Presenter: David Fleeman { D. Juedes, F. Drews, L. Welch and D. Fleeman Center for Intelligent, Distributed & Dependable Systems Ohio University Athens, OH WPDRTS 2004 April 26, 2004 Heuristic Resource Allocation Algorithms for Maximizing Allowable Workload in Dynamic, Distributed, Real-Time Systems

WPDRTS, April 26, 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.

WPDRTS, April 26, 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.

WPDRTS, April 26, 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.

WPDRTS, April 26, 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.

WPDRTS, April 26, 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.

WPDRTS, April 26, A Modified Version of First Fit by Oh and Baker Input: and a workload w. Output: An allocation alloc:T  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.

WPDRTS, April 26, 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 w+1. Use the allocation returned by the last feasible result of FF.

WPDRTS, April 26, Experimental Results We considered n=20,30,40,…,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

WPDRTS, April 26, Experimental Results: Iterations for Simulated Annealing and Random Search