Load balancing techniques in cloud computing environment: A review

State-of-the-Art Load Balancing Algorithms for Mist-Fog-Cloud Assisted Paradigm: A Review and Future Directions

Cloud computing covers a broad range of cloud-based service models such as SaaS: Software as a Service, PaaS: Platform as a Service, IaaS: Infrastructure as a Service, XaaS: Everything as a Service, NaaS: Network as a Service and RaaS: Recovery as a Service. Whether the users are looking at SaaS, PaaS, IaaS, XaaS, NaaS or RaaS, the estimations are the same; fast development of the workloads placed on the VMs in the cloud and a maximized percentage of the IT budget moving toward cloud computing. Therefore, there is a need for an enhanced VM level load balancer to balance the VM level load. In this research, different dynamic load-balancing methods and enhanced algorithms are analyzed to develop a load balancer for allocating the workloads evenly to the VMs and to migrate the excess tasks from the overloaded VMs to the underloaded VMs. The motivation of our research is to propose an Enhanced Throttled Load Balancer for allocating the workloads evenly to all VMs and for minimizing the delay time and response time of the service. The proposed load balancer distributes the arriving requests evenly to all VMs and allocates the resources dynamically. Using the threshold value, the proposed load balancer categorizes the VMs as overloaded VMs, underloaded VMs and balanced VMs. If there is an overloaded VM then the proposed Enhanced Throttled Load Balancer finds out the suitable underloaded VM and automatically begins the task migration process to migrate the task to the underloaded VM from the overloaded VM thereby balance the load on VMs. The main use of the proposed load balancer is to balance the load on VMs and to control the overhead of the VM. This paper presents a dynamic load balancing approach using Enhanced Throttled Load Balancer that aids to reduce the delay time and the response time of the service.

Dynamic Load Balancing Approach for Minimizing the Response Time Using An Enhanced Throttled Load Balancer in Cloud Computing

Cloud computing is the provision of computing services over the web. Cloud Computing's load-balancing algorithms are implemented in static, dynamic, and centralized environments. The article analyzes and outlines various load-balancing techniques in the cloud computing architecture and discusses the pros and cons of different load-balancing algorithms. The research also primarily fixates on exploring machine learning models implemented in LB techniques. The most popular algorithms in the articles reviewed include Statistical Regression, Random Forest Classifier Artificial Neural Networks(RF), Convolutional Neural Networks(CNNs), and Regenerating Memory Neural Networks. long-term (LSTM-RNN). LB specifications have been defined through performance metrics such as throughput, latency, travel time, fault tolerance, and power savings.

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Load Balancing Algorithms with the application of Machine Learning: A Review

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.

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

Cloud computing is a new standard for large-scale distributed and parallel computing that is still in its infancy. At a predetermined time, provides common resources, information, software packages, and other resources in accordance with client requirements. As cloud computing becomes more popular and more people become interested in assistive computing, better and quicker service is necessary. It is necessary to enhance the management of the good load balancing solutions that are already accessible. Recently, numerous clients from all over the world have been seeking various services at a quick rate, negatively impacting cloud computing performance. The various load balancing techniques used in cloud computing are effective in customizing demand by identifying the appropriate virtual machines. In this review, a comparative study will be done between many load balancing algorithms in different cloud computing, as many studies have been done on this important topic. Important research will also be analyzed and defined, and analyzes will be written for each research that talks about dynamic and non-dynamic load balancing techniques and algorithms. Many details will also be mentioned from the researcher's point of view as a summary of each research.

/pdf/a-review-on-load-balancing-algorithms-in-cloud-computing-2usq5hv3.pdf

A Review on Load Balancing Algorithms in Cloud Computing

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A Review on Load Balancing Algorithms in Cloud Computing