Load balancing in cloud computing using optimization techniques: A study

Dave, Akash; Patel, Bhargesh; Bhatt, Gopi

doi:10.1109/cesys.2016.7889883

Cited by 18 publications

(4 citation statements)

References 17 publications

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“…The load balancing technology [17] in cloud computing has always been a research focus. Chen et al [18] measured the load status by setting the number of task request connections for the node resources and assigned the task to the node with the smallest number of connections.…”

Section: Related Workmentioning

confidence: 99%

Task Staggering Peak Scheduling Policy for Cloud Mixed Workloads

Tao

Zheng

et al. 2018

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To address the issue of cloud mixed workloads scheduling which might lead to system load imbalance and efficiency degradation in cloud computing, a novel cloud task staggering peak scheduling policy based on the task types and the resource load status is proposed. First, based on different task characteristics, the task sequences submitted by the user are divided into queues of different types by the fuzzy clustering algorithm. Second, the Performance Counters (PMC) mechanism is introduced to dynamically monitor the load status of resource nodes and respectively sort the resources by the metrics of Central Processing Unit (CPU), memory, and input/output (I/O) load size, so as to reduce the candidate resources. Finally, the task sequences of specific type are scheduled for the corresponding light loaded resources, and the resources usage peak is staggered to achieve load balancing. The experimental results show that the proposed policy can balance loads and improve the system efficiency effectively and reduce the resource usage cost when the system is in the presence of mixed workloads.

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Section: Related Workmentioning

confidence: 99%

Task Staggering Peak Scheduling Policy for Cloud Mixed Workloads

Tao

Zheng

et al. 2018

Information

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“…Day by day, cloud usage and needs increases rapidly in the same manner cloud issues also in a noticeable growth. The cloud users submit multiple tasks through several applications, which requires stabilization on storage, computing, and memory capacity [1,2]. Load balancing is the art of balancing the workload by redistributing to the cloud computing without any, under-loaded, overloaded or idle [3,4].…”

Section: Introductionmentioning

confidence: 99%

An Advanced Dynamic Scheduling for Achieving Optimal Resource Allocation

Prabhu¹,

Rajesh²

2023

Computer Systems Science and Engineering

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Cloud computing distributes task-parallel among the various resources. Applications with self-service supported and on-demand service have rapid growth. For these applications, cloud computing allocates the resources dynamically via the internet according to user requirements. Proper resource allocation is vital for fulfilling user requirements. In contrast, improper resource allocations result to load imbalance, which leads to severe service issues. The cloud resources implement internet-connected devices using the protocols for storing, communicating, and computations. The extensive needs and lack of optimal resource allocating scheme make cloud computing more complex. This paper proposes an NMDS (Network Manager based Dynamic Scheduling) for achieving a prominent resource allocation scheme for the users. The proposed system mainly focuses on dimensionality problems, where the conventional methods fail to address them. The proposed system introduced three -threshold mode of task based on its size STT, MTT, LTT (small, medium, large task thresholding). Along with it, task merging enables minimum energy consumption and response time. The proposed NMDS is compared with the existing Energy-efficient Dynamic Scheduling scheme (EDS) and Decentralized Virtual Machine Migration (DVM). With a Network Manager-based Dynamic Scheduling, the proposed model achieves excellence in resource allocation compared to the other existing models. The obtained results shows the proposed system effectively allocate the resources and achieves about 94% of energy efficient than the other models. The evaluation metrics taken for comparison are energy consumption, mean response time, percentage of resource utilization, and migration.

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“…Load balancing entails task redistribution in a distributed network, such as cloud computing, so that there are no overworked, under-burdened, or idle computer machines (Achar et al, 2013;Magalhaes et al, 2015). It boosts cloud performance by attempting to improve restricting parameters such as reaction time, processing time, stability of the system, and job transfer (Dam et al, 2015;Dave et al, 2016). Researchers have proposed different approaches to improve quality of cloud computing services and consumption of resources.…”

Section: Introductionmentioning

confidence: 99%

Cloud Computing Load Balancing Techniques: Retrospect and Recommendations

Oduwole¹,

Akinboro²,

Lala³

et al. 2022

FUOYEJET

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Load balancing is a research area that seeks to improve the quality of services provided to various clients in cloud computing environments. As cloud users increase around the world, cloud service providers are challenged to develop strategies for distributing tasks to machines for processing at cloud data centers. This work collected and undertook a thorough review of various load balancing techniques, uncovering the key limitations of existing strategies. The publications were chosen from peer-reviewed papers on Google Scholar. Cloud computing, cloud load balancing techniques, approaches to cloud load balancing, and big-data cloud computing systems were among the terms used in the search. Out of 201 studies, 39 met the criteria for inclusion. 5 of the research focused on cloud computing, 6 on cloud load balancing, 7 on resource scheduling in cloud, 16 on techniques for balancing cloud load, and 5 on big-data cloud computing environments. The study identified some research gaps and recommended a throughput-maximization based central-distributive load balancing architecture as a solution to maximize throughput, minimize response time and processing cost, and optimize load balancing architecture. Keywords— Centralized, cloud-computing, distributive, load-balancing.

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Load balancing in cloud computing using optimization techniques: A study

Cited by 18 publications

References 17 publications

Task Staggering Peak Scheduling Policy for Cloud Mixed Workloads

Task Staggering Peak Scheduling Policy for Cloud Mixed Workloads

An Advanced Dynamic Scheduling for Achieving Optimal Resource Allocation

Cloud Computing Load Balancing Techniques: Retrospect and Recommendations

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