Cloud light weight: A new solution for load balancing in cloud computing

Mesbahi, Mohammadreza; Rahmani, Amir Masoud; Chronopoulos, Anthony T.

doi:10.1109/icdse.2014.6974610

Cited by 16 publications

(10 citation statements)

References 18 publications

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“…This assigns weights to each virtual machine (VM) according to the load indicators and indicators of neural networks, dynamically adjusting each VM weight to meet the service level agreement (SLA). Another paper proposed a load-balancing algorithm to balance VM loads and ensure quality of service (QoS) [25]. It reduced the number of VM migrations and migration times during task execution.…”

Section: Focus On System Performancementioning

confidence: 99%

A Multiqueue Interlacing Peak Scheduling Method Based on Tasks’ Classification in Cloud Computing

Zuo

Dong

Shu

et al. 2018

IEEE Systems Journal

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Abstract-In cloud computing, resources are dynamic, and the demands placed on the resources allocated to a particular task are diverse. These factors could lead to load imbalances, which affect scheduling efficiency and resource utilization. A scheduling method called interlacing peak is proposed. First, the resource load information, such as CPU, I/O, and memory usage, is periodically collected and updated, and the task information regarding CPU, I/O, and memory is collected. Second, resources are sorted into three queues according to the loads of the CPU, I/O, and memory: CPU intensive, I/O intensive, and memory intensive, according to their demands for resources. Finally, once the tasks have been scheduled, they need to interlace the resource load peak. Some types of tasks need to be matched with the resources whose loads correspond to a lighter types of tasks. In other words, CPUintensive tasks should be matched with resources with low CPU utilization; I/O-intensive tasks should be matched with resources with shorter I/O wait times; and memory-intensive tasks should be matched with resources that have low memory usage. The effectiveness of this method is proved from the theoretical point of view. It has also been proven to be less complex in regard to time and place. Four experiments were designed to verify the performance of this method. Experiments leverage four metrics: 1) average response time; 2) load balancing; 3) deadline violation rates; and 4) resource utilization. The experimental results show that this method can balance loads and improve the effects of resource allocation and utilization effectively. This is especially true when resources are limited. In this way, many tasks will compete for the same resources. However, this method shows advantage over other similar standard algorithms.

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Section: Focus On System Performancementioning

confidence: 99%

A Multiqueue Interlacing Peak Scheduling Method Based on Tasks’ Classification in Cloud Computing

Zuo

Dong

Shu

et al. 2018

IEEE Systems Journal

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“…In cloud light weight policy, which not only balances the virtual machine work load in cloud computing datacenters, but it also assures QoS for users. It reduces both the number of VM migration processes and the migration time during applications execution [10].…”

Section: Related Workmentioning

confidence: 99%

Service Request Scheduling based on Quantification Principle using Conjoint Analysis and Z-score in Cloud

Rajan¹

2018

IJECE

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Service request scheduling has a major impact on the performance of the service processing design in a large-scale distributed computing environment like cloud systems. It is desirable to have a service request scheduling principle that evenly distributes the workload among the servers, according to their capacities. The capacities of the servers are termed high or low relative to one another. Therefore, there is a need to quantify the server capacity to overcome this subjective assessment. Subsequently, a method to split and distribute the service requests based on this quantified server capacity is also needed. The novelty of this research paper is to address these requirements by devising a service request scheduling principle for a heterogeneous distributed system using appropriate statistical methods, namely Conjoint analysis and Z-score. Suitable experiments were conducted and the experimental results show considerable improvement in the performance of the designed service request scheduling principle compared to a few other existing principles. Areas of further improvement have also been identified and presented.

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“…Cloud computing data centers offer thousands of physical servers networked via high bandwidth network infrastructures that communicate with one another to provide highly available and flexible services. 2 Although cloud computing services suggest many benefits, there are also many issues and open research problems to provide these services such as load balancing solutions, [3][4][5][6][7] security challenges, [8][9][10][11] task scheduling, [12][13][14] and high availability/reliability challenges. [15][16][17][18][19] The large-scale heterogeneity nature of cloud services leads to frequent failures in these systems.…”

Section: Introductionmentioning

confidence: 99%

Dependability analysis for characterizing Google cluster reliability

Mesbahi

Rahmani

Hosseinzadeh

2019

Int J Communication

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Cloud solutions are emerging as a new suitable way of transforming traditional IT data centers to highly available and reliable computing resources for hosting critical applications and data. However, software and hardware failures are a common problem in cloud datacenters that can lead to harmful damages. In this paper, we analyze the physical server failures in the Google cloud datacenter. We study the Google cluster properties to investigate the relationship among physical servers' failure rate and jobs failure events. The failure rate of Google cluster executed jobs and servers is taken into consideration during a 29-day period. We present a reliability model for Google cluster physical machines using the continuous time Markov chains according to this observation. We attempt to analyze the obtained model through SHARPE software packages to improve the understanding of failure events in the Google cloud cluster. We also explore the cluster availability based on parameters like steady-state availability, steady-state unavailability, mean time to failure, and mean time to repair in the Google cluster.

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Cloud light weight: A new solution for load balancing in cloud computing

Cited by 16 publications

References 18 publications

A Multiqueue Interlacing Peak Scheduling Method Based on Tasks’ Classification in Cloud Computing

A Multiqueue Interlacing Peak Scheduling Method Based on Tasks’ Classification in Cloud Computing

Service Request Scheduling based on Quantification Principle using Conjoint Analysis and Z-score in Cloud

Dependability analysis for characterizing Google cluster reliability

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