TY - JOUR
T1 - Stochastic fluid flow models for determining optimal switching thresholds
AU - Aggarwal, Vineet
AU - Gautam, N.
AU - Kumara, Soundar R.T.
AU - Greaves, Mark
N1 - Funding Information:
This work was partially supported by DARPA (Grant: MDA 972-01-1-0038). The authors thank the editor and the anonymous reviewers for their comments and suggestions that led to considerable improvements in the content and presentation of this paper.
PY - 2005/1
Y1 - 2005/1
N2 - This paper is motivated by the problem of capturing and releasing the CPU by a routine software application in order to accommodate other non-routine requests that need the CPU. Specifically, we consider a network of distributed software agents where each agent is assigned with routine tasks that need to be processed by a CPU. The CPU also receives requests from other processes running on the machine. The problem is to select an optimal threshold on the workload of the agent so that the agent releases the CPU and recaptures it from time-to-time based on its workload. In order to do that, we use a stochastic fluid-flow model with two buffers, one for the agent that runs the routine tasks and the other for the remaining non-routine jobs at the CPU. Input to the two buffers are from on-off sources and the processor switches between the two buffers using a threshold-based policy. We develop analytical approximations for the buffer content distribution and determine the Quality of Service (QoS) experienced by the two sources of traffic. We use the QoS performance measures to formulate and solve an optimization problem to design an optimal threshold value. κ 2004 Elsevier B.V. All rights reserved.
AB - This paper is motivated by the problem of capturing and releasing the CPU by a routine software application in order to accommodate other non-routine requests that need the CPU. Specifically, we consider a network of distributed software agents where each agent is assigned with routine tasks that need to be processed by a CPU. The CPU also receives requests from other processes running on the machine. The problem is to select an optimal threshold on the workload of the agent so that the agent releases the CPU and recaptures it from time-to-time based on its workload. In order to do that, we use a stochastic fluid-flow model with two buffers, one for the agent that runs the routine tasks and the other for the remaining non-routine jobs at the CPU. Input to the two buffers are from on-off sources and the processor switches between the two buffers using a threshold-based policy. We develop analytical approximations for the buffer content distribution and determine the Quality of Service (QoS) experienced by the two sources of traffic. We use the QoS performance measures to formulate and solve an optimization problem to design an optimal threshold value. κ 2004 Elsevier B.V. All rights reserved.
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U2 - 10.1016/j.peva.2004.06.002
DO - 10.1016/j.peva.2004.06.002
M3 - Article
AN - SCOPUS:9544220670
SN - 0166-5316
VL - 59
SP - 19
EP - 46
JO - Performance Evaluation
JF - Performance Evaluation
IS - 1
ER -