a multi cloud service co-deployment mechanism yu kang, zibin zheng, and michael r. lyu {ykang,...
TRANSCRIPT
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 & EngineeringThe Chinese University of Hong Kong
Hong Kong, China
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
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BACK GROUND
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 decisionCLOUD 2012, Hawaii, USA, June 24 - 29, 2012
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MOTIVATION EXAMPLE
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MULTI-SERVICE CO-DEPLOYMENT PROBLEM
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
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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
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INDEPENDENT DEPLOYMENT OF SINGLE SERVICE
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SINGLE SERVICE DEPLOYMENT
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
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INDEPENDENT DEPLOYMENT OF SINGLE SERVICE
yj = 1
xij = 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
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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
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MULTI CLOUD SERVICE CO-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
serviceboth 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
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MULTI CLOUD SERVICE CO-DEPLOYMENT
Open at most kh VMs for service h
xhij = 1
yipqrs = 1
zhj = 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
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ITERATIVE SEQUENTIAL CO-DEPLOYMENT ALGORITHM
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
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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
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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
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DATASET STATISTICS
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EXPERIMENT SETTING
Above 106 decision variables Use the tool Ilog CPLEX to solve the MIP
problems Randomly generate user log and calling
sequences as: User id -> service si1 -> service si2 -> … ->
service sim
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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 logs𝑠 One request of a service would involve on
average 5 requests of other services
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EXPERIMENT (ALGORITHM SPECIFICS)
Convergence of Iterative Sequential Procedure
Number of Disturbs
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EXPERIMENT (NUMBER OF SERVICES)
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EXPERIMENT (NUMBER OF SERVICE VMS)
Size of Candidate Set of Service VMs
Number of Service VMs to Deploy
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EXPERIMENT (SERVICES LOGS)
Number of Service Users
Average Call Length of Service
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EXPERIMENT (SERVICES LOGS)
Number of Logs
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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
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CONCLUSION AND FUTURE WORK
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
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Q & A
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