Load balancing in cloud computing using particle swarm optimization on Xen Server

Dave, Akash; Patel, Bhargesh; Bhatt, Gopi; Vora, Yash

doi:10.1109/nuicone.2017.8325618

Cited by 8 publications

(11 citation statements)

References 11 publications

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Section: Related Workmentioning

confidence: 99%

“…Considering above inferences that the migration control under constrained objectives like low delay, low energy consumption, and low SLAV, authors stated VM migration as convexity problem, and hence, heuristic models were suggested for VM scheduling 55,65,70–81 . As heuristic solution, authors 65,70–72 applied PSO algorithm to perform VM–host pair estimation towards load balancing. Here, PSO mapped the best position of the VM (under migration or to be migrated) and connected it to the best host which could preserve the objective function, which was defined as the SLA parameter.…”

Section: Related Workmentioning

confidence: 99%

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Multilevel controller‐assisted intrinsically modified ant colony optimization heuristic‐based load‐balancing model for mega cloud infrastructures

Govardhan

Srinivasan

2022

Int J Communication

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Summary The exponential rise in decentralized computing demands has made cloud computing an inevitable technical need; however, under exceedingly high load dynamism, ensuring service level agreement (SLA) has remained a challenge. Despite of the fact that heuristic‐driven VM migration or load‐balancing methods are effective towards quality of service (QoS) and reliable cloud services, the major existing solutions ignore the issues of local minima and convergence and hence undergo SLA violation (SLAV). Moreover, most of the existing methods are designed as a reactive scheduling concept that often results into delayed scheduling and hence SLAV or QoS compromise. To alleviate such issues, in this paper, a robust intrinsically modified evolutionary computing‐driven dynamic load‐balancing model (intrinsically modified ant colony system [IMACS]) is proposed for mega cloud infrastructures. To address aforesaid issues, IMACS employs stochastic prediction‐based overload detection, followed by SLA‐sensitive VM selection and IMACS for load balancing. In IMACS, the use of stochastic prediction‐based overload detection method enabled timely hotspot detection and hence improved proactive decision ability. Moreover, it applies minimum migration time‐sensitive VM selection concept that too helped in guaranteeing load balancing without undergoing downtime or SLAV. To improve VM migration, unlike classical heuristic methods, we designed IMACS with multitype population, adaptive pheromone update, adaptive pheromone diffusion, and coevolution concept, which not only helped in reducing the search space and allied computation but also enabled time‐efficient scheduling decision. The overall proposed model was realized in sync with two controllers named local controller and global controller. Here, the first controller collects the real‐time VM and VM manager (VMM) information which is used to detect overloaded host and the VM to be migrated. While the global controller is designed with IMACS to exploit the information from local controller to execute QoS‐sensitive VM migration. The proposed IMACS‐driven load‐balancing model was realized with CloudSim platform were simulating it over 800 number of independent VMs, each encompassing autonomous task, the performance was analyzed in terms of SLAV, number of migrations, SLA per active host, and energy consumption. Simulation results revealed that the proposed model exhibits 20% higher migration, while retaining almost 19% better SLA in comparison to the other state‐of‐art methods like genetic algorithm or classical ant colony system‐based load‐balancing methods. IMACS exhibited 30% higher host shutdown signifying better energy efficiency (15% lower power consumption).

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Multilevel controller‐assisted intrinsically modified ant colony optimization heuristic‐based load‐balancing model for mega cloud infrastructures

Govardhan

Srinivasan

2022

Int J Communication

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“…Computing services have become a major subject of Information Technology for academic and industrial study in recent years [16]. Processing microservices in a cloud environment with minimal processing time and cost while efficiently utilizing computing resources is a difficult task [17].…”

Section: Cloud-based Microservicesmentioning

confidence: 99%

A Cloud-Based Container Microservices: A Review on Load-Balancing and Auto-Scaling Issues

Rabiu

Chan

Mohamad

2022

Int. J. Data. Science.

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Microservices are being used by businesses to split monolithic software into a set of small services whose instances run independently in containers. Load balancing and auto-scaling are important cloud features for cloud-based container microservices because they control the number of resources available. The current issues concerning load balancing and auto-scaling techniques in Cloud-based container microservices were investigated in this paper. Server overloaded, service failure and traffic spikes were the key challenges faced during the microservices communication phase, making it difficult to provide better Quality of Service (QoS) to users. The aim is to critically investigate the addressed issues related to Load balancing and Auto-scaling in Cloud-based Container Microservices (CBCM) in order to enhance performance for better QoS to the users.

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“…The evaluation shows that the physical servers are chosen efficiently resulted in resource utilization improvement. Dave et al [20] presented PSO for balancing the load running in cloud environment using different applications to generate the load. The comparison shows considerable improvement in the performance of VMs running applications.…”

Section: Introductionmentioning

confidence: 99%

Optimized load balance scheduling algorithm

Abdul-Hussein

Mohammed

2022

TELKOMNIKA

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Load balancing in cloud computing using particle swarm optimization on Xen Server

Cited by 8 publications

References 11 publications

Multilevel controller‐assisted intrinsically modified ant colony optimization heuristic‐based load‐balancing model for mega cloud infrastructures

Multilevel controller‐assisted intrinsically modified ant colony optimization heuristic‐based load‐balancing model for mega cloud infrastructures

A Cloud-Based Container Microservices: A Review on Load-Balancing and Auto-Scaling Issues

Optimized load balance scheduling algorithm

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