Load Balancing Algorithm Research Articles

A Survey on Generating Load Balancing Algorithm for Cloud Computing

Abstract: The elaboration of IT led Cloud calculating technology crop as a new prototype in furnishing the services to its druggies on rented base at any time or place. Considering the inflexibility of cloud services, innumerous associations switched their businesses to the cloud technology by setting up more data centers. Nonetheless, it has come obligatory to give profitable prosecution of tasks and applicable resource application. A many approaches were outlined in literature to enhance performance, job scheduling, storehouse coffers, QoS and cargo distribution. Cargo balancing conception permits data centers to fore stall over-loading or under- lading in virtual machines that as similar is an issue in cloud computing sphere. Accordingly, it bear the experimenters to layout and apply a proper cargo balancer for cloud terrain. The separate study represents a view of problems and pitfalls faced by the current cargo balancing ways and make the experimenters find more effective algorithms. cargo unbalancing problem is a multi-variant, multi-constraint problem that degrades performance and effectiveness of computing coffers. cargo balancing ways feed the result for cargo unbalancing situation for two undesirable angles- overfilling and under- lading. In disdain of the significance of cargo balancing ways to the stylish of our knowledge, there's no comprehensive, expansive, methodical and hierarchical bracket about the being cargo balancing ways. Further, the factors that beget cargo unbalancing problem are neither studied nor considered in the literature. Keywords: Cloud computing, Taxonomy, Classification, Cloud service consumer, Cloud service provider, Quality of Service, Load unbalancing, Load balancing.

A Novel Load Balancing Technique for Smart Application in a Fog Computing Environment

Internet of things (IoT) induces an immense volume of data every day. Smart IoT-based applications need immediate response and processing of data, for which fog computing was introduced. Fog computing contains many small-scale data centres, which helps to process the incoming data from IoT immediately. More, the data more is the requirement of resources in the fog layer. Hence, there may be overloading of data, which needs to be handled directly. There is a need to provide a framework that can reduce energy consumption and enhance resource utilization during storage, processing, and network functioning. This article proposed smart traffic management architecture, which improves resource utilization and conserves intelligent vehicles' energy. The article also proposes a load balancing algorithm for avoiding the overloading of resources in the proposed architecture while executing a large number of vehicle requests. Further, this paper provides some key challenges and issues of fog computing. The article concludes by providing future directions.

5G environment based CLB-AODV multicast routing for the ZigBee on wireless sensor hetrogenous network

A communication load balanced dynamic topology management algorithm (CLB-AODV) is proposed to extend the wireless sensor network (WSN) lifetime via managing the participation in communication process among all nodes in the network. The idea is that, each time there is a failure in the network topology; the topology is adjusted only on-demand by choosing the best path according to paths weights between source and destination nodes. Simulation results show that CLB-AODV can prolong the lifetime of the network, increase the number of alive nodes and reduce the average routing load when compared with some of the most powerful recent algorithms the integration of Wireless Sensor Networks (WSN), new generation networks or 5G, TCP / IP (IPv6) protocols with the Internet of Things (IoT) that aims to exchange information, applying security, QoS (Quality of Service) and configuration, these three aspects are the problems in the construction of a network in which confidentiality, integrity, availability, authentication, reconfiguration of topology, improvement, high quality of service, addressing, infrastructure, Network and node construction, for M2M (Machine to Machine) communication or end to end. Because 5G cellular networks, in particular, are attractive technologies to provide Internet connectivity to equipment (UE).

Large-System Insensitivity of Zero-Waiting Load Balancing Algorithms

This paper studies the sensitivity (or insensitivity) of a class of load balancing algorithms that achieve asymptotic zero-waiting in the sub-Halfin-Whitt regime, named LB-zero. Most existing results on zero-waiting load balancing algorithms assume the service time distribution is exponential. This paper establishes the large-system insensitivity of LB-zero for jobs whose service time follows a Coxian distribution with a finite number of phases. This result suggests that LB-zero achieves asymptotic zero-waiting for a large class of service time distributions. To prove this result, this paper develops a new technique, called "Iterative State-Space Peeling'' (or ISSP for short). ISSP first identifies an iterative relation between the upper and lower bounds on the queue states and then proves that the system lives near the fixed point of the iterative bounds with a high probability. Based on ISSP, the steady-state distribution of the system is further analyzed by applying Stein's method in the neighborhood of the fixed point. ISSP, like state-space collapse in heavy-traffic analysis, is a general approach that may be used to study other complex stochastic systems.

Load balancing technique based on hybrid resource utilization in cloud computing

Cloud computing uses the internet to supply dynamic services including memory, data, bandwidth and applications. Work schedules have an influence on cloud service reliability and performance. A proper provisioning method is required for a systematic resource allocation, which comprises of large virtual resources. Depending on the present state of the system, load balancing solutions can be distinguished as dynamic or static. Dynamic or static load balancing solutions can be employed to increase server response time or to raise load balancing factors for quicker and more efficient resource utilization. To decrease the load across resources and maximize CPU usage, a hybrid load balancing technique is developed. In the cloud, we have a finite quantity of resources that must be efficiently managed in order to fulfill tasks. Requests are transmitted to a cloud server, which assigns work via quadratic probing. During load balancing, the load is shifted from heavy-weighted servers to lighter-weighted servers, enhancing CPU usage. The suggested methodology's performance was assessed using average mean response time, make-span, average make-span, and average resource utilization. The Load Balancing Algorithm (LBA) is created with the primary purpose of reducing job completion time and increasing the average resource utilization ratio.

A Parallel Fuzzy Load Balancing Algorithm for Distributed Nodes Over a Cloud System

Cloud Computing is an IT organization concept that places the Internet at the heart of business activity, allowing it to use hardware resources. In recent years, Load Balancing has been an active area of research, and has played a very important role in the case of the Cloud environment. There is a wide range of improvements in this arena. In this paper, we propose a new Load Balancing algorithm, based on the weights of the nearest servers in the cloud platform. We use the fuzzy logic to represent the weight of the different nodes. Moreover, we implement separate requests in parallel, and use a token to dispatch tasks efficiently. The several scenarios in this paper are considered for experimentation and compare the result of existing Round Robin, Throttled Load Balancing, Equal Spread Load and proposed algorithm .The experiment results show that this approach improves the Load Balancing process effectively in terms of overall response time, data center processing time, total virtual machine cost, and total data transfer cost.

A Traffic Splitting Algorithm for Load Balancing in Tor.

As the most popular anonymous communication system, Tor provides anonymous protection for users by sending their messages through a series of relays. Due to the use of the bandwidth-weighted path selection algorithm, many more users choose routers with high bandwidth as relays. This will cause the utilization of high bandwidth routers to be much higher than that of low bandwidth routers, which will bring congestion risk. The Quality of Service (QoS) is difficult to guarantee for users who need delay-sensitive services such as web browsing and instant messaging. To reduce the average load of routers and improve the network throughput, we propose a circuit construction method with multiple parallel middle relays and conduct a dynamic load allocation method. The experiment demonstrates that our proposed method can provide better load balancing. Compared with other multipath anonymous communication networks, our proposed method can provide better anonymity.

Dynamic Distributed Multi-Path Aided Load Balancing for Optical Data Center Networks

Benefiting from dense connections in data center networks (DCNs), load balancing algorithms are capable of steering traffic into multiple paths for the sake of preventing traffic congestion. However, given each path’s time-varying and asymmetrical traffic state, this may also lead to worse congestion when some paths are overutilised. Especially in the two-tier hybrid optical/electrical DCNs (Hoe-DCNs), the port contentions and large-grained optical packets of the fast optical switch (FOS) require the top-of-rack (TOR) switch to have microsecond-level load balancing capability for microburst traffic. This paper establishes a leaf-spine Hoe-DCN model to illustrate the principal characteristic of dynamic load balancing in TOR switches for the first time. Moreover, we propose the dynamic distributed multi-path (DDMP) load balancing algorithm that relies on dynamic hashing computing for network flow distribution in DCNs, which dynamically adjusts traffic flow distribution at microsecond level according to the inverse ratio of the buffer occupancy. The simulation results show that our proposed algorithm reduces the TOR-to-TOR latency by 15.88% and decreases the packet loss by 22.06% compared to conventional algorithms under regular load conditions, which effectively improves the overall performance of the Hoe-DCNs. Moreover, our proposed algorithm prevents more than 90% packet loss under low load conditions.

Research on Load Balancing MapReduce Equivalent Join Based on Intelligent Sampling and Multi Knapsack Algorithm

Background: With the rapid development of science, more data is available to human beings. Therefore, the storage and calculation of big data have become the focus of scientific research. MapReduce performs well in the big data processing. However, it is prone to data skew, which affects the overall efficiency of the data processing cluster. Objective: Aiming at the low efficiency of MapReduce data join, this paper proposes an intelligent data join load balancing algorithm based on dynamic programming. The algorithm introduces data sampling and partition algorithms. Due to the high performance of dynamic programming in the data constraint problem, it is used to solve the data skew problem intelligently. Methods: Firstly, the causes of data skew are analyzed and the data partition method is improved. The algorithm introduces a data sampling method. In the task allocation stage, the multidimensional knapsack algorithm is used. Different key values are evenly divided to each computing node through the load cost. Finally, The performance of the improved algorithm is verified by experiments. Results: The experimental results show that compared with the traditional load balancing algorithm and the existing improved algorithm, the new algorithm improves the data processing efficiency, reduces the data skew problem and better solves the problem of data load imbalance. Conclusion: A two-table equivalent join load balancing algorithm based on key cost has been proposed. The algorithm creatively combines dynamic programming with intelligent data sampling, which greatly improves the efficiency and quality of data processing. The algorithm is worthy of popularization and application.

An Ensemble of Bacterial Foraging, Genetic, Ant Colony and Particle Swarm Approach EB-GAP: A Load Balancing Approach in Cloud Computing

Background: In the cloud environment, satisfaction of service level agreement (SLA) is the prime objective. It can be achieved by providing services in minimum time in an efficient manner at the lowest cost by efficiently utilizing the resources. This will create a win-win situation for both consumer and service provider. Through literature analysis, it has been found that the procedure of resource optimization is quite costly and time-consuming. Objective: The research aim is to design and develop an efficient load-balancing technique for the satisfaction of service level agreement and the utilization of resources in an efficient manner. Methods: To achieve this, authors have proposed a new load-balancing algorithm named eB-GAP by picking the best features from Bacterial Foraging, Genetic, Particle-Swarm, and Ant-Colony algorithm. Based on the availability of resources and load on a virtual machine, a fitness value is assigned to all virtual machines. Results: A newly arrived task is mapped with the fittest virtual machine. Whenever a new task is mapped or left the system, the fitness value of the virtual machine is updated. In this manner, the system achieves the satisfaction of service level agreement, the balance of the load, and efficient utilization of resources. To test the proposed approach, the authors have used the real-time cloud environment of amazon web service. In this, waiting time, completion time, execution time, throughput, and cost have been computed in a real-time environment.

GAJEL-DSDN: an intelligent hybrid genetic-Jaya-based switch migration algorithm for efficient load balancing in distributed SDNs

GAJEL-DSDN: an intelligent hybrid genetic-Jaya-based switch migration algorithm for efficient load balancing in distributed SDNs

Retraction Note to: Performance analysis of nature inspired load balancing algorithm in cloud environment

Retraction Note to: Performance analysis of nature inspired load balancing algorithm in cloud environment

Enhancing Response Time of Cloud Resources Through Energy Efficient Cloud Scheduling Algorithm

Cloud Computing is becoming a dominant trend in providing information technology (IT) services. The cloud comprises many hardware and software resources today, and more people are switching to such services. Users' requests for cloud resources must incur a minimum amount of load on the system while getting a rapid response. In the cloud today, there is too much computational power. Load balancing makes it possible for various components of the cloud computing environment to work efficiently. To balance client requests to available resources so that the system is not overloaded, and the requested resources are delivered as quickly as possible, an effective load balancing strategy is essential. In this research article, we have presented a critical analysis of various existing cloud load balancing and scheduling algorithms. Several task scheduling approaches have been proposed in the literature review, but there appears to be a lack of scheduling algorithms for real-time task works based on historical scheduling records (HSR). The proposed algorithm uses information available in HSR to efficiently distributes incoming user requests to available virtual machines. The proposed scheduling algorithm uses the scaleup and scale down resource algorithm which helps in achieving maximum resource utilization. The algorithm tries to balance the load on VMs by scaling up and down cloud resources. WorkflowSim is used to analyze the performance of the algorithm proposed. The simulation results are compared with the existing scheduling algorithm which shows the proposed algorithm outperforms existing scheduling algorithms in terms of makespan.

Efficient Smart Grid Load Balancing via Fog and Cloud Computing

As the cloud data centers size increases, the number of virtual machines (VMs) grows speedily. Application requests are served by VMs be located in the physical machine (PM). The rapid growth of Internet services has created an imbalance of network resources. Some hosts have high bandwidth usage and can cause network congestion. Network congestion affects overall network performance. Cloud computing load balancing is an important feature that needs to be optimized. Therefore, this research proposes a 3-tier architecture, which consists of Cloud layer, Fog layer, and Consumer layer. The Cloud serves the world, and Fog analyzes the services at the local edge of network. Fog stores data temporarily, and the data is transmitted to the cloud. The world is classified into 6 regions on the basis of 6 continents in consumer layer. Consider Area 0 as North America, for which two fogs and two cluster buildings are considered. Microgrids (MG) are used to supply energy to consumers. In this research, a real-time VM migration algorithm for balancing fog load has been proposed. Load balancing algorithms focus on effective resource utilization, maximum throughput, and optimal response time. Compared to the closest data center (CDC), the real-time VM migration algorithm achieves 18% better cost results and optimized response time (ORT). Realtime VM migration and ORT increase response time by 11% compared to dynamic reconFigure with load (DRL) with load. Realtime VM migration always seeks the best solution to minimize cost and increase processing time.

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.

Development of a control algorithm and conditioning monitoring for peak load balancing in smart grids with battery energy storage system

As the traditional electricity grid transitions to the smart grid (SG), some emerging issues such as increased renewable energy penetration in the power system that cause load unbalances require new control methods. Storage of energy seems to be the best option to struggle with such issues. In this manner, energy storage technologies ensure the operating flexibility of the distribution system operator in the power system in terms of both sustainability of energy and peak load balancing. In this study, a grid condition monitoring user-interface and control algorithm is developed for the peak load reduction and supply-demand balancing in a SG system by using an energy storage unit. For this purpose, a battery energy storage system (BESS) is designed, scaled and integrated into the SG didactic test system, designed by the De Lorenzo Company. Online grid condition monitoring and control software is developed for grid connected photovoltaic (PV) system and the BESS in the LabVIEW? program. Moreover, an algorithm is developed that provides the conditions for the integration of the BESS into the system. The proposed algorithm is tested with real daily load data of the Manisa province in Turkey. Also, various case studies are performed to validate the effectiveness of the algorithm. Consequently, the proposed algorithm provides an average load factor improvement of 8.46% and the algorithm-controlled BESS increases the revenue of the system by 3.51% compared to the grid-connected PV system alone.

Using resource allocation for seamless service provisioning in cloud computing

A Resources allocation is one of the most important things in cloud computing because the environment in which work depends is heterogeneous environment and there are many who request the same service at the same time. Two criteria are used to be considered. First, the time for completing all the required tasks on the available resources and secondly the energy consumed and is extracted from the number of processes that used to complete those tasks. While uploading an application to the Cloud, load balancer allocates the appealed instances for physical computers to balance the computational load of several applications through physical computers. this paper presents some resource allocation problems and issues that can solve with help of load balancing techniques and algorithms. In addition, the performance of the algorithm was measured on seven mathematical functions, and the best results were for the algorithm pyogenes.

BLACK BOX APPROACH TO MONITORING CONTAINER MICROSERVICES IN FOG COMPUTING

In recent years IoT has developed very rapidly. IoT devices are used to monitor and control physical objects to transform the physical world into intelligent spaces with computing and communication capabilities. Compared to cloud computing, fog computing is used to support latency-sensitive applications at the edge of the network which allows client requests to be processed faster. This study aims to propose a monitoring framework for containerized black box microservices in a fog computing environment to evaluate CPU overhead, as well as to determine the operating status, service characteristics, and dependencies of each container.
 This study proposes a monitoring framework to integrate computing resource usage and run-time information from service interactions using a black box approach that seeks to integrate service-level information and computing resource information into the same framework. The proposed framework is limited to observing information monitoring after the server receives a request. This study uses JMeter to simulate user actions, which send requests to the server, and this research assumes the user knows the IP address of the server. For container monitoring methods in fog computing, all are indirect monitoring methods.
 The results of this study indicate that the proposed framework can provide operational data for visualization that can help system administrators evaluate the status of running containers using a black box approach. System administrators do not need to understand and modify target microservices to gather service characteristics from containerized microservices. Regarding future research, it is suggested to expand the exploration of modified system information, and that part of the container management tool code can be pre-tried so that the framework proposed in this study can provide real-time quantitative indexes for the load balancing algorithm to help optimize the load balancing algorithm.

Towards Investigation of Various Load Balancing Techniques in Cloud Computing

Cloud computing is an on-demand service where customers may access any time common IT resources, information, software and other equipment. It is a web-based development that offers virtual resources over the Internet as a service. The higher the cloud use, the higher the charge. The allocation of loads to components processing is a challenging phenomenon.In a multi-node system, there is a very good possibility that some nodes will be idle while others will be overloaded. The load balancing algorithms' objective is to keep the load on each processing element constant. A technique for Random Load Balancing in Cloud Computing is devised, in which the amount of user requests is instantly assigned to the resources. This method employs a random strategy to decrease the request's waiting time. This proposed load balancing technique is simulated in Cloud Analyst tool. The performance comparison study between proposed random method and other available load balancing methods is conducted. It is found that the performance of proposed random method is better in terms of response time.Keywords: Cloud Computing, Load Balancing, Response Time, Virtual Machine, Sorting

State-of-the-Art Dynamic Load Balancing Algorithms for Cloud Computing

Cloud computing is a technological commitment that allows it to achieve a set goal, improving business performance. Cloud computing offers many applications like scalability, extensive infrastructure, storage, resource pooling, virtualization, and a wide range of online services. Cloud service providers provide a variety of technologies to cloud computing users all the time. Algorithms for load balancing were applied to enhance the speed and performance of nodes in cloud environments and protect each device from affecting their threshold by reducing their performance. Classification of different algorithms was made according to two factors, i.e., the person initiating the algorithms and the current state of the process. Dynamic load balancing algorithms are based on challenges like high availability, scalability, fault tolerance, virtual machine migration, and low response time. Dynamic load balancing algorithms apply some policies because they use the current state of a system. This paper provides the study of state-of-the-art algorithms used in cloud computing and performance parameters, which are based on different load balancing systems.