A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.
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Transcript of A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.
![Page 1: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/1.jpg)
A User Experience-based Cloud Service
Redeployment MechanismKANG Yu
![Page 2: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/2.jpg)
Introduction
Overview of Cloud-based Services
Redeploying Service Instances
Experiment
Obtaining User Experience
Conclusion and Future Work
![Page 3: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/3.jpg)
Introduction
• In the emerging cloud computing systems, auto scaling and elastic load balance are keys to host the cloud services.– Auto scaling enables a dynamic allocation of computing
resources to a particular application. In other words, the number of service instances can be dynamically adapted to the request load.
– Elastic load balance distributes and balances the incoming application traffic (i.e., the user requests) among the service instances.
![Page 4: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/4.jpg)
Introduction
• Typical approach of auto scaling and load balance (Amazon EC2)
![Page 5: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/5.jpg)
Introduction
• Unfortunately, current auto scaling and elastic load balance techniques are generally not optimized for achieving best service performance.– Typical auto scaling approaches cannot start or terminate a
service instance at the data center selected according to the distributions of the end users.
– Elastic load balance generally redirects user requests to the service instances merely based on loads of the instances. It does not take the user specifics (e.g., user location) into considerations.
![Page 6: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/6.jpg)
Introduction
• Our contribution:– We model the features of user experience in cloud
service.– We propose a new user experience-based service
hosting mechanism which employs a service redeployment method.
![Page 7: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/7.jpg)
Introduction
• Our method has two advantages:1) It improves current auto scaling techniques by
launching the best set of service instances according to the distributions of end users.
2) It extends elastic load balance. Instead of directing user request to the lightest load service instance, it directs user request to a nearby one.
![Page 8: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/8.jpg)
Introduction
Overview of Cloud-based Services
Redeploying Service Instances
Experiment
Obtaining User Experience
Conclusion and Future Work
![Page 9: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/9.jpg)
Framework of Cloud-Based Services
• A cloud contains several data centers. Physical machines are virtualized as instances in the data center. Service providers would deploy service running on these instances. An end user normally connects to the cloud to get data and run applications
/services. User requests are directed to the service instances.
![Page 10: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/10.jpg)
Framework of Cloud-Based Services
• The connection information especially Round Trip Time (RTT) between a user and an instance 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
![Page 11: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/11.jpg)
Challenges of Hosting the Cloud Services
• Difficult of foreseeing user experience before actually running the service.
• Internet delay between users and every cloud data center can either be measured or be predicted. ---Different from existing computing infrastructures.
![Page 12: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/12.jpg)
Introduction
Overview of Cloud-based Services
Redeploying Service Instances
Experiment
Obtaining User Experience
Conclusion and Future Work
![Page 13: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/13.jpg)
Measure the Internet Delay
• A request is responded by an instance inside the cloud thus the cloud provider is able to record the RTT from the user to the instance.
![Page 14: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/14.jpg)
Predict the Internet Delay
• A user may not be able to visit many instances deployed in every data center.
• Find similar users and predict the connection.
![Page 15: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/15.jpg)
Obtaining User Experience
![Page 16: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/16.jpg)
Introduction
Overview of Cloud-based Services
Redeploying Service Instances
Experiment
Obtaining User Experience
Conclusion and Future Work
![Page 17: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/17.jpg)
Minimize Average Cost
![Page 18: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/18.jpg)
Minimize Average Cost
![Page 19: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/19.jpg)
Minimize Average Cost
• k-median problem• Algorithms:
1. Brute Force2. Greedy Algorithm3. Local Search Algorithm (3 + ε approximation)4. Random Algorithm
![Page 20: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/20.jpg)
Maximize Close User Amount
• Part of the users may be extremely far away from most of the data centers. They tend to force some service instances deployed in the data center close to them.
• We should also control number of users connected to a single server instance.
• We believe it is acceptable if some responses take a short time less than a threshold T.
![Page 21: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/21.jpg)
Maximize Close User Amount
![Page 22: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/22.jpg)
Maximize Close User Amount
![Page 23: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/23.jpg)
Maximize Close User Amount
• If we view the red nodes as sets– {1,2,3,5}; {1,2,3}; {1,3,4}; {4,5}
• Max k-cover problem• Algorithms:
1.Greedy Algorithm (1-1/e approximation)2.Local Search Algorithm
![Page 24: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/24.jpg)
Introduction
Overview of Cloud-based Services
Redeploying Service Instances
Experiment
Obtaining User Experience
Conclusion and Future Work
![Page 25: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/25.jpg)
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
![Page 26: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/26.jpg)
Necessity of Redeployment
![Page 27: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/27.jpg)
Weakness of Auto Scaling
![Page 28: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/28.jpg)
Comparing Algorithms for k-Median
![Page 29: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/29.jpg)
Comparing Algorithms for k-Median
• Theoretical time complexity– Brute Force: – Greedy:– Local Search:
)( NMO k
)( NMkO
)( NMkO tt
![Page 30: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/30.jpg)
Redeployment Algorithms for Max k-Cover
• 20 instances are selected to provide service for 4000 users.
• Expect 200 per server.
![Page 31: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/31.jpg)
Redeployment Algorithms for Max k-Cover
• compare the average cost: max k-cover v.s. k-median
![Page 32: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/32.jpg)
Introduction
Overview of Cloud-based Services
Redeploying Service Instances
Experiment
Obtaining User Experience
Conclusion and Future Work
![Page 33: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/33.jpg)
Conclusion and Future Work
• Our work consists two parts– We propose a framework to address the new features
of cloud.– We formulate the redeployment of service instances
as k-median and max k-cover problems.
• Future Work– Formulate the network capability of service instance
carefully with the amount of users.– Figure out potential users and optimize initial service
instances deployment.
![Page 34: A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649de95503460f94ae39eb/html5/thumbnails/34.jpg)