Exploring Computing at the Edge: A Multi-Interface System Architecture Enabled Mobile Device Cloud

Today, mobile applications advancements have overcome limited device capabilities by offloading to costly public cloud. As the edge computing paradigm began to take precedence, a mobile device cloud (MDC) formed at the edge based on idle intra-device resources emerged. This is a result of a customized user-centric composition service request for a time-bound application. Herein, devices volunteer their intra-device resources for producing a compute environment in turn satisfying the needs of the consumer. Now, with the growth of device technology and the available interfaces for accessing multiple radio technologies, a new transport layer protocol called Multipath TCP was introduced in literature. This protocol enables multiple sub-flows to join for transmitting data simultaneously. However, in scenarios like formation of device clouds, there are issues pertaining to sub-flows that are involved in a device cloud composition. One such issue is the management of sub-flow buffer. As each of these sub-flows have their own respective buffering and characteristic delays, it leads to sub-optimal performance in term of buffer occupancy. Thereby, degrading the quality of the device cloud composition. To this end, we propose an OS side architecture that plays a crucial role in managing the traffic coming from different flows. We model an agent that works conservatively satisfying Kleinrock's law and show a proof of concept experiment

[1]  Khaled A. Harras Towards computational offloading in mobile device clouds , 2013 .

[2]  Van Jacobson,et al.  Controlling Queue Delay , 2012, ACM Queue.

[3]  Ellen W. Zegura,et al.  Computing in cirrus clouds: the challenge of intermittent connectivity , 2012, MCC '12.

[4]  Malcolm P. Atkinson,et al.  Ad Hoc Cloud Computing , 2015, 2015 IEEE 8th International Conference on Cloud Computing.

[5]  C. J. Ancker,et al.  Some Queuing Problems with Balking and Reneging. I , 1963 .

[6]  Burak Kantarci,et al.  Social Behaviometrics for Personalized Devices in the Internet of Things Era , 2017, IEEE Access.

[7]  Ahmed Karmouch,et al.  An infrastructure as a Service for Mobile Ad-hoc Cloud , 2017, 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC).

[8]  Ingrid Moerman,et al.  Data-Driven Design of Intelligent Wireless Networks: An Overview and Tutorial , 2016, Sensors.

[9]  Shahbaz Akhtar Abid,et al.  A Survey on DHT-Based Routing for Large-Scale Mobile Ad Hoc Networks , 2014, ACM Comput. Surv..

[10]  Abderrahmane Lakas,et al.  Renting Out Cloud Services in Mobile Vehicular Cloud , 2018, IEEE Transactions on Vehicular Technology.

[11]  Olivier Bonaventure,et al.  Exploring mobile/WiFi handover with multipath TCP , 2012, CellNet '12.

[12]  Ahmed Karmouch,et al.  Optimization of device selection in a Mobile Ad-hoc cloud based on composition score , 2017, 2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA).