Profiling Applications for Virtual Machine Placement in Clouds

Vu, Anh V.; Chen, Junliang; Wang, Chen; Lee, Young Choon; Zomaya, Albert Y.; Zhou, Bing Bing

doi:10.1109/cloud.2011.75

Cited by 61 publications

(29 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several realistic workload datasets from different datacenters are applied to evaluate the system performance. Do et al [13] propose a profiling system for the decision making of job scheduling and resource allocation. Their approach adopts the canonical correlation analysis method to identify the relationship between application performance and resource usage.…”

Section: Related Workmentioning

confidence: 99%

A Framework for Proactive Resource Provisioning in IaaS Clouds

Lee

Huang

et al. 2017

Applied Sciences

View full text Add to dashboard Cite

Abstract:Cloud computing is an emerging technology for rapidly provisioning and releasing resources on-demand from a shared resource pool. When big data is analyzed/mined on the cloud platform, the efficiency of resource provisioning would affect the system performance. This work proposes a framework for proactive resource provisioning in IaaS (Infrastructure as a Service) clouds to improve system performance. The proposed framework consists of the virtual cluster computing system, the profiling system, the resource management system, and the monitoring system. In this framework, the over-commit mechanism is applied to improve resource utilization. Furthermore, a proactive task scheduling approach is also present to prevent the postponement of tasks in critical stages, especially when the amount of aggregated resources requested by virtual machines exceeds that of available resources on the over-committed physical machines. Experimental results show that the over-commit approach indeed improves the resource utilization. However, when the degree of applying the over-commit approach increases, the burden of this proposed approach also conceivably increases. Therefore, the proposed framework further applies the proactive task scheduling approach to execute the time-critical tasks earlier to shorten the processing time. A small-scale cloud system including 3 servers is built for experiments. Preliminary experimental results show the performance improvement of our proposed framework in IaaS clouds.

show abstract

Section: Related Workmentioning

confidence: 99%

A Framework for Proactive Resource Provisioning in IaaS Clouds

Lee

Huang

et al. 2017

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The authors of [16] have investigated the relationship between resource demands and application performance metrics. They have presented an application profiling technique using a Canonical Correlation Analysis (CCA) method.…”

Section: Related Workmentioning

confidence: 99%

Profiling: An application assignment approach for green data centers

Vasudevan

Tian

Tang

et al. 2014

IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society

View full text Add to dashboard Cite

Abstract-In the past few years, there has been a steady increase in the attention, importance and focus of green initiatives related to data centers. While various energy aware measures have been developed for data centers, the requirement of improving the performance efficiency of application assignment at the same time has yet to be fulfilled. For instance, many energy aware measures applied to data centers maintain a trade-off between energy consumption and Quality of Service (QoS). To address this problem, this paper presents a novel concept of profiling to facilitate offline optimization for a deterministic application assignment to virtual machines. Then, a profile-based model is established for obtaining near-optimal allocations of applications to virtual machines with consideration of three major objectives: energy cost, CPU utilization efficiency and application completion time. From this model, a profile-based and scalable matching algorithm is developed to solve the profile-based model. The assignment efficiency of our algorithm is then compared with that of the Hungarian algorithm, which does not scale well though giving the optimal solution.

show abstract

“…We also combine SLA-based control of distributed systems with action scheduling based on previously determined relations between application load and the number of active services, however we consider additional algorithms for processing the monitoring information such as a genetic algorithm and result from applying Little's Law. Anh et al [50] analyse the performance of cloud applications, which share resources with other running applications in the same physical host, by looking at the correlations between application and system performance. We use statistical correlation [47] between the time series corresponding to the performance monitoring metrics of distributed services in order to determine the set of predictors of critical SLA metrics, which can then be used for controlling services scaling.…”

Section: Related Work On Prediction Algorithmsmentioning

confidence: 99%