LSTM-Based Traffic Load Balancing and Resource Allocation for an Edge System

Abstract

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 ultralow 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 an 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.

Remote and Rural Connectivity: Infrastructure and Resource Sharing Principles

“…For the BS system deployed in the remote/rural area, the total energy consumption θ SITE ðtÞ (measured in J) at time slot t consists of the BS communications, denoted by θ COMM ðtÞ, and computing platform processes, related to computing, caching, and communication, which is denoted by θ COMP ðtÞ. Thus, the energy consumption model at time slot t is formulated as follows, inspired by [20]:…”

“…For this, δðtÞ = ð0, 1Þ is the switching status indicator, with 1 indicating the active state and 0 representing the idle state. Then, θ nic max ðtÞ is the maximum energy drained by the network adapter process and it is obtained in a similar way as in [20].…”

“…To minimize the network traffic from the remote/rural site to the remote clouds, some of the frequently requested internet contents are cached locally, more especially viral contents. The caching process contribute to the energy consumption within the site, denoted by θ CH ðtÞ, and it is obtained as [20]:…”

A Hybridly Optimized LSTM‐Based Data Flow Prediction Model for Dependable Online Ticketing

“…Likewise, due to network flow's complex nature and the lack of typical behavior [36], traditional models cannot handle such data well. The LSTM network [32] has achieved good results in nonperiodic event detection [37], traffic load balancing [38], and other fields [8,39]. It has shown promising performance in time series data trend prediction [34].…”

“…Particle swarm optimization (PSO) is a heuristic random search algorithm, which has a lower number of setting parameters, no update and mutation involved, and it can find the extreme function values faster. Some LSTM models use particle swarm optimization (PSO) algorithm to find the optimal super parameters and achieve good results [34,38,39]. However, the PSO algorithm converges faster in the early stage of the optimization process and is easy to fall into the local optimum in the later stage.…”