A User Experience-based Cloud Service Redeployment Mechanism KANG Yu

Introduction Overview of Cloud-based Services Redeploying Service Instances Experiment Obtaining User Experience Conclusion and Future Work

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.

Introduction Typical approach of auto scaling and load balance (Amazon EC2)

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.

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.

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.

Introduction Overview of Cloud-based Services Redeploying Service Instances Experiment Obtaining User Experience Conclusion and Future Work

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.

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 center 3.Time to process the service request

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.

Introduction Overview of Cloud-based Services Redeploying Service Instances Experiment Obtaining User Experience Conclusion and Future Work

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.

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.

Obtaining User Experience

Introduction Overview of Cloud-based Services Redeploying Service Instances Experiment Obtaining User Experience Conclusion and Future Work

Minimize Average Cost

k-median problem Algorithms: 1.Brute Force 2.Greedy Algorithm 3.Local Search Algorithm (3 + ε approximation) 4.Random Algorithm

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.

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

Introduction Overview of Cloud-based Services Redeploying Service Instances Experiment Obtaining User Experience Conclusion and Future Work

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

Necessity of Redeployment

Weakness of Auto Scaling

Comparing Algorithms for k-Median

Theoretical time complexity – Brute Force: – Greedy: – Local Search:

Redeployment Algorithms for Max k-Cover 20 instances are selected to provide service for 4000 users. Expect 200 per server.

Redeployment Algorithms for Max k-Cover compare the average cost: max k-cover v.s. k-median

Introduction Overview of Cloud-based Services Redeploying Service Instances Experiment Obtaining User Experience 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.