Optimization of real-time multiserver system with two different channels and shortage of maintenance facilities

2009

Ann Oper Res

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We consider a real-world problem of military intelligence unit equipped with identical unmanned aerial vehicles producing real-time imagery and responsible for heterogeneous regions (with requests of real-time jobs) required to be under nonstop surveillance. Under certain assumptions these real-time systems can be treated as queueing networks.The use of the system involving unmanned aerial vehicles relies on the principle of availability, namely on its ability to process the maximal portion of real-time tasks. We show that even very large number of vehicles does not guarantee the maximal system availability without proper choice of routing probabilities. We compute analytically (for exponentially distributed service and maintenance times) and via simulation using Cross-Entropy method (for generally distributed service times) optimal routing probabilities which maximize system availability. Notations and abbreviationsCE Cross Entropy CMC Crude Monte Carlo RTS Real-Time System UAV Unmanned Aerial Vehicle N Number of identical UAVs/servers r Number of heterogeneous regions/channels r kNumber of real-time tasks/jobs in k-th region at any instant (r k ≥ 1) λThe maintenance rate (exponential distribution)

Section: Introductionmentioning

confidence: 94%

An optimal routing policy for unmanned aerial vehicles (analytical and cross-entropy simulation approach)

Ianovsky

2009

Ann Oper Res

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“…In [20] the results of [18] for 2 r ≥ different channels operating under a maximum load regime were extended. Optimal assignment probabilities maximizing availability of RTS (with ample and limited number of maintenance teams respectively) with large number of servers and two channels were obtained [21] [22]. Amultiserver and multichannel RTS with identical servers and channels and ample maintenance facilities, working in general regime with exponentially distributed service, maintenance, jobs inter-arrival and duration times were treated as Markov chains [23] in order to obtain various performance characteristics.…”

Section: Introductionmentioning

confidence: 99%

Multiserver Multichannel Real-Time System with Limited Maintenance Facilities under Maximum Load

Ianovsky¹,

2015

OJAppS

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We consider a multi server and multichannel real-time system with identical servers (e.g. unmanned aerial vehicles, machine controllers, etc.) that provide services for requests of real-time jobs arriving via several different channels (e.g. surveillance regions, assembly lines, etc.) working under maximum load regime. Each channel has its own constant numbers of jobs inside at any instant. Each channel has its own specifications, and therefore different kinds of equipment and inventory are needed to serve different channels. There is a limited number of identical maintenance teams (less than the total number of servers in the system). We compute analytically steadystate probabilities of this system, its availability, loss penalty function and other performance characteristics, when both service and maintenance times are exponentially distributed.

“…Kreimer [15] applied the two dimensional birth and death processes in worst case analysis of a multiserver RTS (with ample and limited maintenance facilities respectively) with two different channels, when both service and maintenance times were exponentially distributed. Ianovsky and Kreimer [16] obtained optimal assignment/ routing probabilities to maximize the availability of RTS with limited maintenance facilities, for large number of servers and two different channels. Kreimer [17] obtained the steady state probabilities for RTS with arbitrary number of different channels operating under a maximum load regime.…”

Section: Introductionmentioning

confidence: 99%

Real Time Systems with Nonpreemptive Priorities and Ample Maintenance Facilities

Ianovsky²

2015

JCC

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We consider a real time data acquisition and processing multiserver system with identical servers (such as unmanned aerial vehicles, machine controllers, overhearing devices, medical monitoring devices, etc.) which can be maintained/programmed for different kinds of activities (e.g. passive or active). This system provides a service for real time tasks arriving via several channels (such as surveillance regions, assembly lines, communication channels, etc.) and involves maintenance. We focus on the worst case analysis of the system with ample maintenance facilities exponentially distributed time to failure and maintenance times. We consider two kinds of models (with and without nonpreemptive priorities) and provide balance equations for steady state probabilities and various performance measures, when both operation and maintenance times are exponentially distributed.