NDLI: Resource and performance prediction at high utilization for N-Tier cloud-based service systems

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Resource and performance prediction at high utilization for N-Tier cloud-based service systems

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Resource and performance prediction at high utilization for N-Tier cloud-based service systems

Content Provider	ACM Digital Library
Author	Liu, Lei Cui, Lizhen Zhang, Wenbin Shi, Yuliang Zheng, Yongqing
Abstract	One of the key objectives of cloud computing systems is to meet the service level agreements (SLAs) under conditions of high resource utilization. Cloud service providers often need to design policies for resource sharing and performance optimization. As a result, being able to predict the performance and resource utilizations prior to implementing these policies is important to the dynamic provisioning of services by cloud providers. It is a significant and difficult challenge due to the fact that requests for resources often interact with each other in complex ways. Moreover, the dynamics of the cloud environment bring more problems to predicting the performance of a running query or workload. Hence, an accurate situation-aware model which can capture the complex interactions among resource requests is useful for addressing this challenge. To this end, we propose an efficient and highly accurate resource and performance prediction framework which takes into account the interactions among concurrently running resource requests for n-tier service systems. The proposed framework extends the Gaussian process and kernel canonical correlation analysis techniques and is able to dynamically adapt to variations in workload and physical resource usage. The proposed framework has been trained and evaluated extensively with a realistic multi-tier cloud application benchmark - the RUBiS benchmark system. The results demonstrate that the framework yields highly accurate performance and resource usage predictions, especially under high resource utilization conditions.
Starting Page	1
Ending Page	9
Page Count	9
File Format	PDF
ISBN	9781450347686
DOI	10.1145/3014812.3014857
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-01-30
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Cloud Prediction Benchmark Resource Performance
Content Type	Text
Resource Type	Article

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