Joint Task Offloading Scheduling and Transmit Power Allocation for Mobile-Edge Computing Systems

Abstract

Mobile-edge computing (MEC) has emerged as a prominent technique to provide mobile services with high computation requirement, by migrating the computation-intensive tasks from the mobile devices to the nearby MEC servers. To reduce the execution latency and device energy consumption, in this paper, we jointly optimize task offloading scheduling and transmit power allocation for MEC systems with multiple independent tasks. A low-complexity sub-optimal algorithm is proposed to minimize the weighted sum of the execution delay and device energy consumption based on alternating minimization. Specifically, given the transmit power allocation, the optimal task offloading scheduling, i.e., to determine the order of offloading, is obtained with the help of flow shop scheduling theory. Besides, the optimal transmit power allocation with a given task offloading scheduling decision will be determined using convex optimization techniques. Simulation results show that task offloading scheduling is more critical when the available radio and computational resources in MEC systems are relatively balanced. In addition, it is shown that the proposed algorithm achieves near-optimal execution delay along with a substantial device energy saving.Index Terms-Mobile-edge computing, task offloading scheduling, power control, flow shop scheduling, convex optimization.

Joint Task Assignment and Resource Allocation for D2D-Enabled Mobile-Edge Computing

“…• Optimal The MINLP problem (P0-Eqv) is solved by exhaustive search over feasible Π for 7 with a convex problem (P2) solved each time for a given Π. Note that "Optimal" is of exponential complexity and is thus too costly to implement in practice.…”

“…The intuition behind such assignment is that the selected helper will consume the least amount of energy in transmission (c.f. (7) and (13)), and thus any spare energy can be exploited for further latency reduction. It is also worth noting that the task with the longest (input/output) data flow is executed locally for the sake of saving data-transmission time.…”

Allocation of Computation-Intensive Graph Jobs Over Vehicular Clouds in IoV

“…This fact has resulted in a growing interest in studying efficient computation offloading strategies. The existing literatures on computation-intensive application offloading can be roughly divided into two categories: 1) those where applications are directly mapped as bit streams and considered as a collection of sub-applications without considering the their dependencies, such as [17]- [19]; 2) those explicitly considering the structure of applications which can be modeled as directed/undirected graphs, such as [6], [7], [9], [10], [20], [21]. A reliability-oriented stochastic optimization model in vehicle-infrastructure systems is proposed in [17] based on the dynamic programming for computation offloading considering the deadline constraint on application execution.…”

“…Both sequential and concurrent task offloading algorithms are proposed to minimize the application completion time. It is assumed in [10] that each mobile device has several independent tasks as a set and the task offloading scheduling and transmit power allocation in MEC systems are jointly optimized. Moreover, a lowcomplexity sub-optimal algorithm is proposed to minimize the weighted sum of the execution delay and device energy consumption based on alternating minimization.…”

“…This technology significantly relieves heavy on-board workloads for JOs and accelerates job completion. The feedback duration of computation results is often ignored given that their data size is much smaller than that of the application data [10], [11]; alternatively, results can be transferred back to the JO via a multi-hop V2V routing path or uploaded to RSUs for future delivery in case the V2V connection is disrupted.…”