Self-Adaptive Cloud Capacity Planning

The popularity of cloud service spurs the increasing demands of cloud resources to the cloud service providers. Along with the new business opportunities, the pay-as-you-go model drastically changes the usage pattern and brings technology challenges to effective capacity planning. In this paper, we propose a new method for cloud capacity planning with the goal of fully utilizing the physical resources, as we believe this is one of the emerging problems for cloud providers. To solve this problem, we present an integrated system with intelligent cloud capacity prediction. Considering the unique characteristics of the cloud service that virtual machines are provisioned and de-provisioned frequently to meet the business needs, we propose an asymmetric and heterogeneous measure for modeling the over-estimation, and under-estimation of the capacity. To accurately forecast the capacity, we first divide the change of cloud capacity demand into provisioning and de-provisioning components, and then estimate the individual components respectively. The future provisioning demand is predicted by an ensemble time-series prediction method, while the future de-provisioning is inferred based on the life span distribution and the number of active virtual machines. Our proposed solution is simple and computational efficient, which make it practical for development and deployment. Our solution also has the advantages for generating interpretable predictions. The experimental results on the IBM Smart Cloud Enterprise trace data demonstrate the effectiveness, accuracy and efficiency of our solution.

[1]  Haixun Wang,et al.  An algorithmic approach to event summarization , 2010, SIGMOD Conference.

[2]  Tao Li,et al.  ASAP: A Self-Adaptive Prediction System for Instant Cloud Resource Demand Provisioning , 2011, 2011 IEEE 11th International Conference on Data Mining.

[3]  Darrell D. E. Long,et al.  Design and Implementation of a Predictive File Prefetching Algorithm , 2001, USENIX Annual Technical Conference, General Track.

[4]  Shicong Meng,et al.  Tide: achieving self-scaling in virtualized datacenter management middleware , 2010, Middleware Industrial Track '10.

[5]  Albert G. Greenberg,et al.  The cost of a cloud: research problems in data center networks , 2008, CCRV.

[6]  Vijay Sukthankar,et al.  An optimized capacity planning approach for virtual infrastructure exhibiting stochastic workload , 2010, SAC '10.

[7]  Wei Peng,et al.  Event summarization for system management , 2007, KDD '07.

[8]  Avrim Blum,et al.  On-line Algorithms in Machine Learning , 1996, Online Algorithms.

[9]  Xiaosong Ma,et al.  SigLM: Signature-driven load management for cloud computing infrastructures , 2009, 2009 17th International Workshop on Quality of Service.

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

[11]  Evimaria Terzi,et al.  Constructing comprehensive summaries of large event sequences , 2009, TKDD.

[12]  Michael I. Jordan,et al.  Detecting large-scale system problems by mining console logs , 2009, SOSP '09.

[13]  Darrell D. E. Long,et al.  The case for efficient file access pattern modeling , 1999, Proceedings of the Seventh Workshop on Hot Topics in Operating Systems.

[14]  Manish Marwah,et al.  Sustainable operation and management of data center chillers using temporal data mining , 2009, KDD.