  Mobile-edge computing (MEC) is an emerging technology for enhancing the
computational capabilities of mobile devices and reducing their energy
consumption via offloading complex computation tasks to the nearby servers.
Multiuser MEC at servers is widely realized via parallel computing based on
virtualization. Due to finite shared I/O resources, interference between
virtual machines (VMs), called I/O interference, degrades the computation
performance. In this paper, we study the problem of joint radio-and-computation
resource allocation (RCRA) in multiuser MEC systems in the presence of I/O
interference. Specifically, offloading scheduling algorithms are designed
targeting two system performance metrics: sum offloading throughput
maximization and sum mobile energy consumption minimization. Their designs are
formulated as non-convex mixed-integer programming problems, which account for
latency due to offloading, result downloading and parallel computing. A set of
low-complexity algorithms are designed based on a decomposition approach and
leveraging classic techniques from combinatorial optimization. The resultant
algorithms jointly schedule offloading users, control their offloading sizes,
and divide time for communication (offloading and downloading) and computation.
They are either optimal or can achieve close-to-optimality as shown by
simulation. Comprehensive simulation results demonstrate considering of I/O
interference can endow on an offloading controller robustness against the
performance-degradation factor.
