A Comprehensive survey of Load Balancing Techniques: From Classic Methods to Modern Algorithms

The concept oft Cloud computing has significantly changed the field of parallel and distributed computing systems today. Cloud computing enables a wide range of users to access distributed, scalable, virtualized hardware and/or software infrastructure over the Internet. Load balancing is a methodology to distribute workload across multiple computers, or other resources over the network links to achieve optimal resource utilization, maximize throughput, minimum response time, and avoid overload. With recent advent of technology, resource control or load balancing in cloud computing is main challenging issue. A few existing scheduling algorithms can maintain load balancing and provide better strategies through efficient job scheduling and resource allocation techniques as well. In order to gain maximum profits with optimized load balancing algorithms, it is necessary to utilize resources efficiently. This paper presents a review of a few load balancing algorithms or technique in cloud computing. The objective of this paper is to identify qualitative components for simulation in cloud environment and then based on these components, execution analysis of load balancing algorithms are also presented.

Cloud computing has become popular due to its attractive features. The load on the cloud is increasing tremendously with the development of new applications. Load balancing is an important part of cloud computing environment which ensures that all devices or processors perform same amount of work in equal amount of time. Different models and algorithms for load balancing in cloud computing has been developed with the aim to make cloud resources accessible to the end users with ease and convenience. In this paper, we aim to provide a structured and comprehensive overview of the research on load balancing algorithms in cloud computing. This paper surveys the state of the art load balancing tools and techniques over the period of 2004–2015. We group existing approaches aimed at providing load balancing in a fair manner. With this categorization we provide an easy and concise view of the underlying model adopted by each approach.

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.

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

Cloud computing(CC), which utilizes massively virtualized data centers to deliver quick and affordable computing solutions, has developed ‎into an established industrial standard that is growing quickly. To handle such a massive amount of data effectively, cloud computing mostly ‎relies on automation and dynamic resource management. In cloud computing, load balancing (LB) is a vital technique for maximizing ‎resource utilization and making sure that no resource is used up. Without the requirement for physical infrastructure, cloud ‎LB allows online platforms to adjust their resources in response to traffic demands. In a cloud environment, workload and resource allocation entail determining the best way to divide up work among several servers. For increasingly severe uncertainty problems, traditional LB ‎approaches are simple but ineffective; for this reason, meta-heuristic methods are employed. This algorithm is heuristic and is independent ‎of the complexity of the challenges. Meta-heuristics approaches based on Artificial Intelligence (AI) are employed to analyze real-time data ‎and intelligently distribute workload among servers. This ensures efficient operations by preventing bottlenecks and enabling proactive LB ‎decisions. The review offers a thorough analysis of meta-heuristics techniques based on artificial intelligence (AI) for static and dynamic LB ‎in both homogeneous and heterogeneous cloud systems.

In the past few years, cloud computing has emerged as a vigorous technology in the era of internet. Its economic model fecilitate the client to use software, infrastructure, applications as a service by just paying according to the usage without possessing them. The development of cloud computing needs to balance the load among different replicated services offered by cloud environment. In terms of responsiveness and availability, load balancing is one major aspect to deal with. Load balancing ensures that the load is equally spread over the cloud servers. Efficient load balancing algorithm leads to decrease in maintenance costs and increase in resource availability. There are various factors that affect the efficiency of load balancing algorithm, two of such factors are addressed in the proposed algorithm. The download of large data files is handled by dual direction technique. This technique make use of replicated servers and download the partitions of a file simultaneously from multiple replica servers. Another factor that is taken into consideration is handling of high priority tasks. There are various parameters to define the effectiveness of a load balancing algorithm such as response time, data center processing time etc. In this present work a load balancing algorithm, based on honey bee optimization algorithm is proposed which has shown better response time. Experimental studies on standard data sets showed that the proposed request balanced honey bee algorithm compared to the original honey bee algorithm, has performed 5.2% to 9.7% better with respect to the response time and 13.8% to 68.2% better with respect to the processing time, which is a significant improvement.

In addition to cutting down on energy use, the green cloud-based service also drastically cuts down on operating expenses. Strongly coupled data centers demand controlled energy, constant performance, and overall optimization of excess energy consumption in order to do considerable calculations, which in turn give entire processing influence from a wide collection of resources. An energy-saving scheduling method is the focus of the research, which makes use of green cloud technologies. In recent years, cloud computing has been integrated into several domains, including computing, research, industry, and business. The need for specialized hardware along with additional resources is rendered obsolete by the provision of many services across the internet through cloud computing. Problems with energy efficiency, resource heterogeneity, and resource usage are only a few of the issues that cloud computing systems encounter. Consolidation approaches like as task scheduling as well as virtual machines (VMs) are used to address these concerns. There has been a plethora of research on task scheduling. Researchers have examined the issue using a variety of metrics and aims. The essay delves into the topic of virtualized cloud data centers' energy usage and effective resource use. Task categorization and thresholds form the basis of the proposed method, which aims to improve resource use and scheduling efficiency. Every company needs a highly efficient structure that is both flexible and homogeneous across all of their cloud environments. This research aims to reduce energy consumption in green cloud systems by using a hybrid scheduling strategy that combines a minimal completion time with a priority-based weighted round-robin. An Artificial Neural Network (ANN) based load balancing method is presented in this study. ANN makes demand predictions and then distributes resources accordingly. This method uses less energy than the cautious over-provisioning strategy since it constantly adjusts the number of active servers based on current demand. In addition, a server that is operating at a high utilization may handle more task with the same amount of power, but it will use more power overall. At last, we take a look at the current state of load balancing in cloud computing and compare it to others using different metrics.

Presently, Cloud Computing is an internet-based technology which is growing rapidly, and we are adopting it in a fine way where we have more than enough technologies to develop advance communication technologies such as IOT, Big data, Machine learning and AI. Among all these technologies we have load balancing, which is the process of distributing a set of tasks and network traffic across multiple servers. This has increased the availability of applications and websites for end-users. This paper presents a detailed review about the load balancing techniques. It also ensures the advantage and limitations of the existing methods and highlighted them with extreme challenges. This paper also suggests the new insight and best usage towards load balancing in cloud computing.

Load balancing (LB) is a task to manage the performance and efficiency of multi-attribute, multi-variant cloud computing (CC) resources. CC system is more effective when its whole resources are employed in best probable manner and maintaining its load in proper accessing of its resources. Load includes the CPU load, network load, data traffic load, client requests load etc. So, load balancing process includes the management of all these loads as per the availability of resources. This paper deals with the multiple load balancing techniques and present them in hierarchical form and analysis them in performance and efficiency dynamics. This paper also discusses the comparative analysis of performance of existing techniques related to LB in CC using Machine learning (ML). A brief about hybrid methods will also be discussed through this paper. In conclusion part on the basis of analysis done through this study paper also suggests some new insights for LB in CC.

In Cloud Computing (CC) environment, load balancing refers to the process of optimizing resources of virtual machines. Load balancing in the CC environment is one of the analytical approaches utilized to ensure indistinguishable workload distribution and effective utilization of resources. This is because only by ensuring effective balance of dynamic workload results in higher user satisfaction and optimal allocation of resource, therefore improve cloud application performance. Moreover, a paramount objective of load balancing is task scheduling because surges in the number of clients utilizing cloud lead to inappropriate job scheduling. Hence, issues encircling task scheduling has to be addressed. In this work a method called, Ford Fulkerson and Newey West Regression-based Dynamic Load Balancing (FF-NWRDLB) in CC environment is proposed. The FF-NWRDLB method is split into two sections, namely, task scheduling and dynamic load balancing. First, Ford Fulkerson-based Task Scheduling is applied to the cloud user requested tasks obtained from Personal Cloud Dataset. Here, employing Ford Fulkerson function based on the flow of tasks, energy-efficient task scheduling is ensured. The execution of asymmetrical scientific applications can be smoothly influenced by an unbalanced workload distribution between computing resources. In this context load balancing signifies as one of the most significant solution to enhance utilization of resources. However, selecting the best accomplishing load balancing technique is not an insignificant piece of work. For example, selecting a load balancing model does not work in circumstances with dynamic behavior. In this context, a machine learning technique called, Newey West Regression-based dynamic load balancer is designed to balance the load in a dynamic manner at run time, therefore ensuring accurate data communication. The FF-NWRDLB method has been compared to recent algorithms that use the markov optimization and the prediction scheme to achieve load balancing. Our experimental results show that our proposed FF-NWRDLB method outperforms other state of the art schemes in terms of energy consumption, throughput, delay, bandwidth and task scheduling efficiency in CC environment.

With the rapid development of processing and storage technologies and the success of the Internet, computing resources have become cheaper, more powerful and more ubiquitously available than ever before. This technological trend has enabled the realization of a new computing model, called cloud computing. In cloud, scheduling is an important application. In cloud environments, load balancing task scheduling is an important problem that directly affects resource utilization. Undoubtedly, load balancing scheduling is a serious aspect that should be considered because of its significant impact on both the back end and the front end of the cloud research industry. Good resource utilization is achieved whenever an effective load balance is achieved in the cloud. But, load balancing in cloud computing is an NP-hard optimization problem. In order to accomplish this problem, a novel load balancing task scheduling algorithm in cloud using Adaptive Dragonfly algorithm (ADA) is proposed. The ADA is a combination of dragonfly algorithm and firefly algorithm. Moreover, to attain the better performance, multi-objective function is developed based on three parameters namely, completion time, processing costs and load. Finally, the performance of proposed methodology is evaluated in terms of different metrics namely, execution cost and execution time. The experimental results demonstrate that a proposed approach accomplishes better load balancing result compared to other approaches.

Cloud Computing provides higher level services and shared pools of configurable resources with minimal management effort over the internet. Cloud Computing achieves coherence and economic of scale by means of shared resources. The growth of cloud computing depends on the high availability of low cost computers, storage devices and networks as well as hardware virtualization and utility computing. Service Level Agreement (SLA) is a service commitment tool between a cloud service provider and a cloud customer. Using SLA in cloud computing, reduce the chances of disappointing the cloud customer and manage the expectation between the service provider and the customer. The challenge is, it is very difficult to handle all request by the cloud providers at a time during peak hours and to keep up SLA. So when there is an uneven request arrival pattern, the cloud resources may either be underutilized or over-utilized. In order to balance the load, the load balancer plays the major role in cloud computing. The load balancing algorithm equalizes the workload and computing resources in a cloud computing environment. It allows an organization to manage their workload demands by allocating resources among multiple servers and thus it minimizes the response time, minimize the waiting time, maximize the throughput time, maximize the resource utilization and minimize the communication delays of the server by meeting the SLA. The major goal of this study is to review both the existing static and dynamic load balancing algorithms proposed till now and to design and implement a Load balancer that uses a Meta Heuristics approach – Ant Colony Optimization technique to perform balancing the load so that the SLA is met evenly without any issues. Each of the load balancing algorithms is compared with other algorithms theoretically and experimentally one of it is tested with the proposed system using AWS Cloud PaaS (Platform as a Service).

Cloud computing enables the internet based computing to everywhere. In cloud computing the task scheduling and load balancing is the important issue that must be considered in data centers. In cloud platform service providers allocate the resources based on the available resources this will increase the QoS. The process of distributing workloads is a load balancing Technique. In this paper focusing the load balancing between the virtual machine. Load balancing are different types that is network load, memory capacity, storage capacity and CPU load. Load balancing supports failover, enabling scalability and decreases costs and maximizes the resource availability. This paper presents the detailed review of load balancing with machine learning technique such as naïve bayes, random forest, Decision Tree and Support Vector Machine. By these machine learning model, SVM algorithm significantly to classify the incoming request to reduce the processing time (makespan). Also provide the moth flame optimization technique for dynamic task scheduling to make better scheduling under different load condition. We have experimented and evaluated this algorithm in a cloud simulation environment; we obtain better outcome than some other well-known algorithms. The Simulation results demonstrate that the proposed technique has a better development in terms of reducing the makespan time and load balancing.

In recent years, cloud computing provides a spectacular platform for numerous users with persistent and alternative varying requirements. In the cloud environment, security and service availability are the two most significant factors during the data encryption process. For providing optimal service availability, it is necessary to establish a load balancing technique that is capable of balancing the request from diverse nodes present in the cloud. This paper aims in establishing a dynamic load balancing technique using the APMG approach. Here in this paper, we integrated adaptive neuro-fuzzy interference system-polynomial neural network as well as memory-based grey wolf optimization algorithm for optimal load balancing. The memory-based grey wolf optimization algorithm is employed to enhance the precision of ANFIS-PNN and to maximize the locations of the membership functions respectively. Also, two significant factors namely the turnaround time and CPU utilization involved in optimal load balancing scheme are evaluated. Finally, the performance evaluation of the proposed MG-ANFIS based dynamic load balancing approach is compared with various other load balancing approaches to determine the system performances.

Cloud Computing is an emerging field and prefer by many one at current but it's craze is lot more depend on its performance which in turn is too much depend on the effective scheduling algorithm and load balancing . In this paper we address this issue and propose an algorithm for private cloud which has high throughput and for public cloud which address the issue of environment consciousness also with performance. To improve the throughput in private cloud SJF is used for scheduling and to overcome form the problem of starvation we use bounded waiting. For load balancing we monitor the load and dispatch the job to the least loaded VM. To gain more benefit and to have opportunity for future enhancement in public cloud environment consciousness isthe key factor and for better performance and load balancing also desired.While load balancing improve the performance, the environment consciousness increase the profit of cloud providers.