M. Pinedo. Scheduling: Theory, Algorithms, and Systems. Prentice-Hall, 2nd edition, 2002.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... : e.g. minimizing the probability that the quantity we want to maximize is less than a certain value ( KRT97] use the expected value for the makespan problem, but have over ow probability as a parameter for bin packing and knapsack) Other measures of stochastic optimization can be found in [Pin95] It would be interesting to get simple proofs of convergence for a large class of distributions. Pro t maximization for other problems can also be studied using the approach proposed here. Two problems that seem particularly attractive are minimum spanning tree and facility location. ....

M. Pinedo. Scheduling - Theory, Algorithms, and Systems. Prentice Hall, 1995.

....to the host by selecting the best match from the pool of applications and pool of the available hosts. The selecting strategy can be based on the prediction of the computing power of the host [GoSW02] Before moving to the scheduling heuristics, let us review some terms and definitions [MaAS99, Pine95]. The expected execution time ET ij of task t i on machine m is defined as the amount of time taken by m to execute t given that m has no load when t is assigned. The expected completion time CT ij of task t i on machine m is defined as the wall clock time at which m completes t (after having ....

M. Pinedo, Scheduling: Theory, Algorithms, and Systems, Prentice Hall, Englewood Cliffs, NJ, 1995.

....SMDPs implies the existence of optimal (randomized) Markov policies for non homogeneous SMDPs. A nite step SMDP is an important example of a non homogeneous SMDP. An important application of nite step SMDPs is scheduling of a nite number of jobs with random durations; Ross [19] Pinedo [16]. For a nite step SMDP, the assumption 0 can be omitted when the functions rk (x; a) k = 0; K, are bounded above. It is also possible to de ne SMDPs with parameters depending on time t. We do not expect that the results of this paper can be applied to such models. For example, ....

Pinedo, M. (1995). Scheduling: Theory, Algorithms, and Systems. Prentice Hall, Englewood Clis.

....the future research work. 2 SCHEDULING IN A PARALLEL MACHINES SHOP In many manufacturing and assembly facilities, every job can be processed in the same type of machines. This kind of environment, where the machines are set up in parallel, is usually referred to as parallel machines environment [5]. The study of this environment is very important from both a theoretical and a practical point of view: the occurrence of resources in parallel is common in the real world, e.g. in the flexible flow shop configurations, and the techniques used for machines in parallel are often used in ....

....with m identical machines (P m ) with setup times in the machines that depend on the job (called sequence dependent setup times s ij ) and where the function to minimize is the makespan Cmax . This machine environment is highly complex; the problem with m = 2 is already an NP hard problem. In [5], a general survey of heuristics to solve this problem is presented. A new heuristic to solve the parallel machine environment using an extension of the Traveling Salesman Problem (TSP) is proposed in [6] This algorithm finds the optimal scheduling of the makespan problem with sequence dependent ....

Michael Pinedo. Scheduling: Theory, Algorithms, and Systems. Prentice Hall, second edition, 2002.

....size. Our goal is to devise e#cient algorithms for job scheduling, such that job response time is kept low. Back end server 1 Dispatcher Back end server 2 Back end server n . Figure 1: A clustered Web server The literature contains a considerable body of work on job scheduling (see [2, 8, 9, 10, 13] and references therein) but most of the work models the randomness of arrivals and job sizes using exponential type distributions. When job sizes are iid (independently identically distributed) exponential and their arrivals follow a Poisson process, then the scheduling problem is fairly well ....

....therein) but most of the work models the randomness of arrivals and job sizes using exponential type distributions. When job sizes are iid (independently identically distributed) exponential and their arrivals follow a Poisson process, then the scheduling problem is fairly well understood [10, 11, 13, 17]. However, there is a great deal of evidence suggesting that the sizes of files traveling on the Internet do not follow an exponential type distribution. Rather, these sizes appear to follow power law distributions, which by definition satisfy the following equation: #x IP[X x] c x , ....

Michael Pinedo. Scheduling: Theory, Algorithms, and Systems, Prentice Hall, 2002.

.... problem formulation covers well known scheduling problems: The restriction J i J i is total in the Job Shop scheduling problem (JSP) trivial ( f(o; o) j o 2 J i g) in the Open Shop scheduling problem (OSP) and either total or trivial for each i in the Mixed Shop scheduling problem (MSP) [3, 11]. For the Group Shop scheduling problem (GSP) we consider a weaker restriction on which includes the above scheduling problems by looking at a re nement of the partition J to a partition into groups G = fG 1 ; G g g. We demand that G i G i has to be trivial and that for o; o ....

M. Pinedo. Scheduling: Theory, Algorithms, and Systems. Prentice-Hall, Englewood Clis, 1995.

.... problem formulation covers well known scheduling problems: The restriction J i J i is total in the Job Shop Scheduling problem (JSP) trivial ( f(o; o) j o 2 J i g) in the Open Shop Scheduling problem (OSP) and either total or trivial for each i in the Mixed Shop Scheduling problem (MSP) [17, 5]. For the Group Shop Scheduling problem (GSP) we consider a weaker restriction on which includes the above scheduling problems by looking at a re nement of the partition J to a partition into groups G = fG 1 ; G g g. We demand that G i G i has to be trivial and that for o; o 2 ....

M. Pinedo. Scheduling: Theory, Algorithms, and Systems. Prentice-Hall, Englewood Clis, 1995.

....t i be a i , and let the time t begins execution be b i . From the above definitions, c ij =b i e ij .Letc i be the completion time for task t i , and it is equal to c ij where machine m j is assigned to execute task t i . Themakespan for the complete schedule is then defined as max t # K (c i ) [21]. Makespan is a measure of the throughput of the HC system and does not measure the quality of service imparted to an individual task. One other performance metric is considered in [17] In the immediate mode heuristics, each task is considered only once for matching and scheduling, i.e. the ....

M. Pinedo, Scheduling: Theory, Algorithms, and Systems," Prentice Hall, Englewood Cliffs, NJ, 1995.

.... Parallel Shop scheduling) in the following called FOPSSP was de ned by ten Eikelder and Liekens in [11] as a generalization of scheduling problems covering the well known Job Shop scheduling problem (JSSP) the Open Shop scheduling problem (OSSP) and the Mixed Shop Scheduling problem (MSSP) [8, 2]. An instance of the the FOP Shop scheduling problem consists of the following elements: A set O = fo 1 ; o n g of operations which is partitioned into jobs J 1 ; J r . For o 2 O, we denote o 2 J i by j(o) i. The partition of O into jobs is further re ned to a partition into ....

M. Pinedo. Scheduling: theory, algorithms, and systems. Prentice-Hall, Englewood Clis, 1995.

.... problem formulation covers well known scheduling problems: The restriction J i J i is total in the Job Shop scheduling problem (JSP) trivial ( f(o; o) j o 2 J i g) in the Open Shop scheduling problem (OSP) and either total or trivial for each i in the Mixed Shop scheduling problem (MSP) [11, 3]. For the Group Shop scheduling problem (GSP) we consider a weaker restriction on which includes the above scheduling problems by looking at a re nement of the partition J to a partition into groups G = fG 1 ; G g g. We demand that G i G i has to be trivial and that for o; o ....

M. Pinedo. Scheduling: theory, algorithms, and systems. Prentice-Hall, Englewood Clis, 1995.

....which provides a fundamental building block of the AppLeS Parameter Sweep Template. In this subsection, we review those results in order to make this paper self contained. For PSAs, we focus on scheduling algorithms whose objective is to minimize the application s makespan (as de ned in [29]) In [27] we proposed an adaptive scheduling algorithm that we call sched( The general strategy is that sched( takes into account resource performance estimates to generate a plan for assigning le transfers to network links and tasks to hosts. To account for the Grid s dynamic nature, our ....

M. Pinedo. Scheduling: Theory, Algorithms, and Systems. Prentice Hall, Englewood Clis, NJ, 1995.

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M. Pinedo. Scheduling: Theory, Algorithms, and Systems. Prentice-Hall, 2nd edition, 2002.

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M. Pinedo. Scheduling: Theory, Algorithms, and Systems. Prentice-Hall, New Jersey, second edition, 2002.

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M. Pinedo. Scheduling: Theory, Algorithms, and Systems. Prentice-Hall, Englewood Cli#s, NJ, 1995.

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Pinedo, M. 1995. Scheduling: Theory, Algorithms, and Systems, Prentice-Hall, Englewood Cliffs, NJ.

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M. Pinedo, Scheduling: Theory, Algorithms, and Systems, Prentice Hall, Englewood Cliffs, NJ, 1995.

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Pinedo, Michael (2002). Scheduling: Theory, Algorithms, and Systems. Precentice Hall, Upper Saddle River, NJ, 2nd edition.

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Michael Pinedo. Scheduling: Theory, Algorithms, and Systems. Prentice Hall, 1995.

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Pinedo, M.L. (2001) Scheduling: Theory, Algorithms and Systems, Prentice-Hall, Englewood Cliffs, New Jersey.

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Pinedo, M. (1995) Scheduling : Theory, Algorithms, and Systems. Prenctice Hall, Englewood Clis, NJ 3

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M. Pinedo. Scheduling: Theory, Algorithms, and Systems. Prentice Hall, 1995.

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Michael Pinedo. Scheduling: Theory, Algorithms, and Systems. Prentice Hall, 1995.

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M. Pinedo. Scheduling: Theory, Algorithms, and Systems. Prentice Hall, Englewood Cliffs, NJ, 1995.

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M. Pinedo, Scheduling: Theory, Algorithms, and Systems. Prentice-Hall, 2002.

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M. Pinedo, Scheduling: Theory, Algorithms, and Systems. Prentice-Hall, 2002.

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