Performance and Resource Prediction at High Utilization for N-Tier Service Systems in Cloud an Experiment Driven Approach

Zhang, Wenbin; Shi, Yuliang; Liu, Lei; Cui, Lizhen; Zheng, Yongqing

doi:10.1109/cit/iucc/dasc/picom.2015.125

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…Many studies have been conducted on various predictions in cloud computing. From the perspective of research objectives, some researchers have studied server load prediction [ [6][7][8][9][10], VM load prediction [11,12], VM utilization prediction [13,14], host utilization prediction [15], web application workload prediction [16], cloud service workload prediction [17][18][19], workflow workload prediction [20], service quality prediction [21], and workload characterization [22][23][24]. Toumi et al [6] described a server load according to the submitted task types and the submission rate and applied a stream mining technique to predict server loads.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Method for Short-Term Host Utilization Prediction in Cloud Computing

Jing

Wang

2019

Journal of Electrical and Computer Engineering

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Dynamic resource scheduling is a critical activity to guarantee quality of service (QoS) in cloud computing. One challenging problem is how to predict future host utilization in real time. By predicting future host utilization, a cloud data center can place virtual machines to suitable hosts or migrate virtual machines in advance from overloaded or underloaded hosts to guarantee QoS or save energy. However, it is very difficult to accurately predict host utilization in a timely manner because host utilization varies very quickly and exhibits strong instability with many bursts. Although machine learning methods can accurately predict host utilization, it usually takes too much time to ensure rapid resource allocation and scheduling. In this paper, we propose a hybrid method, EEMD-RT-ARIMA, for short-term host utilization prediction based on ensemble empirical mode decomposition (EEMD), runs test (RT), and autoregressive integrated moving average (ARIMA). First, the EEMD method is used to decompose the nonstationary host utilization sequence into relatively stable intrinsic mode function (IMF) components and a residual component to improve prediction accuracy. Then, efficient IMF components are selected and then reconstructed into three new components to reduce the prediction time and error accumulation due to too many IMF components. Finally, the overall prediction results are obtained by superposing the prediction results of three new components, each of which is predicted by the ARIMA method. An experiment is conducted on real host utilization traces from a cloud platform. We compare our method with the ARIMA model and the EEMD-ARIMA method in terms of error, effectiveness, and time-cost analysis. The results show that our method is a cost-effective method and is more suitable for short-term host utilization prediction in cloud computing.

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Section: Related Workmentioning

confidence: 99%

A Hybrid Method for Short-Term Host Utilization Prediction in Cloud Computing

Jing

Wang

2019

Journal of Electrical and Computer Engineering

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Towards a software product line-based approach to adapt IaaS cloud configurations

Ruiz

Durán-Limón

Parlavantzas

2016

Proceedings of the 9th International Conference on Utility and Cloud Computing

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International audienceCloud computing is nowadays one of the most promising IT technologies, since it provides seemingly unlimited resources on demand at low costs. Hence, different types of applications have been migrated to IaaS environments, e.g. multi-tier (distributed) applications. However, in order to benefit from such characteristics, cloud configurations (i.e. virtual resource configurations) should be designed accordingly to the necessities of the applications. Furthermore, such configurations have to provide the required resources not only at the application deployment-time, but also during the whole application execution time. Hence, adaptive paradigms are required when designing solutions to cloud applications with dynamic resource requirements. Software Product Lines (SPLs) provide great flexibility and a high level of abstraction to describe complete system configurations. Even though SPLs are not commonly used to describe changes after an initial product (configuration) has been created, their inherent characteristics can enable producing the required virtual resource configuration to adapt applications after their initial deployment, i.e., at runtime. In this paper, we present an approach to create and adapt cloud configurations at the IaaS level by using SPLs. We focus on the architectural design of our solution as well as on the possible implementation challenges we could face

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Performance and Resource Prediction at High Utilization for N-Tier Service Systems in Cloud an Experiment Driven Approach

Cited by 2 publications

References 10 publications

A Hybrid Method for Short-Term Host Utilization Prediction in Cloud Computing

A Hybrid Method for Short-Term Host Utilization Prediction in Cloud Computing

Towards a software product line-based approach to adapt IaaS cloud configurations

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