scholarly journals Time and resource constrained offloading with multi-task in a mobile edge computing node

2020 ◽  
Vol 10 (4) ◽  
pp. 3757
Author(s):  
Mohamed El Ghmary ◽  
Youssef Hmimz ◽  
Tarik Chanyour ◽  
Mohammed Ouçamah Cherkaoui Malki
Keyword(s):  
Mobile Device ◽  
Simulated Annealing Algorithm ◽  
Decision Time ◽  
Edge Computing ◽  
Full Time ◽  
Mobile Edge Computing ◽  
Heuristic Solution ◽  
Battery Capacity ◽  
Computing Node ◽  
Computationally Intensive

In recent years, the importance of the mobile edge computing (MEC) paradigm along with the 5G, the Internet of Things (IoT) and virtualization of network functions is well noticed. Besides, the implementation of computation-intensive applications at the mobile device level is limited by battery capacity, processing capabalities and execution time. To increase the batteries life and improve the quality of experience for computationally intensive and latency-sensitive applications, offloading some parts of these applications to the MEC is proposed. This paper presents a solution for a hard decision problem that jointly optimizes the processing time and computing resources in a mobile edge-computing node. Hence, we consider a mobile device with an offloadable list of heavy tasks and we jointly optimize the offloading decisions and the allocation of IT resources to reduce the latency of tasks’ processing. Thus, we developped a heuristic solution based on the simulated annealing algorithm, which can improve the offloading rate and reduce the total task latency while meeting short decision time. We performed a series of experiments to show its efficiency. Finally, the obtained results in terms of full-time treatrement are very encouraging. In addition, our solution makes offloading decisions within acceptable and achievable deadlines.

scholarly journals A cost efficient model for minimizing energy consumption and processing time for IoT tasks in a Mobile Edge Computing environment

2020 ◽  
Author(s):  
João Luiz Grave Gross ◽  
Cláudio Fernando Fernando Resin Geyer
Keyword(s):  
Energy Consumption ◽  
Cost Minimization ◽  
Cost Model ◽  
Scheduling Algorithm ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Total Energy Consumption ◽  
Data Intensive ◽  
Battery Capacity ◽  
Cost Efficient

In a scenario with increasingly mobile devices connected to the Internet, data-intensive applications and energy consumption limited by battery capacity, we propose a cost minimization model for IoT devices in a Mobile Edge Computing (MEC) architecture with the main objective of reducing total energy consumption and total elapsed times from task creation to conclusion. The cost model is implemented using the TEMS (Time and Energy Minimization Scheduler) scheduling algorithm and validated with simulation. The results show that it is possible to reduce the energy consumed in the system by up to 51.61% and the total elapsed time by up to 86.65% in the simulated cases with the parameters and characteristics defined in each experiment.

scholarly journals Wireless Powered Mobile Edge Computing Systems: Simultaneous Time Allocation and Offloading Policies

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 965
Author(s):  
Amna Irshad ◽  
Ziaul Haq Abbas ◽  
Zaiwar Ali ◽  
Ghulam Abbas ◽  
Thar Baker ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Time Allocation ◽  
Minimum Cost ◽  
Access Point ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Computational Power ◽  
Local Computation ◽  
Computing Systems ◽  
Battery Capacity

To improve the computational power and limited battery capacity of mobile devices (MDs), wireless powered mobile edge computing (MEC) systems are gaining much importance. In this paper, we consider a wireless powered MEC system composed of one MD and a hybrid access point (HAP) attached to MEC. Our objective is to achieve a joint time allocation and offloading policy simultaneously. We propose a cost function that considers both the energy consumption and the time delay of an MD. The proposed algorithm, joint time allocation and offload policy (JTAOP), is used to train a neural network for reducing the complexity of our algorithm that depends on the resolution of time and the number of components in a task. The numerical results are compared with three benchmark schemes, namely, total local computation, total offloading and partial offloading. Simulations show that the proposed algorithm performs better in producing the minimum cost and energy consumption as compared to the considered benchmark schemes.

scholarly journals Energy-Efficient Allocation for Multiple Tasks in Mobile Edge Computing

2021 ◽  
Author(s):  
Xi Liu ◽  
Jun Liu
Keyword(s):  
Approximation Algorithm ◽  
Mobile Device ◽  
Task Allocation ◽  
Maximum Energy ◽  
Edge Computing ◽  
Computation Offloading ◽  
Integer Programming Problem ◽  
Mobile Edge Computing ◽  
Total Energy Consumption ◽  
Multiple Tasks

Abstract Mobile edge computing (MEC) allows a mobile device to offload tasks to the nearby server for remote execution to enhance the performance of user equipment. A major challenge of MEC is to design an efficient algorithm for task allocation. In contrast to previous work on MEC, which mainly focuses on single-task allocation for a mobile device with only one task to be completed, this paper considers a mobile device with multiple tasks or an application with multiple tasks. This assumption does not hold in real settings because a mobile device may have multiple tasks waiting to execute. We address the problem of task allocation with minimum total energy consumption considering multi-task settings in MEC, in which a mobile device has one or more tasks. We consider the binary computation offloading mode and formulate multi-task allocation as an integer programming problem that is strongly $NP$-hard. We propose an approximation algorithm and show it is a polynomial-time approximation scheme that saves the maximum energy. Therefore, our proposed algorithm achieves a tradeoff between optimality loss and time complexity. We analyze the performance of the proposed algorithm by performing extensive experiments. The results of the experiments demonstrate that our proposed approximation algorithm is capable of finding near-optimal solutions, and achieves a good balance of speed and quality.

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

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>

Sign in / Sign up

Export Citation Format

Share Document