Research on application classification method in cloud computing environment

Energy consumption is a very important issue that has attracted the attention of many cloud providers as it takes a large quotient of the operation cost for cloud data center. To decrease the energy consumption in cloud data center, one possible solution is to process different types of applications with different strategies. To reach this goal, it is important to know the type of application before it be dealt with. In this paper, we present an application type classification method by monitoring the usage of the resources of application. Through analysis, we find that only part of the parameters are much related to different types of applications. Using these parameters we put forward a feature model that can effectively classify the types of different applications. Extensive experiments show that the model put forward can effectively and accurately classify CPU intensive application, I/O intensive application and network intensive application. It can be used as the basis of efficient utilization of the cloud resources.

[1]  Suman Nath,et al.  Energy-Aware Server Provisioning and Load Dispatching for Connection-Intensive Internet Services , 2008, NSDI.

[2]  Kevin Lee,et al.  Empirical prediction models for adaptive resource provisioning in the cloud , 2012, Future Gener. Comput. Syst..

[3]  Zhenhuan Gong,et al.  PRESS: PRedictive Elastic ReSource Scaling for cloud systems , 2010, 2010 International Conference on Network and Service Management.

[4]  Meikang Qiu,et al.  Modeling for CPU-Intensive Applications in Cloud Computing , 2015, 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security, and 2015 IEEE 12th International Conference on Embedded Software and Systems.

[5]  Fatos Xhafa,et al.  A Taxonomy of Data Scheduling in Data Grids and Data Centers: Problems and Intelligent Resolution Techniques , 2011, 2011 International Conference on Emerging Intelligent Data and Web Technologies.

[6]  Ruay-Shiung Chang,et al.  An Adaptive Scoring Job Scheduling algorithm for grid computing , 2012, Inf. Sci..

[7]  Jian Peng,et al.  Task scheduling algorithm based on improved genetic algorithm in cloud computing environment , 2011 .

[8]  Sriram Ramabhadran,et al.  Cloud control with distributed rate limiting , 2007, SIGCOMM 2007.

[9]  Feng Zhao,et al.  Fine-grained energy profiling for power-aware application design , 2008, PERV.

[10]  Ladislau Bölöni,et al.  A comparison study of static mapping heuristics for a class of meta-tasks on heterogeneous computing systems , 1999, Proceedings. Eighth Heterogeneous Computing Workshop (HCW'99).

[11]  Aniruddha S. Gokhale,et al.  Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[12]  Norman W. Paton,et al.  Optimizing Utility in Cloud Computing through Autonomic Workload Execution , 2009 .

[13]  Amaury Lendasse,et al.  Methodology for long-term prediction of time series , 2007, Neurocomputing.

[14]  Junjie Peng,et al.  Modeling for I/O Intensive Applications in Cloud Computing , 2015, 2015 IEEE Symposium on Service-Oriented System Engineering.

[15]  José María Valls,et al.  Time Series Forecasting by means of Evolutionary Algorithms , 2007, 2007 IEEE International Parallel and Distributed Processing Symposium.

[16]  R. F. Freund,et al.  Dynamic matching and scheduling of a class of independent tasks onto heterogeneous computing systems , 1999, Proceedings. Eighth Heterogeneous Computing Workshop (HCW'99).

[17]  F. Berman,et al.  Adaptive Performance Prediction for Distributed Data-Intensive Applications , 1999, ACM/IEEE SC 1999 Conference (SC'99).