On the Use of Resource Reservation for Web Services Load Balancing

Nakai, Alan Massaru; Madeira, Edmundo Roberto Mauro; Buzato, Luiz Eduardo

doi:10.1007/s10922-014-9303-y

Cited by 18 publications

(5 citation statements)

References 36 publications

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“…Since our objective in load balancing is to reduce the response time, we state some new studies in this field. Nakai et al have presented a load‐balancing mechanism based on the reserve policy to disperse requests among replicated servers . This allowed overloaded servers to reserve a part of the capacity of remote servers before receiving a new request, and, if requests were higher than the amount of average capacity of remote servers, some of the requests were discarded.…”

Section: Related Workmentioning

confidence: 99%

“…25 Now, we address the load-balancing algorithms. A lot of related work has been done on load balancing with different objectives such as reducing response time, 3,26,27 scalability, 26,28 and reducing migration time of requests. 28,29 Since our objective in load balancing is to reduce the response time, we state some new studies in this field.…”

Section: Related Workmentioning

confidence: 99%

“…Nakai et al have presented a load-balancing mechanism based on the reserve policy to disperse requests among replicated servers. 26 This allowed overloaded servers to reserve a part of the capacity of remote servers before receiving a new request, and, if requests were higher than the amount of average capacity of remote servers, some of the requests were discarded. Simulation results showed that their proposed method reduces the response time and increases overload.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A PSO‐based task scheduling algorithm improved using a load‐balancing technique for the cloud computing environment

Ebadifard

Babamir

2017

Concurrency and Computation

126

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Summary Dynamic on‐demand resource provisioning is one of the primary goals of the cloud computing task scheduling process. Task scheduling is a nondeterministic polynomial time (NP)‐hard problem and is responsible for assigning tasks to virtual machines (VMs) in a way that increases the resource utilization and performance, reduces response time, and keeps the whole system balanced. In this paper, we present a static task scheduling method based on the particle swarm optimization (PSO) algorithm where the tasks are assumed to be non‐preemptive and independent. We have improved the performance of the basic PSO method using a load‐balancing technique. We have compared our proposed method with round robin (RR) task scheduling, improved PSO task scheduling and a load‐balancing technique. The simulation results show that our method outperforms these algorithms by an increase of resource utilization of 22% and a decrease of makespan by 33%, compared with the basic PSO algorithm. The results illustrate that our proposed method converges to the near optimal solution faster than the basic PSO algorithm and is more efficacious with more tasks.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A PSO‐based task scheduling algorithm improved using a load‐balancing technique for the cloud computing environment

Ebadifard

Babamir

2017

Concurrency and Computation

126

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“…• Scalability: the ability of the system to accomplish the load balancing algorithm, taking into consideration the number of machines or hosts [43], [45].…”

Section: The Performance Metrics Measurementsmentioning

confidence: 99%

Load Balancing and Server Consolidation in Cloud Computing Environments: A Meta-Study

Ala’anzy

Othman

2019

IEEE Access

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The data-center is considered the heart of cloud computing. Recently, the growing demand for cloud computing services has caused a growing load on data centers. In terms of system behavior and workload, patterns of cloud computing are very dynamic; and that might serve to imbalance the load among data center resources. Eventually, some data-center resources could come to be over-loaded/under-loaded, which leads to an increase in energy consumption in addition to decreased functioning and wastage of resources. Just considering energy-efficiency (that can be attained efficiently by consolidate the servers) may not be enough for real applications because it may cause problems such as unbalanced load for each Physical Machine (PM). Therefore, this paper surveys published load balancing algorithms that achieved by server consolidation via a meta-analysis. Load balancing with server consolidation enriches the exploitation of resource utilization and can enhance Quality of Service (QoS) metrics, since data-centers and their applications are increasing exponentially. This meta-study, reviews the literature on load balancing and server consolidation and presents a ready reference taxonomy on the most efficient algorithms that achieve load balancing and server consolidation. This work attempts to present a taxonomy with a new classification for load balancing and server consolidation, such as migration overhead, hardware threshold, network traffic, and reliability.

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“…But it is about homogenous virtual machines. Nakai et al proposed server based load balancing policy. It uses some rules for permanent policy.…”

Section: Related Workmentioning

confidence: 99%

Magnum opus of an efficient hospitality technique for load balancing in cloud environment

Sakthivelmurugan

Vimala

Britto

2018

Concurrency and Computation

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Summary The load balancing approach in the cloud environment is an open issue in the recent days. It helps obtain better resource utilization in cloud environment. It also improves the performance of the system. A good load balancing algorithm in cloud ensures no under loading or over loading in a single host. This paper proposes an algorithm named Hospitality Load Balancing (HLB) algorithm, which aims to balance the load among virtual machines (VM) for increasing the performance and throughput. The proposed algorithm handles preemptive or nonpreemptive tasks, which may be independent. It helps to reduce make span and obtain low task migration. The HLB algorithm has been analyzed and compared with plenty of load balancing technique. An experimental result shows that it is an excellent and effective algorithm when compared with the existing load balancing algorithm. It also illustrates the performance of proposed load balancing scheme with analysis and proves that it attains low makespan and low task migration.

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On the Use of Resource Reservation for Web Services Load Balancing

Cited by 18 publications

References 36 publications

A PSO‐based task scheduling algorithm improved using a load‐balancing technique for the cloud computing environment

A PSO‐based task scheduling algorithm improved using a load‐balancing technique for the cloud computing environment

Load Balancing and Server Consolidation in Cloud Computing Environments: A Meta-Study

Magnum opus of an efficient hospitality technique for load balancing in cloud environment

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