  An edge computing environment features multiple edge servers and multiple
service clients. In this environment, mobile service providers can offload
client-side computation tasks from service clients' devices onto edge servers
to reduce service latency and power consumption experienced by the clients. A
critical issue that has yet to be properly addressed is how to allocate edge
computing resources to achieve two optimization objectives: 1) minimize the
service cost measured by the service latency and the power consumption
experienced by service clients; and 2) maximize the service capacity measured
by the number of service clients that can offload their computation tasks in
the long term. This paper formulates this long-term problem as a stochastic
optimization problem and solves it with an online algorithm based on Lyapunov
optimization. This NP-hard problem is decomposed into three sub-problems, which
are then solved with a suite of techniques. The experimental results show that
our approach significantly outperforms two baseline approaches.

Distributed, Parallel, and Cluster Computing;Networking and Internet Architecture