Statistical static capacity management in virtualized data centers supporting fine grained QoS specification

Hoyer, Marko; Schröder, Kiril; Nebel, Wolfgang

doi:10.1145/1791314.1791322

Cited by 16 publications

(21 citation statements)

References 16 publications

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“…Similarly Borgetto et al [2012a] use linear program modeling, taking into account some SLA for jobs, and they propose vector packing heuristics to solve it. In Hoyer et al [2010], statistical allocation planning is proposed through two methods. The first approach allocates pessimistically to each job the maximum resource ratio it might need, developing an allocation directed by vector packing.…”

Section: Vm Placementmentioning

confidence: 99%

Cloud Computing

Mastelić

Oleksiak

Claußen³

et al. 2014

ACM Comput. Surv.

231

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Cloud computing is today's most emphasized Information and Communications Technology (ICT) paradigm that is directly or indirectly used by almost every online user. However, such great significance comes with the support of a great infrastructure that includes large data centers comprising thousands of server units and other supporting equipment. Their share in power consumption generates between 1.1% and 1.5% of the total electricity use worldwide and is projected to rise even more. Such alarming numbers demand rethinking the energy efficiency of such infrastructures. However, before making any changes to infrastructure, an analysis of the current status is required. In this article, we perform a comprehensive analysis of an infrastructure supporting the cloud computing paradigm with regards to energy efficiency. First, we define a systematic approach for analyzing the energy efficiency of most important data center domains, including server and network equipment, as well as cloud management systems and appliances consisting of a software utilized by end users. Second, we utilize this approach for analyzing available scientific and industrial literature on state-of-the-art practices in data centers and their equipment. Finally, we extract existing challenges and highlight future research directions.

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Section: Vm Placementmentioning

confidence: 99%

Cloud Computing

Mastelić

Oleksiak

Claußen³

et al. 2014

ACM Comput. Surv.

231

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“…These efforts can be divided into two categories: static VM consolidation and dynamic VM consolidation. In [2], researchers proposed a statistical static capacity management in virtualized data centers that can support fine grained QoS.…”

Section: Related Workmentioning

confidence: 99%

“…By partitioning the resource utilization, the resource utilization status is defined to be proportional to its utilization level w j as shown in equation (2). VM i has higher probability to be hosted in the PM j with higher utilization level.…”

Section: B Energy Efficiencymentioning

confidence: 99%

Energy-Efficient Statistical Live Virtual Machine Placement for Big Data Information Systems in Cloud Computing Environments

Zheng

Cai

2015

2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity)

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With the increasing applications of big data computing on large scale cloud platforms, virtual machines (VM) are utilized to provide flexibility and availability for big data information systems. Energy efficient VM management and distribution on cloud platforms has become an important research subject. Mapping VMs "correctly" into PMs (Physical Machines) requires knowing the capacity of each PM and the resource requirements of the VMs. It must also take into accounts VM operation overheads, the reliability of PMs, Quality of Service (QoS) in addition to energy efficiency. However, the current VM placement approaches are mostly built in a server cluster with homogeneous nodes. Moreover, those approaches are not effective for live migrations with multiple considerations at the same time. In this paper, we propose an energy efficient statistical live VM placement scheme in a heterogeneous server clusters. Our scheme supports VM requests scheduling and live migration to minimize the number of active servers in order to save the overall energy in a virtualized server cluster. Specifically, the proposed VM placement scheme incorporates all the VM operation overheads in the dynamic migration process. In addition, it considers other important factors in relation to energy consumption, and it is ready to be extended with more considerations on users demand. We conduct extensive evaluations based on HPC jobs in a simulated environment. The results prove the effectiveness of our scheme.

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“…Resource allocation for distributed cluster platforms is currently an active area of research, with application placement [24], load balancing [1], [25], and avoiding QoS constraint violations [26], [27] being primary areas of concern. Some authors have also chosen to focus on optimizing fairness or other utility metrics [28].…”

Section: Related Workmentioning

confidence: 99%

Virtual Machine Resource Allocation for Service Hosting on Heterogeneous Distributed Platforms

Stillwell

Vivien²,

Casanova³

2012

2012 IEEE 26th International Parallel and Distributed Processing Symposium

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International audienceWe propose algorithms for allocating multiple resources to competing services running in virtual machines on heterogeneous distributed platforms. We develop a theoretical problem formulation and compare these algorithms via simulation experiments based in part on workload data supplied by Google. Our main finding is that vector packing approaches proposed in the homogeneous case can be extended to provide high-quality solutions in the heterogeneous case, and combined to provide a single efficient algorithm. We also consider the case when there may be bounded errors in estimates of performance-related resource needs. We provide a heuristic for compensating for such errors that performs well in simulation, as well as a proof of the worst-case competitive ratio for the single-resource, single-node case when there is no bound on the error

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Statistical static capacity management in virtualized data centers supporting fine grained QoS specification

Cited by 16 publications

References 16 publications

Cloud Computing

Cloud Computing

Energy-Efficient Statistical Live Virtual Machine Placement for Big Data Information Systems in Cloud Computing Environments

Virtual Machine Resource Allocation for Service Hosting on Heterogeneous Distributed Platforms

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