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Heuristic Resource Allocation Algorithms for Maximizing Allowable Workload in Dynamic, Distributed,

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Title: Heuristic Resource Allocation Algorithms for Maximizing Allowable Workload in Dynamic, Distributed,


1
Heuristic Resource Allocation Algorithms for
Maximizing Allowable Workload in Dynamic,
Distributed, Real-Time Systems
Presenter David Fleeman fleeman_at_ohio.edu
D. Juedes, F. Drews, L. Welch and D.
Fleeman Center for Intelligent, Distributed
Dependable Systems Ohio University Athens,
OH WPDRTS 2004 April 26, 2004
2
Motivating Example
  • Tasks which have workload dependent execution
    times.
  • Example originated in the generic air defense
    system
  • The detect task is periodic and identifies
    threats by filtering and evaluating radar tracks.
  • The engage task is event-driven and fires a
    missile at the threat
  • The guide missile task is event-driven periodic
    and uses sensor data to track a specific threat,
    recalculates flight path, and issues guidance
    commands to the missile.
  • Familiar shooter-to-target requirement.

3
Motivating Example (continued)
  • All three tasks have resource and timing
    requirements that are environment-dependent.
  • The detect task depends on both the number of
    radar tracks to process and the number of tracks
    that are actually threats.
  • The engage task is activated by events which
    occur at rates that are determined by the
    external environment.
  • The guide missile task depends on the number of
    missiles in flight.

4
Motivating Example (continued)
  • Traditional WCET analysis causes poor utilization
    of resources whenever there are little or no
    threats to be analyzed.
  • We characterize the resource needs of these tasks
    by execution profile functions that compute the
    resource needs as a function of workload.
  • These functions are used in this work to choose
    allocations that allow the applications
  • To better utilize the resources.
  • Allow real-time constraints to be met
  • Minimize the need for reallocations which create
    system overhead at the worst possible times.

5
The Maximum Allowable Workload Problem (RMS)
  • Allocation of n independent tasks to m
    processors.
  • Running times of each task t is given as function
    of the system workload w.
  • Problem Find an allocation of tasks to
    processors and a setting of w such that this
    allocation is feasible for all workloads w w,
    such that w is maximized.

6
Known Analytical Results
  • If the running-times of all of the tasks are
    well-behaved, then a modified version of First
    First is guaranteed to be within 41 of optimal,
    asymptotically.
  • If less than 12 of the system utilization is
    used up by input independent tasks (i.e.,
    constant time tasks), then First First is within
    33 of optimal, asymptotically.

7
A Modified Version of First Fit by Oh and Baker
  • Input ltT,Pgt and a workload w.
  • Output An allocation allocT ? P and Feasible
    or Not Feasible
  • for each job i do
  • place job i on the first processor j such that
    all tasks already assigned to processor j and
    task i can meet their deadlines when running with
    workload w.
  • if no such processor j exists, return Not
    Feasible
  • Return Feasible and alloc.

8
Using FF to Approximate MAW-RMS
  • Use binary search to find a workload w such that
    the algorithm given on the previous page return
    Feasible, but the same algorithm returns Not
    Feasible for workload w1. Use the allocation
    returned by the last feasible result of FF.

9
Experimental Results
  • We considered n20,30,40,…,100 tasks
  • 10 non-identical processors, each of which
    described by its speed factor ranging within
    10,30
  • Periods of tasks were choosen from 2500,5000
  • Random polynomials for workload functions
  • Choosen from

10
Experimental Results 100.000 Iterations for
Simulated Annealing and Random Search
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