Risk Aware Provisioning and Resource Aggregation Based Consolidation of Virtual Machines

Halder, Kishaloy; Bellur, Umesh; Kulkarni, Purushottam

doi:10.1109/cloud.2012.86

Cited by 27 publications

(21 citation statements)

References 12 publications

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“…Authors in [27] present a control-oriented model that considers cyber and physical dynamics in datacenters to study the potential impact of coordinating the IT and cooling controls. To achieve further power savings while maintaining the QoS level, joint relationships among VMs, like load correlations, have been exploited in recent works [36,24,19]. For instance, in [24], Meng et al proposed a VM sizing technique that pairs two uncorrelated VMs into a super-VM by predicting the workloads.…”

Section: Related Workmentioning

confidence: 99%

“…This favors consolidation and leads to power savings by lowering the number of active servers. In this context, due to the distributed operations of multiple VMs in a cluster, a high correlation within a cluster of VMs is observed, called intra-cluster correlation, rather than the correlation among different clusters targeted in other correlationaware schemes [36,19]. The correlation-aware VM allocation method has been proposed, in [22], while sharing cores among colocated VMs based on defining a cost function depending on QoS requirement to efficiently quantify the correlation between the VMs across a certain time horizon.…”

Section: Datacenter Energy Controllermentioning

confidence: 99%

See 1 more Smart Citation

Joint Computing and Electric Systems Optimization for Green Datacenters

Pahlevan

Rossi

Valle

et al. 2016

Handbook of Hardware/Software Codesign

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This chapter presents an optimization framework to manage green datacenters using multi-level energy reduction techniques in a joint approach. A green datacenter exploits renewable energy sources and active Uninterruptible Power Supply (UPS) units to reduce the energy intake from the grid while improving its Quality of Service (QoS). At server level, the state-of-the-art correlation-aware Virtual Machines (VMs) consolidation technique allows to maximize server's energy efficiency. At system level, heterogeneous Energy Storage Systems (ESS) replace standard UPSs while a dedicated optimization strategy aims at maximizing the lifetime of the battery banks and to reduce the energy bill, considering the load of the servers. Results demonstrate, under different number of VMs in the system, up to 11.6% energy savings, 10.4% improvement of QoS compared to existing correlation-aware VM allocation schemes for datacenters and up to 96% electricity bill savings. IntroductionEver increasing demands for computing and growing number of clusters and servers in datacenters have ramped up the power consumption costs as an undesirable effect [21]. On the other hand, traditional fossil fuel concerns, carbon emissions and global warming impose the introduction of more sustainable energy sources and behavioural change of people [41], since 10% of the global consumption of electrical energy has been estimated to be consumed by Information Technology (IT) infrastructures [14].

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Section: Related Workmentioning

confidence: 99%

Section: Datacenter Energy Controllermentioning

confidence: 99%

Joint Computing and Electric Systems Optimization for Green Datacenters

Pahlevan

Rossi

Valle

et al. 2016

Handbook of Hardware/Software Codesign

View full text Add to dashboard Cite

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“…In [51], Verma et al found out that workloads running on datacenters are strongly correlated one another. In order to achieve further power savings while maintaining quality of service (QoS) level, correlations among VMs' workload have been exploited in recent works [51][52][53][54]. In [51], Verma et al presented a clustering-based correlation-aware VM placement solution.…”

Section: Correlation-aware Power and Temperature Managementmentioning

confidence: 99%

“…Innovative DCIM support systems for datacenter management are thus needed. PMSM (i.e., Power Monitor System and Management) [44][45][46][47][48][49][50][51][52][53][54][55][56], developed at EPFL in cooperation with Credit Suisse [45], is an example of such an innovation.…”

Section: Monitoring System For Datacentersmentioning

confidence: 99%

Power-Thermal Modeling and Control of Energy-Efficient Servers and Datacenters

Kim

Sabry

Ruggiero

et al. 2015

Handbook on Data Centers

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“…This algorithm places VMs by pair without considering the correlation among the pair of VMs to be placed and the VMs that are already placed on a particular PM. Halder et al [13] proposed a first fit decreasing (FFD) based correlationaware VM placement algorithm that iteratively updates the correlation matrix for each placement decision, and places the VM having the minimal correlation with the placed VMs on a PM. A dynamic power management solution for scaleout applications in data centers was proposed by Kim et al [14].…”

Section: Related Workmentioning

confidence: 99%

Scalable correlation-aware virtual machine consolidation using two-phase clustering

Ventresque

Iglesias

et al. 2015

2015 International Conference on High Performance Computing &Amp; Simulation (HPCS)

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Abstract-Server consolidation is the most common and effective method to save energy and increase resource utilization in data centers, and virtual machine (VM) placement is the usual way of achieving server consolidation. VM placement is however challenging given the scale of IT infrastructures nowadays and the risk of resource contention among co-located VMs after consolidation. Therefore, the correlation among VMs to be colocated need to be considered. However, existing solutions do not address the scalability issue that arise once the number of VMs increases to an order of magnitude that makes it unrealistic to calculate the correlation between each pair of VMs. In this paper, we propose a correlation-aware VM consolidation solution ScalCCon 1 , which uses a novel two-phase clustering scheme to address the aforementioned scalability problem. We propose and demonstrate the benefits of using the two-phase clustering scheme in comparison to solutions using one-phase clustering (up to 84% reduction of execution time when 17, 446 VMs are considered). Moreover, our solution manages to reduce the number of PMs required, as well as the number of performance violations, compared to existing correlation-based approaches.

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Risk Aware Provisioning and Resource Aggregation Based Consolidation of Virtual Machines

Cited by 27 publications

References 12 publications

Joint Computing and Electric Systems Optimization for Green Datacenters

Joint Computing and Electric Systems Optimization for Green Datacenters

Power-Thermal Modeling and Control of Energy-Efficient Servers and Datacenters

Scalable correlation-aware virtual machine consolidation using two-phase clustering

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