Joint Resource Management and Pricing for Task Offloading in Serverless Edge Computing

We consider the problem of resource allocation, pricing and application caching for latency sensitive task offloading in serverless edge computing. We model the interaction between a profit-maximizing operator and cost-minimizing
Wireless Device
s (WDs) as a Stackelberg game where the operator is the leader and decides the price, resource allocation and set of applications to cache, while the WDs are the followers and decide whether to offload their tasks. We first show that the game has a
Subgame Perfect Equilibrium
(SPE), but computing it, is NP-hard. Importantly, we show that an SPE, which maximizes the operator's revenue, results in minimal energy consumption among the WDs. For computing an approximate SPE, we propose a linear time approximation algorithm with bounded approximation ratio for resource allocation and pricing, and we propose an efficient heuristic based on the utility density of individual applications for the joint optimization of caching, resource allocation and pricing. Our results show that the proposed algorithm outperforms state-of-the-art methods by up to an order of magnitude both in terms of revenue and total energy savings and has small computational overhead. An interesting feature of our results is that the utility of the operator is maximized by a solution that maximizes the WDs’ energy savings through computation offloading, which makes it a promising candidate for energy efficient edge cloud deployments.