1. Auto Scaling 2012/04/30

2. Introduction Auto-scaling is a technique that dynamically adjust the resource utilization for an application based on actual workload demand. ◦ Resource utilization: the amount of resource that can be used (by an application) in an fixed time interval.

3. Why We Need Auto-scaling? Workloads of an application can be fluctuating and unpredictable. Neither over-provision nor under-provision is a good solution. ◦ Over-provision: costly and make resource under-utilization. ◦ Under-provision: performance suffering.

4. Advantages of Auto-scaling From user perspective: ◦ Only pay for the virtual machines in need. ◦ Money-saving. From cloud provider perspective: ◦ Only deploy those virtual machines in need to physical servers. ◦ Power-saving and money-saving.

5. Scaling Algorithms Majority vote ◦ For general applications Workload-based ◦ For web applications Trend prediction ◦ For applications with periodic workload

6. Majority Vote Majority Vote selects the choice with the most props among all choices. ◦ Each virtual machine makes choice according to the chosen standard metrics. ◦ A final scaling decision is made by majority vote. Add/remove one VM at a time. ◦ Improvement: scale multiple VMs at a time.

7. Workload-based Workload-based determines the number of VMs needed based on the current workload, which is request per second. ◦ Decision maker makes scaling decisions according to the difference between the number of VMs needed and the number of current running VMs. ◦ Add/remove multiple VMs at a time.

8. Trend Prediction Trend prediction acts as a helper for scaling algorithms. Trend is the direction of workload change. ◦ Instead of accurately predict the workload value, we focus on the trend only. If a scale-in decision contradict with the trend, it will be canceled.

9. Performance Prediction Predict the performance according to the current status of running VMs. ◦ If current performance is going to violate SLA, scale out. “Parameter selection” ◦ Select metrics that can significantly affect the performance of an application.

10. Our Approach Application Workloads Performance VM Status (Standard metrics) Dimension Reduction

11. Our Approach(Cont.) We apply different amount of workloads to N VMs and collect corresponding VM status and performance. ◦ Workload: requests/sec. ◦ N: 1~10 Apply PCA to exploit the relation between metrics.

12. Principle Component Analysis [1] A mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of uncorrelated variables called principal components. [1] http://en.wikipedia.org/wiki/Principal_component_analysis

13. Dimension Reduction CPU, Load_one, Load_five, Load_fifteen pkg_in, pkg_out, byte_in, byte_out PCA CPU’ PCA Network’ Memory Memory’

14. Our Approach(Cont.) Having the three new dimensions(CPU’, Memory’, Network’), apply regression analysis. ◦ Use SPSS to do regression analysis. ◦ y =a 1 *CPU’ + a 2 *Memory’ + a 3 *Network’ + a 4 y is the predict performance of an application.

15. Issues 目前有提供 Auto-scaling 機制的 cloud providers (Amazon) 或 software (Scalr, Rightscale) 都需要使用者提 供 scaling threshold 。如何能夠自動決定，或者根本不 使用 threshold 來做 auto-scaling 。 ◦ Performance prediction. Cloud provider 本身提供的服務可以知道 workload 類 型，但是使用者上傳 applications 的 workload 較難掌 握。是否有除了請使用者提供 workload 資訊外的解 決方法。 ◦ Can affect workload-based algorithm and performance prediction.

16. Issues(Cont.) Auto scaling 在遇到大流量的時候如何分辨是否為 DDoS 攻擊。 ◦ Block DDoS instead of scaling.