Virtual machine placement for elastic infrastructures in overbooked cloud computing datacenters under uncertainty

Lopez-Pires, Fabio; Barán, Benjamín; Benítez, Leonardo; Zalimben, Saúl; Amarilla, Augusto

doi:10.1016/j.future.2017.09.021

Cited by 31 publications

(25 citation statements)

References 19 publications

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“…Due to the elastic nature of cloud computing systems, dynamic resource management is a difficult problem for resource providers. Many studies [7,[16][17][18][19] resolved this problem with auto scaling methods. Most of the current providers [27,30] such as Amazon and Rightscale use horizontal scaling method.…”

Section: Related Workmentioning

confidence: 99%

“…To evaluate the efficiency of the proposed MIMO control based on RBF neural network approach, and according to the comparison in Section 2 (Related Works), we compare a horizontal scaling approach [7], a vertical scaling approach based on control theory [5], and the proposed approach. Hereafter, we call the horizontal approach "Horizontal" for short, the vertical scaling approach based on control theory "SISO" for short, and the proposed approach "MIMO" for short.…”

Section: Cpu Resource Utilization Ratementioning

confidence: 99%

“…Email: gongsiqian@buaa.edu.cn essential to maximize resource utilization and maintain a high QoS according to the dynamic workload. Existing solutions [7][8][9][10] have typically only focused on one objective, for example, maximizing resource utilization rate or ensuring a high QoS. However, these two objectives are conflict some times, and focusing on a single objective cannot address the reasonable trade-off between a high QoS and effective resource utilization, especially in dynamic workload situations.…”

Section: Introductionmentioning

confidence: 99%

“…two scaling architectures, that is, horizontal scaling and vertical scaling, are used to dynamically adjust the resource allocation. Horizontal scaling methods [7,[16][17][18][19] perform dynamic resource allocation by changing the number of virtual machines (VMs) for services. In such methods, one VM is treated as a scaling unit.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Adaptive Control Method for Resource Provisioning with Resource Utilization Constraints in Cloud Computing

Gong¹,

Yin²,

Zhang³

et al. 2019

IJCIS

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A B S T R A C TCloud computing enables users to purchase virtual resources on demand; therefore, the service requests change over time. Dynamic resource provisioning for cloud computing has become a key challenge. To reduce the associated costs, resource utilization must be improved, and ensure the Quality of Service (QoS) to meet the service-level agreements (SLAs). However, it is difficult to improve the resource utilization level and maintain a high QoS with fluctuating workloads in shared cloud computing systems. In this paper, we propose an adaptive control method for resource provisioning in cloud computing systems to simultaneously improve the resource utilization, achieve a satisfactory QoS, and react to the dynamic workload. We proposed an approach integrating adaptive multi-input and multi-output (MIMO) control and radial basis function (RBF) neural network to react to the highly dynamic workloads, and precisely control the resource allocation to improve resource utilization based on the maximum allowable QoS requirements. Experiments based on real-world workloads show that the proposed approach jointly improves resource utilization, maintains a satisfactory QoS, and handles workload fluctuations in a coordinated manner.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Cpu Resource Utilization Ratementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Adaptive Control Method for Resource Provisioning with Resource Utilization Constraints in Cloud Computing

Gong¹,

Yin²,

Zhang³

et al. 2019

IJCIS

View full text Add to dashboard Cite

show abstract

“…Like [24] here also authors have not taken into account elasticity at all. Another two phase approached for VMP proposed by Fabio et al, [27] considers complex IaaS environment including elasticity but did not consider the intercommunication patterns while placing the additional VM of an elastic requests. Mishra et al, [28] proposed algorithm to map the task onto VM and the VMs to PM.…”

mentioning

confidence: 99%

Placement Strategy for Intercommunicating Tasks of an Elastic Request in Fog-Cloud Environment

Sridharan

Domnic

2019

SCPE

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Cloud computing hosts large number of modern day applications using the virtualization concept. However, end-to-end network latencies are detrimental to the performance of IoT (Internet of Things) applications like video surveillance and health monitoring. Although edge/fog computing alleviates the latency concerns to some extent, it is still not suitable for applications having intercommunicating tasks. Further, these applications can be elastic in nature and demand more tasks during their life-time. To address this gap, in this paper a network aware co-allocation strategy for the tasks of an individual applications is proposed. After modelling the problem using bin packing approach with additional constraints, the authors propose a novel heuristic IcAPER,(Inter-communication Aware Placement for Elastic Requests) algorithm. The proposed algorithm uses the network neighborhood machine for placement, once current resource is fully utilized by the application. Using CloudsimPlus simulator the performance IcAPER algorithm is compared with First Come First Serve (FCFS), Random and First Fit Decreasing (FFD) algorithms for the parameters (a) resource utilization (b) resource fragmentation and (c) Number of requests having intercommunicating tasks placed on to same PM. Extensive simulation results shows IcAPER maps 34% more tasks on to the same PM and also increase the resource utilization by 13% while decreasing the resource fragmentation by 37.8% when compared to other algorithms in our consideration.

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Nature‐inspired virtual machine placement mechanisms: A systematic review

Kong

He²,

Abnoosian

2022

Concurrency and Computation

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Cloud data centers do not completely use their resources, resulting in resource underutilization. Cloud computing companies primarily leverage virtualization technologies to supply cost-effective service provision. In order to optimize cloud performance, virtual machines (VMs) must be placed among physical machines (PMs). When it comes to concentrating on the issues in the cloud computing environment, effective VM placement (VMP) is one of the primary difficulties that might cost suppliers money. VMP may be applied in a variety of ways in cloud computing. In terms of lowering related processing overhead, consolidating the cloud environment to become a highly on-demand method, balancing the load among PMs, power usage, and refining performance, VMP techniques still require improvement in the computing environment. This study aims to provide a comprehensive overview of VMP approaches. This article provides an up-to-date survey of the most related VMP literature to highlight study possibilities in cloud settings utilizing nature-inspired metaheuristic algorithms. The findings suggest that placing VMs in the most efficient place saves power usage substantially. The key problem is to minimize data center energy usage without compromising performance or breaking service level agreements. Finally, we will discuss and look at what further may be accomplished in this line of science.

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Virtual machine placement for elastic infrastructures in overbooked cloud computing datacenters under uncertainty

Cited by 31 publications

References 19 publications

An Adaptive Control Method for Resource Provisioning with Resource Utilization Constraints in Cloud Computing

An Adaptive Control Method for Resource Provisioning with Resource Utilization Constraints in Cloud Computing

Placement Strategy for Intercommunicating Tasks of an Elastic Request in Fog-Cloud Environment

Nature‐inspired virtual machine placement mechanisms: A systematic review

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