.png){kind=icon}
返回
An SDN based framework for maximizing throughput and balanced load distribution in a Cloudlet network
.png){kind=icon}
PRE
AI
DOI：10.1016/j.future.2020.04.009.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Although mobile devices have experienced voluminous proliferation throughout the last decade, there are limited resources in terms of their portable size. Such limitations could be mitigated by remote execution of the computation-intensive tasks to the cloud. By creating a cluster of servers (a.k.a. Cloudlets) to the network edge and close to the mobile devices, task offloading could be performed with a more acceptable delay in comparison with a cloud-based solution. Nevertheless, once the user requests mount, the resource constraints in a Cloudlet will lead to resource shortages. However, this challenge can be obviated using a network of Cloudlets for sharing their resources. This paper proposes a novel framework to optimally manage the resources and balance an equitable load across a network of Cloudlets via software-defined networking (SDN) techniques. To achieve this, firstly, the problem is considered as a mixed-integer linear programming (MILP) optimization model in order to balance the distribution of independent tasks offloaded from the mobile devices along with optimal use of resources. The MILP model guarantees meeting the tasks' deadlines and maximizes overall system throughput. Secondly, by showing that the addressed problem is NP-hard, an LP-relaxation model is proposed to enable the SDN controller on a large-scale network. Finally, we conduct experiments by emulating the proposed framework in Mininet-WiFi, with the Floodlight usage as the SDN controller. The simulation results indicate that the proposed architecture can achieve a significant throughput maximization of a system, which satisfactorily performs load balancing, and offers adequate proof, as well. (C) 2020 Elsevier B.V. All rights reserved.
Keyword:
SDN
Mobile Cloud Computing (MCC)
Cloudlet
Load balancing
Mobile Edge computing (MEC)
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
F
IF:
6.1
论文数:
6.9K
被引数:
2.3W
机构
引用论文
.png){kind=icon}
Optimal Cloudlet Placement and User to Cloudlet Allocation in Wireless Metropolitan Area Networks
PREAI
分享收藏
查看更多内容