Computation Offloading for Tasks With Bound Constraints in Multiaccess Edge Computing

Abstract

Multi-Access Edge Computing (MEC) provides task offloading services to facilitate the integration of idle resources with the network and bring cloud services closer to the end user. By selecting suitable servers and properly managing resources, task offloading can reduce task completion latency while maintaining the Quality of Service (QoS). Prior research, however, has primarily focused on tasks with strict time constraints, ignoring the possibility that tasks with soft constraints may exceed the bound limits and failing to analyze this complex task constraint issue. Furthermore, considering additional constraint features makes convergent optimization algorithms challenging when dealing with such complex and high-dimensional situations. In this paper, we propose a new computational offloading decision framework by minimizing the long-term payment of computational tasks with mixed bound constraints. In addition, redundant experiences are gotten rid of before the training of the algorithm. The most advantageous transitions in the experience pool are used for training in order to improve the learning efficiency and convergence speed of the algorithm as well as increase the accuracy of offloading decisions. The findings of our experiments indicate that the method we have presented is capable of achieving fast convergence rates while also reducing sample redundancy.

Collaborative Computation Offloading and Resource Management in Space–Air–Ground Integrated Networking: A Deep Reinforcement Learning Approach

“…The access fee charged by ground edge server a random value in [1,2] units/bps The access fee charged by air edge server a random value in [3,4] units/bps The access fee charged by space edge server a random value in [5,7] units/bps The usage cost of spectrum paid by ground edge server [1 × 10 −4 , 2 × 10 −4 ] units/Hz The usage cost of spectrum paid by air edge server [3 × 10 −4 , 4 × 10 −4 ] units/Hz The usage cost of spectrum paid by space edge server [5 × 10 −4 , 7 × 10 −4 ] units/Hz The computation fee charged by ground edge server 0.2 units/J The computation fee charged by air edge server 0.4 units/J The computation fee charged by space edge server 0.6 units/J The storage fee charged by ground edge server 10 units/byte The storage fee charged by air edge server 15 units/byte The storage fee charged by space edge server 20 units/byte…”

“…Scholars have conducted extensive research on the optimization of computation offloading performance [4]. Some are committed to optimizing the resource allocation of edge servers, and they usually establish the computation offloading problem as an optimization model [5]. Due to the limitations of wireless spectrum, there is a communication bottleneck between the terminal device and the road side unit, which affects the quality of computation offloading service.…”

Enhancing Healthcare Efficacy Through IoT-Edge Fusion: A Novel Approach for Smart Health Monitoring and Diagnosis

“…Researchers [33] also developed novel cipher scheme to enhance the security of transmitted dat.aTo overcome the problems such as power, memory, and life of battery multi-access edge computing has recently evolved [34].Authors [35] introduce FTPipeHD, a faulttolerant DNN training method for distributed heterogeneous devices, in this work.This study [36]presents FedMint, a game theory and bootstrapping-based intelligent client selection technique for IoT federated learning.In this study [37], researchers provide a novel computational offloading choice paradigm aimed at reducing the long-term payment associated with computing jobs that include mixed bound restrictions.…”

“…This study [36]presents FedMint, a game theory and bootstrapping-based intelligent client selection technique for IoT federated learning. In this study [37], researchers provide a novel computational offloading choice paradigm aimed at reducing the long-term payment associated with computing jobs that include mixed bound restrictions. A unique architectural framework was developed and executed, combining four deep learning techniques using edge computing devices [38].…”

An Improved Gravitational Search Algorithm for Task Offloading in a Mobile Edge Computing Network with Task Priority

“…Unlike traditional cloud computing, Mobile edge computing (MEC) [2] deploys computing and storage resources at the edge of a mobile network to provide an information technology (IT) service environment and cloud computing capabilities for mobile networks, thus providing users with ultra-low latency, low power consumption, and high broadband network service solutions [3][4][5]. As one of the key technologies in MEC, computing offloading [6][7][8][9][10] enables terminal devices to unload partial or all computational tasks to mobile edge servers for assistance, aiming to address the inherent issues of limited storage space, inadequate computing power, and energy constraints on terminal devices.…”