Load balancing techniques in cloud computing environment: A review

Shafiq, Dalia Abdulkareem; Jhanjhi, N. Z.; Abdullah, Azween

doi:10.1016/j.jksuci.2021.02.007

Cited by 98 publications

(36 citation statements)

References 53 publications

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“…The percentage value of the factors affected by load balancing is applied here. User request response time [5], [9], [10], [15], [17], [22] 2 Data processing time [5], [9], [10] 3…”

Section: Resultsmentioning

confidence: 99%

Load balancing in cloud computing: an analytical review and proposal

Maurya

Sinha

2022

IJEECS

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Task scheduling <span>or mapping of requests to machines with specified requirements becomes a very important problem to get optimized results. With the requests of many users, the machine can go under different conditions such as heavy load and minimum load. These various aspects can lead to system failure and loss of data. Hence the load must be balanced to beat the losses of the system. A load balancing concept described how different types of loads could be balanced with each machine. The load can be of memory, computation, and network. The paper mainly focuses on all kinds of load to provide a big view for load balancing in cloud computing. Along with a review of all load balancing techniques, the paper is also presenting a proposal to balance the load with decision theory. The proposed paper introduces the basic techniques and types for load balancing and their implications for innovation.</span>

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“…The percentage value of the factors affected by load balancing is applied here. User request response time [5], [9], [10], [15], [17], [22] 2 Data processing time [5], [9], [10] 3…”

Section: Resultsmentioning

confidence: 99%

Load balancing in cloud computing: an analytical review and proposal

Maurya

Sinha

2022

IJEECS

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“…Numerous strategies aimed to improve task scheduling and consequently affect load balancing improvement in different Cloud environments. Approaches to solving task scheduling problems implement various heuristic, metaheuristic, and hybrid algorithms [28], among which metaheuristics [29] algorithms predominate. Dynamic strategies based on historical data scheduling requirements [30] or VM status at runtime [31] have improved performance management, resource use, and task response time.…”

Section: Related Workmentioning

confidence: 99%

Scalable Management of Heterogeneous Cloud Resources Based on Evolution Strategies Algorithm

Lončar

Loncar

2022

IEEE Access

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The emergence and rapid development of Cloud computing have intensively changed the Information Technology paradigm. Cloud computing found its wide application in science and engineering. Cloud-based systems provide new solutions for scientific experiments and access to data distributed across data centers. Optimization of Cloud resource management is becoming a significant area of research. This research focuses on scheduling tasks and processing scientific data in a heterogeneous Cloud environment where most jobs require a large number of resources and computing power. Our approach proposes a strategy for optimizing task scheduling across different virtual machines and data centers based on the metaheuristic Evolution Strategies algorithm. The Evolution Strategies algorithm was tested, which has not been used in this domain. As an essential property of the system, scalability has played an important role in selecting the algorithm. We created a model and added a Longest Job First broker policy. Compared to the standard Genetic Algorithm, our approach has shown improvements in measured metrics. After testing under different loads, the proposed strategy gave promising results and achieved a better makespan, larger average resource utilization, better throughput, less average execution time, a smaller degree of imbalance, and scalability.

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“…Different load balancers discussed here are random method, round robin method, weighted round robin method and dynamic round robin method. Another survey appears in [Shafiq et al (2021)]. It discusses throttled algorithm, equally spread current execution method, round robin method, weighted round robin method, honey-bee method and genetic algorithm for load balancing in cloud computing to ensure quality of services (QoS) parameters of different virtual machines in cloud computing framework.…”

Section: Review Of Literaturementioning

confidence: 99%

Greedy Load Balancing for Cloud Computing Framework

Datta¹,

Pan²

2021

INDJCSE

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Cloud computing technology helps in resource and application sharing at large scale. Load balancing or resource sharing is one key task which controls quality of services in a cloud computing framework. This proposed work focuses on optimal task management by scheduling through greedy resource allocation strategy. Literature study reveals that greedy resource allocation concept is mostly unexplored in this domain. This work relies on that to propose a task allocation schedule. Acquired schedule is also verified with service level agreement (SLA) protocol for its validity to control quality of services. This new method has been simulated and tested under various load scenarios and also compared with two other widely used models. Experimental study shows promising outcome.

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Load balancing techniques in cloud computing environment: A review

Cited by 98 publications

References 53 publications

Load balancing in cloud computing: an analytical review and proposal

Load balancing in cloud computing: an analytical review and proposal

Scalable Management of Heterogeneous Cloud Resources Based on Evolution Strategies Algorithm

Greedy Load Balancing for Cloud Computing Framework

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