scholarly journals Mobility- and Energy-Aware Cooperative Edge Offloading for Dependent Computation Tasks

Network ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 191-214
Author(s):  
Mahshid Mehrabi ◽  
Shiwei Shen ◽  
Yilun Hai ◽  
Vincent Latzko ◽  
George Koudouridis ◽  
...  
Keyword(s):  
Mobility Model ◽  
Edge Computing ◽  
Mixed Integer ◽  
Task Completion ◽  
Energy Aware ◽  
Coverage Area ◽  
Wide Range ◽  
Battery Energy ◽  
Multi Access ◽  
Quadratically Constrained

Cooperative edge offloading to nearby end devices via Device-to-Device (D2D) links in edge networks with sliced computing resources has mainly been studied for end devices (helper nodes) that are stationary (or follow predetermined mobility paths) and for independent computation tasks. However, end devices are often mobile, and a given application request commonly requires a set of dependent computation tasks. We formulate a novel model for the cooperative edge offloading of dependent computation tasks to mobile helper nodes. We model the task dependencies with a general task dependency graph. Our model employs the state-of-the-art deep-learning-based PECNet mobility model and offloads a task only when the sojourn time in the coverage area of a helper node or Multi-access Edge Computing (MEC) server is sufficiently long. We formulate the minimization problem for the consumed battery energy for task execution, task data transmission, and waiting for offloaded task results on end devices. We convert the resulting non-convex mixed integer nonlinear programming problem into an equivalent quadratically constrained quadratic programming (QCQP) problem, which we solve via a novel Energy-Efficient Task Offloading (EETO) algorithm. The numerical evaluations indicate that the EETO approach consistently reduces the battery energy consumption across a wide range of task complexities and task completion deadlines and can thus extend the battery lifetimes of mobile devices operating with sliced edge computing resources.

scholarly journals Energy-aware Resource Management for Federated Learning in Multi-access Edge Computing Systems

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Chit Wutyee Zaw ◽  
Shashi Raj Pandey ◽  
Kitae Kim ◽  
Choong Seon Hong
Keyword(s):  
Resource Management ◽  
Edge Computing ◽  
Energy Aware ◽  
Computing Systems ◽  
Multi Access

scholarly journals Efficient Computation Offloading in Multi-Tier Multi-Access Edge Computing Systems: A Particle Swarm Optimization Approach

2019 ◽  
Vol 10 (1) ◽  
pp. 203 ◽  
Author(s):  
Luan N. T. Huynh ◽  
Quoc-Viet Pham ◽  
Xuan-Qui Pham ◽  
Tri D. T. Nguyen ◽  
Md Delowar Hossain ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mixed Integer ◽  
5G Networks ◽  
Swarm Optimization ◽  
Multi Access

In recent years, multi-access edge computing (MEC) has become a promising technology used in 5G networks based on its ability to offload computational tasks from mobile devices (MDs) to edge servers in order to address MD-specific limitations. Despite considerable research on computation offloading in 5G networks, this activity in multi-tier multi-MEC server systems continues to attract attention. Here, we investigated a two-tier computation-offloading strategy for multi-user multi-MEC servers in heterogeneous networks. For this scenario, we formulated a joint resource-allocation and computation-offloading decision strategy to minimize the total computing overhead of MDs, including completion time and energy consumption. The optimization problem was formulated as a mixed-integer nonlinear program problem of NP-hard complexity. Under complex optimization and various application constraints, we divided the original problem into two subproblems: decisions of resource allocation and computation offloading. We developed an efficient, low-complexity algorithm using particle swarm optimization capable of high-quality solutions and guaranteed convergence, with a high-level heuristic (i.e., meta-heuristic) that performed well at solving a challenging optimization problem. Simulation results indicated that the proposed algorithm significantly reduced the total computing overhead of MDs relative to several baseline methods while guaranteeing to converge to stable solutions.

scholarly journals Optimal Design of Hierarchical Cloud-Fog&Edge Computing Networks with Caching

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1582 ◽  
Author(s):  
Xiaoqian Fan ◽  
Haina Zheng ◽  
Ruihong Jiang ◽  
Jinyu Zhang
Keyword(s):  
Optimal Design ◽  
Mode Selection ◽  
Optimal Solution ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mixed Integer ◽  
Task Completion ◽  
Transmit Power ◽  
Content Caching ◽  
Power Threshold

This paper investigates the optimal design of a hierarchical cloud-fog&edge computing (FEC) network, which consists of three tiers, i.e., the cloud tier, the fog&edge tier, and the device tier. The device in the device tier processes its task via three computing modes, i.e., cache-assisted computing mode, cloud-assisted computing mode, and joint device-fog&edge computing mode. Specifically, the task corresponds to being completed via the content caching in the FEC tier, the computation offloading to the cloud tier, and the joint computing in the fog&edge and device tier, respectively. For such a system, an energy minimization problem is formulated by jointly optimizing the computing mode selection, the local computing ratio, the computation frequency, and the transmit power, while guaranteeing multiple system constraints, including the task completion deadline time, the achievable computation capability, and the achievable transmit power threshold. Since the problem is a mixed integer nonlinear programming problem, which is hard to solve with known standard methods, it is decomposed into three subproblems, and the optimal solution to each subproblem is derived. Then, an efficient optimal caching, cloud, and joint computing (CCJ) algorithm to solve the primary problem is proposed. Simulation results show that the system performance achieved by our proposed optimal design outperforms that achieved by the benchmark schemes. Moreover, the smaller the achievable transmit power threshold of the device, the more energy is saved. Besides, with the increment of the data size of the task, the lesser is the local computing ratio.

scholarly journals Joint Node Selection and Resource Allocation for Task Offloading in Scalable Vehicle-Assisted Multi-Access Edge Computing

Symmetry ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 58 ◽  
Author(s):  
Xuan-Qui Pham ◽  
Tien-Dung Nguyen ◽  
VanDung Nguyen ◽  
Eui-Nam Huh
Keyword(s):  
Resource Allocation ◽  
High Reliability ◽  
Resource Limitation ◽  
Optimization Techniques ◽  
Edge Computing ◽  
Mixed Integer ◽  
Node Selection ◽  
Computing Services ◽  
Iot Devices ◽  
Multi Access

The resource limitation of multi-access edge computing (MEC) is one of the major issues in order to provide low-latency high-reliability computing services for Internet of Things (IoT) devices. Moreover, with the steep rise of task requests from IoT devices, the requirement of computation tasks needs dynamic scalability while using the potential of offloading tasks to mobile volunteer nodes (MVNs). We, therefore, propose a scalable vehicle-assisted MEC (SVMEC) paradigm, which cannot only relieve the resource limitation of MEC but also enhance the scalability of computing services for IoT devices and reduce the cost of using computing resources. In the SVMEC paradigm, a MEC provider can execute its users’ tasks by choosing one of three ways: (i) Do itself on local MEC, (ii) offload to the remote cloud, and (iii) offload to the MVNs. We formulate the problem of joint node selection and resource allocation as a Mixed Integer Nonlinear Programming (MINLP) problem, whose major objective is to minimize the total computation overhead in terms of the weighted-sum of task completion time and monetary cost for using computing resources. In order to solve it, we adopt alternative optimization techniques by decomposing the original problem into two sub-problems: Resource allocation sub-problem and node selection sub-problem. Simulation results demonstrate that our proposed scheme outperforms the existing schemes in terms of the total computation overhead.

scholarly journals A ROBUST CALCULUS OFFLOADING STRUCTURE FOR AMBULANT HEALTH INQUIRIES

2021 ◽  
pp. 144-151
Author(s):  
Pitta Rebecca Alekhya ◽  
K. Tulasi Krishna Kumar Nainar
Keyword(s):  
Resource Utilization ◽  
Mobile Health ◽  
Completion Time ◽  
Edge Computing ◽  
Computation Offloading ◽  
Utilization Rate ◽  
Task Completion ◽  
Base Line ◽  
Internet Of Vehicles ◽  
Multi Access

Recently, research intergrading medicine and Artificial Intelligence has attracted extensive attention. Mobile health has emerged as a promising paradigm for improving people’s work and life in the future. However, high mobility of mobile devices and limited resources pose challenges for users to deal with the applications in mobile health that require large amount of computational resources. In this paper, a novel computation offloading mechanism is proposed in the environments combining of the Internet of Vehicles and Multi-Access Edge Computing. Through the proposed mechanism, mobile health applications are divided into several parts and can be offloaded to appropriate nearby vehicles while meeting the requirements of application completion time, energy consumption, and resource utilization. A particle swarm optimization based approach is proposed to optimize the aforementioned computation offloading problem in a specific medical application. Evaluations of the proposed algorithms against local computing method serves as base line method are conducted via extensive simulations. The average task completion time saved by our proposed task allocation scheme increases continually compared with the local solution. Specially, the global resource utilization rate increased from 71.8% to 94.5% compared with the local execution time. KEY WORDS: Computation Offloading, Mobile Health, Internet of Vehicles, Multi-Access Edge Computing.

Evolving Multi-Access Edge Computing to Support Enhanced IoT Deployments

2019 ◽  
Vol 3 (2) ◽  
pp. 26-34 ◽  
Author(s):  
Lanfranco Zanzi ◽  
Flavio Cirillo ◽  
Vincenzo Sciancalepore ◽  
Fabio Giust ◽  
Xavier Costa-Perez ◽  
...  
Keyword(s):  
Edge Computing ◽  
Multi Access

scholarly journals New general mixed-integer linear programming model for mobile workforce management

2021 ◽  
Author(s):  
András Éles ◽  
István Heckl ◽  
Heriberto Cabezas
Keyword(s):  
Programming Model ◽  
Time Windows ◽  
Mutual Exclusion ◽  
Parallel Execution ◽  
Mixed Integer ◽  
Management Problem ◽  
Routing Problem ◽  
Workforce Management ◽  
Computational Performance ◽  
Wide Range

AbstractA mathematical model is introduced to solve a mobile workforce management problem. In such a problem there are a number of tasks to be executed at different locations by various teams. For example, when an electricity utility company has to deal with planned system upgrades and damages caused by storms. The aim is to determine the schedule of the teams in such a way that the overall cost is minimal. The mobile workforce management problem involves scheduling. The following questions should be answered: when to perform a task, how to route vehicles—the vehicle routing problem—and the order the sites should be visited and by which teams. These problems are already complex in themselves. This paper proposes an integrated mathematical programming model formulation, which, by the assignment of its binary variables, can be easily included in heuristic algorithmic frameworks. In the problem specification, a wide range of parameters can be set. This includes absolute and expected time windows for tasks, packing and unpacking in case of team movement, resource utilization, relations between tasks such as precedence, mutual exclusion or parallel execution, and team-dependent travelling and execution times and costs. To make the model able to solve larger problems, an algorithmic framework is also implemented which can be used to find heuristic solutions in acceptable time. This latter solution method can be used as an alternative. Computational performance is examined through a series of test cases in which the most important factors are scaled.

Sign in / Sign up

Export Citation Format

Share Document