Workload characteristics-aware virtual machine consolidation algorithms

Yang, Jyun-Shiung; Liu, Pangfeng; Wu, Jan‐Jan

doi:10.1109/cloudcom.2012.6427540

Cited by 20 publications

(22 citation statements)

References 12 publications

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“…In [34], it was emphasized that, VMs can be broadly classified as data intensive or CPU intensive based on their respective workloads. For this new framework, the BPP was modified, and suitable approximation schemes were presented.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Model for Load Balancing and Server Consolidation in Cloud

Megharaj¹,

Kabadi²

2019

IJIES

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One of the most pressing and critical issues in cloud is Power Optimization. Due to the popularity of cloud, many computing applications are being hosted in the cloud. Naturally, many Cloud Centers (CCs) are experiencing huge power consumption problem, which leads to higher operational cost and environmental hazards due to carbon emissions. The two important concepts used to achieve power optimization are: load balancing and server consolidation. The first concept aims to achieve fair distribution of computing load on different Physical Machine (PM); whereas, the second concept aims to shutdown PMs, which are having limited computational load. One of the most popular techniques to achieve load balancing and server consolidation is Virtual Machine (VM) migration technique; where, the VMs are packed inside limited PMs; such that, load is fairly balanced among PMs, and limited usage PMs are relieved of their computational load and can be shutdown. However, VM migration requires excessive cost, and results in excessive computed task wastage. Hence, in this work, a new hybrid scheme which does not migrate VMs, and migrates only suitable tasks from overloaded VMs and lightly loaded PMs is presented. The proposed hybrid model is compared against contemporary VM migration technique to assess resource usage merits. The proposed hybrid model significantly outperforms the contemporary VM migration techniques in resource usage efficiency.

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Section: Related Workmentioning

confidence: 99%

Hybrid Model for Load Balancing and Server Consolidation in Cloud

Megharaj¹,

Kabadi²

2019

IJIES

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“…But it only adapts to unchanged workload. In [16], the authors addressed that an energy conservation algorithm must consider the workload characteristics of VMs. Different workload will affect the CPU utilization because of its task types.…”

Section: Introductionmentioning

confidence: 99%

Towards optimal CPU frequency and different workload for multi-objective VM allocation

Liu

Xiang

2015

2015 12th Annual IEEE Consumer Communications and Networking Conference (CCNC)

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In the problem of VMs consolidation for cloud energy saving, workload characteristics should be considered to make a more reasonable solution for VM placement. Different workload works in a varied CPU utilization during its work time according to its task characteristics. However, there are many works that evaluate energy consumption have the basic assumption that the CPU works on 100% full load. In this paper, we have theoretically verified that there will be a CPU frequency best suited for a certain CPU utilization that can make the minimum energy consumption. According to this deduction, We put forward a CPU frequency scaling algorithm VP-FS(Virtual machine Placement with Frequency Scaling). We simulate three groups of VM tasks. We also design and implement three typical greedy algorithms for VMs placement. We then carry the experiments using these four algorithms to allocate the three groups of VMs respectively. Our efforts show that different workloads will affect VMs allocation results. Each group of workload has its most suitable algorithm when considering the minimum used number of physical machines. And because of the CPU frequency scaling, VP-FS has the best results on the total energy consumption compared with the other three algorithms under any of the three groups of workloads.Index Terms-cloud datacenter; CPU frequency scaling; energy consumption; workload awareness 978-1-4799-6390-4/15/$31.00 ©2015 IEEE

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“…But it only adapts to unchanged workload. In [22], the authors addressed the idea that an energy conservation algorithm must consider the workload characteristics of virtual machines. The typical or random workload will actually affect the final energy consumption.…”

Section: Introductionmentioning

confidence: 99%

“…However, its assumption is when the CPU operates on full load and the CPU utilization is 100%. Paper [22] has addressed the idea that the different workload will affect the CPU utilization because of its task kinds and algorithms. For example, some workload is CPU intensive while others are data intensive or I/O intensive.…”

Section: Introductionmentioning

confidence: 99%

Workload-Aware and CPU Frequency Scaling for Optimal Energy Consumption in VM Allocation

Liu¹,

Xiang²,

Qu³

2014

Mathematical Problems in Engineering

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In the problem of VMs consolidation for cloud energy saving, different workloads will ask for different resources. Thus, considering workload characteristic, the VM placement solution will be more reasonable. In the real world, different workload works in a varied CPU utilization during its work time according to its task characteristics. That means energy consumption related to both the CPU utilization and CPU frequency. Therefore, only using the model of CPU frequency to evaluate energy consumption is insufficient. This paper theoretically verified that there will be a CPU frequency best suit for a certain CPU utilization in order to obtain the minimum energy consumption. According to this deduction, we put forward a heuristic CPU frequency scaling algorithm VP-FS (virtual machine placement with frequency scaling). In order to carry the experiments, we realized three typical greedy algorithms for VMs placement and simulate three groups of VM tasks. Our efforts show that different workloads will affect VMs allocation results. Each group of workload has its most suitable algorithm when considering the minimum used physical machines. And because of the CPU frequency scaling, VP-FS has the best results on the total energy consumption compared with the other three algorithms under any of the three groups of workloads.

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Workload characteristics-aware virtual machine consolidation algorithms

Cited by 20 publications

References 12 publications

Hybrid Model for Load Balancing and Server Consolidation in Cloud

Hybrid Model for Load Balancing and Server Consolidation in Cloud

Towards optimal CPU frequency and different workload for multi-objective VM allocation

Workload-Aware and CPU Frequency Scaling for Optimal Energy Consumption in VM Allocation

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