A comparative review analysis for load balancing techniques in Cloud Computing using Machine Learning

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.

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.

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.

Load unbalancing problem is a multi-variant, multi-constraint problem that degrades performance and efficiency of computing resources. Load balancing techniques cater the solution for load unbalancing situation for two undesirable facets- overloading and under-loading. In contempt of the importance of load balancing techniques to the best of our knowledge, there is no comprehensive, extensive, systematic and hierarchical classification about the existing load balancing techniques. Further, the factors that cause load unbalancing problem are neither studied nor considered in the literature. This paper presents a detailed encyclopedic review about the load balancing techniques. The advantages and limitations of existing methods are highlighted with crucial challenges being addressed so as to develop efficient load balancing algorithms in future. The paper also suggests new insights towards load balancing in cloud computing.

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.

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 transformed how companies utilize and manage resources, but effectively balancing loads presents a challenge. Traditional load balancing techniques have resulted in efficiency loss and increased latency. With artificial intelligence (AI) and machine learning, predictive load balancing has emerged as a viable solution, providing real-time resource distribution and performance improvement. In this study, a systematic literature review of AI-based techniques for load balancing is conducted. Findings highlight AI capabilities in terms of increased scalability, reduced energy consumption, and increased service availability. Computational complexity and training overhead present ongoing challenges, necessitating continued investigation into hybrid AI models to enhance adaptability and efficiency.

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.

Now-a-days, cloud computing has become a cornerstone of modern technology, driving innovation, efficiency and accessibility across various industries and application. Distributed computing solves the difficulty of using distributed autonomous machines and communicating with each other over a network. Cloud computing provides clients with a range of services and capabilities that enhances productivity, accessibility and scalability while reducing the need for extensive hardware and infrastructure investments. When interest in distributed computing rises, it implies that more individuals, businesses, and organizations are exploring, adopting, and implementing distributed computing solutions. This surge in interest leads to increase in data traffic. There are two solutions for the issue of increase in data traffic, one is to server optimization (or) server performance enhancement (upgrade single server to a high performance server) but upgraded server may exceed its capacity (overload).Another one is multi servers. Ulti-server configurations are common in scenarios where the demands of an application or service exceed the capabilities of a single server, and the distribution of tasks across multiple servers is necessary for optimal performance and reliability. When there are multiple servers, there is an issue i.e. Load Adjusting. [1] Load balancing is one of the critical issues in cloud computing. In a cloud environment, where resources are often dynamically allocated and distributed, load balancing plays a central role in managing workloads efficiently. Load balancing in cloud computing is a technique used to distribute computing workloads and network traffic across multiple servers or resources within a cloud environment. The primary goal of load balancing is to optimize resource utilization, prevent individual servers from becoming overloaded, and ensure that the overall system can handle varying levels of demand efficiently. Here we also discussed about the hybrid of Cat and Mouse Optimization and Grey Wolf Optimization algorithms. This paper refers to Cloud Computing, Load balancing techniques, Load balancing algorithms.

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.

Load balancing is the core of virtual resource management and scheduling in cloud computing. For network applications, the cost of user would be greatly saved if load balancer could dynamically adjust cluster resources in accordance with the current applied load. The current load balancing products of cloud, such as Amazon's ELB, can be used to manage virtual machines in the cloud. The main drawbacks are still only supporting template-based deployment of new virtual machines, not supporting the trend prediction, failing to gain resources dynamically, and not sufficiently providing the elastic management of resources. Since the virtual machine for load balancing management in cloud computing can be dynamically applied and released, an algorithm of prediction-based elastic load balancing resource management (TeraScaler ELB) is presented to overcome the drawbacks. Experiments have shown that the required number of virtual machines change in compliance with the change of network load, thus TeraScaler ELB is able to dynamically adjust the processing capacity of back-end server cluster with the applied load. Besides it could make full use of the 'use on demand' feature of cloud computing, TeraScaler ELB leads to a better application of prediction based load balancing in cloud computing. It concludes that compared with the traditional elastic resource management algorithm, TeraScaler ELB is more reasonable for providing scalability and high availability.

The evolution of IT led Cloud computing technology emerge as a new prototype in providing the services to its users on rented basis at any time or place. Considering the flexibility of cloud services, innumerable organizations switched their businesses to the cloud technology by setting up more data centers. Nevertheless, it has become mandatory to provide profitable execution of tasks and appropriate resource utilization. A few approaches were outlined in literature to enhance performance, job scheduling, storage resources, QoS and load distribution. Load balancing concept permits data centers to avert over-loading or under-loading in virtual machines that as such is an issue in cloud computing domain. Consequently, it necessitate the researchers to layout and apply a proper load balancer for cloud environment. The respective study represents a view of problems and threats faced by the current load balancing techniques and make the researchers find more efficient algorithms.

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.

Cloud computing provides facilities. These facilities increased demand for its using as institutions and individuals moved to the cloud service. Therefore, cloud service providers must provide services to users based on the expected quality. One of the main challenges presented by the cloud computing is the Quality of Service management. QoS management is defined as allocating resources to applications to ensure service based on reliability, performance and availability. It is necessary to allocate resources based on load balancing that allows avoiding overloading or low loading in virtual machines, and this is a challenge for researchers in the field of cloud computing. This research highlights the importance of cloud computing, its types and importance, It also reviews some researches in the field of quality assurance of service in computing.

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.