Proposed Load Balancing Algorithm Research Articles

Load balancing in 5G heterogeneous networks based on automatic weight function

Load balancing is a major challenge in heterogeneous networks (HetNets) consisting of 5G and 6G ultra-dense small cells with long-term evaluation advanced (LTE-A) networks. A key factor in achieving efficient load balancing during user mobility is creating appropriate optimisation for handover control parameters (HCP). This paper proposes a coordinated load balancing algorithm for LTE-A/fifth generation (5G) HetNets. The algorithm automatically optimises HCP settings for a given user based on three bounded functions (the signal-to-interference-plus-noise ratio (SINR) of the user equipment (UE), the number of physical resource blocks (PRBs) per UE and the UE’s speed) as well as their automatic weight levels. A two-step target cell determination strategy is implemented according to the cell load level and RSRP criteria, ensuring that users are handed over to low-loaded target cells. A new HO procedure that considers the pilot signal power is also proposed, which includes the number of PRBs per UE and the RSRP. Cells with freer PRBs are prioritised in user association to provide load balance and enhanced throughput. The proposed load balancing algorithm is compared with five other load balancing algorithms selected from the literature. The simulation results reveal that under various mobile speed scenarios, the proposed load balancing scheme enhances network performance in terms of load level, throughput, spectral efficiency and call dropping ratio (CDR).

Game Theory-Based Load-Balancing Algorithms for Small Cells Wireless Backhaul Connections

5G wireless networks have as one of the main characteristics the large-scale deployment of small cells (microcells, picocells, etc.), which is expected to bring several advantages in what concerns the high speed and low latency connectivity of the users. This large-scale deployment of small cells also raises several technical challenges, provisioning the backhaul connectivity being one of them. The paper considers the situations when small cells are deployed temporarily or are deployed in a vehicle transporting many passengers, situations when the traditional wired or wireless backhaul solutions could be too costly to be used. The paper proposes, as an alternative solution, the use as backhaul connections of the wireless links set up in the macro cells which cover the location of the small cell. The paper proposes several Game Theory (GT)-based Load-Balancing (LB) algorithms for distributing the traffic of the small cell users over the macro cell links. The proposed LB algorithms are evaluated by computer simulations and are compared with “classical” LB algorithms considered as references. The performed computer simulations show that the auction-based algorithms have the best performance in terms of delay suffered by the transmitted data packets, while the selfish routing type algorithm has weaker performance, even behaving poorly than some of the reference non-GT-based algorithms. The paper also considers the situation when several small cell APs are deployed in a limited area or a vehicle and the user groups that attach to different APs should be identified. The paper proposes two GT-based user clustering algorithms, and the performance of these algorithms are evaluated by computer simulations. These simulations show that even simple clustering algorithms could improve the distribution of the traffic over the neighbor small cell APs and reduce the delay experienced by the data packets in the transmission system.

Enhanced performance of long-term evolution small-cell networks using improved mobility algorithms

With an ever-increasing number of user equipment (UE) and bandwidth demands of new applications, the deployment of dense heterogeneous cellular networks has been adopted in many network scenarios. The small cells are unable to unload the traffic due to the random UEs mobility and cells deployment. This degrades the network performance such as handover success, throughput, and load distribution. To address such a problem, we propose an enhanced mobility load-balancing algorithm for small cells. The conventional mobility load balancing (MLB) algorithms study only the fixed or adaptive thresholds of the network separately to consider the load balance process, while other MLB algorithms consider the neighboring cells of the network those experience unnecessary MLB actions. The proposed load balancing algorithms study overloaded cells and neighbors using the proposed efficiency parameter, B. To identify the overloaded cells, we propose B which compares between a pre-defined threshold and the network threshold to categorize medium and overloaded cells, based on B, the algorithm is triggered. Then, to control the shifted load to a target neighbor cell, we propose the Rescue factor, f. the f ensures the load of the target cell after handover is equal or less than Ɓ. The simulation results showed a lower standard deviation and higher throughput and physical resource block utilization than surveyed algorithms.

OpenDaylight vs. Floodlight: Comparative Analysis of a Load Balancing Algorithm for Software Defined Networking

This paper presents the proposal of a load balancing algorithm implemented in two of the most popular controllers for Software Defined Networks (SDN): OpenDaylight and Floodlight. A comparative study in terms of the available bandwidth and delay time of the packet forwarding was performed by means of simulation modeling in a base network in which a shortest path algorithm was implemented as well. The results show that the proposed load balancing algorithm improves significantly the performance of a SDN in terms of the offered QoS of a OpenDaylight based controller. The effect of the proposed load balancing algorithm in the Floodlight controller shows a smaller impact mainly on the bandwidth allocation due to its in-build modules that by default perform specific routing and forwarding operations efficiently according to the traffic demand.

An elephant herd grey wolf optimization (EHGWO) algorithm for load balancing in cloud

Purpose The advancements in the cloud computing has gained the attention of several researchers to provide on-demand network access to users with shared resources. Cloud computing is important a research direction that can provide platforms and softwares to clients using internet. However, handling huge number of tasks in cloud infrastructure is a complicated task. Thus, it needs a load balancing (LB) method for allocating tasks to virtual machines (VMs) without influencing system performance. This paper aims to develop a technique for LB in cloud using optimization algorithms. Design/methodology/approach This paper proposes a hybrid optimization technique, named elephant herding-based grey wolf optimizer (EHGWO), in the cloud computing model for LB by determining the optimal VMs for executing the reallocated tasks. The proposed EHGWO is derived by incorporating elephant herding optimization (EHO) in grey wolf optimizer (GWO) such that the tasks are allocated to the VM by eliminating the tasks from overloaded VM by maintaining the system performance. Here, the load of physical machine (PM), capacity and load of VM is computed for deciding whether the LB has to be done or not. Moreover, two pick factors, namely, task pick factor (TPF) and VM pick factor (VPF), are considered for choosing the tasks for reallocating them from overloaded VM to underloaded VM. The proposed EHGWO decides the task to be allocated in the VM based on the newly derived fitness functions. Findings The minimum load and makespan obtained in the existing methods, constraint measure based LB (CMLB), fractional dragonfly based LB algorithm (FDLA), EHO, GWO and proposed EHGWO for the maximum number of VMs is illustrated. The proposed EHGWO attained minimum makespan with value 814,264 ns and minimum load with value 0.0221, respectively. Meanwhile, the makespan values attained by existing CMLB, FDLA, EHO, GWO, are 318,6896 ns, 230,9140 ns, 1,804,851 ns and 1,073,863 ns, respectively. The minimum load values computed by existing methods, CMLB, FDLA, EHO, GWO, are 0.0587, 0.026, 0.0248 and 0.0234. On the other hand, the proposed EHGWO with minimum load value is 0.0221. Hence, the proposed EHGWO attains maximum performance as compared to the existing technique. Originality/value This paper illustrates the proposed LB algorithm using EHGWO in a cloud computing model using two pitch factors, named TPF and VPF. For initiating LB, the tasks assigned to the overloaded VM are reallocated to under loaded VMs. Here, the proposed LB algorithm adapts capacity and loads for the reallocation. Based on TPF and VPF, the tasks are reallocated from VMs using the proposed EHGWO. The proposed EHGWO is developed by integrating EHO and GWO algorithm using a new fitness function formulated by load of VM, migration cost, load of VM, capacity of VM and makespan. The proposed EHGWO is analyzed based on load and makespan.

RETRACTED ARTICLE: To improve user key security and cloud user region-based resource scheduler using rail fence region-based load balancing algorithm

Computer services, such as cloud computing and distributed computing, offer very low costs. These services can be calculated in the form of hardware, operating system or application software. However, the prospect of a cloud service provider (CSP), then they are not easy to find. Cloud-based companies are offering customer computing servers that have proven to be less expensive for energy. In this proposed work, a systematic method of balancing tasks with the consumption of loads and security is addressed. The energy consumption of the cloud data center is minimized through our proposed load balancing algorithm. And also introduced a security model to improve the user’s trust model providing in the cloud. In public key with encryption, properties are based on cryptography. In this proposed, a trusted and scalable load balancing approach named Rail Fence Region-based Load balancing (RFRLB) it is adaptive, dynamic, scalable and efficient. When one server fails, the task transfer to another VM server. It takes rows, queue lengths and heavy workloads, as well as account server confidence values. The load balancing technique deployment model used in various computing environments. In this regard, the main focus is on improving bandwidth usage and improving cloud user security performance, rather than issues such as power efficiency. This proposed RFLP method performs well in comparison with parameters analysis and its protection method.

A Load Balancing Algorithm for Mobile Devices in Edge Cloud Computing Environments

As current data centers and servers are growing in size by orders of magnitude when needed, load balancing is a great concern in scalable computing systems, including mobile edge cloud computing environments. In mobile edge cloud computing systems, a mobile user can offload its tasks to nearby edge servers to support real-time applications. However, when users are located in a hot spot, several edge servers can be overloaded due to suddenly offloaded tasks from mobile users. In this paper, we present a load balancing algorithm for mobile devices in edge cloud computing environments. The proposed load balancing technique features an efficient complexity by a graph coloring-based implementation based on a genetic algorithm. The aim of the proposed load balancing algorithm is to distribute offloaded tasks to nearby edge servers in an efficient way. Performance results show that the proposed load balancing algorithm outperforms previous techniques and increases the average CPU usage of virtual machines, which indicates a high utilization of edge servers.

Cloud computing based load balancing algorithm for erlang concurrent traffic

<p>The distribution of scheduler from user inquiries in the clouds is complex. In keeping up with the cloud computing environment and the inquirers, the clouds meet with some problematic load balancing complications as an improving load balancing tool induces the rigorous efficiency of the cloud based website’s user access. Overloaded or underloaded conditions originate processing catastrophe regarding the prolonged execution time, bandwidth hog, malfunction, and etc. Besides, to manipulate Erlang concurrent tasks is another skyward situation. Hence, the load balancing is obliged to exhaust all mentioned conditions. The proposed load balancing algorithm for Erlang concurrent tasks (those are and could also be autonomous and unstable.) on VMware workstations is introduced. There are several load patterns within the clouds corresponding to CPU’s load (utilization), memory load (queue size), link capacity load (bandwidth), and so on. The proposed load balancing is to spot underloaded and overloaded conditions then stabilizes the weight amidst computing nodes. There are countless load balancing approaches in the cloud environment to examine performance parameters. A short outline of corresponding performance metrics in the review and their findings are presented. To investigate the fit efficiency of the proposed algorithm, the simulation is applied then results based on the proposed method are compared to the existing ones. The outcomes settle the weight balancing, outperform others when executing Erlang traffic, and are catered in the context.</p>

Load balancing in cloud computing environments based on adaptive starvation threshold

SummaryClouds provide to users on‐demand access to large computing and storing resources and offer over on premise IT infrastructures many advantages in terms of cost, flexibility, and availability. However, this new paradigm still faces many challenges, and in this paper, we address the load balancing problem. Even though many approaches have been proposed to balance the load among the servers, most of them are too sensitive to the fluctuation in the clouds load and produce unstable systems. In this paper, we propose a new distributed load balancing algorithm, based on adaptive starvation threshold. It tries to balance the load between the servers while minimizing the response time of the cloud, maximizing the utilization rate of the servers, decreasing the overall migration cost, and maintaining the stability of the system. The performance of the proposed algorithm was compared to a well‐known load balancing algorithm, inspired from the honey bee behavior (HBB). The experimental results showed that the application of the proposed load balancing algorithm gives considerable performance gains and a significant reduction in number of migrations when compared to the performance of the HBB algorithm.

Load Balancing Problem on Hyper Hexa Cell Interconnection Network

Dynamic load balancing techniques prevents computer nodes from overloading unevenly while leaving other idle. It is considered as one of the most challenging topics in parallel computing. Moreover, it is essential for increasing the efficiency of highly parallel systems especially in solving multitask problems with unpredictable load estimates. Particularly, over each processor in the parallel systems and interconnection networks. This paper focuses on developing an efficient algorithm for load balancing on Hyper Hexa Cell (HHC) interconnection network, namely, HHCLB algorithm. Basically, the Dimension Exchange Method (DEM) approach is used in this paper to construct a new load balancing approach on the network of HHC interconnections. Thus, an algorithm was introduced and simulated using java threads, where the performance of the algorithm is evaluated both analytically and experimentally. The evaluation was in terms of various performance metrics, including, execution time, load balancing accuracy, communication cost. By implementing the proposed load balancing algorithm to the HHC network, a high degree of accuracy and minimal execution time was achieved. It is important to highlight that the algorithm recorded small gap between the execution time for small number of processors and large number of processors. For instance, the algorithm achieved 0.14 seconds for balancing the load of 6 processors while 0.59 seconds for balancing the load of 3072 processors. This proves how effective the algorithm is in balancing the load for different network sizes from small to large number of processors, with a slight difference in execution time.

Performance evaluation of proposed load balancing algorithm with unstable concurrent programs

<span>IoT is the succeeding cohort of the digital computing environment. A swift progression in the IoT deployment and its applications are on the rise. Improving load balancing mechanisms induces healthier performance of the internet based computing as higher number of users can be comfortable. Implementing full services for tasks with unstable concurrency is an uphill process. One of the encounters allied with this administration is the task partition among the applications, regularly referred as concurrent programs. Through load balancing not only resources are equally utilized but also concurrent job’s response time can be promoted. Therefore, in this paper the widely used load balancing algorithms are investigated and yet the proposed algorithm is introduced. Simulation is employed in order to compare the performance metrics such as mean queue length, utilization and throughput between the recommended and existing algorithms. The proposed algorithm confirms the load balancing and outperforms when processing unstable concurrent programs.</span>

Dynamic load balancing algorithm for large data flow in distributed complex networks

Abstract Information society brings convenience to people, but also produces a lot of data. Relational databases are not suitable for processing big data due to architecture defects. The most commonly used system to store and process large amounts of data is the NoSQL (Not only Structured Query Language) database. Obviously, it is very important to cooperate with these independent computers to accomplish processing tasks efficiently, which is the function of load balancing. This paper studies the commonly used NoSQL database and load balancing algorithms, and designs and implements a more efficient load balancing algorithm. By introducing the relationship between nodes and the children of their brother nodes, we reduce the height of the whole sorted binary tree. The time cost of the algorithm is reduced versus the commonly used weighted polling algorithm O(N) to O(log N), while the spatial cost remains unchanged. The equalization algorithm synthetically utilizes the characteristics of big data processing systems and has good performance. At the same time, the algorithm can quickly find the sub-optimal nodes when the optimal nodes have been occupied, so it is very suitable for load balancing in highly concurrent systems. Finally, the effectiveness of the proposed load balancing algorithm is verified by simulation.

OpenDaylight vs. Floodlight: Comparative Analysis of a Load Balancing Algorithm for Software Defined Networking

This paper presents the proposal of a load balancing algorithm implemented in two of the most popular controllers for Software Defined Networks (SDN): OpenDaylight and Floodlight. A comparative study in terms of the available bandwidth and delay time of the packet forwarding was performed by means of simulation modeling in a base network in which a shortest path algorithm was implemented as well. The results show that the proposed load balancing algorithm improves significantly the performance of a SDN in terms of the offered QoS of a OpenDaylight based controller. The effect of the proposed load balancing algorithm in the Floodlight controller shows a smaller impact mainly on the bandwidth allocation due to its in-build modules that by default perform specific routing and forwarding operations efficiently according to the traffic demand.

Analysis and Development of Load Balancing Algorithms in Cloud Computing

This article describes how cloud computing utilizes the benefits of web engineering and its applications by improving the performance and reducing the load on cloud providers. As the cloud is one of the emerging technology in the field of computing, it is used to provide various services to the user through the internet. One of the major concerns in cloud computing is accessibility of cloud. For estimating the availability of cloud, various load balancing algorithms are deployed in data centers of the cloud environment. Load balancing is a technique that distributes a signal load across various computers for optimizing resource usage, reducing response time, etc. There are different load balancing algorithms, for performing the load distribution across various centers. This article analyses different load balancing algorithms and develop a new algorithm for efficient load balancing. The proposed load balancing algorithm utilizes the concepts of web engineering to prioritize the request of end user using parsing technique, which will assign the resources to the end users based on the priority set by the data centers.

AN IMPROVED RESOURCE ALLOCATION STRATEGY BASED ON PACKAGE LEVEL ACCESS IN CLOUD ENVIRONMENT

Cloud computing is an emerging field of computer science, where heterogeneous services such as applications, servers and storage are delivered to an individual or organization's computer and devices through the Internet. In the proposed research work, a load balancing algorithm is designed, to provide proper utilization of all resources while processing the requests received from the users. We have implemented the ‘package’ level access at the Virtual Machines. We have proposed three types of packages like basic, medium and premium according to the requirements of the Cloudlets. In the basic package, we have the Virtual Machines with low capacity and in the premium package we are having the Virtual Machines with higher capacity, whereas the medium is of intermediate capacity of MIPS and RAM. In the proposed approach, the capacity, status and current load of every Virtual Machine is computed before allocating the new request of the user. Different parameters like waiting time, execution time and turnaround time of the Cloudlets are computed and analyzed. The proposed research provides the anticipated results with the implementation of the proposed algorithm. Compared with the other job scheduling algorithms, the proposed load balancing algorithm can outperform them in circumstances where the load and Virtual Machines are heterogeneous.

Shuffled Complex Evolution Approach for Load Balancing of Gateways in Wireless Sensor Networks

Energy consumption is one of the important factor of Wireless Sensor Networks (WSN). It has much attention in many fields. From recent studies, it is observed that energy consumption in WSN is challenging task as the energy is limited resource. This energy is needed for sensor nodes operation. In order to maximize the network lifetime, energy consumption should be mitigated. In cluster based WSN, cluster head i.e., the leader of cluster performs various activities, such as data collection from its member nodes, data aggregation and data exchange with base station. Hence, load balancing in WSNs is one of the challenging task to maximize network lifetime. In order to address this problem, in this paper, Shuffled Complex Evolution (SCE) algorithm is used. A novel fitness function is also designed to evaluate fitness of solutions produced by SCE algorithm. In SCE, the solutions with best and worst fitness value exchange their information to produce new off-spring. We have simulated proposed load balancing algorithm along with other state-of-the-art load balancing algorithms, namely Node Local Density Load Balancing, Score Based Load Balancing, Simple Genetic Algorithm based load balancing, Novel Genetic Algorithm based Load Balancing. It is observed from experimental results that proposed load balancing algorithm outperforms state-of-the-art load balancing algorithms in terms of load balancing, energy consumption, execution time, number of sensor nodes and number of heavy loaded sensor nodes.

An Efficient Load Balancing of Gateways Using Improved Shuffled Frog Leaping Algorithm and Novel Fitness Function for WSNs

Energy consumption is one of the important factors in wireless sensor networks (WSNs) design. As energy is a limited resource, energy consumption problem in WSNs has become a fast growing problem, and there is a need of efficient and robust algorithms for load balancing in WSNs. This energy is needed for sensor nodes operations. In order to maximize the network lifetime, energy consumption should be optimized. In cluster-based WSNs, cluster heads or gateways perform activities, such as data collection from its member nodes, data aggregation, and data exchange with the base station. Hence, load balancing of gateways in WSNs is one of the crucial and challenging tasks to maximize network lifetime. In order to address this problem, in this paper, shuffled frog leaping algorithm (SFLA) is improved by suitably modifying the frog’s population generation and off-spring generation phases in SFLA and by introducing a transfer phase. A novel fitness function is also designed to evaluate the quality of the solutions produced by the improved SFLA. We performed extensive simulations of the proposed load balancing algorithm in terms of various performance parameters. The experimental results are encouraging and demonstrated the efficiency of the proposed algorithm.

A Relay-Aided Device-to-Device-Based Load Balancing Scheme for Multitier Heterogeneous Networks

As a key feature of the next generation wireless networks (5G), heterogeneous networks (HetNets) architecture is embraced as the fundamental network technology to meet the immensely diverse service requirements and characteristics of various devices. By introducing underlaying device-to-device (D2D) communication into HetNets, it is possible to offload the unevenly distributed load from the overloaded macrocell to the uncongested femtocell in multitier HetNets. However, the performance of load balancing (LB) scheme heavily depends on the D2D relay selection method as well as the potential resource reuse interference brought by underlay D2D relaying. In order to improve resources utilization ratio and mitigate the resource reuse interference, an LB strategy based on D2D relaying is proposed. To accommodate more new users into the already overloaded macrocell, the macro user equipment with poor link quality will be first transferred into nearby uncongested femtocells by D2D relaying. Then, the released macrocell resource can be allocated to new users who cannot access femtocell due to location restriction. Furthermore, we also propose a two-stage relay selection and resource allocation scheme which not only minimizes the potential interference caused by resource reuse but also guarantees the transmission requirement of different users. Extensive simulation results demonstrate that by taking advantage of D2D relaying, the proposed LB algorithm can effectively adjust the unbalanced load between macrocell and femtocells, which achieves improved performance of the whole HetNets.

A combination of game theory and genetic algorithm for load balancing in distributed computer systems

High demand of computation and communication in recent decades has increased the importance of distributed computer systems. Because of the heterogeneity of resources, resource management is one of the main challenges in designing distributed computer systems. In this paper, a new method has been proposed for solving load balancing problem in distributed computer systems using game theory and a genetic algorithm. The load balancing problem has been modelled as a non-cooperative game among users of the system. The payoff function of players was computed using an introduced parameter in order to decrease the users' expected response time. Certain Nash equilibriums are unstable in a multi agent problem. Thus, a genetic algorithm based on the concept of Nash equilibrium is used for solving the load balancing game. Simulation results show the performance of the proposed load balancing algorithm in terms of the expected response time and fairness index as parameters in evaluating the performance of load balancing algorithms.

A combination of game theory and genetic algorithm for load balancing in distributed computer systems

High demand of computation and communication in recent decades has increased the importance of distributed computer systems. Because of the heterogeneity of resources, resource management is one of the main challenges in designing distributed computer systems. In this paper, a new method has been proposed for solving load balancing problem in distributed computer systems using game theory and a genetic algorithm. The load balancing problem has been modelled as a non-cooperative game among users of the system. The payoff function of players was computed using an introduced parameter in order to decrease the users' expected response time. Certain Nash equilibriums are unstable in a multi agent problem. Thus, a genetic algorithm based on the concept of Nash equilibrium is used for solving the load balancing game. Simulation results show the performance of the proposed load balancing algorithm in terms of the expected response time and fairness index as parameters in evaluating the performance of load balancing algorithms.