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LSTM-based Traffic Load Balancing and Resource Allocation for an Edge System
arXiv - CS - Networking and Internet Architecture Pub Date : 2020-11-20 , DOI: arxiv-2011.10602 Thembelihle Dlamini, Sifiso Vilakati
arXiv - CS - Networking and Internet Architecture Pub Date : 2020-11-20 , DOI: arxiv-2011.10602 Thembelihle Dlamini, Sifiso Vilakati
The massive deployment of small cell Base Stations (SBSs) empowered with
computing capabilities presents one of the most ingenious solutions adopted for
5G cellular networks towards meeting the foreseen data explosion and the
ultra-low latency demanded by mobile applications. This empowerment of SBSs
with Multi-access Edge Computing (MEC) has emerged as a tentative solution to
overcome the latency demands and bandwidth consumption required by mobile
applications at the network edge. The MEC paradigm offers a limited amount of
resources to support computation, thus mandating the use of intelligence
mechanisms for resource allocation. The use of green energy for powering the
network apparatuses (e.g., Base Stations (BSs), MEC servers) has attracted
attention towards minimizing the carbon footprint and network operational
costs. However, due to their high intermittency and unpredictability, the
adoption of learning methods is a requisite. Towards intelligent edge system
management, this paper proposes a Green-based Edge Network Management (GENM)
algorithm, which is a online edge system management algorithm for enabling
green-based load balancing in BSs and energy savings within the MEC server. The
main goal is to minimize the overall energy consumption and guarantee the
Quality of Service (QoS) within the network. To achieve this, the GENM
algorithm performs dynamic management of BSs, autoscaling and reconfiguration
of the computing resources, and on/off switching of the fast tunable laser
drivers coupled with location-aware traffic scheduling in the MEC server. The
obtained simulation results validate our analysis and demonstrate the superior
performance of GENM compared to a benchmark algorithm.
中文翻译:
边缘系统的基于LSTM的流量负载平衡和资源分配
具有计算功能的小型基站(SBS)的大规模部署是5G蜂窝网络采用的最巧妙的解决方案之一,旨在满足可预见的数据爆炸和移动应用程序所需的超低延迟。一种具有多路访问边缘计算(MEC)功能的SBS授权已经成为一种临时解决方案,可以克服网络边缘移动应用程序所要求的延迟要求和带宽消耗。MEC范例提供了有限数量的资源来支持计算,因此强制要求使用智能机制进行资源分配。使用绿色能源为网络设备(例如,基站(BS),MEC服务器)供电已引起了人们的关注,它们将碳足迹和网络运营成本降至最低。然而,由于它们的高间歇性和不可预测性,必须采用学习方法。面向智能边缘系统管理,本文提出了一种基于绿色的边缘网络管理(GENM)算法,该算法是一种在线边缘系统管理算法,用于在BS中实现基于绿色的负载平衡并在MEC服务器内实现节能。主要目标是最大程度地降低总体能耗,并保证网络内的服务质量(QoS)。为此,GENM算法执行BS的动态管理,计算资源的自动缩放和重新配置,以及快速可调激光驱动器的开/关切换以及MEC服务器中的位置感知流量调度。
更新日期:2020-11-25
中文翻译:
边缘系统的基于LSTM的流量负载平衡和资源分配
具有计算功能的小型基站(SBS)的大规模部署是5G蜂窝网络采用的最巧妙的解决方案之一,旨在满足可预见的数据爆炸和移动应用程序所需的超低延迟。一种具有多路访问边缘计算(MEC)功能的SBS授权已经成为一种临时解决方案,可以克服网络边缘移动应用程序所要求的延迟要求和带宽消耗。MEC范例提供了有限数量的资源来支持计算,因此强制要求使用智能机制进行资源分配。使用绿色能源为网络设备(例如,基站(BS),MEC服务器)供电已引起了人们的关注,它们将碳足迹和网络运营成本降至最低。然而,由于它们的高间歇性和不可预测性,必须采用学习方法。面向智能边缘系统管理,本文提出了一种基于绿色的边缘网络管理(GENM)算法,该算法是一种在线边缘系统管理算法,用于在BS中实现基于绿色的负载平衡并在MEC服务器内实现节能。主要目标是最大程度地降低总体能耗,并保证网络内的服务质量(QoS)。为此,GENM算法执行BS的动态管理,计算资源的自动缩放和重新配置,以及快速可调激光驱动器的开/关切换以及MEC服务器中的位置感知流量调度。