We consider a general multi-user Mobile Cloud Computing (MCC) system where
each mobile user has multiple independent tasks. These mobile users share the
computation and communication resources while offloading tasks to the cloud. We
study both the conventional MCC where tasks are offloaded to the cloud through
a wireless access point, and MCC with a computing access point (CAP), where the
CAP serves both as the network access gateway and a computation service
provider to the mobile users. We aim to jointly optimize the offloading
decisions of all users as well as the allocation of computation and
communication resources, to minimize the overall cost of energy, computation,
and delay for all users. The optimization problem is formulated as a non-convex
quadratically constrained quadratic program, which is NP-hard in general. For
the case without a CAP, an efficient approximate solution named MUMTO is
proposed by using separable semidefinite relaxation (SDR), followed by recovery
of the binary offloading decision and optimal allocation of the communication
resource. To solve the more complicated problem with a CAP, we further propose
an efficient three-step algorithm named MUMTO-C comprising of generalized MUMTO
SDR with CAP, alternating optimization, and sequential tuning, which always
computes a locally optimal solution. For performance benchmarking, we further
present numerical lower bounds of the minimum system cost with and without the
CAP. By comparison with this lower bound, our simulation results show that the
proposed solutions for both scenarios give nearly optimal performance under
various parameter settings, and the resultant efficient utilization of a CAP
can bring substantial cost benefit.

此研究探讨了一个多用户移动云计算系统中的任务离线决策、资源分配等问题，并且通过提出有效算法使用计算接入点 (CAP) 来优化整个系统的能量、计算和时延成本，从而带来了相当的成本效益。