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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
2CLOUD 2011, Washington DC, USA, July 4 - 9, 2011
Introduction
Cloud Computing Systems–Auto scaling
Dynamic allocation of computing resources
–Elastic load balanceDistributes and balances the incoming traffic
3CLOUD 2011, Washington DC, USA, July 4 - 9, 2011
Introduction
• Typical approach of auto scaling and load balance (Amazon EC2)
4CLOUD 2011, Washington DC, USA, July 4 - 9, 2011
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
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
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
Framework of Cloud-Based Services
• Data centers• Instances• Users
8CLOUD 2011, Washington DC, USA, July 4 - 9, 2011
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
9CLOUD 2011, Washington DC, USA, July 4 - 9, 2011
Framework of Cloud-Based Services
10CLOUD 2011, Washington DC, USA, July 4 - 9, 2011
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
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
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
14CLOUD 2011, Washington DC, USA, July 4 - 9, 2011
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
Minimize Average Cost
16
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
17CLOUD 2011, Washington DC, USA, July 4 - 9, 2011
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
18CLOUD 2011, Washington DC, USA, July 4 - 9, 2011
Minimize Average Cost
• Approximate Algorithms:1. Exhaustive Search2. Greedy Algorithm3. Local Search Algorithm (3 + ε
approximation)4. Random Algorithm
19CLOUD 2011, Washington DC, USA, July 4 - 9, 2011
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
Maximize Close User Amount
21
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
22
{v1,v2,v3,v5}
v1 v2 v3 v4 v5
{v1,v2,v4}{v1,v3,v4}
{v4,v5}
CLOUD 2011, Washington DC, USA, July 4 - 9, 2011
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
24CLOUD 2011, Washington DC, USA, July 4 - 9, 2011
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
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
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
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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