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Evaluating the Cost-Benefit of Using Cloud Computing to Extend the

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					 Evaluating the Cost-Benefit of
Using Cloud Computing to Extend
     the Capacity of Clusters

          Presenter: Xiaoyu Sun
              Cluster Computing


Users have to know cluster very well

  administrative privileges
           What is Cloud Computing?

Cloud computing provides computation,
software, data access, and storage resources
without requiring cloud users to know the
location and other details of the computing
infrastructure.
Characteristics of Cloud Computing


   Empowerment
        Users control resource by themselves not by a centralized IT service
   Agility
        users' ability to re-provision technological infrastructure resources.
   Application Programing Interface
   Cost
   Device and Location Independence
        enable users to access systems using a web browser regardless of their location or what device they are using
   Virtualization
        servers and storage devices to be shared and utilization be increased
   Reliability and Scalability
   Performance
        Monitor by web services as the system interface
   Security
        providers are able to devote resources to solving security issues that many customers cannot afford
   Maintenance
        Applications don’t need to be installed on each user's computer and can be accessed from different places
                      Purpose


 Describe a system that enables an organization to
  augment its computing infrastructure by allocating
  resources from a Cloud provider.
 Provide various scheduling strategies that aim to minimize
  the cost of utilizing resources from the Cloud provider.
 Evaluate the proposed strategies, considering different
  performance metrics; namely average weighted response
  time, job slowdown, number of deadline violations,
  number of jobs rejected, and the   money spent for
  using the Cloud.
        Cloud Computing


Strategy sets




         Figure 1:The resource provisioning scenario
                  Backfilling Policies


 Conservative
    each request is scheduled when it arrives in the system, and requests are allowed
     to jump ahead in the queue if they do not delay the execution of other requests.
 Aggressive
    Only the request at the head of the waiting queue called the pivot is granted a
     reservation. Other requests are allowed to move ahead in the queue if they do not
     delay the pivot.
 Selective
    Requests are given reservations if they have waited long enough in the queue.
     Long enough is determined by the requests’ expansion factor:

                                   Xfactor = (wait time + run time)/run time (1)
               The threshold is given by the average slowdown of previously completed
   requests.
                 Strategy Sets


 Naïve:
   Both site and cloud schedulers use Conservative
    backfilling to schedule the requests
   The redirection algorithm is executed at the arrival of
    each job at the site
   Use cloud provider when the request cannot start
    immediately on local cluster
                 Strategy Sets


 Shortest Queue:
   Aggressive backfilling
   First-Come-First-Served (FCFS) manner
   At the arrival or complete of each job at the site
   Compute the ratio of number of VMs required by
    requests to the number of VMS available
   Redirect request if cloud provider’s number is smaller
                Strategy Sets


 Weighted Queue:
   Aggressive backfilling
   First-Come-First-Served (FCFS) manner
   Number of VMs that can be borrowed from cloud
    provider is the number of VMs required by requests
    minus VMs in use
                Strategy Sets


 Selective
   Selective backfilling
   Compute the ratio of number of VMs required by
    requests to the number of VMS available
   When the request’s xFactor exceeds the threshold, the
    scheduler makes a reservation at the place that
    provides the earliest start time.
                Experiments


 Simulation of two-month-long periods
 SDSC Blue Horizon machine with 144 nodes
   Number of VMs
 Price of a virtual machine per hour
   Amazon EC2’s small instance: US $0.10
   Network and storage are not considered
 Values are average of 5 simulation runs
         Performance Metrics


 Average Weighted Response Time(AWRT) of site k:
              å          p j × m j × (ct j - st j )
    AWRTk =       jÎTk
                                                      (2)
                         å p ×m  j     j
                         jÎTk
   Tk: requests submitted to site k
   Pj: the runtime of request j
   mj: the number of VMs required by request j
   ctj: request j’s completion time
   stj: the submission time of request j
            Performance Metrics


 Performance Improvement Cost of a strategy set st:

         Amount _ spend
PICst =                   * AWRTst                                           (3)
        AWRTbase - AWRTst
    Amount spent is the amount spent running virtual machines on the Cloud provider
    AWRTbase is the AWRT achieved by a base strategy(FCFS with aggressive
     backfilling) that schedules requests using only the site's resources
    AWRTst is the AWRT reached by the strategy st when Cloud resources are also
     utilized.
Performance Improvement Cost


 Using Lublin99's model to generate different workloads:

   Umed: the mean number of virtual machines required by a
    request to log2m-umed where m is the maximum number of
    virtual machines allowed in the system, from 1.5 to 3.5.
   Barr: the inter-arrival time of requests at rush hours, from 0.45
    to 0.55 .
   PB: the proportion p of the first gamma in Lublin99's model is given
    by p = pa * nodes + PB, from 0.5 to 1.0.
  Performance Improvement Cost

These three graphs show the site's utilization using the base aggressive
backfilling strategy without Cloud resources

 The larger the value of Umed, the smaller the requests.




                      The larger the value of PB, the smaller the duration
                      of the requests
 Performance Improvement Cost




Requests’ size   Requests’ arrive time   Requests’ duration
Deadline Constrained Applications


 Users may have stringent requirement on when the
  virtual machines are required
 Deadline constrained requests have:
   Ready time
   Duration
   Deadline
 Cost of using Cloud resources used to meet requests’
  deadlines and decrease the number of deadline
  violations and request rejections
      Deadline Aware Strategies


 Conservative
   both local site and Cloud schedule requests using conservative
    backfilling.
   Places a request where it achieves the best start time
   If rejections are allowed and deadline cannot be met, reject the request
 Aggressive
     both local site and Cloud use aggressive backfilling to schedule requests
     Earliest Deadline First
     If request deadlines are broken in the local cluster, try the cloud provider
     If rejections are allowed and deadlines are broken, reject the request
Cost of Reducing Deadline Violations


  The non-violation cost is given by:
                             Amount _ spentst
    non - violation costst =                          (4)
                              violbase - violst
  Where:
    Amount_spentst: amount spent with Cloud resources
    Violbase: the number of deadline violations under the base
     strategy set (aggressive backfilling and an Earliest Deadline
     First manner)
    Violst:the number of deadline violations under the evaluated
     strategy set
             Deadline calculation


 The deadline calculation is given by:




 Where:
 stj: the request j's submission time
 ctj: the completion time.
 taj: the difference between the request's completion and submission times.
 sf : a stringency factor that indicates how urgent the deadlines are.
Cost of Reducing Deadline Violations

   sf=0.9        sf=1.3   sf=1.7
Cost of Reducing Deadline Violations



  Tight deadlines   Normal deadlines   Relaxed deadlines
Cost to Reduce Job Rejections:
   Aggressive Strategy Set
Cost to Reduce Job Rejections:
   Aggressive Strategy Set
              Conclusions


 Different strategy sets can yield different ratios of
  performance improvement to money spent
   Naïve strategy has a higher performance improvement
    cost
   Selective strategy provides a good ratio of money spent
    to job slowdown improvement
 Using cloud provider to meet job deadlines
   Less than $3,000 were spent to keep the number of
    rejections close to zero

				
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posted:6/5/2013
language:English
pages:26