Optimal server resource reservation policies for priority classes of users under cyclic non-homogeneous markov modeling

Koutras, Vasilis P.; Platis, Agapios Ν.; Gravvanis, George A.

doi:10.1016/j.ejor.2008.09.031

Cited by 12 publications

(21 citation statements)

References 29 publications

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“…In the work presented in , a server that provides resources, in particular hard disk space, is taken under consideration. Users’ classes, denoted by A, B, C, and D, with descending priority, are served.…”

Section: Resource Reservation and Software Rejuvenationmentioning

confidence: 99%

“…Notice also that the number of reserved resources g 1 , g 2 , and g 3 that appear in Figure actually correspond to , and that are derived according to the work in as already stated.…”

Section: Resource Reservation and Software Rejuvenationmentioning

confidence: 99%

“…In fact, some predefined levels P b;B;0 , P b;C;0 , P b;D;0 are defined, and the blocking probabilities P b;B , P b;C , P b;D have to be eliminated below these levels. Using the algorithm proposed in [8], the optimization problem is solved for both high and low traffic periods, resulting in the optimal solution g high D g 1;high ; g 2;high ; g 3;high Á and g low D g 1;low ; g 2;low ; g 3;low Á . The corresponding optimal values of the blocking probabilities are also derived:…”

Section: Computing Resource Availability With Rejuvenationmentioning

confidence: 99%

“…The procedure that is followed to examine whether rejuvenation manages to minimize the blocking probabilities of each class consists first in solving the multiobjective optimization problem introduced in [8] for deriving the optimal RRVs for the high and the low traffic periods. By setting P high b;ADP;0 D 5 10 5 , P high b;PR;0 D 5 10 4 , P high b;UN;0 D 5 10 3 for the high traffic period and P low b;ADP;0 D 5 10 4 , P low b;PR;0 D 5 10 3 , P low b;UN;0 D 5 10 2 for the low traffic period, the optimization results in g 1;high D 2; g 2;high D 3, g 3;high D 7 and g 1;low D 2, g 2;low D 3, g 3;low D 10.…”

Section: Case I: Optimal Reservation and Rejuvenation Policiesmentioning

confidence: 99%

“…Two different traffic periods are distinguished: high and low traffic period. Through the work presented in the optimal number of reserved resources for each period and each priority class are determined by solving a multiobjective optimization problem . Innovatively, software rejuvenation is adopted to prevent server's software aging.…”

Section: Introductionmentioning

confidence: 99%

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Software rejuvenation and resource reservation policies for optimizing server resource availability using cyclic nonhomogeneous Markov chains

Koutras

Platis

Gravvanis

2012

Appl Stoch Models Bus & Ind

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Software rejuvenation is modeled in a client–server system, which provides resources to priority classes of users. To assure availability, resource reservation policies are adopted for the higher priority classes. In addition software rejuvenation is proposed to optimize resource availability. The system is modeled by a cyclic nonhomogeneous Markov chain to capture the variation of the arrival and service rates during a day period. An optimization problem is solved based on a similar previous work and given the optimal resource reservation policy obtained by its solution, rejuvenation is performed and the optimal rejuvenation policy is determined. As a measure of resource availability the blocking probability of each priority class is used. Performability indicators expressing the total cost are also derived, with respect to the optimal resource reservation and optimal rejuvenation policies, to examine whether rejuvenation benefits the system in terms of cost. To derive the blocking probabilities, the limiting probability distribution is computed using explicit generalized approximate inverse preconditioning for solving efficiently sparse linear systems of algebraic equations. Copyright © 2012 John Wiley & Sons, Ltd.

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Section: Resource Reservation and Software Rejuvenationmentioning

confidence: 99%

“…Notice also that the number of reserved resources g 1 , g 2 , and g 3 that appear in Figure actually correspond to , and that are derived according to the work in as already stated.…”

Section: Resource Reservation and Software Rejuvenationmentioning

confidence: 99%

Section: Computing Resource Availability With Rejuvenationmentioning

confidence: 99%

Section: Case I: Optimal Reservation and Rejuvenation Policiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Software rejuvenation and resource reservation policies for optimizing server resource availability using cyclic nonhomogeneous Markov chains

Koutras

Platis

Gravvanis

2012

Appl Stoch Models Bus & Ind

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A cyclic non‐homogeneous Markov chain model for resource availability optimization in a two‐parking lots system with priority classes and resource reservation

Koutras

Kadrefi

Platis

2021

Appl Stoch Models Bus & Ind

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Resource availability optimization has gained a lot of research effort recently, especially in client-server systems. A parking lot can be considered such a client-server system. Parking lot resource availability importance increased during the last decades since there is a tremendous increase of vehicles' usage not only in urban areas but in rural areas too. Academia and industry have extensively studied the problem of parking availability under various constraints and limitations. In this article, we extend previous work on parking lot resource availability optimization by providing an integrated theoretical framework for modeling resource reservation in a two-parking lot problem which can be accessed by clients of different priorities under nonconstant arrival and service rates. The aim is to determine the optimal parking place reservation policy, which minimizes the blocking probability for each clients' class at each parking lot for different traffic periods. Under the optimal policy, we compute two different performance indicators; from clients' perspective we compute the total expected walking time from the parking lot to the point of interest, and from company's point of view, the total expected hourly profit. To model the fact that clients' arrival and service rates are time dependent, we use a cyclic nonhomogeneous Markov chain to model the entire system's evolution in time. Additionally, an empirical numerical case study is provided to examine the effectiveness of the proposed resource reservation approach.

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Software rejuvenation in cluster computing systems with dependency between nodes

et al. 2014

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Optimal server resource reservation policies for priority classes of users under cyclic non-homogeneous markov modeling

Cited by 12 publications

References 29 publications

Software rejuvenation and resource reservation policies for optimizing server resource availability using cyclic nonhomogeneous Markov chains

Software rejuvenation and resource reservation policies for optimizing server resource availability using cyclic nonhomogeneous Markov chains

A cyclic non‐homogeneous Markov chain model for resource availability optimization in a two‐parking lots system with priority classes and resource reservation

Software rejuvenation in cluster computing systems with dependency between nodes

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