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Combining Optimism and Pessimism in Managing Replicas in Distributed

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Combining Optimism and Pessimism in Managing Replicas in Distributed Powered By Docstoc
					Combining Optimism and
Pessimism in Accessing
Replicas in Distributed
Systems
Joel M. Crichlow
INTRODUCTION
 The system contains a network of computing
 nodes.
 The data that can be accessed can have any
 level of replication across the nodes.
 Replication is employed in order to increase
 availability.
 Replication increases the need for effective
 control measures to preserve some level of
 mutual consistency.
 The scheme under discussion combines
 Optimism and Pessimism.
Introduction
                                         Front
                                          End



     x,y,z     x,y,z         x,y,z




                       x,y           x
Rationale
   Simplicity – KIS (note only one S)
   Increasingly more powerful computers
   Faster networks
   Wireless?
Objectives
   Provide a high level of availability at a
   known penalty to the application
   Preserve data integrity
   Build a system that is conceptually
   simple
The Cost Bound
   Maximum Numerical Error
   Determines the extent to which a
   replica can act independently
   We are considering resources that can
   be counted and are of the same type
The Cost Bound
  A request for r resources can be
  satisfied if the reachable pool is
  controlled by a cost bound of C
  resources and r  C.
  We can split the cost bound C among n
  replicas such that each replica i has
  local access to ci resources where
                  n

                 c
                 i 1
                        i   C
The Cost Bound
   Application characteristics can permit
   temporary over-allocation of resources
   (like over-booking), hence, if we let R
   be the no. of resources initially
   available, then a cost bound ci can be
   set at each replica i such that
                    n

                   c
                   i 1
                          i   C R
The Cost Bound
    We propose a dynamic C value which
    changes in two ways:
    In response to pessimistic global processing,
    and
    In response to optimistic local processing.
Dynamic C
  On pessimistic run


        Cnew  (1  ) * CinitRA
                             R
  Cnew is new cost bound;
  Cinit is cost bound initially set;
  RA is no. of resources allocated via requests;
  R is no. of resources initially available.
Dynamic C
   On pessimistic run

    ci  wi C
                             n
                 where     w
                            i 1
                                   i     1

   Let the resources allocated at replica i after a
   pessimistic cycle be RAi then
                                        n
                             RA   RAi
                                       i 1


   Then let

      wi  ( RAi  1) /( RA  n)
Dynamic C
  On optimistic run
                        )x (  ic  ic
  where
   x  {allocatereq,deallocatereq}
Testing
   There are published performance
   results for a prototype first coded in C
   and now coded in Java.
   In each cycle we used wi = 1/n.
   Due to the expert work of Prof. Steve
   Hartley the system now runs on a Linux
   four-node platform in Rob.308.
   Thanks Steve!!!
Testing
   Three replicas process transactions that
   are generated by a transaction
   generator located at a remote
   computer.
Testing
   Each transaction is expressed as a
   parent and child transactions.
   The parent remains at the owner while
   the children are sent to all the replicas.
   Parents are processed pessimistically.
   Children are processed optimistically.
Testing
   Link to recent paper.
Conclusion
   A system has been designed to
   investigate the use of optimism and
   pessimism in accessing replicas in a
   distributed system. The viability of the
   system has been established and a Java
   test bed is being used to examine
   alternative features.

				
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posted:5/3/2012
language:English
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