3 edition of Static assignment of complex stochastic tasks using stochastic majorization found in the catalog.
Static assignment of complex stochastic tasks using stochastic majorization
by National Aeronautics and Space Administration, Langley Research Center in Hampton, Va
Written in English
|Statement||David Nicol, Rahul Simha, Don Towsley.|
|Series||ICASE report ;, no. 92-51, NASA contractor report ;, 189716, NASA contractor report ;, 189716.|
|Contributions||Simha, Rahul., Towsley, Don., Institute for Computer Applications in Science and Engineering.|
|LC Classifications||TL521.3.C6 A3 no. 189716, QA76.58 A3 no. 189716|
|The Physical Object|
|Pagination||22 p. ;|
|Number of Pages||22|
|LC Control Number||93122336|
Serving as the foundation for a one-semester course in stochastic processes for students familiar with elementary probability theory and calculus, Introduction to Stochastic Modeling, Third Edition, bridges the gap between basic probability and an intermediate level course in stochastic processes. The objectives of the text are to introduce students to the standard concepts and methods of 5/5(1). formula, it should be proved without the use of the more general theorem. We provide a long yet elementary proof here. All three parts of the following proof are based on a simple operation, namely, removing one edge and one vertex at a time. We assume without loss of generality that G has at least one edge. First we need a Size: KB.
Computational stochastic optimization represents an effort to create an umbrella over the many communities of stochastic optimization. This website is intended to introduce people to the different fields of stochastic optimization, while also identifying important bridges between communities. Outline Outline Convergence Stochastic Processes Conclusions - p. 2/19 Outline Illustration of CLT, WLLN, SLLN. Stochastic processes. Poisson process. Smooth processes in 1D. Fractal and smooth processes in 2+ Size: 7MB.
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We consider the problem of statically assigning tasks to processors when the tasks have unknown random processing times and a certain type of stochastic structure.
The structure we examine embodies the notion of one task spawning a set of others; we examine static assignments, under the assumption that all offspring of a task are executed on the same processor as the task. Get this from a library. Static assignment of complex stochastic tasks using stochastic majorization.
[David Nicol; Rahul Simha; Don Towsley; Langley Research Center.]. We show how the theory of majorization can be used to obtain a partial order among possible task assignments. Our results show that if the vector of numbers of tasks assigned to each processor under one mapping is majorized by that of another mapping, then the former mapping is better than the latter with respect to a large number of objective : David Nicol, Rahul Simha and Don Towsley.
Static assignment is likely to be use(l when a task's state is large, thereby making dynamic assignment very costly in terms o)f cOlmminui nication. This paper examines theoretical issues associated with compamiNg different static mappings of a set of complex stochastic tasks. We consider the problem of statically assigning tasks to processors when the tasks have unknown random processing times and a certain type of stochastic structure.
The structure we examine embodies the notion of one task spawning a set of others; we examine static assignments, under the assumption that all offspring of a task are executed on the same processor as the : David Nicol and Rahul Simha. Nicol D, Simha R and Towsley D () Static Assignment of Stochastic Tasks Using Majorization, IEEE Transactions on Computers,(), Online publication date: 1-Jun Gudaitis M, Lamont G and Terzuoli A Multicriteria vehicle route-planning using parallel A * search Proceedings of the ACM symposium on Applied computing, ().
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In this paper a notion of directional convexity (concavity) is introduced and its stochastic analog is studied. Using the notion of stochastic directional convexity (concavity), a sufficient. Given a flight schedule, which is a set of non-stop flights, called legs, with specified departure and arrival times, and a set of aircraft types, called subfleets, the fleet assignment problem is to determine which aircraft type should fly each leg.
The objective is to maximize the overall by: 4. Online Stochastic GAP 7. The optimal value of this linear program, which corresponds to the expected in- stance, is an upper bound on the expected value of the optimal ofﬂine assignment.
Theorem 1. The optimal value of the linear program (OPT) is an upper bound on the expected value of the ofﬂine optimal assignment. in this book. In Section we present a brief historical overview on the develop-ment of the theory of stochastic processes in the twentieth century.
In Section we introduce the one-dimensional random walk an we use this example in order to introduce several concepts such Brownian motion, the Markov property. In Sec-File Size: 1MB. The online stochastic algorithm described in this paper draws inspiration from the primal-dual algorithm for online optimization and the sample average approximation, and is built upon an existing static nurse scheduling by: 1.
Figure (c) shows a data partitioning using a logarithmic complexity measure. Data clustering by a self-organizing chain is shown in (d), neighboring clusters being connected. with y = P N k=1 M k Author: Joachim M Buhmann.
The mathematical theory of stochastic dynamics has become an important tool in the modeling of uncertainty in many complex biological, physical, and chemical systems and in engineering applications - for example, gene regulation systems, neuronal networks, geophysical flows, climate dynamics, chemical reaction systems, nanocomposites, and communication by: This book is intended as a beginning text in stochastic processes for stu-dents familiar with elementary probability calculus.
Its aim is to bridge the gap between basic probability know-how and an intermediate-level course in stochastic processes-for example, A First Course in Stochastic. Static Assignment of Complex Tasks using Stochastic Majorization David Nicol, Rahul Simha and Don Towsley IEEE Transactions on Computers 45(6), June A Comparative Study of Parallel Algorithms for Simulating Continuous Time Markov Chains.
When the number of offspring of a task has a geometric distribution whose parameter is decreasing and convex in the level, then the breadth-first policy stochastically minimizes the makespan. If, however, this parameter is increasing and concave, then the depth-first policy stochastically minimizes the Cited by: 3.
A Stochastic Generalized Assignment Problem, Spoerl and Wood, 14 January Page 5 of 32 and d represents the amount of resource available to each our basic model of the SEGAP, only B is actually random, but extensions to random f and d are straightforward. SEGAP may also be classified as a stochastic integer program (SIP).
Static Assignment of Stochastic Tasks Using Majorization, IEEE Trans. Computers, Vol. 45, No. 6, June Dynamic Processor Assignment in a Task System with Time-varying Load, Load Balancing of Complex Stochastic Tasks Using Stochastic Majorization, Proceedings of INFO San Francisco, pp.
(with and y. Static assignment is likely t(_ I)e used when a task's state is large, thereby making dynamic ent very costly in tel'IllS of C()llllllllnic_lli()ll. This paper examines theoretical issues associated with COlnparing different static mappings of a set of complex stochastic tasks.
In particular, we show how the theory of majorization can be. I’d like to recommend you the book following： Probability, Random Variables and Stochastic Processes * Author： Athanasios Papoulis；Unnikrishna Pillai * Paperback: pages * Publisher: McGraw-Hill Europe; 4th edition (January 1, ) * Language.
1. Construction of Time-Continuous Stochastic Processes: Brownian Motion Probably the most basic stochastic process is a random walk where the time is discrete. The process is defined by X(t+1) equal to X(t) + 1 with probabilityand to X(t) - 1 with probability It constitutes an infinite sequence of auto-correlated random variables.Theorem The transition matrix P is a stochastic matrix, which is to say that pij ≥0 for all i,j and P j pij = 1 for all i.
Proof. Trivial. 1Andrey Andreyevich Markov, Russian mathematician Tried to develop the theory of stochas-tic processes. 1File Size: KB.