Optimal MultiDecision Mobile Computation Offloading With Hard Task Deadlines Multidecision mobile computation offloading occurs when a task to be remotely executed is uploaded in separate parts. Since the upload is partitioned, separate decisions are needed to determine the best time to initiate each upload. The multidecision problem is considered for the case where execution completion times are subject to hard deadline constraints and where task offloads occur over a Markovian wireless channel. An online energyoptimal computation offloading algorithm, Multiopt (Multidecision online Optimum), is introduced, whose optimality is proven using Markovian stopping theory. The paper presents results using the GilbertElliott channel model, where task completion time probabilities can be efficiently computed using Dynamic Programming. Although the proposed algorithm is proven to be energy optimal, its performance is also compared to four others, namely, Immediate Offloading, Channel Threshold, Local Execution, as well as optimal singlepart offloading. Results show that the proposed algorithm can significantly improve mobile device energy consumption compared to the other approaches while guaranteeing hard task execution deadlines.

LatencyMinimized and EnergyEfficient Online Task Offloading for Mobile Edge Computing with Stochastic Heterogeneous Tasks Mobile edge computing (MEC) is a promising paradigm to meet the low latency requirement of applications like virtualaugmented reality, which moves computation workloads from remote cloud to network edge. Task offloading is a crucial problem in a MEC system, which significantly concerns with the achieved latency of tasks and the energy consumption on edge nodes. Although many efforts have been paid on the problem, most of them ignore the hierarchical architecture of MEC and the heterogeneity of tasks generated by different applications. In this paper, we consider a threelayer MEC system with tasks of various types stochastically arrive in real time, in which each task can be executed on mobile smart devices, offloaded to the edge server, or offloaded to the remote cloud. The task offloading problem in such a MEC system with the objective to minimize the averaged latency over time is NPhard. To solve it, we employ Lyapunov optimization and duality theory to reformulate the problem and decompose it into a set of subproblems. Each subproblem can be distributedly solved by a mobile device or the edge server individually. Extensive simulations are also performed to verify the feasibility and superiority of our approach.