  Mobile edge computing (MEC) is a new paradigm that provides cloud computing
services at the edge of networks. To achieve better performance with limited
computing resources, peer offloading between cooperative edge servers (e.g.
MEC- enabled base stations) has been proposed as an effective technique to
handle bursty and spatially imbalanced arrival of computation tasks. While
various performance metrics of peer offloading policies have been considered in
the literatures, the worst-case response time, a common Quality of Service(QoS)
requirement in real-time applications, yet receives much less attention. To
fill the gap, we formulate the peer offloading problem based on a stochastic
arrival model and propose two online algorithms for cases with and without
prior knowledge of task arrival rate. Our goal is to maximize the utility
function of time-average throughput under constraints of energy consumption and
worst-case response time. Both theoretical analysis and numerical results show
that our algorithms are able to produce close to optimal performance.
