DDMTS: A novel dynamic load balancing scheduling scheme under SLA constraints in cloud computing

Tong, Zhao; Deng, Xiaomei; Chen, Hongjian; Mei, Jing

doi:10.1016/j.jpdc.2020.11.007

Cited by 37 publications

(13 citation statements)

References 24 publications

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“…Figure 12 and Table 10 show the degree of imbalance of both existing 33 and proposed load balancing model with respect to varying number of tasks. Here, the degree of imbalance is used as an indicator for evaluating the balanced loads across the VMs in the cloud system.…”

Section: Resultsmentioning

confidence: 99%

PGWO‐AVS‐RDA: An intelligent optimization and clustering based load balancing model in cloud

Laxman

Lathigara

Aluvalu

et al. 2022

Concurrency and Computation

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Load balancing and task scheduling in cloud have gained a significant attention by many researchers, due to the increased demand of computing resources and services. For this purpose, there are various load balancing methodologies are developed in the existing works, which are mainly focusing on allocating the tasks to Virtual Machines (VMs) based on their priority, order of tasks, and execution time.Still, it facing the major difficulties in finding the best tasks for allocation, because the sequence of patterns are normally used to categorize the relevant tasks with respect to the load. Thus, this research work intends to develop an intelligent group of mechanisms for efficiently allocating the tasks to the VMs by finding the best tasks with respect to the scheduling parameters. Initially, the user tasks are given to the load balancer unit, where the Probabilistic Gray Wolf Optimization (PGWO) technique is used to find the best fitness value for selecting the tasks. Then, the Adaptive Vector Searching (AVS) methodology is utilized to cluster the group of tasks for efficiently allocating the tasks with improved Quality of Service (QoS). Finally, the Recursive Data Acquisition (RDA) based scheduler unit can allocate the clustered tasks to the appropriate VMs in the cloud system by analyzing the properties of storage capacity, balancing load of VM, CPU usage, memory consumption, and execution time of tasks. During evaluation, the performance of the proposed load balancing model is validated by using various measures. Then, the obtained results are compared with some state-of-the-art models for proving the betterment of the proposed scheme.

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Section: Resultsmentioning

confidence: 99%

PGWO‐AVS‐RDA: An intelligent optimization and clustering based load balancing model in cloud

Laxman

Lathigara

Aluvalu

et al. 2022

Concurrency and Computation

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“…others scheduling algorithm like ipso [20], map reducing hybrid scheduling algorithm [21], and Bayes theorem [22], fuzzy logic [23], load balancing and rebalancing [24], randomized optimization [25], [26] also been proposed. Intensive task scheduling framework using deep reinforcement learning (DRL) with deep Q-network (DQN) [27] has been proposed for suitable VM selection for current situation. Another research team has proposed a method for improving load balancing using QoS, service-level agreement (SLA) violation and energy consumption [28].…”

Section: Related Workmentioning

confidence: 99%

A load balancing strategy for reducing data loss risk on cloud using remodified throttled algorithm

Johora

Ahmed

Shajal

et al. 2022

IJECE

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<p><span>Cloud computing always deals with new problems to fulfill the demand of the challenging organizations around the whole world. Reducing response time without the risk of data loss is a very critical issue for the user requests on cloud computing. Load balancing ensures quick response of virtual machine (VM), proper usage of VMs, throughput, and minimal cost of VMs. This paper introduces a re-modified throttled algorithm (RTMA) that reduces the risk of data hampering and data loss considering the availability of VM which increases system’s performance. Response time of virtual machines have been considered in our work, so that when migration process is running, data will not be overflowed in the VMs. Thus, the data migration process becomes high and reliable. We have completed the overall simulation of our proposed algorithm on the cloud analyst tool and successfully reduced the risk of data loss as well as maintains the response time.</span></p>

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“…[ Tong et al, (2021)] discusses the constraint of service level agreement during dynamic load balancing for cloud computing. Here the upcoming task is first dynamically allocated to an available virtual machine and then that allocation is cross verified with service level agreement for deadline constraint.…”

Section: Review Of Literaturementioning

confidence: 99%

“…Role of service level agreement has been mostly overlooked in literature. [Tong et al, (2021)] has recently kept the scope of service level agreement in their proposal for the validation. Thus there remains a profound need for exploring optimal scheduling strategy under the validation measure of service level agreement.…”

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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DDMTS: A novel dynamic load balancing scheduling scheme under SLA constraints in cloud computing

Cited by 37 publications

References 24 publications

PGWO‐AVS‐RDA: An intelligent optimization and clustering based load balancing model in cloud

PGWO‐AVS‐RDA: An intelligent optimization and clustering based load balancing model in cloud

A load balancing strategy for reducing data loss risk on cloud using remodified throttled algorithm

Greedy Load Balancing for Cloud Computing Framework

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