Optimization of Dynamic Virtual Machine Consolidation in Cloud Computing Data Centers

Najari, Alireza; Alavi, Seyed Mohammad; Noorimehr, Mohammad Reza

doi:10.14569/ijacsa.2016.070929

Cited by 3 publications

(4 citation statements)

References 24 publications

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“…This research work focuses on analyzing the effect of VM consolidation when different combinations of algorithms are applied for an IoT-based eye tracking big data workload. A static threshold-based underload detection is commonly applied as CPU utilization seldom drops below a threshold and thus complex algorithms cause unnecessary overhead [74]. On the other hand, sudden peaks are observed with varying workloads that overloads hosts and causes performance degradation [75].…”

Section: Consolidation Of Virtual Machines (Vm)mentioning

confidence: 99%

“…On the other hand, sudden peaks are observed with varying workloads that overloads hosts and causes performance degradation [75]. Three categories of algorithms are proposed for overload detection in [71,74]: Static threshold-based algorithms, adaptive utilization-based algorithms and regression-based algorithms. We created a composite selection (by choosing one algorithm from each category) to analyze its suitability for a big data workload.…”

Section: Consolidation Of Virtual Machines (Vm)mentioning

confidence: 99%

“…QoS requirements of a system are devised in the form of SLA (service level agreement), determined by attributes such as throughput or response time which are application dependent. In the case of IaaS, QoS can be evaluated using SLA metrics that depend on VM and compute resources [74]. IaaS-SLA violations (SLAV) can be measured using two metrics below.…”

Section: Power State Changesmentioning

confidence: 99%

“…Effective identification of overloaded/underloaded hosts and VMs to be migrated is crucial in VM consolidation as aggressive VM migrations lead to unnecessary energy loss [74]. In addition, power state changes between sleep and on states should be kept to a minimum [64].…”

Section: Performance Evaluationmentioning

confidence: 99%

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Green Cloud Software Engineering for Big Data Processing

et al. 2020

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Internet of Things (IoT) coupled with big data analytics is emerging as the core of smart and sustainable systems which bolsters economic, environmental and social sustainability. Cloud-based data centers provide high performance computing power to analyze voluminous IoT data to provide invaluable insights to support decision making. However, multifarious servers in data centers appear to be the black hole of superfluous energy consumption that contributes to 23% of the global carbon dioxide (CO2) emissions in ICT (Information and Communication Technology) industry. IoT-related energy research focuses on low-power sensors and enhanced machine-to-machine communication performance. To date, cloud-based data centers still face energy–related challenges which are detrimental to the environment. Virtual machine (VM) consolidation is a well-known approach to affect energy-efficient cloud infrastructures. Although several research works demonstrate positive results for VM consolidation in simulated environments, there is a gap for investigations on real, physical cloud infrastructure for big data workloads. This research work addresses the gap of conducting real physical cloud infrastructure-based experiments. The primary goal of setting up a real physical cloud infrastructure is for the evaluation of dynamic VM consolidation approaches which include integrated algorithms from existing relevant research. An open source VM consolidation framework, Openstack NEAT is adopted and experiments are conducted on a Multi-node Openstack Cloud with Apache Spark as the big data platform. Open sourced Openstack has been deployed because it enables rapid innovation, and boosts scalability as well as resource utilization. Additionally, this research work investigates the performance based on service level agreement (SLA) metrics and energy usage of compute hosts. Relevant results concerning the best performing combination of algorithms are presented and discussed.

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Section: Consolidation Of Virtual Machines (Vm)mentioning

confidence: 99%