EFFECT: Energy-efficient Fog Computing Framework for Real-time Video Processing
Xiaojie Zhang, Amitangshu Pal, Saptarshi Debroy
Abstract
Energy efficient task offloading within a fog computing environment comprising of end-devices and edge servers remains a challenging problem to solve, especially for real-time video processing applications due to such tasks' strict latency deadline demands. In this paper we propose an Energy-efficient Fog Computing framework (EFFECT) for real-time applications within mission-critical use cases. The proposed framework runs a Unified Resource Broker (URB) that implements: a) centralized sub-channel and transmission power allocation as well as end-device/edge server computation speed allocation algorithms, along with b) distributed multi-device, multi-server task offloading game based Directed Acyclic Graph (DAG) partition and edge server selection algorithms. The framework is designed, developed, implemented, and evaluated on an Amazon EC2 virtual testbed built using Apache Storm, which is a distributed computing platform. The results from the testbed experiments along with realistic simulations validate the utility of EFFECT task offloading strategy in minimizing energy consumption yet satisfying latency deadlines.