Enhanced resource allocation in mobile edge computing using reinforcement learning based MOACO algorithm for IIOT

Data centers (DCs) is becoming increasingly an integral part of the computing infrastructures of most enterprises. Therefore, the concept of DC networks (DCNs) is receiving an increased attention in the network research community. Most DCNs deployed today can be classified as wired DCNs as copper and optical fiber cables are used for intra- and inter-rack connections in the network. Despite recent advances, wired DCNs face two inevitable problems; cabling complexity and hotspots . To address these problems, recent research works suggest the incorporation of wireless communication technology into DCNs. Wireless links can be used to either augment conventional wired DCNs, or to realize a pure wireless DCN. As the design spectrum of DCs broadens, so does the need for a clear classification to differentiate various design options. In this paper, we analyze the free space optical (FSO) communication and the 60 GHz radio frequency (RF), the two key candidate technologies for implementing wireless links in DCNs. We present a generic classification scheme that can be used to classify current and future DCNs based on the communication technology used in the network. The proposed classification is then used to review and summarize major research in this area. We also discuss open questions and future research directions in the area of wireless DCs.

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Wireless Communication in Data Centers: A Survey

The fifth generation (5G) network is an upcoming standard for wireless communications that coexists with the current 4G network to increase the throughput. The deployment of ultra-dense small cells (UDSC) over a macro-cell layer yields multi-tier networks, which are known as heterogeneous networks (HetNets). HetNets play a key role in the cellular network to provide services to numerous users. However, the number of handovers (HOs) and radio link failure (RLF) greatly increase due to the increase in the UDSC in the network. Therefore, mobility management becomes a very important function in a self-organizing network to improve the system performance. In this paper, we propose a velocity-based self-optimization algorithm to adjust the HO control parameters in 4G/5G networks. The proposed algorithm utilizes the user’s received power and speed to adjust the HO margin and the time to trigger during the user’s mobility in the network. Simulation results demonstrate that the proposed algorithm achieves a remarkable reduction in the rate of ping-pong HOs and RLF compared with other existing algorithms, thereby outperforming such algorithms by an average of more than 70% for all HO performance metrics.

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Velocity-Aware Handover Self-Optimization Management for Next Generation Networks

Network virtualization enables increasingly diverse network services to cohabit and share a given physical infrastructure and its resources, with the possibility to rely on different network architectures and protocols optimized towards specific requirements. In order to ensure a predictable performance despite shared resources, network virtualization requires a strict performance isolation and hence, resource reservations. Moreover, the creation of virtual networks should be fast and efficient. The underlying NP-hard algorithmic problem is known as the Virtual Network Embedding (VNE) problem and has been studied intensively over the last years. This paper presents NeuroViNE, a novel approach to speed up and improve a wide range of existing VNE algorithms: NeuroViNE is based on a search space reduction mechanism and preprocesses a problem instance by extracting relevant subgraphs, i.e., good combinations of substrate nodes and links. These subgraphs can then be fed to an existing algorithm for faster and more resource-efficient embeddings. NeuroViNE relies on a Hopfield network, and its performance benefits are investigated in simulations for random networks, real substrate networks, and data center networks.

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NeuroViNE: A Neural Preprocessor for Your Virtual Network Embedding Algorithm

5G New Radio (NR) is envisioned to provide three major services: enhanced Mobile Broadband (eMBB), Ultra Reliable Low Latency Communications (URLLC), and massive Machine Type Communication (mMTC). URLLC services (i.e., autonomous vehicles, industrial Internet of Things (IoT),…) require strict latency, on-way latency of 1 ms, with 99.999% reliability. eMBB applications aims extreme data rate while mMTC is designed to serve a large number of IoT devices that send a small data sporadically. In this paper, we address the resource scheduling problem of URLLC and eMBB traffics. First, the Resource Blocks (RBs) are allocated to eMBB users at the beginning of each time slot based on the channel state of each eMBB user and his previous average data rate up to current time slot. The RBs allocation problem modeled as as a 2-Dimensions Hopfield Neural Networks (2D-HNN) and the energy function of 2D-HNN is investigated to solve the RBs allocation problem. Then, the resource scheduling problem of URLLC and eMBB is formulated as an optimization problem with chance constraint. The chance constraint based problem aims to maximize the eMBB data rate while satisfying the URLLC critical constraints. The cumulative Distribution Function (CDF) of the stochastic URLLC traffic is investigated to relax the chance constraint into a linear constraint. The simulation results show efficiency of the proposed dynamic scheduling approach.

A Chance Constrained Based Formulation for Dynamic Multiplexing of eMBB-URLLC Traffics in 5G New Radio

Small cells are deployed in the long-term evolution—advanced (LTE-A) data standard to satisfy rapidly increasing data rates at hotspots and enhance coverage in buildings. Small cells are low-cost, ...

Novel type-2 fuzzy logic technique for handover problems in a heterogeneous network

In previous cellular networks, changing the parameters of handover manually was time consuming. So, automatic change of the parameters of handover will decrease expenditures, save time and guarantee the best quality of service. A new efficient handover optimization technique for Long Term Evolution (LTE) based on fuzzy logic is proposed in this paper. It is evaluated in terms of average number of handover, system throughput, and system delay based on user equipment (UE) speed. The most important contribution of this work that UE speed is considered in the handover process. The suggested optimization technique will select effective time-to-trigger (TTT) based on the UE speed, and it will also find the optimum handover margin (HOM) required for the handover process. This new handover optimization technique is evaluated for the four well-known handover algorithms then compared together. The proposed algorithm achieves a significantly improve in the handover performance when compared with the standard LTE and self-optimization technique.

An enhanced fuzzy logic optimization technique based on user mobility for LTE handover

In this paper, an extensive study is performed to understand the effect of the various parameters used in Genetic Algorithms (GA) to solve the spectrum allocation problem in cognitive radio networks. Two utilization functions, namely, Mean-Reward, and Max-Proportional-Fair, are used to evaluate the performance of the GA under different parameters values. Extensive simulation shows that, the parameters of the GA can be fine-tuned to achieve up to 90% and 62% improvements in speed and error, respectively, as compared to the GA used in the literature.

On the effectiveness of using Genetic Algorithm for spectrum allocation in cognitive radio networks

One of the key objectives of evolving communication technologies is to maximize the utilization of the available spectrum by increasing the number of simultaneous users while reducing interferences among users. In cognitive radio networks, this problem is referred to as the spectrum allocation problem, and is shown to be NP-Hard. This chapter studies the use of evolutionary algorithms to solve the spectrum allocation problem in cognitive radio networks. In particular, a Binary Harmony Search Algorithm (BHSA) is proposed and used, for the first time, to solve the spectrum allocation problem. The performance of the proposed BHSA algorithm is evaluated via simulation and is compared with an optimized Genetic Algorithm (GA) under three utilization functions, namely, Mean-Reward (MR), Max-Min-Reward (MMR), and Max-Proportional-Fair (MPF). Extensive simulation results confirm that the BHSA is not only faster, but it also finds better solutions compared to those obtained by the GA. For instance, under the MMR function, the BHSA requires less than 4% of the time needed by the GA in order to find a solution that is 10% better than that obtained by the GA.

Spectrum Allocation in Cognitive Radio Networks Using Evolutionary Algorithms

One of the main goals of LTE is to provide seamless and fast handover from one cell to another to meet a strict requirements. Hence, the decision of handover is a critical part in the design process of handover. The design of an efficient and successful handover requires a careful selection of handover (HO) parameters and the optimal setting of these. In this paper a new handover optimization algorithm for long term evolution (LTE) network based on fuzzy logic is presented. It consists of finding the optimum handover margin (HOM) required for handover process and also finding appropriate time-to-trigger (TTT) to perform a success handover using fuzzy logic. The proposed handover optimization technique is evaluated and compared with the four well-known handover algorithms and achieves minimum average number of handover per user and also have maximum throughput than the self-optimization technique.

Mona El-Ghoneimy

Papers

Novel type-2 fuzzy logic technique for handover problems in a heterogeneous network

An enhanced fuzzy logic optimization technique based on user mobility for LTE handover

On the effectiveness of using Genetic Algorithm for spectrum allocation in cognitive radio networks

Spectrum Allocation in Cognitive Radio Networks Using Evolutionary Algorithms

A new fuzzy logic technique for handover parameters optimization in LTE