Energy-Efficient Proactive Caching for Fog Computing with Correlated Task Arrivals

• Published in: SPAWC : signal processing advances in wireless communications pp. 1 - 5
• Main Authors: Xing, Hong, Cui, Jingjing, Deng, Yansha, Nallanathan, Arumugam
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2019
• Subjects: Approximation algorithms; Benchmark testing; computation caching; computation offloading; Correlation; Fog computing; mobile edge computing; online algorithm; Optimization; Prediction algorithms; Robustness; Signal processing; Signal processing algorithms; Upper bound; Wireless communication
• ISSN: 1948-3252
• DOI: 10.1109/SPAWC.2019.8815493

Ahstract- With the proliferation of latency-critical applications, fog-radio network (FRAN) has been envisioned as a paradigm shift enabling distributed deployment of cloud-clone facilities at the network edge. In this paper, we consider proactive caching for a one-user one-access point (AP) fog computing system over a finite time horizon, in which consecutive tasks of the same type of application are temporarily correlated. Under the assumption of predicable length of the task-input bits, we formulate a long-term weighted-sum energy minimization problem with three-slot correlation to jointly optimize computation offloading policies and caching decisions subject to stringent per-slot deadline constraints. The formulated problem is hard to solve due to the mixed-integer non-convexity. To tackle this challenge, first, we assume that task-related information are perfectly known a priori, and provide offline solution leveraging the technique of semi-definite relaxation (SDR), thereby serving as theoretical upper bound. Next, based on the offline solution, we propose a sliding-window based online algorithm under arbitrarily distributed prediction error. Finally, the advantage of computation caching as well the proposed algorithm is verified by numerical examples by comparison with several benchmarks.