Artificial Neural Network Based Load Balancing 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.

Web technology has gained a huge momentum these days due to the digitization in almost all sectors. In cloud computing environment, the cloud data centers and the users of cloud computing are distributed extensively throughout the world and thus it is a huge challenge to manage these data in an orderly fashion. Load Balancing in this context means the distribution of load (data) in a smart manner such that the efficiency of the nodes are increased and the resources are used to their maximum potential which in turn would result in high user satisfaction. The algorithm proposed in this paper provides an optimal way of distributing load in servers connected to the Virtual Machines such that unnecessary overloading has been avoided in the cloud data centers. In this paper, the mixture of traditional algorithms like Weighted Round Robin and Weighted Least Connection have been used, which is showing better result in balancing the load among servers connected to the virtual machines.

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.

Live virtual machine (VM) migration is a technique for achieving system load balancing in a cloud environment by transferring an active VM from one physical host to another. This technique has been proposed to reduce the downtime for migrating overloaded VMs, but it is still time- and cost-consuming, and a large amount of memory is involved in the migration process. To overcome these drawbacks, we propose a Task-based System Load Balancing method using Particle Swarm Optimization (TBSLB-PSO) that achieves system load balancing by only transferring extra tasks from an overloaded VM instead of migrating the entire overloaded VM. We also design an optimization model to migrate these extra tasks to the new host VMs by applying Particle Swarm Optimization (PSO). To evaluate the proposed method, we extend the cloud simulator (Cloudsim) package and use PSO as its task scheduling model. The simulation results show that the proposed TBSLB-PSO method significantly reduces the time taken for the load balancing process compared to traditional load balancing approaches. Furthermore, in our proposed approach the overloaded VMs will not be paused during the migration process, and there is no need to use the VM pre-copy process. Therefore, the TBSLB-PSO method will eliminate VM downtime and the risk of losing the last activity performed by a customer, and will increase the Quality of Service experienced by cloud customers.

Hybrid grey wolf and improved particle swarm optimization with adaptive intertial weight-based multi-dimensional learning strategy for load balancing in cloud environments

The paper concentrates on the Fog Computing (FC) application to a Smart Grid (SG), that comprises of a Distribution Generation System recognized as a Microgrid (MG). FC acts as an additional layer of computation and communication. It decreases the load on the Cloud and provides same facilities as Cloud. The main concern in FC environment is Load Balancing. Fog contains many software and hardware resources and handling these will play a significant role in completing a client’s request. Today, from different regions of the world clients are requesting for the numerous services in a continuous frequency. The Fog manages the load by assigning the Virtual Machines (VMs) to clients’ requests. In this regard, the techniques that should be employed to stabilize the load on the Fog should be very effective in assigning the VM to user requests. In the proposed work, for load balancing we have used four different load balancing algorithms: Round Robin (RR), Throttled, Particle Swarm Optimization (PSO) and Active VM Load Balancing Algorithm (AVMLB). Further, the Cloud Analyst simulator is used to analyze and compare the performances of the algorithms.

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.

Goal: Load balancing policies often map workloads on virtual machines, and are being sought to achieve their goals by creating an almost equal level of workload on any virtual machine. In this research, a hybrid load balancing algorithm is proposed with the aim of reducing response time and processing time.
 Design / Methodology / Approach: The proposed algorithm performs load balancing using a table including the status indicators of virtual machines and the task list allocated to each virtual machine. The evaluation results of response time and processing time in data centers from four algorithms, ESCE, Throttled, Round Robin and the proposed algorithm is done.
 Results: The overall response time and data processing time in the proposed algorithm data center are shorter than other algorithms and improve the response time and data processing time in the data center. The results of the overall response time for all algorithms show that the response time of the proposed algorithm is 12.28%, compared to the Round Robin algorithm, 9.1% compared to the Throttled algorithm, and 4.86% of the ESCE algorithm.
 Limitations of the investigation: Due to time and technical limitations, load balancing has not been achieved with more goals, such as lowering costs and increasing productivity.
 Practical implications: The implementation of a hybrid load factor policy can improve the response time and processing time. The use of load balancing will cause the traffic load between virtual machines to be properly distributed and prevent bottlenecks. This will be effective in increasing customer responsiveness. And finally, improving response time increases the satisfaction of cloud users and increases the productivity of computing resources.
 Originality/Value: This research can be effective in optimizing the existing algorithms and will take a step towards further research in this regard.

PurposeSea Lion Optimization (SLnO) algorithm involves the ability of exploration and exploitation phases, and it is able to solve combinatorial optimization problems. For these reasons, it is considered a global optimizer. The scheduling operation is completed by imitating the hunting behavior of sea lions.Design/methodology/approachCloud computing (CC) is a type of distributed computing, contributory in a massive number of available resources and demands, and its goal is sharing the resources as services over the internet. Because of the optimal using of these services is everlasting challenge, the issue of task scheduling in CC is significant. In this paper, a task scheduling technique for CC based on SLnO and multiple-objective model are proposed. It enables decreasing in overall completion time, cost and power consumption; and maximizes the resources utilization. The simulation results on the tested data illustrated that the SLnO scheduler performed better performance than other state-of-the-art schedulers in terms of makespan, cost, energy consumption, resources utilization and degree of imbalance.FindingsThe performance of the SLnO, Vocalization of Whale Optimization Algorithm (VWOA), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO) and Round Robin (RR) algorithms for 100, 200, 300, 400 and 500 independent cloud tasks on 8, 16 and 32 VMs was evaluated. The results show that SLnO algorithm has better performance than VWOA, WOA, GWO and RR in terms of makespan and imbalance degree. In addition, SLnO exhausts less power than VWOA, WOA, GWO and RR. More precisely, SLnO conserves 5.6, 21.96, 22.7 and 73.98% energy compared to VWOA, WOA, GWO and RR mechanisms, respectively. On the other hand, SLnO algorithm shows better performance than the VWOA and other algorithms. The SLnO algorithm's overall execution cost of scheduling the cloud tasks is minimized by 20.62, 39.9, 42.44 and 46.9% compared with VWOA, WOA, GWO and RR algorithms, respectively. Finally, the SLnO algorithm's average resource utilization is increased by 6, 10, 11.8 and 31.8% compared with those of VWOA, WOA, GWO and RR mechanisms, respectively.Originality/valueTo the best of the authors’ knowledge, this work is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere.

Cloud computing is the means of accessing a shared pool of configurable computing resources (including hardware, software, networks, servers, storage applications and services) that can be rapidly provided, used and released with minimal effort on the part of users or service providers. But it has some of the main concerns like load management and fault tolerance. In this paper we are discussing load balancing approach in cloud computing. Load balancing is helped to distribute the workload across multiple nodes to ensure that no single node is overloaded. It helps in proper utilization of resources .It also improves the performance of the system This paper focuses on the load balancing algorithm which distributes the incoming jobs among VMs optimally in cloud data centers. In this paper, we have reviewed several existing load balancing mechanisms and we have tried to address the problems associated with them.

Infrastructure-as-a-service cloud environment provides infrastructure services per the user's workflow requirements using the pay-you-go model on demand. Workflow applications are highly used in IT industries, project management, supply chain management, large distributed management, and many more. Load-balanced workflow allocation to solve all the requirements of the cloud users is already proven NP-hard. In this scenario, meta-heuristic algorithms are one of the best candidates to solve the load balancing problem in the cloud to achieve an optimal allocation schedule. In this paper, we proposed a load-balanced workflow allocation method using stochastic fractal search (SFS) algorithms to maximize the resource utilization of the virtualized cloud resources. In this way, the load imbalance on virtual machines can be minimized. SFS is an evolutionary optimization method that uses the natural growth behavior in the search process. The performance analysis is conducted by implementing the workflow load balancer in MATLAB. The study shows that the solutions achieved by the proposed SFS strategy are far better than those obtained by particle swarm optimization (PSO).

This paper objective to investigate the application of PSO for load balancing in cloud computing, optimize to solve the load balancing challenges in cloud environment and focus to distribute the workload across different virtual machines [1]. PSO is a metaheuristic optimization technique inspired by collective behavior of fish schooling and bird flocking., is used to explore the resource utilization and increase the system performance by redistribute the load across nodes. This study focuses to solve the challenges occurred during dynamic nature of cloud environment. Every node has its own capacity to handle the workload so various task scheduling algorithm applied to manage the workload. By using the load balancing approach maximize the global convergence with various parameter. In this research develops a robust load-balancing model that adopt to changing conditions and improves the responsiveness and efficiency of cloud services. Cloudsim simulator is also used to compare using PSO with various traditional algorithms.

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.

Scalable, high performance, and fault-tolerant distributed applications are realized with virtual machines in server cluster systems. Application processes are performed on virtual machines in each server. Processing load of virtual machines to perform application processes has to be balanced in a server cluster system to satisfy the application requirements like response time. On the other hand, a server cluster system consumes a large amount of electric energy since multiple servers consume electric energy to perform application processes. It is critical to discuss how to reduce the total electric energy consumption of a server cluster to perform application processes on virtual machines. In our previous studies, the transmission model and power consumption model of a server to perform communication processes on multiple virtual machines are proposed. In this paper, we newly propose the transmission energy consumption laxity based (TECLB) algorithm to allocate communication processes to virtual machines in a server cluster based on the proposed transmission model and power consumption model of a server so that the total energy consumption of a server cluster can be reduced. We evaluate the TECLB algorithm in terms of the total energy consumption of a server cluster and transmission time of each process compared with the basic round-robin (RR) algorithm. The evaluation results show the average total energy consumption of a server cluster is maximumly reduced to 9% of the RR algorithm.

Load Balancing is an important factor handling resource during running and execution time in real time applications. Virtual machines are used for dynamically access and share the resources. As per current scenario cloud computing is played major for storage, resource accessing, resource pooling and internet based service offering. Usage of cloud computing services is dynamically increased such as online shopping, education, ticketing, etc. Many users can use the cloud resources and load balancing is used for adjusting the virtual machine and balance the node. Our proposed virtualized genetic algorithms are to provide balanced virtual machine services in Hybrid cloud. The proposed algorithm and experiments are implemented by using Cloud simulator. In this paper the experiments are done with cloud computing models, Virtual Machine allocation, load balancing and simulations. Also compare the results using response time, throughput and turnaround time using cloud sim. The accuracy can be compared with existing load balancing techniques.