Load Balancing In Cloud Environment Research Articles

Reinforcement Learning Based Metaheuristic Algorithm For Optimized Load Balancing In Cloud Environment

In cloud computing, workload balancing remains a difficult issue, as overburdened or underburdened servers are capable of causing issues with computation speed or even cause an entire network to collapse, subsequently this is a scenario that is never supposed to occur when using the cloud. Therefore, in order to avoid these issues, the system should be load balanced—that is, allocate job across each of the accessible computing assets by taking into account an acceptable schedule of utilization. The Virtual Machines are supposed to be used effectively, in response to the distributed load mechanism. In this work, a multifaceted load balancing optimization based on the Enhanced Firefly algorithm (EFA) with a Partial Order Markov Decision (POMD) process algorithm is proposed as an autonomous job allocation strategy in cloud computing. In order to overcome the limits of concurrent considerations, the suggested solution seeks to maximise VM productivity, streamline task allocation and the use of resources, and provide load balancing amongst virtual machines based on Time to Finish, expenditure, and consumption of resources. Using CloudSim, the performance study of the suggested approach was contrasted with the load balancing algorithms now in use in datasets like Google Cloud Jobs (GoCJ). According to the trial results, the suggested POMD-EFA strategy performed better than the other algorithms in terms of decreasing the Time to Finish, minimizing expenses, and increasing the efficiency in the application of resources.

Soft Computing Techniques to Analyze the Load Balancing in Cloud Environment

An emerging method of digital computing known as "cloud computing" has recently gained immense popularity. While there are many benefits to using the cloud over the internet, there are also serious challenges that must be addressed in order to boost the efficiency of this method. Challenges to cloud computing via the internet include load balancing, work scheduling, fault tolerance, and several security concerns. The effectiveness of the cloud may be enhanced by fixing a number of problems, one of the most pressing being load balancing. To prevent any one node from being too overburdened or underused, a system employs a technique known as load balancing. In order to increase utilization and minimize overall task execution time, load balancing algorithms are developed to distribute work fairly across available resources. In this article, a review and comparison of different load-balancing algorithms is provided. This paper provides a basis of the application of different load-balancing techniques, which utilize the approach of soft computing within the cloud computing environment.

Load Balancing in Cloud Environment: A State-of-the-Art Review

Load Balancing in Cloud Environment: A State-of-the-Art Review

Load balancing in cloud environment using enhanced migration and adjustment operator based monarch butterfly optimization

In the decades before the advent of computers, humans tend to make mistakes while calculating and remembering tasks. Distributed computing helped to reduce the workload of each computer by distributing the workload evenly among computers connected in the network. Cloud computing have eradicated most of the problems that occurred in distributed computing but were also prone to different types of issues. Major issues in cloud computing relate to security and load balancing. Load balance of a node relates to two important parameters namely request time and response time. Meta heuristics algorithms can be used to provide proper load balancing techniques in cloud. This paper provides a mechanism namely EMAMBO to ensure that each node is properly load-balanced in cloud. Based on different metrics considered, it could be inferred that the proposed system fares better when compared to different benchmarked existing systems.

A distributed quality of service-enabled load balancing approach for cloud environment

PurposeDuring high demand for the virtualized resources in cloud environment, efficient task scheduling achieves the desired performance criteria by balancing the load in the system.Design/methodology/approachIt is a task scheduling approach used for load balancing in cloud environment. Task scheduling in such an environment is used for the task execution on a suitable resource by considering some parameters and constraints to achieve performance.FindingsThe presented mechanism is an extension of the previous proposed work quality of service (QoS)-enabled join minimum loaded queue (JMLQ) (Sharma et al., 2019c). The proposed approach has been tested in the CloudSim simulator, and the results show that the proposed approach achieves better results in comparison to QoS-enabled JMLQ and its other variants in the cloud environment.Originality/value90%

Load balancing using dynamic algorithms for cloud environment: A survey

Numerous researchers have worked on different load balancing algorithms in cloud Environment. But, no efficient algorithm for load balancing in cloud environment is developed yet. For optimizing the solution and fully utilizing the resources authors moved to the nature inspired algorithms. This paper presents a survey of different dynamic and nature inspired algorithms with their advantages and disadvantages. The objective of this study is to bring out the conclusion that disadvantages of various nature inspired algorithms can be overcome if different algorithms can be used as a hybrid to suit the usage of application and new optimization technique can be formulated by the combination of two or more algorithm. Researchers are also proposing a new hybrid technic which is more effective and robust and will try to balance the load without being complex. Proposed algorithm will use Ant Colony and Bird swarm Optimization techniques through which the degree of imbalance will be reduced. By using the proper fitness function makespan can be minimized and resource utilization can be maximized. Good part of the Ant colony optimization and Bird Swarm optimization is that by using these algorithms best optimal solution can be find out and both can be suited for static and dynamic environment and adaptive as well.

Novel hybrid ANN and clustering inspired load balancing algorithm in cloud environment

Load balancing in the cloud environment is a NP-hard problem. To address this problem, a new hybridized soft computing inspired technique named Clustering based Artificial Neural Network for Dynamic Load Balancing (CANN-DLB) is introduced. Artificial Neural Network aims to achieve an optimized load of VMs by training the cloud environment. Back-propagation ANN is performed to calculate the optimized Virtual Machine (VM) load in cloud systems for improving QoS. The CANN-DLB technique uses K-means clustering algorithm over calculated VM loads and clusters them into under-loaded and over-loaded VMs. The scheduling of homogeneous independent non-preemptive tasks is performed using Particle Swarm Optimization (PSO) technique. CloudSim (Cloud Simulator) tool has been used to implement the proposed algorithm. The performance of the CANN-DLB technique has been achieved for Space Shared and Time Shared VM task scheduler methods. The simulation results of the CANN-DLB algorithm are compared with existing load balancing and scheduling algorithms with the objectives to improve load balance fairness. Results of CANN-DLB have achieved 69.5%, 96.9%, 96.0% and 97.4% less processing cost and 87.12%, 87.12%, 87.57% and 81.78% less degree of imbalance compared to CM-eFCFS, Static RR, MinMin and MaxMin method respectively. The system fairness with respect to system load is calculated to analyze CANN-DLB algorithm performance on heavy load. The proposed algorithm achieved 12% higher load fairness than the existing Two-level method and proves that the introduced model works better in dynamic scenarios. Plotted graphs show that the proposed idea is innovative for load balancing in a dynamic cloud environment. This hybridization of the ANN and K-means clustering method produces remarkable results as compared to existing algorithms for different cases.

A Bio-Inspired and Heuristic-Based Hybrid Algorithm for Effective Performance With Load Balancing in Cloud Environment

In a cloud computing environment, effective scheduling policies and load balancing have always been the aim. An efficient task scheduler must be proficient in a dynamically distributed environment and to the policy of efficient scheduling of jobs based upon the workload. In this research, a novel hybrid heuristic algorithm is developed for balancing the load among cloud nodes. This is achieved by hybridizing the existing ant colony optimization (ACO), artificial bee colony algorithm (ABC), and AHP (analytical hierarchy process) algorithm. The AHP algorithm and the artificial bee colony (ABC) algorithm is used for figuring out the best servers suitable for a particular job, and the ant colony algorithm is used to find the most efficient path to that particular server. The proposed algorithm is better in resource utilization. It also performs better load balancing, which keeps on improving with time. The result analysis shows better average response time and better average makespan time compared to other two existing algorithms.

Hybrid Max-Min Genetic Algorithm for Load Balancing and Task Scheduling in Cloud Environment

In recent decades, task scheduling and load balancing in the cloud is a growing research area, due to the vast amount of data stored in the server highly increases the load. In order to address this concern, Hybrid Max-Min Genetic Algorithm (HMMGA) is proposed for task scheduling and load balancing in the cloud environment. At first, the load is evaluated for every Virtual Machine (VM), if the load is high, then HMMGA is used for balancing the load. HMMGA selects the best VMs to assign the tasks and migrates the over-loaded VMs tasks to the under-loaded VMs. HMMGA significantly avoids the imbalanced workload performance in the cloud environment. In this research paper, the proposed HMMGA performance is compared to Max-Min algorithm, Low time complexity and low cost binary Particle Swarm Optimizer (IBPSO-LBS) and PSO with Technique of Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm to examine the efficacy of HMMGA. From the experimental simulation, the result shows that HMMGA averagely delivers 1.63 and 3.88 seconds less make span compared to the Max-Min and TOPSIS-PSO algorithm for five VMs. In addition, HMMGA averagely enhances 10% to 40% of resource utilization than the MaxMin and TOPSIS-PSO algorithm. In another experiment, the HMMGA approximately showed 1.7 to 25.99 seconds less average waiting time compared to the Max-Min and IBPSO-LBS.

Credit Based Scheduling with Load Balancing in Cloud Environment

Credit Based Scheduling with Load Balancing in Cloud Environment

Load Balancing in Cloud Environment Exploiting Hybridization of Chicken Swarm and Enhanced Raven Roosting Optimization Algorithm

Load Balancing in Cloud Environment Exploiting Hybridization of Chicken Swarm and Enhanced Raven Roosting Optimization Algorithm

Dynamic Load Balancing Ant Colony Optimization (DLBACO) Algorithm for Task Scheduling in Cloud Environment

Cloud computing is a framework which provides on-demand services to the user for scalability, security, and reliability based on pay as used service anytime & anywhere. For load balancing, task scheduling is the most critical issues in the cloud environment. There are so many meta-heuristic algorithms used to solve the load balancing problem. A good task scheduling algorithm should be used for optimum load balancing in cloud environment. Such scheduling algorithm must have some vital characteristic like minimum makespan, maximum throughput, and maximum resource utilization, etc. In this paper, a dynamic load balancing and task scheduling algorithm based on ant colony optimization (DLBACO) has been proposed. This algorithm assigns the task the VM which has highest probability of availability in minimum time. The proposed algorithm balances the whole system by minimizing the makespan of the task and maximizing the throughput. CloudSim simulator is used to simulate the proposed scheduling algorithm and results show that the proposed (DLBACO) algorithm is better than the existing algorithms such as FCFS, LBACO (Load balancing ACO), and primary ACO.

Improving Security and Load Balancing in Cloud Environment

The demand of cloud computing is increasing day by day. The whole scenario is changing due to rise in issues with regard to the speed of access, privacy and security of data. The data repositories are showing a real time shift from general data base management systems to cloud storage. This step in technology has made our data prone to attacks from intruders and moreover, this has also created an increased load on the technology used. The sole aim of this research work is to improve the performance of computing systems and their security in cloud environment. A new technology is proposed to balance the load on cloud computing and to improve the quality of cloud service with PSO is presented in this research work.

Crow Search based Scheduling Algorithm for Load Balancing in Cloud Environment

Task scheduling is a vital aspect in computer science, as it is the essence of how a computer executes various tasks and performs the related activities with accuracy and efficiency. In cloud computing, task scheduling is a typical NP-hard problem and scientists have been attempting to handle this problem for quite a long time. Although it is anything but difficult to accomplish a global optimum solution utilizing the ant colony algorithm and achieve promising outcomes. This research paper proposes a crow search based load balancing algorithm (CSLBA) for multi-objective task scheduling environment which concentrates on allocating best suitable resources for the task to be implemented with the consideration of various parameters like average makespan time (AMT), average waiting time (AWT) and average data center processing time (ADCPT). The present work provides a comparative analysis of proposed algorithm and Ant Colony Optimization based load balancing algorithm (ACOLBA). The experimentation is performed in steps and comparative analysis proved that proposed CSLBA is the optimal technique among the other scheduling technique considered in this research paper.

Load balancing in cloud environment with switching mechanism and token-based algorithm

Cloud computing as a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. Cloud environment comprises of abundant nodes with distributed computing resources in different geographic locations. The main aim is to maintain all the data centres to be available and also to distribute the load uniformly to all the data centres. Load balancing with switching mechanism contains two different algorithms in which, one algorithm is used to distribute the jobs in idle state divisions and another, token-based load balancing (TLB) algorithm is used to distribute the jobs in the normal state divisions. The basic demand of each user is to complete requested jobs in the short time, so the public cloud need a mechanism, which can complete the jobs of all users with reasonable response time. The proposed work chooses different strategies for different load conditions. Whenever the server is overloaded, the TLB algorithm migrates the job to the available data centre (DC) either within a cloud division or to the data centre in other cloud division.

A Novel Approach Approaches for Load Balancing in Cloud Environment and Detection of Duplication Data

The cloud computing is a sort of processing framework that comprises of a collection of interconnected computing nodes, servers, and other equipment and also programming administrations and applications that are powerfully provisioned among clients. Cloud provide the services to the users and users easily access the data from the cloud without performing any operation. The most of the IT companies like IBM, Google, Amazon, Microsoft, and so on are using cloud computing technique to develop their business or technical strategies. Cloud computing has changed the Information and Communication Technology (ICT) industry by allowing the on-demand services of flexible computing resources on a pay-as-you-go basis. In society different organization contract to the cloud for its computational requirement with high investment to reduce the private computing infrastructure maintenance cost as well as upgrades, to create the private cloud Data Centers, which develops the provisioning process along with resource management.

Load balancing in cloud environment with switching mechanism and token-based algorithm

Cloud computing as a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. Cloud environment comprises of abundant nodes with distributed computing resources in different geographic locations. The main aim is to maintain all the data centres to be available and also to distribute the load uniformly to all the data centres. Load balancing with switching mechanism contains two different algorithms in which, one algorithm is used to distribute the jobs in idle state divisions and another, token-based load balancing (TLB) algorithm is used to distribute the jobs in the normal state divisions. The basic demand of each user is to complete requested jobs in the short time, so the public cloud need a mechanism, which can complete the jobs of all users with reasonable response time. The proposed work chooses different strategies for different load conditions. Whenever the server is overloaded, the TLB algorithm migrates the job to the available data centre (DC) either within a cloud division or to the data centre in other cloud division.

Magnum opus of an efficient hospitality technique for load balancing in cloud environment

SummaryThe load balancing approach in the cloud environment is an open issue in the recent days. It helps obtain better resource utilization in cloud environment. It also improves the performance of the system. A good load balancing algorithm in cloud ensures no under loading or over loading in a single host. This paper proposes an algorithm named Hospitality Load Balancing (HLB) algorithm, which aims to balance the load among virtual machines (VM) for increasing the performance and throughput. The proposed algorithm handles preemptive or nonpreemptive tasks, which may be independent. It helps to reduce make span and obtain low task migration. The HLB algorithm has been analyzed and compared with plenty of load balancing technique. An experimental result shows that it is an excellent and effective algorithm when compared with the existing load balancing algorithm. It also illustrates the performance of proposed load balancing scheme with analysis and proves that it attains low makespan and low task migration.

Meta-heuristic based framework for workflow load balancing in cloud environment

Cloud services are based on datacenter which provides resources on demand with higher capacity, lowest response time and improved resource utilization. The data center comprises of physical hosts which are effectively utilized in the form of Virtual Machines. The task scheduling problem is the mapping of tasks to suitable resources (VMs) as required and it is NP-hard problem. Further, the scheduling algorithms are followed by load balancing techniques for efficient utilization of VMs. In this paper a framework for Load balancing in Cloud Environment has been proposed and implemented for overflow and underflow VM identification. Two metaheuristics and one heuristic have been used in the proposed framework to achieve effective and efficient utilization of VMs in cloud environment. Further, the performance of the proposed framework has been analysed on the basis of makespan and cost metrics which are computed while executing scientific workflow tasks.

Task based approach towards Load Balancing in Cloud Environment

Task based approach towards Load Balancing in Cloud Environment