Efficient Live Migration of Edge Services Leveraging Container Layered Storage

Mobile users across edge networks require seamless migration of offloading services. Edge computing platforms must smoothly support these service transfers and keep pace with user movements around the network. However, live migration of offloading services in the wide area network poses significant service handoff challenges in the edge computing environment. In this paper, we propose an edge computing platform architecture which supports seamless migration of offloading services while also keeping the moving mobile user “in service” with its nearest edge server. We identify a critical problem in the state-of-the-art tool for Docker container migration. Based on our systematic study of the Docker container storage system, we propose to leverage the layered nature of the storage system to reduce file system synchronization overhead, without dependence on the distributed file system. In contrast to the state-of-the-art service handoff method in the edge environment, our system yields a 80 percent (56 percent) reduction in handoff time under 5 Mbps (20 Mbps) network bandwidth conditions.

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

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

[3]  Mahadev Satyanarayanan,et al.  You can teach elephants to dance: agile VM handoff for edge computing , 2017, SEC.

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

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

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

[7]  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).

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

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

[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]  Kin K. Leung,et al.  Live Service Migration in Mobile Edge Clouds , 2017, IEEE Wireless Communications.

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

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

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

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

[16]  Qun Li,et al.  Efficient service handoff across edge servers via docker container migration , 2017, SEC.

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

[18]  Raja Lavanya,et al.  Fog Computing and Its Role in the Internet of Things , 2019, Advances in Computer and Electrical Engineering.

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

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