts, Applications and Issues , 2015, Mobidata@MobiHoc.

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

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

[18]  Adam Freeman.  Docker Images and Containers , 2017 .

[19]  Andrew Warfield,et al.  Live migration of virtual machines , 2005, NSDI.

[20]  Qun Li,et al.  Fog Computing: Platform and Applications , 2015, 2015 Third IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb).

[21]  Mahadev Satyanarayanan,et al.  Adaptive VM Handoff Across Cloudlets , 2015 .

[22]  Qun Li,et al.  Security and Privacy Issues of Fog Computing: A Survey , 2015, WASA.

[23]  Ken Thompson,et al.  The use of name spaces in plan 9 , 1992, EW 5.