Offloading Schemes in Mobile Edge Computing With an Assisted Mechanism

Abstract

Mobile edge computing (MEC) is a promising paradigm for providing computing and storage capabilities in close proximity to mobile devices. To solve the scenario in which massive mobile devices have tasks to be processed at the same time, this paper proposes an assisted mechanism for the MEC system. When the primary MEC server is unable to meet the delay requirements of the mobile devices within its coverage area, a portion of the tasks can be offloaded to secondary MEC servers to obtain extra resources for processing. This MEC framework effectively reduces the computing and communication burden of the primary MEC server and improves the resource utilization of the secondary MEC servers. To maximize the system offloading utility in terms of latency, we formulated an optimization problem that jointly optimizes the task assignment, computing resource allocation and offloading decision of all mobile devices. Since the formulated problem is a mixed integer nonlinear problem, we use the decomposition method to convert the optimization problem into several subproblems. In addition, a heuristic algorithm based on the priorities of mobile devices and the MEC servers is proposed to obtain the suboptimal device offloading strategy. The numerical results show that the assisted mechanism can effectively reduce system latency and improve system reliability. In addition, the performance of our proposed algorithm is close to the optimal solution. INDEX TERMS Assisted mechanism, computation offloading scheme, mobile edge computing, resource allocation.

Vehicle Collaborative Partial Offloading Strategy in Vehicular Edge Computing

“…Approaches from different perspectives have been proposed to tackle the problem of a single server with insufficient resources, such as adding backup servers [2][3][4][5][6], balancing loads between MEC servers [7,8], and collaborative offloading using existing equipment [9][10][11]. Wang et al [6] used a secondary MEC server to reduce the computational and communication load on a primary MEC server.…”

“…Approaches from different perspectives have been proposed to tackle the problem of a single server with insufficient resources, such as adding backup servers [2][3][4][5][6], balancing loads between MEC servers [7,8], and collaborative offloading using existing equipment [9][10][11]. Wang et al [6] used a secondary MEC server to reduce the computational and communication load on a primary MEC server. Furthermore, they decomposed the mixed-integer nonlinear problem of minimizing the latency of system processing tasks into several subproblems and proposed a heuristic algorithm based on the priority of mobile devices and MEC servers to obtain a suboptimal device offloading policy.…”

Hardware-Accelerated Platforms and Infrastructures for Network Functions: A Survey of Enabling Technologies and Research Studies

“…The wide-spread adoption of Multi-Access Edge Computing (MEC) [14] with cloud-native management is accelerating the trend towards softwarized NFs, which run on GPC platforms. The MEC aims to deliver low-latency services by bringing computing platforms closer to the users [15]- [19]. A key MEC implementation requirement is to inherit the flexibility of hosting a variety of NFs as opposed to a specific dedicated NF.…”

On the Provisioning of Ultra-Reliable Low-Latency Services in IoT Networks with Multipath Diversity. (c2020)

“…Simulation results show that the proposed algorithms obtain a good trade-off between the latency and reliability in ultra-Reliable Low Latency Communications (uRLLC). The authors in [63] propose supporting a primary MEC server with multiple secondary MEC servers to reduce the latency experienced by mobile devices. To maximize the system offloading utility in terms of latency, a mixed integer nonlinear problem is formulated with the objective of the joint optimization of task assignment, computing resource allocation and offloading decision of all mobile devices.…”