An Energy-Aware Task Offloading Mechanism in Multiuser Mobile-Edge Cloud Computing

Abstract

Mobile-edge cloud computing, an emerging and prospective computing paradigm, can facilitate the complex application execution on resource-constrained mobile devices by offloading computation-intensive tasks to the mobile-edge cloud server, which is usually deployed in close proximity to the wireless access point. However, in the multichannel wireless interference environment, the competition of mobile users for communication resources is not conducive to the energy efficiency of task offloading. Therefore, how to make the offloading decision for each mobile user and select its suitable channel become critical issues. In this paper, the problem of the offloading decision is formulated as a 0-1 nonlinear integer programming problem under the constraints of channel interference threshold and the time deadline. Through the classification and priority determination for the mobile devices, a reverse auction-based offloading method is proposed to solve this optimization problem for energy efficiency improvement. The proposed algorithm not only achieves the task offloading decision but also gives the facility of resource allocation. In the energy efficiency performance aspects, simulation results show the superiority of the proposed scheme.

Pattern-Identified Online Task Scheduling in Multitier Edge Computing for Industrial IoT Services

“…e intermediate data of the tasks are prioritized and assigned at appropriate levels of cache memory to optimize latency and energy in data access. In order to make the offloading decision, Li et al formulated the problem in a 0-1 nonlinear integer programming with a consideration of channel interference threshold and the time deadline [16]. Based on that assumption, a reverse auction based offloading policy has been proposed to obtain energy efficiency improvement for task execution.…”

A New Context-Sensitive Decision Making System for Mobile Cloud Offloading

“…Proposed algorithm compared with competition-based algorithm and user-satisfaction algorithm. The simulation results validate that the proposed algorithm can achieve better performances [15]. Islam and others have done a study on the processing of mass health data for smart cities [16].…”

“…Generally, studies can be categorized as either collecting data on the mobile environment, making decisions about offloading to the cloud, or offloading a process piece-by-piece. Generally, special algorithms have been developed for solving the specified problems [2,3,6,13,14,15,16] . In a few studies, machine-learning algorithms have been used [17][18][19][20].…”

Energy-Efficient Computation Offloading and Transmit Power Allocation Scheme for Mobile Edge Computing

“…where k > 0 is the iteration index and θ(k) is the positive iteration step size. en, the updated Lagrange multiplier in equation (31) can be used to update the transmission power allocation in equations (28) and (29) and the offloading policy in equation 30. Algorithm 1 gives an outline of the proposed algorithm.…”

“…Delay. In this subsection, the energy consumption and completion time of the proposed scheme, considering the variance of the task size, are compared with the local computing approach, full offloading approach, and Li's et al binary offloading approach in [28]. Figure 2 depicts the energy consumption and completion time for the four schemes.…”