A SELF-TUNING ALGORITHM FOR OPTIMAL ENERGY EFFICIENT LOAD BALANCING IN WIRELESS CELLULAR HETEROGENEOUS NETWORKS

Design of energy efficient load balancing models in cloud environments requires in-depth analysis of the cloud architecture and nature of requests served by the cloud. Depending upon these parameters, machine learning models are designed which aim at assigning best possible resource combination to serve the given tasks. This assignment varies w.r.t. multiple task and cloud parameters; which include task time, virtual machine (VM) performance, task deadline, energy consumption, etc. In order to perform this task, a wide variety of algorithms are developed by researchers cloud designers. Each of these algorithms aim at optimizing certain load balancing related parameters; for instance, a Genetic Algorithm (GA) designed for optimization of VM utilization might not consider task deadline before task allocation to the VMs. While, algorithms aimed at performing deadline aware load balancing might not provide effective cloud-to-task-mapping before allocation of tasks. Thus, it becomes difficult for researchers to select the best possible algorithms for their cloud deployment. In order to reduce this ambiguity, the underlying text compares different energy efficient cloud load balancing algorithms; and evaluates their performance in terms of computational complexity, and relative energy efficiency. This performance evaluation is further extended via inter architecture comparison; in order to evaluate the most optimum load balancer implementation for a given energy efficient application. Thus, after referring this text, researchers and cloud system designers will be able to select optimum algorithmic implementations for their given deployment. This will assist in reducing cloud deployment delay, and improving application specific load balancer performance.

Load balancing (LB) aims at tackling the hot-spot cell problem caused by uneven load distribution and resources shortage. Traditional LB schemes mainly focus on the load reduction of the hot-spot cell, while some partner cells might suffer from aggravating load after receiving inappropriate traffic, which triggers ripple LB and reduces the LB efficiency. To deal with this problem, a self- optimizing LB scheme in fixed relay station (RS) cellular networks is proposed. In the scheme, partner cells share the load jointly to guarantee each partner has similar load after LB. Meanwhile, the partner response mechanism considers the resources usage of partner's RSs to further reduce the ripple LB. Simulation results show that the proposed scheme can effectively reduce the number of ripple LB and reduce the call blocking rate.

With the augmentation of affordable multimedia wireless gadgets, the ubiquitous availability of the internet access, and the rapid pace of mobile traffic motivate research towards fifth generation (5G) communications to realize energy-efficient cloud radio access networks (C-RAN) with guaranteed quality of experience. Exploiting green energy harvesting for powering the C-RAN substantially alleviates the energy procurement from the utility grid, carbon footprint, and operational expenses. In this paper, we propose a new dynamic point selection coordinated multipoint (DPS CoMP) based load balancing paradigm emphasizing achievable throughput and energy efficiency (EE) by reducing utility grid consumption from a network level perspective. This paper investigates the radio efficiency, EE, and average on-grid energy saving addressing the key challenges of tempo-spatial dynamics of traffic intensity and renewable energy (RE) generation under a wide range of network setup. Endeavoring load balancing technique strives a balance in network utilities such as green energy utilization and user association based on BS coordination technique in a cluster. Provision of cell sleep approach is contemplated for further energy saving by turning off lightly loaded base stations (BSs) during low traffic arrivals. The proposed CoMP based load balancing algorithm proficiently manages resource block allocation to the new users and elevated the energy efficiency over the conventional location and traffic centric mechanisms. Extensive system-level simulations manifest that the suggested framework enables an adjustable trade-off between radio efficiency and EE, and saves 22% on-grid power consumption and increases EE index by 32%. Afterward, an exhaustive comparison of the proposed method with the existing schemes is pledged for further validation highlighting sustainable 5G wireless systems.

Energy efficiency of wireless networks is becoming more and more important because of the steadily rising energy cost and environmental concerns. In a real network with unequal traffic load distributions among cells, by offloading some edge users in a heavily-loaded cell to neighboring lightly-loaded cell, load balance, aims to utilize the radio resource more efficiently and consequently can improve the end-user performance. In this paper an energy efficient load balancing algorithm is proposed. In the proposed algorithm, the target cell in load balance is selected based on the prediction of the possible performance. Furthermore, an energy efficiency scaling factor is introduced into the criteria when selecting the target cell in load balance to achieve the joint optimization of both load balance and energy saving. Simulation results show that the proposed algorithm can reduce the power consumption in the network and at the same time improve the cell throughput.

Objectives: In this paper various task scheduling algorithm along with load distribution techniques investigated to ensure efficient mapping of tasks to resources and for coherent resource utilization in heterogeneous environment. Statistical Analysis: A detailed comparative approach is conferred for different job scheduling and load balancing algorithm available in the literature. Considered parameters for comparisons are scheduling approaches, techniques, findings, benefits, pros and cons. This study facilitates to identify the efficient scheduling and load balancing approach which optimizes resource use, minimizes completion time as well as task waiting time and balanced load among resources. Findings: Make span time and flow time are two important parameters to compute and minimize job execution time. This paper explores an intense survey of minimization of task execution time with minimum make span time and flow time. Apart from this a joint review on job scheduling, load balancing and minimizing job completion time using Expected Time to Compute (ETC) matrix is presented. At the end of this paper a research model is proposed which is a novel and hybrid approach with a combination of the efficiency of scheduling and load balancing. Application: The Load Balancing (LB) is an important feature of the grid environment. The efficient LB algorithm assures cost effective resource usage by the provisioning of resources to users on demand schedule in pay-as-you-say-manner.

The influences of network characteristics on the task performance in a desktop collaborative virtual environment (CVE) system were studied. In the experiment, two participants worked together to accomplish a collaborative task and a network emulator was utilized to simulate real network characteristics. The task used in the experiment was designed to be highly interactive and had rigid requirements on network characteristics (e.g. latency and packet loss). The objective performance and subjective assessment were recorded and analyzed quantitatively as a function of network latency and packet loss rate. The experiment results show that network latency has no distinct effect on task performance when network latency is less than 200 ms and lightly affect task performance when network latency is between 200 and 600 ms; packet loss has no effect on task performance when there are 40 state update messages received per second and lightly affect task performance when there are between 15 and 40 state update messages received per second.

Virtual reality (VR) and augmented reality (AR) applications with interactions are usually delay-sensitive. Fast delivery of three-dimensional (3-D) models in VR/AR over wireless networks has been a crucial task due to limited bandwidth and packet error. In this paper we propose an application layer content-aware joint source-channel coding scheme based on forward error correction (FEC). A 3D model is progressively encoded to a base mesh and several refinement layers consisting of connectivity and geometric data. A new design of LT code with a special coding graph has been proposed to provide unequal error protection (UEP) to these two kinds of data. Then a statistical measure is used to optimize the rate allocation for each refinement layer, which is scalable to both network bandwidth and packet loss rate (PLR). Experimental results show that the proposed streaming scheme can achieve the objectives of low transmission delay and small distortion under serious loss.

In order to reduce video network transmission delay, congestion and packet loss rate, the authors propose an algorithm of video network transmission based on unbalanced multiple description coding. Based on the detection of network link available bandwidth, we predict congestion state of the network link according to Markov model, then adjust video transmission path. Comparing with the random early detection (RED) algorithm, experimental results show that the algorithm can more effectively estimate network congestion, reduce video packet loss rate and network latency, and ensure the quality of the video network transmission effectively.

With the demand of Internet video multicast service and the advantages of SDN (soft defined network), a multi-factor adaptive SDN video multicast routing algorithm is proposed. The algorithm combines hop count, bandwidth and load balancing, and realizes the multicast system simulation based on this routing algorithm. The simulation results show that with the increase of the number of members in multicast group, the packet loss rate and RTT and packet-in of the controller of the whole system do not change much, and the network performance of the whole system is stable. It is suitable for solving the problem of unstable video communication effect caused by network congestion and packet loss.

Voice over IP (VoIP) services are now offered on many mobile networks. However, delay spikes, which are sudden large increases in packet delay, affect the quality of VoIP on mobile networks. This paper proposes a quality of experience (QoE) indicator considering delay spikes. The QoE indicator combines a voice quality indicator such as ITU-T P.862 and the delay part of ITU-T G.107, which is a quality indicator for VoIP. This paper also proposes a transmission control method that selects the codec type and the sending interval of voice packets so as to maximize the QoE indicator. The method improves the mean opinion score (MOS), which indicates QoE as a value between 1 and 5, by up to 0.4.

YouTube, the killer application of today's Internet, is changing the way ISPs and network operators manage quality monitoring and provisioning on their IP networks. YouTube is currently the most consumed Internet application, accounting for more than 30% of the overall Internet's traffic worldwide. Coupling such an overwhelming traffic volume with the ever intensifying competition among ISPs is pushing operators to integrate Quality of Experience (QoE) paradigms into their traffic management systems. The need for automatic QoE assessment solutions becomes even more critical in mobile broadband networks, where over-provisioning solutions can not be foreseen and bad user experience translates into churning clients. This paper presents a complete study on the problem of YouTube Quality of Experience monitoring and assessment in mobile networks. The paper considers not only the QoE analysis, modeling and assessment based on real users' experience, but also the passive monitoring of the quality provided by the ISP to its end-customers in a large mobile broadband network.

Perceived speech quality is the key metric for QoS in VoIP applications. Our primary aims are to carry out a fundamental investigation of the impact of packet loss and talkers on perceived speech quality using an objective method and, thus, to provide the basis for developing an artificial neural network (ANN) model to predict speech quality for VoIP. The impact on perceived speech quality of packet loss and of different talkers was investigated for three modern codecs (G.729, G.723.1 and AMR) using the new ITU PESQ algorithm. Results show that packet loss burstiness, loss locations/patterns and the gender of talkers have an impact. Packet size has, in general, no obvious influence on perceived speech quality for the same network conditions, but the deviation in speech quality depends on packet size and codec. Based on the investigation, we used talkspurt-based conditional and unconditional packet loss rates (which are perceptually more relevant than network packet loss rates), codec type and the gender of the talker (extracted from decoder) as inputs to an ANN model to predict speech quality directly from network parameters. Results show that high prediction accuracy was obtained from the ANN model (correlation coefficients for the test and validation datasets were 0.952 and 0.946 respectively). This work should help to develop efficient, nonintrusive QoS monitoring and control strategies for VoIP applications.

As the demand for mobile services grows exponentially, the focus on wireless network optimization has been changed from Quality of Service (QoS) for the network to Quality of Experience (QoE) for users. The network optimization research about QoS in the past cannot surely meet the requirements of the users' QoE. Therefore, in this paper, a handover solution is proposed to improve the QoE while considering the QoE balance for a LTE network that provides different services. Compared with other optimization of QoE nowadays, the proposed solution balances and improves the QoE of users. The proposed solution controls handover parameter by running a handover optimization algorithm based on the dynamic particle swarm optimization (DPSO) in a central controller, which finally optimizes the overall QoE and reduces the proportion of users with extremely poor QoE. The simulation results show that the DPSO algorithm ensures the quality of the solution and increases the speed of convergence by nearly twice that of the standard particle swarm optimization algorithm (SPSO). After adopting the DPSO handover solution, the overall QoE of users is increased by 6.22 % and 4.59 % while the variance of users' QoE is decreased by 14.2 % and 22.6 %, compared with the full handover solution and the traditional handover solution respectively. The number of users with QoE less than 1.9 is reduced to 0 with the proposed solution.

A present issue in the evolution of mobile cellular networks is determining whether, how and where to deploy adaptive content and cloud distribution solutions at the base station and backhauling network level. Intuitively, an adaptive placement of content and computing resources in the most crowded regions can grant important traffic offloading, improve network efficiency and user quality of experience. In this paper we document the content consumption in the Orange cellular network for the Paris metropolitan area, from spatial and application-level extensive analysis of real data from a few million users, reporting the experimental distributions. In this scope, we propose a hotspot cell estimator computed over user's mobility metrics and based on linear regression. Evaluating our estimator on real data, it appears as an excellent hotspot detection solution of cellular and backhauling network management. We show that its error strictly decreases with the cell load, and it is negligible for reasonable hotspot cell load upper thresholds. We also show that our hotspot estimator is quite scalable against mobility data volume and against time variations.

Due to extensive usage of smartphone, laptop, tablet, etc., there is an increase in volume of mobile data traffic. So, the best remedy to tackle with mobile data traffic issue is Wi-Fi offloading. It is the cost-effective approach that offloads mobile traffic in the existing Wi-Fi networks. Mobile users obtain data through Wi-Fi instead of cellular network; hence, it is the efficient technique to improve the spectrum efficiency and reduce the cellular network congestion. Wi-Fi access points have the advantage on amount of offload data and also provide quality of service in offloading data. To provide fairness between LTE and Wi-Fi, load balance is the technique addressed here. The load balancing problem can be solved by sharing traffic between adjacent cells. This work aims to provide an efficient dynamic load balance algorithm for offloading the data, and the simulation is done using MATLAB and NS2 software.