A survey on virtual machine scheduling in cloud computing

Liu, Li; Qiu, Zhe

doi:10.1109/compcomm.2016.7925192

Cited by 27 publications

(10 citation statements)

References 19 publications

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“…Previous studies on the problem of VM-PM placement were mainly intended to optimize the utilization of PMs in IaaS cloud infrastructure and to maintain certain Quality of Service (QoS) requirements of the applications submitted to IaaS clouds. Much of this research was conducted to investigate VM placement and provisioning on PMs, including static and dynamic scheduling, and optimal PM utilization was their main objective [9][10][11].…”

Section: Placement Of Vms On Pms (Vm-pm)mentioning

confidence: 99%

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A placement architecture for a container as a service (CaaS) in a cloud environment

2019

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Unlike a traditional virtual machine (VM), a container is an emerging lightweight virtualization technology that operates at the operating system level to encapsulate a task and its library dependencies for execution. The Container as a Service (CaaS) strategy is gaining in popularity and is likely to become a prominent type of cloud service model. Placing container instances on virtual machine instances is a classical scheduling problem. Previous research has focused separately on either virtual machine placement on physical machines (PMs) or container, or only tasks without containerization, placement on virtual machines. However, this approach leads to underutilized or overutilized PMs as well as underutilized or overutilized VMs. Thus, there is a growing research interest in developing a container placement algorithm that considers the utilization of both instantiated VMs and used PMs simultaneously. The goal of this study is to improve resource utilization, in terms of number of CPU cores and memory size for both VMs and PMs, and to minimize the number of instantiated VMs and active PMs in a cloud environment. The proposed placement architecture employs scheduling heuristics, namely, Best Fit (BF) and Max Fit (MF), based on a fitness function that simultaneously evaluates the remaining resource waste of both PMs and VMs. In addition, another meta-heuristic placement algorithm is proposed that uses Ant Colony Optimization based on Best Fit (ACO-BF) with the proposed fitness function. Experimental results show that the proposed ACO-BF placement algorithm outperforms the BF and MF heuristics and maintains significant improvement of the resource utilization of both VMs and PMs.

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Section: Placement Of Vms On Pms (Vm-pm)mentioning

confidence: 99%

“…The placement algorithms should maximize the resource utilization on represent the number of cores available and the amount of memory on vm ij after assigning the requested virtual resources, respectively, and they are calculated using Eqs. (10) and (11…”

Section: Problem Formulationmentioning

confidence: 99%

A placement architecture for a container as a service (CaaS) in a cloud environment

2019

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“…27 In the literature, lots of work were conducted to improve the scheduler in the context of containerization. 27 In the literature, lots of work were conducted to improve the scheduler in the context of containerization.…”

Section: Related Workmentioning

confidence: 99%

“…The optimization problem of deploying a task on a cluster was originally studied for virtual machines (VMs). 27 In the literature, lots of work were conducted to improve the scheduler in the context of containerization.…”

Section: Related Workmentioning

confidence: 99%

Optimizing scheduling decisions of container management tool using many‐objective genetic algorithm

Imdoukh

Ahmad

Alfailakawi

2019

Concurrency and Computation

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Container virtualization is an emerging technology in cloud computing mainly due to its portability and lightweight features. Scheduling is a key task, performed by container management tool, which indirectly affects the characteristics of distributed software system in terms of availability, realizability, scalability, resources utilization, as well as power consumption. However, current schedulers only focus on some of the aforementioned aspects but not all. In this paper, a Many-Objective Genetic Algorithm Scheduler (MOGAS) is proposed to handle all such objectives to realize solutions with better characteristics. The proposed scheduler is compared with the Ant Colony Optimization (ACO)-based scheduler. Based on the proposed objective functions, simulation results show that MOGAS is better than the ACO scheduler in equally distributing tasks by 50%, assigning unique set of tasks per node by 40%, and reducing power consumption by 7%, on average.

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“…A job, which exhibits low parallelism and needs low computational power and it is given a low priority [10], let take the following Example: Min-Min Scheduling Algorithm is the most simple and well known algorithm for efficient job scheduling .The algorithm first calculates the expected execution time of tasks, and then scheduling the task with the minimum execution time to the corresponding VMs [11]. In other words Min-Min scheduling algorithm work on determining the minimum completion time for each task [6]. Advantage of Min-Min Scheduling Algorithm is Applicable in small scale distributed systems.…”

Section: B Shortest Job First Schedulingmentioning

confidence: 99%

Study and Comparison of VM Scheduling Algorithm in Cloud Computing Using CloudSim Simulator

Joshi¹

2018

IJRASET

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Scheduling in cloud computing is a technique which is used to improve the overall execution time of the job. A good scheduling algorithm can help in load balancing as well. Scheduling in cloud can be done in three areas i.e. Scheduling Cloudlets (where Cloudlet is small scale data center that is located at the edge of Internet) within the virtual machine, Scheduling Virtual Machine on the host, and scheduling cloudlets to the Virtual Machine. There are used CloudSim Simulator which is control environment in which programmer can run application instead of practically running it. This paper presents Different VM Scheduling algorithm using CloudSim and trying to find out best algorithm.

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A survey on virtual machine scheduling in cloud computing

Cited by 27 publications

References 19 publications

A placement architecture for a container as a service (CaaS) in a cloud environment

A placement architecture for a container as a service (CaaS) in a cloud environment

Optimizing scheduling decisions of container management tool using many‐objective genetic algorithm

Study and Comparison of VM Scheduling Algorithm in Cloud Computing Using CloudSim Simulator

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