Online Virtual Machine Placement for Increasing Cloud Provider’s Revenue

Zhao, Laiping; Lu, Liangfu; Zhou, Jin; Yu, Ce

doi:10.1109/tsc.2015.2447550

Cited by 35 publications

(12 citation statements)

References 35 publications

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“…Ding et al [ 17 ] proposed a Linux scheduling policy with a priority queue rather than a first-in-first-out (FIFO) queue to improve kernel-based virtual machine performance. Zhao et al [ 18 ] proposed online VM placement algorithms to cost efficiently allocate resources to VMs to increase revenues in a managed server farm. First-fit (FF), FF migration (FFM), least reliable first (LRF), and decreased density greedy (DDG) algorithms are packing strategies relevant to task optimization for achieving desirable performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization-Based Resource Management Algorithms with Considerations of Client Satisfaction and High Availability in Elastic 5G Network Slices

Hsiao

Lin

Fang

et al. 2021

Sensors

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A combined edge and core cloud computing environment is a novel solution in 5G network slices. The clients’ high availability requirement is a challenge because it limits the possible admission control in front of the edge cloud. This work proposes an orchestrator with a mathematical programming model in a global viewpoint to solve resource management problems and satisfying the clients’ high availability requirements. The proposed Lagrangian relaxation-based approach is adopted to solve the problems at a near-optimal level for increasing the system revenue. A promising and straightforward resource management approach and several experimental cases are used to evaluate the efficiency and effectiveness. Preliminary results are presented as performance evaluations to verify the proposed approach’s suitability for edge and core cloud computing environments. The proposed orchestrator significantly enables the network slicing services and efficiently enhances the clients’ satisfaction of high availability.

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Section: Literature Reviewmentioning

confidence: 99%

Optimization-Based Resource Management Algorithms with Considerations of Client Satisfaction and High Availability in Elastic 5G Network Slices

Hsiao

Lin

Fang

et al. 2021

Sensors

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“…In [11], designs an online VMP algorithm to increase cloud providers revenue for the multi dimensional resources.…”

Section: Related Workmentioning

confidence: 99%

Multi-Core Aware Virtual Machine Placement for IaaS Cloud DataCenters

S¹,

Raja²

2020

International Journal of Engineering Research and Technology

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Cloud computing provides elastic computational resources that have been widely deployed in data centres to provide Infrastructure as a Service (IaaS). Virtualization is a key technology in cloud computing for resource sharing. In the virtualization technology the most challenging issue is Virtual Machine Placement Problem (VMPP). Virtual Machine Placement is a significant process executed as a branch of VM migration, which involves placing a Virtual Machine (VM) on a suitable physical machine (PM) in order to improve the resource utilization efficiently. Many existing VMP algorithms considered the overall CPU capacity of a PM, without considering the number of cores available in a PM and core-CPU capacity of a PM. i.e. VMs are mapped onto PMs, if the CPU capacity of the VM is less than or equal to the total CPU capacity of a PM. Such an allocation results in core overload which leads to performance degradation and violation of Service Level Agreement. However, in the real scenario, PM and VM consists of multiple cores. So, to place VM on PM, the core-CPU capacity of a VM must be mapped to a core-CPU capacity of a PM. i.e., the core of a VM should be mapped to a core of a PM. In this paper, we have proposed a Multi-Core Aware Virtual Machine Placement Algorithm (MCA-VMP). In MCA-VMP, the number of cores available in a PM and core-CPU capacity of a PM is considered instead of total CPU capacity of a PM. We used, Google Cluster Traces to generate the virtual machine configurations. Based on Google Cluster Traces the dataset is generated. Monte Carlo Simulation method is used to produce Google Cloud Jobs (GoCJ). Our simulation results shows that MCA-VMP is efficient than traditional non-Core Aware VMP algorithms. Our proposed algorithm MCA-VMP improves the performance of a DataCenter in terms of resource utilization, PM overload and resource wastage.

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“…Zhao et al [18] proposed two algorithms of VM placement, Least-Reliable-First (LRF) and Decreased-Density-Greedy (DDG) to increase cloud provider's revenue by reducing the cost of SLA violation. Algorithms have produced results on the revenue of cloud providers under different conditions.…”

Section: Related Workmentioning

confidence: 99%

Intra-Balance Virtual Machine Placement for Effective Reduction in Energy Consumption and SLA Violation

2019

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Cloud computing has emerged as one of the most important technological revolutions globally. However, the rapid growth of cloud computing has imposed a massive financial burden and resulted in environmental side effects due to excessive energy consumption. The high power consumption is not only attributed to the size of data centers but also to the ineptitude of resource usage. Most of the extant research has focused on reducing power consumption by an aggressive VM consolidation, which leads to the violation of the service level agreement (SLA). Furthermore, the unbalanced resource consumption exacerbates the unavailable wasted resources that are referred to as unavailable resource fragmentation. In this paper, we propose the use of a balanced resources consumption algorithm called BRC-IBMMT in order to enhance the efficiency of resource consumption while achieving an acceptable balance between conflicting correlation objectives of power consumption as well as SLA violation. The extensive simulation results of different types of workload validate and lend credence to the significance of the proposed method in reducing power consumption and SLA violation of the cloud data center. INDEX TERMS Balanced resource placement, VM consolidation, energy consumption, cloud computing.

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Online Virtual Machine Placement for Increasing Cloud Provider’s Revenue

Cited by 35 publications

References 35 publications

Optimization-Based Resource Management Algorithms with Considerations of Client Satisfaction and High Availability in Elastic 5G Network Slices

Optimization-Based Resource Management Algorithms with Considerations of Client Satisfaction and High Availability in Elastic 5G Network Slices

Multi-Core Aware Virtual Machine Placement for IaaS Cloud DataCenters

Intra-Balance Virtual Machine Placement for Effective Reduction in Energy Consumption and SLA Violation

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