A novel load balancing technique for cloud computing platform based on PSO

Pradhan, Arabinda; Bisoy, Sukant Kishoro

doi:10.1016/j.jksuci.2020.10.016

Cited by 53 publications

(32 citation statements)

References 5 publications

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“…It is measured to increase the efficiency in utilizing the resources in the cloud environment. It is calculated using the equations below proposed by authors in [37], where ExT denotes total execution time, and MT denotes total Makespan. The average resource utilization can determine how efficient the proposed algorithm in terms of utilizing the CPU.…”

Section: Ext = Actmentioning

confidence: 99%

A Load Balancing Algorithm for the Data Centres to Optimize Cloud Computing Applications

et al. 2021

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Despite the many past research conducted in the Cloud Computing field, some challenges still exist related to workload balancing in cloud-based applications and specifically in the Infrastructure as service (IaaS) cloud model. Efficient allocation of tasks is a crucial process in cloud computing due to the restricted number of resources/virtual machines. IaaS is one of the models of this technology that handles the backend where servers, data centers, and virtual machines are managed. Cloud Service Providers should ensure high service delivery performance in such models, avoiding situations such as hosts being overloaded or underloaded as this will result in higher execution time or machine failure, etc. Task Scheduling highly contributes to load balancing, and scheduling tasks much adheres to the requirements of the Service Level Agreement (SLA), a document offered by cloud developers to users. Important SLA parameters such as Deadline are addressed in the LB algorithm. The proposed algorithm is aimed to optimize resources and improve Load Balancing in view of the Quality of Service (QoS) task parameters, the priority of VMs, and resource allocation. The proposed LB algorithm addresses the stated issues and the current research gap based on the literature's findings. Results showed that the proposed LB algorithm results in an average of 78% resource utilization compared to the existing Dynamic LBA algorithm. It also achieves good performance in terms of less Execution time and Makespan.

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Section: Ext = Actmentioning

confidence: 99%

A Load Balancing Algorithm for the Data Centres to Optimize Cloud Computing Applications

et al. 2021

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“…Constraint (6) guarantees that the workload for all edge servers is less than or equal to η, which is the largest workload among the edge servers. Constraint (7) guarantees that tasks originating from a particular node will be allocated to a single edge server.…”

Section: Figure 1: Illustration Of Task Allocation In Edge Computingmentioning

confidence: 99%

“…Some previous studies have considered load balancing by monitoring certain utilization metrics for load generated by server-side computation [4] [5], but rarely considered communication distance as the main factor impacting workload balancing. The transmission time is usually determined by the data size and data transmission rate [6] [7], with longer transmission distances also creating greater loads within the network. Traditional models for load balancing usually consider only the computational load on the servers and do not consider the transmission load due to distance.…”

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confidence: 99%

Server Placement and Task Allocation for Load Balancing in Edge-Computing Networks

Huang

Chin²,

Chuang

2021

IEEE Access

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Offloading tasks to cloud servers has increasingly been used to provide terminal users with powerful computation capabilities for a variety of services. Recently, edge computing, which offloads tasks from user devices to nearby edge servers, has been exploited to avoid the long latency associated with cloud computing. However, edge server placement and task allocation strongly affect the offloading process and the quality of a user's experience. Therefore, appropriately deploying the edge servers within a network and evenly allocating the workload to the servers are vital. This paper thus considers both the workload of edge servers and the distances involved in offloading tasks to these servers. To improve the user experience, edge server locations are carefully selected and the workload for the servers are allocated in a balanced manner. This scenario is formulated as a mixed-integer linear programming problem, and a novel solution that searches for the best server placement using simulated annealing while integrating task allocation using the Lagrangian duality theory with the sub-gradient method is proposed. Numerical simulations verify that the proposed algorithm can achieve better results than conventional heuristics. INDEX TERMSCloud computing, Edge computing, Server placement, Task allocation I. INTRODUCTION

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“…Yao et al [24] suggested a load balancing technique by concerning an artificial bee colony (ABC) algorithm and presented an enhanced artificial bee colony algorithm for increasing the system's throughput. A load balancing technique using modified PSO task scheduling (LBMPSO) [54] considered the execution time, and starting time variation of the tasks and assigned them to the ideal VMs. It minimized the make-span and maximized the resource utilization value.…”

Section: Related Workmentioning

confidence: 99%

A Double Threshold-Based Power-Aware Honey Bee Cloud Load Balancing Algorithm

et al. 2021

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Present-day advancement in cloud computing provides ICT infrastructure as a service on a pay per use. Cloud computing provides this infrastructure as a service and as service demand increases, service providers organize large-scale data centers with a lot of resources, and cause of huge greenhouse gases' emission. This data center's huge power demand necessitates the balancing of cloud load. To attain the optimum resource utilization, least processing time of CPU, minimal average response time, and avoiding over-load, cloud load balancing algorithms distributes workload across virtual machines. The key challenge here is to develop such a load balancing algorithm which consumes the least resources to fulfill the service demands. In this paper, a double threshold-based power-aware honey bee load balancing algorithm is proposed for the fair and even distribution of the incoming task requests to all the virtual machines. This paper compares the proposed algorithm with five widely used existing load balancing algorithms. Moreover, we have done the performance analysis using the popular CloudAnalyst simulation toolkit. Results of simulation showed that the proposed algorithm gives a note-worthy outcome for average response time, CPU cost, storage cost, memory cost, and energy consumption in cloud computing to show the resource utilization.

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A novel load balancing technique for cloud computing platform based on PSO

Cited by 53 publications

References 5 publications

A Load Balancing Algorithm for the Data Centres to Optimize Cloud Computing Applications

A Load Balancing Algorithm for the Data Centres to Optimize Cloud Computing Applications

Server Placement and Task Allocation for Load Balancing in Edge-Computing Networks

A Double Threshold-Based Power-Aware Honey Bee Cloud Load Balancing Algorithm

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