Improving the Load Balancing and Dynamic Placement of Virtual Machines in Cloud Computing using Particle Swarm Optimization Algorithm

Yousefipour, Amin; Rahmani, Amir Masoud; Jahanshahi, Mohsen

doi:10.5829/ije.2021.34.06c.05

Cited by 4 publications

(4 citation statements)

References 29 publications

(52 reference statements)

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“…Particle swarm algorithm is an intelligence algorithm to simulate the bird flock foraging. Each particle is characterized as a possible solution vector [21][22][23][24]. The quality of particles is judged by the value of fitness function.…”

Section: Basic Principles Of Improved Particle Swarm Algorithmmentioning

confidence: 99%

Modal Optimization Design of Supporting Structure Based on the Improved Particle Swarm Algorithm

2022

IJE

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To cope with the strong vibration of a supporting structure excited by external loads under operating conditions, and in order to achieve the purpose of vibration reduction by structural optimization through modal modification, a modal modification method was proposed, through structural vibration theory. Subsequently, the search performance of an improved particle swarm optimization method was analyzed before conducting a case study on the structural optimization. Finally, aiming at the problem of strong vibration of gun mount at the time of firing, a finite element model of the gun mount was constructed and the type and natural frequency of the gun vibration in a free state was analyzed. Meanwhile, taking the thickness, height and width of the stiffening structure of the bracket as the design variables, combined with the improved particle swarm algorithm, an optimized mathematical model was developed with the first-order natural frequency of the gun mount as the objective function. The secondary development of Abaqus finite element software by using Python is used as a tool to calculate the optimization model. By virtue of optimation, thickness, width and height of the stiffening structure are 156.4mm, 453.7mm and 238.9mm at the range of [100,600]mm, [100,700]mm, [100,700]mm, respectively, and the base frequency of the gun mount has been increased by 11.3%. The effect is remarkable.

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Section: Basic Principles Of Improved Particle Swarm Algorithmmentioning

confidence: 99%

Modal Optimization Design of Supporting Structure Based on the Improved Particle Swarm Algorithm

2022

IJE

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“…The results showed that this algorithm performs well in case of increasing the number of tasks. Yousefipour et al [13] proposed a method for task scheduling improvement of cloud computing through an improved particle swarm optimization algorithm. In this method, selection of a proper objective function has led to balanced workload of virtual machines, decreased time of all tasks as well as maximum utilization of all resources and increased productivity in addition to dynamic placement of virtual machine on physical machine.…”

Section: Related Workmentioning

confidence: 99%

Toward Energy-efficient Communication Protocol in WBAN: A Dynamic Scheduling Policy Approach

2022

IJE

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Wireless body area network (WBAN) is an emerging technology that has been able to provide a better experience of mobility and flexibility for humans using tiny and low power sensors inside, outside, or around the body compared to the traditional wired monitoring systems. Due to numerous constraints in size, energy consumption, and security of implant devices in the human body, it is still a significant research challenge to design these systems in a reliable and energy-efficient fashion. To provide quality of service, timely and secure delivery of real-time data needs to be done without any loss. This paper attempts to provide a communication protocol in order to upgrade QoS levels in WBANs and reduce energy consumption in sensor nodes. To do so, the earliest deadline first (EDF) real-time scheduling algorithm and its combination with the least laxity first (LLF) scheduling algorithm were employed to prioritize sensor nodes for sending data packets. The proposed method could optimize the system performance when it is in the event of an overload and tasks miss their deadlines in a row. The OMNET++ simulation environment is used to evaluate the proposed solution's efficiency which checks packet delivery rate and mean-power consumption evaluation criteria in the sink and sensor nodes. This is done with different numbers of nodes in the network. The results show that the proposed strategy could provide an appropriate improvement in sending and receiving packets for body area networks.

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“…An improved Particle Swarm Optimization algorithm was designed [11] for the balanced workload of virtual machines. However, the higher efficiency of workload balancing was a challenging task.…”

Section: Introductionmentioning

confidence: 99%

Resource Aware Orthogonal Projected Regressive MapReduce Lottery Load Balancing in Cloud Computing

Ellakkiya,

Ravi

2024

J. Sci. Res.

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Cloud Computing is an internet-based network technology that provides various services and requirements to customers through online computing resources. In the cloud, Load balancing is the most significant issue that includes both hardware and software platforms for the execution of demand of the user request. Furthermore, for handling multiple user requests, load balancing is necessary. Therefore, an efficient load-balancing technique is required to optimize and ensure user satisfaction by utilizing the virtual machine's resources efficiently. A novel Orthogonal Projected Regressive MapReduce Lottery Load Balancing (PORLOB) technique is introduced for resource-efficient task scheduling with minimal Makespan and complexity. In the PORLOB technique, many cloud user requests are transmitted to the cloud server from different locations. The load balancer uses the index table for maintaining the virtual machines. The MapReduce function includes two steps, namely, map and reduce. Based on the resource estimation, the map function performs the regression analysis and provides three resource statuses of the virtual machine: overloaded, less loaded, and balanced. In the reduction phase, the load balancer uses the lottery scheduling technique to balance the workload by migrating the task from an overloaded Virtual Machine to a less-loaded VM.

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Improving the Load Balancing and Dynamic Placement of Virtual Machines in Cloud Computing using Particle Swarm Optimization Algorithm

Cited by 4 publications

References 29 publications

Modal Optimization Design of Supporting Structure Based on the Improved Particle Swarm Algorithm

Modal Optimization Design of Supporting Structure Based on the Improved Particle Swarm Algorithm

Toward Energy-efficient Communication Protocol in WBAN: A Dynamic Scheduling Policy Approach

Resource Aware Orthogonal Projected Regressive MapReduce Lottery Load Balancing in Cloud Computing

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