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A MULTI CLOUD SERVICE CO-DEPLOYMENT MECHANISM Yu Kang, Zibin Zheng, and Michael R. Lyu {ykang, zbzheng, Department of Computer Science.

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Presentation on theme: "A MULTI CLOUD SERVICE CO-DEPLOYMENT MECHANISM Yu Kang, Zibin Zheng, and Michael R. Lyu {ykang, zbzheng, Department of Computer Science."— Presentation transcript:

1 A MULTI CLOUD SERVICE CO-DEPLOYMENT MECHANISM Yu Kang, Zibin Zheng, and Michael R. Lyu {ykang, zbzheng, lyu}@cse.cuhk.edu.hk Department of Computer Science & Engineering The Chinese University of Hong Kong Hong Kong, China

2 Back Ground Independent Deployment of Single Service Co-deployment of Multi-service Experiment and Discussion Conclusion and Future Work CLOUD 2012, Hawaii, USA, June 24 - 29, 2012 2

3 B ACK G ROUND Rapid growing of cloud-based services Different cloud-based services may cooperate YouTube & Facebook Google Doc & Gmail Taobao & Alipay Necessary to deploy together Interactions between services Independent deployment is not enough Critical to make global decision CLOUD 2012, Hawaii, USA, June 24 - 29, 2012 3

4 M OTIVATION E XAMPLE CLOUD 2012, Hawaii, USA, June 24 - 29, 2012 4

5 M ULTI -S ERVICE C O - DEPLOYMENT P ROBLEM Independent services with different target users (may overlap) Interactions between services Deployed in different data centers even different clouds for users One company to host and deploy CLOUD 2012, Hawaii, USA, June 24 - 29, 2012 5

6 Back Ground Independent Deployment of Single Service Co-deployment of Multi-service Experiment and Discussion Conclusion and Future Work CLOUD 2012, Hawaii, USA, June 24 - 29, 2012 6

7 I NDEPENDENT D EPLOYMENT OF S INGLE S ERVICE CLOUD 2012, Hawaii, USA, June 24 - 29, 2012 7

8 S INGLE S ERVICE D EPLOYMENT Indicator whether VM j is used Indicator whether user i is connected to VM j Times of user i call service Distance between user i and VM j Minimize total distance for all user requests Every user i can only connect to one VM Can only connect to open VMs Open at most k VMs CLOUD 2012, Hawaii, USA, June 24 - 29, 2012 8

9 I NDEPENDENT D EPLOYMENT OF S INGLE S ERVICE y j = 1 x ij = 1 Every user i can only connect to one VM × Can only connect to open VMs × Open at most k VMs CLOUD 2012, Hawaii, USA, June 24 - 29, 2012 9

10 Back Ground Independent Deployment of Single Service Co-deployment of Multi-service Experiment and Discussion Conclusion and Future Work CLOUD 2012, Hawaii, USA, June 24 - 29, 2012 10

11 M ULTI C LOUD S ERVICE C O - DEPLOYMENT Times of user i call service h connect at most one VM Times of interaction between service q service s for request of user i Limit number of instances every service both VMs should be selected at most one connection indicator whether interaction between services q and s go through VM p to r for requests of user i indicator whether VM j for service h is used indicator whether user i would use VM j for service h CLOUD 2012, Hawaii, USA, June 24 - 29, 2012 11

12 M ULTI C LOUD S ERVICE C O - DEPLOYMENT Open at most k h VMs for service h x hij = 1 y ipqrs = 1 z hj = 1 connect at most one VM Can only connect to open VMs × First VM is chosen by user i for service h, next is open CLOUD 2012, Hawaii, USA, June 24 - 29, 2012 12

13 I TERATIVE S EQUENTIAL C O - DEPLOYMENT A LGORITHM First Generate Random Deployment Sequentially improve the deployment of each service Treat requests from other services the same as these from users Record the best till now Disturb and do local search CLOUD 2012, Hawaii, USA, June 24 - 29, 2012 13

14 Back Ground Independent Deployment of Single Service Co-deployment of Multi-service Experiment and Discussion Conclusion and Future Work CLOUD 2012, Hawaii, USA, June 24 - 29, 2012 14

15 OBTAIN THE DATASET 1. Conduct the experiment on 597 planetlab instances 2. Ping 2,213 web-services and all other planetlab peers in random order 3. Delete records of several instances and web- services to obtain two non-negative matrices, finally 307 * 1,881 remains 4. Mapping: Planetlab nodes -> available data centers Web-services -> users CLOUD 2012, Hawaii, USA, June 24 - 29, 2012 15

16 D ATASET S TATISTICS CLOUD 2012, Hawaii, USA, June 24 - 29, 2012 16

17 EXPERIMENT SETTING Above 10 6 decision variables Use the tool Ilog CPLEX to solve the MIP problems Randomly generate user log and calling sequences as: User id -> service s i1 -> service s i2 -> … -> service s im CLOUD 2012, Hawaii, USA, June 24 - 29, 2012 17

18 DEFAULT EXPERIMENT SETTING 1881 users 10 services Deploy10 service VMs among a candidate set in 100 data centers A user of service  would have 5 request logs One request of a service would involve on average 5 requests of other services CLOUD 2012, Hawaii, USA, June 24 - 29, 2012 18

19 EXPERIMENT (ALGORITHM SPECIFICS) Convergence of Iterative Sequential Procedure Number of Disturbs CLOUD 2012, Hawaii, USA, June 24 - 29, 2012 19

20 EXPERIMENT ( NUMBER OF SERVICES) CLOUD 2012, Hawaii, USA, June 24 - 29, 2012 20

21 EXPERIMENT ( NUMBER OF SERVICE VMS) Size of Candidate Set of Service VMs Number of Service VMs to Deploy CLOUD 2012, Hawaii, USA, June 24 - 29, 2012 21

22 EXPERIMENT ( SERVICES LOGS) Number of Service Users Average Call Length of Service CLOUD 2012, Hawaii, USA, June 24 - 29, 2012 22

23 EXPERIMENT ( SERVICES LOGS) Number of Logs CLOUD 2012, Hawaii, USA, June 24 - 29, 2012 23

24 Back Ground Independent Deployment of Single Service Co-deployment of Multi-service Experiment and Discussion Conclusion and Future Work CLOUD 2012, Hawaii, USA, June 24 - 29, 2012 24

25 C ONCLUSION AND F UTURE W ORK Conclusion Model the latency-aware cloud-based multiple services co-deployment problem Give a new iterative algorithm to solve the problem Conduct experiments on real world data set Limitation of model No limitation on requests to one service VM Computation time is not constant in real world Possible solution: add a term of computational time in the model CLOUD 2012, Hawaii, USA, June 24 - 29, 2012 25

26 Q & A CLOUD 2012, Hawaii, USA, June 24 - 29, 2012 26

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