Web Service in Cloud by malj

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									 A User Experience-based
      Cloud Service
Redeployment Mechanism
                  KANG Yu
Introduction

    Overview of Cloud-based Services

      Obtaining User Experience

      Redeploying Service Instances

    Experiment

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

      Obtaining User Experience

      Redeploying Service Instances

    Experiment

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

      Obtaining User Experience

      Redeploying Service Instances

    Experiment

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

      Obtaining User Experience

      Redeploying Service Instances

    Experiment

Conclusion and Future Work
Minimize Average Cost
Minimize Average Cost
 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
Maximize Close User
     Amount
   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

      Obtaining User Experience

      Redeploying Service Instances

    Experiment

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
Comparing Algorithms for
        k-Median




• Theoretical time complexity
  – Brute Force: O( M  N )
                      k



  – Greedy: O(k  M  N )
  – Local Search: O(k  M
                       t      t
                                   N)
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

      Obtaining User Experience

      Redeploying Service Instances

    Experiment

Conclusion and Future Work
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.

								
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