Online Offloading Scheduling and Resource Allocation Algorithms for Vehicular Edge Computing System

Abstract

To accommodate the exponentially increasing computation demands of vehicle-based applications, vehicular edge computing (VEC) system was introduced. This paper considers a three-layer VEC architecture and proposes an online offloading scheduling and resource allocation (OOSRA) algorithm to improve the system performance. Specifically, this study designs a game-theoretic online algorithm to solve the problem of computation task offloading scheduling, and employs an online bin-packing algorithm to compute the resource allocation modified from the First Fit algorithm, which can be adapted to various traffic flow and service attributes. Extensive simulations are conducted, and a numerical analysis of simulation results verifies the effectiveness of the OOSRA-VEC system. The algorithms proposed in this paper are online, adaptive, and distributed, which can provide useful references for future development in VEC system protocols. INDEX TERMS Intelligent vehicles, intelligent transportation systems, edge-computing, game theory.

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

“…A very few works concern the QoE as a metric directly. energy Gao et al [49] independent full cost Chen et al [50] independent full cost Chen et al [51] independent full profit Yuan et al [52] independent full profit Lin et al [53] independent full performance, energy Du et al [54] independent full performance, energy Duan et al [55] independent full performance, energy Mahmud et al [56] independent full performance, profit Li et al [57] independent full Performance, cost Sun et al [58] independent full performance, cost Adhikari et al [59] independent full performance, utilization Ma et al [60] independent full QoE, cost Miao et al [61] independent partial performance Kai et al [62] independent partial performance Guo et al [63] independent partial performance Meng et al [64], [65] independent partial performance hop-e Cui et al [66], [67] independent partial performance hop-d, hop-e Sarkar et al [68] independent partial performance hop-e Ouyang et al [69] independent partial performance Y Cheng et al [70] independent partial energy Xia et al [71] independent partial energy Zhang et al [72] independent partial cost Chabbouh et al [73] independent partial performance, balance Y Wang et al [74] independent partial performance, cost Zhao et al [75] independent partial performance, cost Khayyat et al [76] independent partial performance, energy Alshahrani et al [77] independent partial performance, energy Chen et al [78] independent partial performance, cost, energy Hong et al [16] independent partial performance, energy hop-d Sun et al [79] independent partial performance, energy Long et al [80] independent partial performance, energy Nguyen et al…”

“…Wang et al [74] study on the tradeoff between delays and costs for vehicular applications exploiting the edge-cloud resources. They first employ a game-theoretic online algorithm to make task offloading decisions, where applications are the players trying to obtain both fewer service delays and smaller rent fees.…”

An Optimization Framework for Edge-to-Cloud Offloading of Kubernetes Pods in V2X Scenarios

“…In the scenario where the CPU constraint (8) is considered, but not the memory constraint (9), problem (5) can be simplified as…”

“…However, this and other works [8] only considered the benefit of users, and did not study the resource usage cost optimization. [9] studied the tradeoff between endto-end delay of tasks and resource usage cost, but considered separate allocation of tasks with small and large workloads.…”

Decentralized Cache-aided Offloading in Edge Cloud Collaborative Environment using Deep Q Reinforcement Learning

“…In all other research works ( [61], [62], [63], [40], [64], [65]), weighted sum of energy consumption and latency is considered as objective function. Constraints differed in most of the research, such as the number of computation resources available and computation capacity.…”