1. A User Experience-based Cloud Service Redeployment Mechanism KANG Yu Yu Kang, Yangfan Zhou, Zibin Zheng, and Michael R. Lyu {ykang,yfzhou, zbzheng,lyu}@cse.cuhk.edu.hk Department of Computer Science & Engineering The Chinese University of Hong Kong Hong Kong, China School of Computer Science National University of Defence Technology Changsha, China

2. Introduction Overview of Cloud-based Services Redeploying Service Instances Experiment Obtaining User Experience Conclusion and Future Work 2CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

3. Introduction Cloud Computing Systems – Auto scaling Dynamic allocation of computing resources – Elastic load balance Distributes and balances the incoming traffic 3CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

4. Introduction Typical approach of auto scaling and load balance (Amazon EC2) 4CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

5. Introduction Current approaches are not optimized for users – Auto scaling Do not consider distributions of the end users – Elastic load balance Do not take the user specifics (e.g., user location) into considerations 5CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

6. Introduction Our contribution: – User experience model in cloud – A new service redeployment method Two advantages: 1)Improve auto scaling techniques Launch best set of service instances 2)Extend elastic load balance Directs user request to a nearby one. 6CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

7. Introduction Overview of Cloud-based Services Redeploying Service Instances Experiment Obtaining User Experience Conclusion and Future Work 7CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

8. Framework of Cloud-Based Services Data centers Instances Users 8CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

9. Framework of Cloud-Based Services Round Trip Time (RTT) can be kept by the cloud provider. User experience contains three elements: 1.Internet delay between a user and a cloud data center (This is the most significant part) 2.Delay inside the data center 3.Time to process the service request 9CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

10. Framework of Cloud-Based Services 10CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

11. Challenges of Hosting the Cloud Services Difficult to foresee user experience Delay can be measured (should take advantage of it) 11CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

12. Introduction Overview of Cloud-based Services Redeploying Service Instances Experiment Obtaining User Experience Conclusion and Future Work 12CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

13. Obtaining User Experience Measuring Internet delay – RTT can be recorded Predict the Internet Delay – Not every data center is visited – Find similar users and predict the connection. 13CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

14. Obtaining User Experience 14CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

15. Introduction Overview of Cloud-based Services Redeploying Service Instances Experiment Obtaining User Experience Conclusion and Future Work 15CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

16. Minimize Average Cost 16 Given: Z = the set of data centers C = the set of users d ij = distance between every pair (i,j) ∈ C╳Z Minimize: Subject to:  ′ ⊂ , ∣  ′∣ =  CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

17. Minimize Average Cost 17CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

18. Minimize Average Cost k-median problem NP-hard W[2]-hard with k as parameter W[1]-hard with capacity l as parameter In FPT with both as parameter algorithm: O(f(k,l)n o(1) ) time 18CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

19. Minimize Average Cost Approximate Algorithms: 1.Exhaustive Search 2.Greedy Algorithm 3.Local Search Algorithm (3 + ε approximation) 4.Random Algorithm 19CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

20. Problems of the Model Local Optimizer Number of users connected to an instance Acceptable whenever response time less than a threshold T 20CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

21. Maximize Close User Amount 21 Given Bipartite graph  (  1, 2,  ) where ∣  1 ∣ = , ∣  2 ∣ =   ∈  1,  ∈  2 ( ,  ) ∈ ,   ≤  ; ( ,  ) ∉ , otherwise. Maximize: ∣   (  ′)∣ Subject to:  ′ ⊂  1, ∣  ′∣ =  CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

22. Maximize Close User Amount 22 {v1,v2,v3,v5} v1 v2 v3 v4v5 {v1,v2,v4} {v1,v3,v4} {v4,v5} CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

23. Maximize Close User Amount Max k-cover problem NP-hard W[2]-hard with k as parameter W[2]-hard (general) and FPT (tree-like) with maximum subset size as parameter FPT if both maximum subset size and capacity as parameter 23CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

24. Maximize Close User Amount Approximate Algorithms: 1.Greedy Algorithm (1-1/e approximation) 2.Local Search Algorithm 24CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

25. Introduction Overview of Cloud-based Services Redeploying Service Instances Experiment Obtaining User Experience Conclusion and Future Work 25CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

26. Dataset Description Deploy our WSEvaluator to 303 distributed computers of PlanetLab invoke to 4302 the Internet services A 303 * 4302 matrix containing response- time values 26CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

27. Introduction Overview of Cloud-based Services Redeploying Service Instances Experiment Obtaining User Experience Conclusion and Future Work 27CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

28. Conclusion and Future Work Our work – A framework of new features – Formulate the redeployment problems. Future Work – Formulate the network capability in detail – Optimize initial service instances deployment 28CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

29. Q & A 29CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

30. Necessity of Redeployment 30CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

31. Weakness of Auto Scaling 31CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

32. Comparing Algorithms for k-Median 32CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

33. Comparing Algorithms for k-Median Theoretical time complexity – Exhaustive search: – Greedy: – Local Search: 33CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

34. Redeployment Algorithms for Max k-Cover 20 instances are selected to provide service for 4000 users. Expect 200 per server. 34CLOUD 2011, Washington DC, USA, July 4 - 9, 2011

35. Redeployment Algorithms for Max k-Cover compare the average cost: max k-cover v.s. k- median 35CLOUD 2011, Washington DC, USA, July 4 - 9, 2011