Sheldon Ross. Introduction to Stochastic Dynamic Programming. Academic Press, New York, 1983. Home/Search Document Not in Database Summary Related Articles Check |
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Optimum Scheduling and Memory Management in Input Queued - Switches With Finite (Correct)
....of the scheduling and the memory management problem in either case, as the solution for one may be used in the other case as well. The optimal scheduling and memory management policy is the one which minimizes the average packet loss. We present a generic markov decision process(MDP) [11, 12] based technique for computing the optimal loss rates and the optimal scheduling and memory management decisions in section 3. However, the computations become intensive with increase in B or N: This happens because the computation needs several iterations and each iteration has a complexity ....
.... case and certain properties deduced from the MDP framework to design near optimal heuristics for the more general cases of 2 2 switches with arbitrary arrival rates and N N switches with N 2: 3 A Generic Framework for Computing the Optimal Strategy We present a markov decision process (MDP)[11, 12] based framework for computing the optimal strategy. Refer to [11, 12] for details on markov decision processes. At any time t the system is characterized by the system state vector x(t) x 11 (t) x ij (t) xNN (t) consisting of the queue lengths of the packets waiting at ....
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S. Ross. Introduction to Stochastic Dynamic Programming. Academic Press, 1998.
Ergodic Theorems for Stochastic Operators and Discrete Event .. - Baccelli, Mairesse (9 citations) (Correct)
....A, there is an immediate reward for being originally in state i which is a i (ff) It is well known that the optimal decision and the reward vector are obtained as lim n T (x) see for example [45] Chapter 3.2. There is a very important literature on deterministic operators of type (8. 5) see [40] or [45] and the references there. The next theorem is classical, for a proof see for example [45] Chapter 4.3. Theorem 8.3. Let T be an operator verifying Equation (8.5) A suOEcient condition for the existence of a unique generalized xed point for T is : 8ff 2 A, matrix P (ff) is ergodic, i.e. ....
S. Ross. Introduction to Stochastic Dynamic Programming. Academic Press, New York, 1983.
Stochastic Routing in Ad Hoc Wireless Networks - Lott, Teneketzis (2001) (Correct)
....policy . The system of Problem (P 1 ) is a time homogeneous Markov chain, hence we are faced with a nite state Markovian Decision Problem with perfect information. We can thus restrict attention to Markov policies on 2 , and we are guaranteed that such an optimal Markov policy exists (cf. Ross 83] Ch.3 p.51) We seek an optimal Markov policy : 2 (1; N) which minimizes (11) To solve Problem (P 1 ) we could directly apply stochastic dynamic programming. But since the number of states is 2 , the complexity of such an approach is at least O(2 ) and generally higher ....
S. Ross, Introduction to Stochastic Dynamic Programming, Academic Press, 1983
A Jointly Optimum Scheduling and Memory Management for Matching.. - Sarkar (Correct)
....sum of throughput and loss rates is equal to the sum of the arrival rates. Thus a strategy which minimizes loss maximizes throughput and vice versa. The optimal scheduling and memory management policy is the one which minimizes the average job loss. We present a generic markov decision process(MDP)[7, 8] based technique for computing the optimal loss rates and the optimal scheduling and memory management decisions. We define some notations next. Let S( x) be the set of possible next states when the current state is x and the next event is completion of job transmission. Let M = f M 1 ; M 2 ....
.... ( x) is the unique solution of the following equations[7] x) TJ ( x) 2) where T is a linear operator on any function f( x) defined as follows: Tf( x) ij (c( x; y; i; j) f( y) min f ( y) 3) The optimal average job loss is given by lim 1 (1 )J ( 0)[7, 8]. Consider a scheduling strategy s ( x) and a memory management strategy for arrival in the ith input, jth output a ( x; i; j) which choose the next states which have the minimum possible discounted cost: s ( x) arg min ( y) 4) a ( x; i; j) arg min (c( x; y; i; j) ....
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S. Ross. Introduction to Stochastic Dynamic Programming. Academic Press, 1998.
Efficient Buffer Sharing in Shared Memory ATM - Systems With Space (2002) (Correct)
....of high and low priority cells dropped from the system up to the end of slot , respectively. and are positive real numbers, is the expectation when policy is used. One can write with this value function the dynamic programming equations if the arrival statistics are completely characterized [9]. The optimal class of policies is defined as the following. Suppose we are given the number of buffer positions that will 1089 7798 02 17.00 2002 IEEE ROY AND PANWAR: BUFFER SHARING IN SHARED MEMORY ATM SYSTEMS 163 be allocated to a particular logical queue after the drop pushout decision at a ....
S. M. Ross, Introduction to Stochastic Dynamic Programming.New York: Academic, 1983.
Constrained Discounted Semimarkov Decision Processes - Feinberg (2002) (Correct)
....for homogeneous 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 ....
Ross, S.M. (1983). Introduction to Stochastic Dynamic Programming. Academic Press, New York.
Optimum Scheduling and Memory Management in Input Queued Switches .. - Sarkar (2003) (Correct)
....of the scheduling and the memory management problem in either case, as the solution for one may be used in the other case as well. The optimal scheduling and memory management policy is the one which minimizes the average packet loss. We present a generic markov decision process(MDP) 10] [11] based technique for computing the optimal loss rates and the optimal scheduling and memory management decisions in technical report [13] However, the computations become intensive with increase in B or N. This happens because the computation needs several iterations and each iteration has a ....
S. Ross. Introduction to Stochastic Dynamic Programming. Academic Press, 1998.
The Origins of the Deadline: - Optimizing Communication In (Correct)
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Sheldon Ross. Introduction to Stochastic Dynamic Programming. Academic Press, New York, 1983.
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S. Ross. Introduction to stochastic dynamic programming. Academic Press, 1983.
Closed-Loop Control with Delayed Information - Eitan Altman And (1992) (10 citations) (Correct)
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Ross, S. M., Introduction to Stochastic Dynamic Programming . Academic Press, New York, 1983.
An Explicit Solution for the Value Function of a Priority Queue - Koole, Nain (2004) (Correct)
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S. M. Ross, Introduction to Stochastic Dynamic Programming. Academic Press, 1983.
Ergodic Theorems for Stochastic Operators and Discrete Event .. - Baccelli, Mairesse (1995) (9 citations) (Correct)
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S. Ross. Introduction to Stochastic Dynamic Programming. Academic Press, New York, 1983.
Rate of Convergence of Empirical Measures and Costs in.. - Altman, Zeitouni (1994) (1 citation) (Correct)
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Ross S., Introduction to Stochastic Dynamic Programming, Academic Press, 1984. 26
Optimal Buffer Sharing - Cidon, Georgiadis, Guérin.. (1995) (25 citations) (Correct)
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S. Ross, Introduction to Stochastic Dynamic Programming, Academic Press.
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S. M. Ross, Introduction to Stochastic Dynamic Programming, Academic Press, 1983
Dynamic Server Allocation in Heterogeneous Clusters - Palmer, Mitrani (2003) (Correct)
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S. M. Ross, Introduction to Stochastic Dynamic Programming, Academic Press, 1983
Modeling Correlations in Web-Traces and Implications .. - Psounis, Zhu.. (2004) (3 citations) (Correct)
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S. Ross. Introduction to Stochastic Dynamic Programming. Academic Press, 1983.
Dynamic Frequency Scaling with Buffer Insertion for Mixed.. - Lu, Benini, De Micheli (2002) (1 citation) (Correct)
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S. Ross, Introduction to Stochastic Dynamic Programming. NewYork: Academic, 1983.
Policy Optimization for Dynamic Power Management - Benini, Bogliolo, Paleologo, .. (1999) (45 citations) (Correct)
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S. Ross, Introduction to Stochastic Dynamic Programming. New York: Academic, 1983.
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S. M. Ross, Introduction to Stochastic Dynamic Programming, Academic Press, 1983
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S. Ross. Introduction to Stochastic Dynamic Programming. Academic Press, New York, 1983.
Optimal Server Allocation in Reconfigurable Clusters with.. - Palmer, Mitrani (2003) (1 citation) (Correct)
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S. M. Ross, Introduction to Stochastic Dynamic Programming, Academic Press, 1983
September 21, 1994 - On The Poisson (Correct)
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S.M. Ross, Introduction to Stochastic Dynamic Programming, Academic Press, NY (1984).
The Optimality Of Lept In Parallel Machine Scheduling - Chang, Righter (Correct)
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Ross, S. M. (1983) Introduction to Stochastic Dynamic Programming. New York: Academic Press. 10
Scheduling Two Classes of Exponential Jobs on Parallel.. - Chang, Nelson, Pinedo (1991) (1 citation) (Correct)
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Ross, S. (1983) Introduction to Stochastic Dynamic programming, Academic Press.
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