a user experience-based cloud service redeployment mechanism kang yu yu kang, yangfan zhou, zibin...
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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 KongHong Kong, China
School of Computer ScienceNational University of Defence Technology
Changsha, China
Introduction
Overview of Cloud-based Services
Redeploying Service Instances
Experiment
Obtaining User Experience
Conclusion and Future Work
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Introduction
Cloud Computing Systems–Auto scaling
Dynamic allocation of computing resources
–Elastic load balanceDistributes and balances the incoming traffic
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Introduction
• Typical approach of auto scaling and load balance (Amazon EC2)
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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
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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.
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Introduction
Overview of Cloud-based Services
Redeploying Service Instances
Experiment
Obtaining User Experience
Conclusion and Future Work
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Framework of Cloud-Based Services
• Data centers• Instances• Users
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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 center3. Time to process the service request
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Framework of Cloud-Based Services
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Challenges of Hosting the Cloud Services
• Difficult to foresee user experience
• Delay can be measured (should take advantage of it)
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Introduction
Overview of Cloud-based Services
Redeploying Service Instances
Experiment
Obtaining User Experience
Conclusion and Future Work
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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.
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Obtaining User Experience
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Introduction
Overview of Cloud-based Services
Redeploying Service Instances
Experiment
Obtaining User Experience
Conclusion and Future Work
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Minimize Average Cost
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Given:Z = the set of data centersC = the set of usersdij = distance between every pair (i,j) ∈ C╳Z
Minimize:
Subject to:𝑍′ ⊂ 𝑍, ∣𝑍′∣ = 𝑘
N
1i'
min ijZjd
CLOUD 2011, Washington DC, USA, July 4 - 9, 2011
Minimize Average Cost
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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)no(1)) time
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Minimize Average Cost
• Approximate Algorithms:1. Exhaustive Search2. Greedy Algorithm3. Local Search Algorithm (3 + ε
approximation)4. Random Algorithm
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Problems of the Model
• Local Optimizer• Number of users connected to an
instance• Acceptable whenever response time
less than a threshold T
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Maximize Close User Amount
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Given Bipartite graph 𝐵(𝑉1,��2,𝐸) where
∣𝑉1∣ = 𝑀, ∣𝑉2∣ = 𝑁 ∈ 𝑖 𝑉1, ∈ 𝑗 𝑉2
(𝑖, 𝑗) ∈ 𝐸, 𝑑𝑖𝑗 ≤ 𝑇;
(𝑖, 𝑗) ∉ 𝐸, otherwise.Maximize:
∣𝑁𝐵(𝑉′)∣Subject to:
𝑉 ′ ⊂ 𝑉1, ∣ 𝑉 ′∣ = 𝑘CLOUD 2011, Washington DC, USA, July 4 - 9, 2011
Maximize Close User Amount
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{v1,v2,v3,v5}
v1 v2 v3 v4 v5
{v1,v2,v4}{v1,v3,v4}
{v4,v5}
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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
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Maximize Close User Amount
• Approximate Algorithms:1. Greedy Algorithm (1-1/e
approximation)2. Local Search Algorithm
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Introduction
Overview of Cloud-based Services
Redeploying Service Instances
Experiment
Obtaining User Experience
Conclusion and Future Work
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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
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Introduction
Overview of Cloud-based Services
Redeploying Service Instances
Experiment
Obtaining User Experience
Conclusion and Future Work
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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
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Q & A
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Necessity of Redeployment
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Weakness of Auto Scaling
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Comparing Algorithms for k-Median
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Comparing Algorithms for k-Median
• Theoretical time complexity– Exhaustive search:– Greedy:– Local Search:
)( NMO k
)( NMkO
)( NMkO tt 33CLOUD 2011, Washington DC, USA, July 4 - 9, 2011
Redeployment Algorithms for Max k-Cover
• 20 instances are selected to provide service for 4000 users.
• Expect 200 per server.
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Redeployment Algorithms for Max k-Cover
• compare the average cost: max k-cover v.s. k-median
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