Optimal Offloading for Streaming Applications in Mobile Edge Computing

Journal of Circuits System and Computers ◽

10.1142/s021812662250102x ◽

2021 ◽

Author(s):

Pengfei Sun ◽

Xue-Yang Zhu ◽

Ya Gao

Keyword(s):

Mobile Devices ◽

Rapid Development ◽

Heuristic Method ◽

Exact Algorithm ◽

Edge Computing ◽

Computation Offloading ◽

Mobile Edge Computing ◽

Computationally Efficient ◽

Streaming Applications ◽

Smart Mobile

With the rapid development of smart mobile devices, mobile applications are becoming more and more popular. Since mobile devices usually have constrained computing capacity, computation offloading to mobile edge computing (MEC) to achieve a lower latency is a promising paradigm. In this paper, we focus on the optimal offloading problem for streaming applications in MEC. We present solutions to find offloading policies of streaming applications to achieve an optimal latency. Streaming applications are modeled with synchronous data flow graphs. Two architecture assumptions are considered — with sufficient processors on both the local device and the MEC server, and with a limited number of processors on both sides. The problem is generally NP-complete. We present an exact algorithm and a heuristic algorithm for the former architecture assumption and a heuristic method for the latter. We carry out our experiments on a practical application and thousands of synthetic graphs to comprehensively evaluate our methods. The experimental results show that our methods are effective and computationally efficient.

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Security and Cost-Aware Computation Offloading via Deep Reinforcement Learning in Mobile Edge Computing

Wireless Communications and Mobile Computing ◽

10.1155/2019/3816237 ◽

2019 ◽

Vol 2019 ◽

pp. 1-20 ◽

Cited By ~ 6

Author(s):

Binbin Huang ◽

Yangyang Li ◽

Zhongjin Li ◽

Linxuan Pan ◽

Shangguang Wang ◽

...

Keyword(s):

Reinforcement Learning ◽

Mobile Devices ◽

Mobile Applications ◽

Mobile User ◽

Edge Computing ◽

Computation Offloading ◽

Security Threats ◽

Mobile Edge Computing ◽

Computing Environment ◽

Markov Decision

With the explosive growth of mobile applications, mobile devices need to be equipped with abundant resources to process massive and complex mobile applications. However, mobile devices are usually resource-constrained due to their physical size. Fortunately, mobile edge computing, which enables mobile devices to offload computation tasks to edge servers with abundant computing resources, can significantly meet the ever-increasing computation demands from mobile applications. Nevertheless, offloading tasks to the edge servers are liable to suffer from external security threats (e.g., snooping and alteration). Aiming at this problem, we propose a security and cost-aware computation offloading (SCACO) strategy for mobile users in mobile edge computing environment, the goal of which is to minimize the overall cost (including mobile device’s energy consumption, processing delay, and task loss probability) under the risk probability constraints. Specifically, we first formulate the computation offloading problem as a Markov decision process (MDP). Then, based on the popular deep reinforcement learning approach, deep Q-network (DQN), the optimal offloading policy for the proposed problem is derived. Finally, extensive experimental results demonstrate that SCACO can achieve the security and cost efficiency for the mobile user in the mobile edge computing environment.

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Efficient Multi-task offloading with energy and computational resources optimization in a mobile edge computing node

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v9i6.pp4908-4919 ◽

2019 ◽

Vol 9 (6) ◽

pp. 4908

Author(s):

Mohamed El Ghmary ◽

Tarik Chanyour ◽

Youssef Hmimz ◽

Mohammed Ouçamah Cherkaoui Malki

Keyword(s):

Mobile Devices ◽

Edge Computing ◽

Mobile Edge Computing ◽

Heuristic Solution ◽

Huge Impact ◽

Energy Constrained ◽

Processing And Storage ◽

Smart Mobile ◽

Computational Resources ◽

And Storage

<span>With the fifth-generation (5G) networks, Mobile edge computing (MEC) is a promising paradigm to provide near computing and storage capabilities to smart mobile devices. In addition, mobile devices are most of the time battery dependent and energy constrained while they are characterized by their limited processing and storage capacities. Accordingly, these devices must offload a part of their heavy tasks that require a lot of computation and are energy consuming. This choice remains the only option in some circumstances, especially when the battery drains off. Besides, the local CPU frequency allocated to processing has a huge impact on devices energy consumption. Additionally, when mobile devices handle many tasks, the decision of the part to offload becomes critical. Actually, we must consider the wireless network state, the available processing resources at both sides, and particularly the local available battery power. In this paper, we consider a single mobile device that is energy constrained and that retains a list of heavy offloadable tasks that are delay constrained. Therefore, we formulated the corresponding optimization problem, and proposed a Simulated Annealing based heuristic solution scheme. In order to evaluate our solution, we carried out a set of simulation experiments. Finally, the obtained results in terms of energy are very encouraging. Moreover, our solution performs the offloading decisions within an acceptable and feasible timeframes.</span>

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Optimization of Cooperative Offloading Model with Cost Consideration in Mobile Edge Computing

10.21203/rs.3.rs-165316/v1 ◽

2022 ◽

Author(s):

Bin Xu ◽

Tao Deng ◽

Yichuan Liu ◽

Yunkai Zhao ◽

Zipeng Xu ◽

...

Keyword(s):

Energy Consumption ◽

Mobile Devices ◽

Simulated Annealing Algorithm ◽

Minimum Energy ◽

Edge Computing ◽

Computation Offloading ◽

Neighborhood Search ◽

Mobile Edge Computing ◽

Edge Network ◽

Computing Capacity

Abstract The combination of idle computing resources in mobile devices and the computing capacity of mobile edge servers enables all available devices in an edge network to complete all computing tasks in coordination to effectively improve the computing capacity of the edge network. This is a research hotspot for 5G technology applications. Previous research has focused on the minimum energy consumption and/or delay to determine the formulation of the computational offloading strategy but neglected the cost required for the computation of collaborative devices (mobile devices, mobile edge servers, etc.); therefore, we proposed a cost-based collaborative computation offloading model. In this model, when a task requests these devices' assistance in computing, it needs to pay the corresponding calculation cost; and on this basis, the task is offloaded and computed. In addition, for the model, we propose an adaptive neighborhood search based on simulated annealing algorithm (ANSSA) to jointly optimize the offloading decision and resource allocation with the goal of minimizing the sum of both the energy consumption and calculation cost. The adaptive mechanism enables different operators to update the probability of selection according to historical experience and environmental perception, which makes the individual evolution have certain autonomy. A large number of experiments conducted on different scales of mobile user instances show that the ANSSA can obtain satisfactory time performance with guaranteed solution quality. The experimental results demonstrate the superiority of the mobile edge computing (MEC) offloading system. It is of great significance to strike a balance between maintaining the life cycle of smart mobile devices and breaking the performance bottleneck of MEC servers.

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Cross-Server Computation Offloading for Multi-Task Mobile Edge Computing

Information ◽

10.3390/info11020096 ◽

2020 ◽

Vol 11 (2) ◽

pp. 96 ◽

Cited By ~ 2

Author(s):

Yongpeng Shi ◽

Yujie Xia ◽

Ya Gao

Keyword(s):

Approximation Algorithm ◽

Mobile Devices ◽

Computational Efficiency ◽

Network Architecture ◽

Edge Computing ◽

Computation Offloading ◽

Mobile Edge Computing ◽

Optimal Solutions ◽

Resource Constrained ◽

And Storage

As an emerging network architecture and technology, mobile edge computing (MEC) can alleviate the tension between the computation-intensive applications and the resource-constrained mobile devices. However, most available studies on computation offloading in MEC assume that the edge severs host various applications and can cope with all kinds of computation tasks, ignoring limited computing resources and storage capacities of the MEC architecture. To make full use of the available resources deployed on the edge servers, in this paper, we study the cross-server computation offloading problem to realize the collaboration among multiple edge servers for multi-task mobile edge computing, and propose a greedy approximation algorithm as our solution to minimize the overall consumed energy. Numerical results validate that our proposed method can not only give near-optimal solutions with much higher computational efficiency, but also scale well with the growing number of mobile devices and tasks.

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A Multilevel Optimization Framework for Computation Offloading in Mobile Edge Computing

Mathematical Problems in Engineering ◽

10.1155/2020/4124791 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17

Author(s):

Nanliang Shan ◽

Yu Li ◽

Xiaolong Cui

Keyword(s):

Mobile Devices ◽

Language Processing ◽

Scheduling Algorithm ◽

Edge Computing ◽

Computation Offloading ◽

Potential Game ◽

Mobile Edge Computing ◽

Computing Paradigm ◽

Channel Interference ◽

Selection Stage

Mobile edge computing is a new computing paradigm that can extend cloud computing capabilities to the edge network, supporting computation-intensive applications such as face recognition, natural language processing, and augmented reality. Notably, computation offloading is a key technology of mobile edge computing to improve mobile devices’ performance and users’ experience by offloading local tasks to edge servers. In this paper, the problem of computation offloading under multiuser, multiserver, and multichannel scenarios is researched, and a computation offloading framework is proposed that considering the quality of service (QoS) of users, server resources, and channel interference. This framework consists of three levels. (1) In the offloading decision stage, the offloading decision is made based on the beneficial degree of computation offloading, which is measured by the total cost of the local computing of mobile devices in comparison with the edge-side server. (2) In the edge server selection stage, the candidate is comprehensively evaluated and selected by a multiobjective decision based on the Analytic Hierarchy Process based on Covariance (Cov-AHP) for computation offloading. (3) In the channel selection stage, a multiuser and multichannel distributed computation offloading strategy based on the potential game is proposed by considering the influence of channel interference on the user’s overall overhead. The corresponding multiuser and multichannel task scheduling algorithm is designed to maximize the overall benefit by finding the Nash equilibrium point of the potential game. Amounts of experimental results show that the proposed framework can greatly increase the number of beneficial computation offloading users and effectively reduce the energy consumption and time delay.

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Energy Efficient Computation Offloading Mechanism in Multi-Server Mobile Edge Computing—An Integer Linear Optimization Approach

Electronics ◽

10.3390/electronics9061010 ◽

2020 ◽

Vol 9 (6) ◽

pp. 1010 ◽

Cited By ~ 6

Author(s):

Prince Waqas Khan ◽

Khizar Abbas ◽

Hadil Shaiba ◽

Ammar Muthanna ◽

Abdelrahman Abuarqoub ◽

...

Keyword(s):

Energy Harvesting ◽

Mobile Devices ◽

Energy Efficient ◽

Linear Optimization ◽

Edge Computing ◽

Computation Offloading ◽

Optimization Approach ◽

Mobile Edge Computing ◽

Efficient Computation ◽

Integer Linear Optimization

Conserving energy resources and enhancing computation capability have been the key design challenges in the era of the Internet of Things (IoT). The recent development of energy harvesting (EH) and Mobile Edge Computing (MEC) technologies have been recognized as promising techniques for tackling such challenges. Computation offloading enables executing the heavy computation workloads at the powerful MEC servers. Hence, the quality of computation experience, for example, the execution latency, could be significantly improved. In a situation where mobile devices can move arbitrarily and having multi servers for offloading, computation offloading strategies are facing new challenges. The competition of resource allocation and server selection becomes high in such environments. In this paper, an optimized computation offloading algorithm that is based on integer linear optimization is proposed. The algorithm allows choosing the execution mode among local execution, offloading execution, and task dropping for each mobile device. The proposed system is based on an improved computing strategy that is also energy efficient. Mobile devices, including energy harvesting (EH) devices, are considered for simulation purposes. Simulation results illustrate that the energy level starts from 0.979 % and gradually decreases to 0.87 % . Therefore, the proposed algorithm can trade-off the energy of computational offloading tasks efficiently.

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Computation Offloading Game in Multiple UAV-Enabled Mobile Edge Computing Networks

IET Communications ◽

10.1049/iet-com.2020.0131 ◽

2020 ◽

Author(s):

Yanling Ren ◽

Zhibin Xie ◽

Zhenfeng Ding ◽

xiyuan sun ◽

Jie Xia ◽

...

Keyword(s):

Edge Computing ◽

Computation Offloading ◽

Mobile Edge Computing

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Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing

Sensors ◽

10.3390/s21082628 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2628

Author(s):

Mengxing Huang ◽

Qianhao Zhai ◽

Yinjie Chen ◽

Siling Feng ◽

Feng Shu

Keyword(s):

Optimization Algorithm ◽

Edge Computing ◽

Solution Set ◽

Whale Optimization Algorithm ◽

Computation Offloading ◽

Mobile Edge Computing ◽

Multi Objective ◽

Whale Optimization ◽

Artificial Immune System Algorithm

Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it is very important to decide quickly how many tasks should be executed on servers and how many should be executed locally. Only computation tasks that are properly offloaded can improve the Quality of Service (QoS). Some existing methods only focus on a single objection, and of the others some have high computational complexity. There still have no method that could balance the targets and complexity for universal application. In this study, a Multi-Objective Whale Optimization Algorithm (MOWOA) based on time and energy consumption is proposed to solve the optimal offloading mechanism of computation offloading in mobile edge computing. It is the first time that MOWOA has been applied in this area. For improving the quality of the solution set, crowding degrees are introduced and all solutions are sorted by crowding degrees. Additionally, an improved MOWOA (MOWOA2) by using the gravity reference point method is proposed to obtain better diversity of the solution set. Compared with some typical approaches, such as the Grid-Based Evolutionary Algorithm (GrEA), Cluster-Gradient-based Artificial Immune System Algorithm (CGbAIS), Non-dominated Sorting Genetic Algorithm III (NSGA-III), etc., the MOWOA2 performs better in terms of the quality of the final solutions.

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