1. Automatic Resource Scaling for Web Applications in the Cloud Ching-Chi Lin Institute of Information Science, Academia Sinica Department of Computer Science and Information Engineering, Nation Taiwan University Jeng-An Lin, Li-Chung Song Department of Computer Science and Information Engineering, Nation Taiwan University Pangfeng Liu Department of Computer Science and Information Engineering, Nation Taiwan University Graduate Institute of Networking and Multimedia, Nation Taiwan University Jan-Jan Wu Institute of Information Science, Academia Sinica Research Center for Information Technology Innovation, Academia Sinica

2. Introduction Web applications face fluctuating loads. ◦ Using a fixed number of VM as web server is not enough. ◦ Over-provision or under provision. Auto-scaling

3. Auto-Scaling Estimate the load. Up-scale or down-scale the resources. Purpose ◦ Maintains application service quality. ◦ Reduces wasted resources.

4. Related Works Cloud service: Amazon EC2, Google App Engine, … Software: Scalr, RightScale, … Constraints: ◦ Replying on user-provided scaling metrics and threshold values. ◦ Employing the simple Majority Vote scaling algorithm. ◦ Lack of capability for predicting workload change.

5. Our Contribution WebScale ◦ A modularized auto scaling system for dynamic resource provision in data centers. Consider different algorithms Propose a trend analysis technique.

6. Technical approach Majority Vote ◦ Each VM makes their choice according to their current loading.  Compare with threshold. ◦ The scaling decision equals to the majority one among all choices.

7. Workload-Based Determines the number of running VMs needed based on the incoming workload. Has the advantage of knowing the needed number of VMs in advance compare to Majority Vote.

8. Trend Analysis Works as a helper to the scaling algorithms. ◦ More ”correct” decision. Only predict the trend of workload change instead of accurately value. ◦ Trend is the direction of workload changing.

9. Example of Trend Analysis

10. Experiment Setting Environment: ◦ 24 physical servers, each with 4 core X5460 CPU * 2 with hyperthreading,16 GB memory, and 250 GB disk. Web application: MediaWiki Performance measuring tool: Httperf

11. Experiment Workload

12. Experiment Results – Majority Vote

13. Experiment Results – Workload- based

14. Experiment Results – Database

15. Experiment results

16. Experiment results - Summary Majority vote is not an effective. ◦ Especially with frequently changing workloads. Workload-based with trend analysis performs the best among all three strategies.

17. Conclusion Auto-scaling technique provides on- demand resources according to workload in cloud computing system. We implemented an modularized auto- scaling system, WebScale. Our experiment results show that for workloads with periodic behavior, using workload-based algorithm with trend analysis performs the best among all three strategies.