Energy-efficiency enhanced virtual machine scheduling policy for mixed workloads in cloud environments

Hu, Zhigang; Liu, Dongbo; Zhang, Xizheng; Qu, Xiaobo

doi:10.1016/j.compeleceng.2014.03.002

Cited by 9 publications

(3 citation statements)

References 8 publications

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“…The results show that the algorithms reduce the number of migration operations, rebooting servers, and energy consumption, while achieving SLA guarantee. A separation mechanism of I/O tasks to perform computation-intensive tasks in a batch in virtualized servers to mitigate virtualization overheads is proposed in [17]. Because energy consumption and the frequency of SLA violations determine the quality of service [18], in this paper, we balance the tradeoff between energy consumption and SLA violations using the double threshold schemes based on tack classification and none of the abovementioned studies consider the energy saving objectives in the context of task classification.…”

Section: Related Workmentioning

confidence: 99%

Task Classification Based Energy-Aware Consolidation in Clouds

Choi

Lim

et al. 2016

Scientific Programming

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We consider a cloud data center, in which the service provider supplies virtual machines (VMs) on hosts or physical machines (PMs) to its subscribers for computation in an on-demand fashion. For the cloud data center, we propose a task consolidation algorithm based on task classification (i.e., computation-intensive and data-intensive) and resource utilization (e.g., CPU and RAM). Furthermore, we design a VM consolidation algorithm to balance task execution time and energy consumption without violating a predefined service level agreement (SLA). Unlike the existing research on VM consolidation or scheduling that applies none or single threshold schemes, we focus on a double threshold (upper and lower) scheme, which is used for VM consolidation. More specifically, when a host operates with resource utilization below the lower threshold, all the VMs on the host will be scheduled to be migrated to other hosts and then the host will be powered down, while when a host operates with resource utilization above the upper threshold, a VM will be migrated to avoid using 100% of resource utilization. Based on experimental performance evaluations with real-world traces, we prove that our task classification based energy-aware consolidation algorithm (TCEA) achieves a significant energy reduction without incurring predefined SLA violations.

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Section: Related Workmentioning

confidence: 99%

Task Classification Based Energy-Aware Consolidation in Clouds

Choi

Lim

et al. 2016

Scientific Programming

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“…So-called workload categorization problem plays a critical role towards improving the efficiency and the reliability of Cloud-based big data applications (e.g., [47], [48]). Implementation-wise, our method proposes deploying Cloud entities that participate to the distributed classification approach on top of virtual machines (e.g., [23]), which represent classical "commodity" settings for Cloud-based big data applications.…”

Section: Introductionmentioning

confidence: 99%

Cloud-Based Machine Learning Tools for Enhanced Big Data Applications

Cuzzocrea

Mumolo

Corona

2015

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

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We propose Cloud-based machine learning tools for enhanced Big Data applications, where the main idea is that of predicting the "next" workload occurring against the target Cloud infrastructure via an innovative ensemble-based approach that combine the effectiveness of different well-known classifiers in order to enhance the whole accuracy of the final classification, which is very relevant at now in the specific context of Big Data. So-called workload categorization problem plays a critical role towards improving the efficiency and the reliability of Cloudbased big data applications. Implementation-wise, our method proposes deploying Cloud entities that participate to the distributed classification approach on top of virtual machines, which represent classical "commodity" settings for Cloud-based big data applications. Preliminary experimental assessment and analysis clearly confirm the benefits deriving from our classification framework. 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing 978-1-4799-8006-2/15 $31.00

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“…The creating cost of tuning and managing PC structures is provoking out-sourcing of business organizations to core interests. The features of distributed framework fuse on self-association, broad structure, asset pooling, smart flexibility and assessed association (Xiao et al, 2014). On intrigue self-association, it recommends that clients (ordinarily affiliations) can ask for and deal with their own particular computing assets.…”

Section: Introductionmentioning

confidence: 99%

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JST

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Pertanika Journal of Science & Technology (JST) is the official journal of Universiti Putra Malaysia published by UPM Press. It is an open-access online scientific journal which is free of charge. It publishes the scientific outputs. It neither accepts nor commissions third party content.Recognized internationally as the leading peer-reviewed interdisciplinary journal devoted to the publication of original papers, it serves as a forum for practical approaches to improving quality in issues pertaining to science and engineering and its related fields.JST is a quarterly (January, April, July and October) periodical that considers for publication original articles as per its scope. The journal publishes in English and it is open to authors around the world regardless of the nationality.

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Energy-efficiency enhanced virtual machine scheduling policy for mixed workloads in cloud environments

Cited by 9 publications

References 8 publications

Task Classification Based Energy-Aware Consolidation in Clouds

Task Classification Based Energy-Aware Consolidation in Clouds

Cloud-Based Machine Learning Tools for Enhanced Big Data Applications

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