Dynamic optimization of virtual machine placement by resource usage prediction

Sato, Katsunori; Samejima, Masaki; Komoda, Norihisa

doi:10.1109/indin.2013.6622863

Cited by 20 publications

(12 citation statements)

References 12 publications

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“…The result shows that the efficiency will become higher and the throughput capacity will be improved greatly. Katsunori Sato [9] proposed an idea of monitoring the running situation of virtual memory according to the real-time allocation situation of memory. They implemented a tool named VMMB(Virtual Machine Monitor Balancer)with a new mechanism of balance every memory state of virtual machine.…”

Section: Research Of Memory Workloadmentioning

confidence: 99%

“…Page reuse technology which is researched in [9,20] and dynamic adjusting memory mechanism in literature [3] has provided the basic support method, but they can not solve the problem of when and how to allocate the memory space. Literature [21] and literature [22] are absent of the global management system frame and assisted scheduling methods.…”

Section: Classification Of Memory Balancing Technologymentioning

confidence: 99%

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A Memory Configuration Method for Virtual Machine Based on User Preference in Distributed Cloud

2018

TIIS

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It is well-known that virtualization technology can bring many benefits not only to users but also to service providers. From the view of system security and resource utility, higher resource sharing degree and higher system reliability can be obtained by the introduction of virtualization technology in distributed cloud. The small size time-sharing multiplexing technology which is based on virtual machine in distributed cloud platform can enhance the resource utilization effectively by server consolidation. In this paper, the concept of memory block and user satisfaction is redefined combined with user requirements. According to the unbalanced memory resource states and user preference requirements in multi-virtual machine environments, a model of proper memory resource allocation is proposed combined with memory block and user satisfaction, and at the same time a memory optimization allocation algorithm is proposed which is based on virtual memory block, makespan and user satisfaction under the premise of an orderly physical nodes states also. In the algorithm, a memory optimal problem can be transformed into a resource workload balance problem. All the virtual machine tasks are simulated in Cloudsim platform. And the experimental results show that the problem of virtual machine memory resource allocation can be solved flexibly and efficiently.

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Section: Research Of Memory Workloadmentioning

confidence: 99%

Section: Classification Of Memory Balancing Technologymentioning

confidence: 99%

A Memory Configuration Method for Virtual Machine Based on User Preference in Distributed Cloud

2018

TIIS

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“…Solving a multi-objective optimization problem as monoobjective and considering the other objective functions as constraints of the problem is also a studied method in the VMP literature [20], [42]. Kord et al proposed a two-objective approach and made a trade-off between these two goals using a fuzzy Analytic Hierarchy Process (AHP) [29].…”

Section: B Multi-objective Solved As Mono-objective Approachmentioning

confidence: 99%

A Virtual Machine Placement Taxonomy

Pires¹,

Barán²

2015

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

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Cloud computing datacenters dynamically provide millions of virtual machines (VMs) in actual cloud markets. In this context, Virtual Machine Placement (VMP) is one of the most challenging problems in cloud infrastructure management, considering the large number of possible optimization criteria and different formulations that could be studied. VMP literature include relevant research topics such as energy efficiency, Service Level Agreement (SLA), Quality of Service (QoS), cloud service pricing schemes and carbon dioxide emissions; all of them with high economical and ecological impact. This work classifies an extensive up-to-date survey of the most relevant VMP literature proposing a novel taxonomy in order to identify research opportunities and define a general vision on this research area.

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“…Based on fluctuations in network performance (latency), they propose a method based on network flow maximization to estimate VM migration cost by amortizing it to the latency of every access. Song et al [30] define a bin packing approach to allocation and migration of VMs in data centers and similarly, Sato et al [28] combine bin packing with resource usage prediction to dynamically optimize VM placement. Autoregressive models are used for predictions and the number of VM migrations is minimized (avoiding ping-pong effects) based on the predictions.…”

Section: Related Workmentioning

confidence: 99%

Continuous Datacenter Consolidation

Svärd

Wadbro

et al. 2015

2015 IEEE 7th International Conference on Cloud Computing Technology and Science (CloudCom)

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Abstract-Efficient mapping of Virtual Machines (VMs) onto physical servers is a key problem for cloud infrastructure providers as hardware utilization directly impacts revenue. Today, this mapping is commonly only performed when new VMs are created, but as VM workloads fluctuate and server availability varies, any initial mapping is bound to become suboptimal over time. We introduce a set of heuristic methods for continuous optimization of the VM-to-server mapping based on combinations of fundamental management actions, namely suspending and resuming physical machines, migrating VMs, and suspending and resuming VMs. Using these methods cloud infrastructure providers can continuously optimize their server resources regardless of the predictability of the workload. To verify that our approach is applicable in real-world scenarios, we build a proof-of-concept datacenter management system that implements the proposed algorithms. The feasibility of our approach is evaluated through a combination of simulations and real experiments where our system provisions a workload of benchmark applications. Our results indicate that the proposed algorithms are feasible, that the combined management approach achieves the best results, and that the VM suspend and resume mechanism has the largest impact.

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Dynamic optimization of virtual machine placement by resource usage prediction

Cited by 20 publications

References 12 publications

A Memory Configuration Method for Virtual Machine Based on User Preference in Distributed Cloud

A Memory Configuration Method for Virtual Machine Based on User Preference in Distributed Cloud

A Virtual Machine Placement Taxonomy

Continuous Datacenter Consolidation

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