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A Computational Study on Bounding the Makespan Distribution in Stochastic Project Networks
 ANNALS OF OPERATIONS RESEARCH
, 1998
"... Given a stochastic project network with independently distributed activity durations, several approaches to bound the distribution function of the project completion time have been proposed. We have implemented the most promising of these algorithms and compare their behavior on a basis of nearly 20 ..."
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Cited by 14 (1 self)
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Given a stochastic project network with independently distributed activity durations, several approaches to bound the distribution function of the project completion time have been proposed. We have implemented the most promising of these algorithms and compare their behavior on a basis of nearly 2000 instances with up to 1200 activities of different testbeds. We propose a suitable numerical representation of the given distributions which is the basis for excellent computational results.
A heuristic for optimizing stochastic activity networks with applications to statistical digital circuit sizing
 IEEE Transactions on Circuits and SystemsI
, 2004
"... A deterministic activity network (DAN) is a collection of activities, each with some duration, along with a set of precedence constraints, which specify that activities begin only when certain others have finished. One critical performance measure for an activity network is its makespan, which is th ..."
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Cited by 12 (4 self)
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A deterministic activity network (DAN) is a collection of activities, each with some duration, along with a set of precedence constraints, which specify that activities begin only when certain others have finished. One critical performance measure for an activity network is its makespan, which is the minimum time required to complete all activities. In a stochastic activity network (SAN), the durations of the activities and the makespan are random variables. The analysis of SANs is quite involved, but can be carried out numerically by Monte Carlo analysis. This paper concerns the optimization of a SAN, i.e., the choice of some design variables that affect the probability distributions of the activity durations. We concentrate on the problem of minimizing a quantile (e.g., 95%) of the makespan, subject to constraints on the variables. This problem has many applications, ranging from project management to digital integrated circuit (IC) sizing (the latter being our motivation). While there are effective methods for optimizing DANs, the SAN optimization problem is much more difficult; the few existing methods cannot handle largescale problems.
CIPDATA LIBRARY TECHNISCHE UNIVERSITEIT EINDHOVEN
"... Towards predictable deepsubmicron manufacturing ..."
Diffusion Activity Networks
, 1999
"... An activity network (AN) is a directed acyclic graph with n nodes and A arcs. The nodes are numbered from 1 to n so that an arc always leads from a smaller numbered node to a higher numbered node. The graph has only one node with no incident arcs, which is called the starting node and numbered 1. No ..."
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An activity network (AN) is a directed acyclic graph with n nodes and A arcs. The nodes are numbered from 1 to n so that an arc always leads from a smaller numbered node to a higher numbered node. The graph has only one node with no incident arcs, which is called the starting node and numbered 1. Node n is the only node with no emanating arcs and is named the terminal node. An arc represents an activity and a node the start or the culmination of that activity. The terminal node represents the end of the project. These kinds ofgraphsarealsoreferredtoasActivity on Arc (AoA) representation of AN. In DiAN the process represented by the arcs is a diffusion process, the state of which is identified with the remaining work content (rwc). The process starts at time ‘0 ’ at rwc = 1 with a negative drift coefficient. An absorbing barrier is placed at rwc = 0 to identify with the end of the process. The completion time of an activity is thus the first passage time of such a diffusion process. The paradigm of DiAN, while offering an enhanced modeling concept, raises many questions regarding computational challenges, definition of project management metrics and applicability of such a tool in areas beyond project management. The thesis primarily focuses
SystemLevel ProcessDriven Variability Analysis for Single and Multiple VoltageFrequency Island Systems*
"... The problem of determining bounds for application completion times running on generic systems comprised of single or multiple voltagefrequency islands (VFIs) with arbitrary topologies is addressed in the context of manufacturingdriven variability. The approach provides an exact solution for the sys ..."
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The problem of determining bounds for application completion times running on generic systems comprised of single or multiple voltagefrequency islands (VFIs) with arbitrary topologies is addressed in the context of manufacturingdriven variability. The approach provides an exact solution for the systemlevel timing yield in single clock, single voltage (SSV) and VFI systems with an underlying treebased topology, and a tight upper bound for generic, nontree based topologies. The results show that: (a) timing yield for overall sourcetosink completion time for generic systems can be modeled in an exact manner for both SSV and VFI systems; and (b) multiple VFI, latencyconstrained systems can achieve 1190 % higher timing yield than their SSV counterparts. The results are proven formally and supported by experimental results on two embedded applications, namely software defined radio and MPEG2 encoder.
Halt or Continue: Estimating Progress of Queries in the Cloud
"... Abstract. With cloudbased data management gaining more ground by day, the problem of estimating the progress of MapReduce queries in the cloud is of paramount importance. This problem is challenging to solve for two reasons: i) cloud is typically a largescale heterogeneous environment, which requi ..."
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Abstract. With cloudbased data management gaining more ground by day, the problem of estimating the progress of MapReduce queries in the cloud is of paramount importance. This problem is challenging to solve for two reasons: i) cloud is typically a largescale heterogeneous environment, which requires progress estimation to tailor to nonuniform hardware characteristics, and ii) cloud is often built with cheap and commodity hardware that is prone to fail, so our estimation should be able to dynamically adjust. These two challenges were largely unaddressed in previous work. In this paper, we propose PEQC, a Progress Estimator of Queries composed of MapReduce jobs in the Cloud. Our work is able to apply to a heterogeneous setting and provides a dynamically update mechanism to repair the network when failure occurs. We experimentally validate our techniques on a heterogeneous cluster and results show that PEQC outperforms the state of the art.
A hierarchical approach for bounding the completion time distribution of
, 1999
"... stochastic task graphs ..."
On the Complexity of NonOverlapping Multivariate Marginal Bounds for Probabilistic Combinatorial Optimization Problems
, 2010
"... Given a combinatorial optimization problem with an arbitrary partition of the set of random objective coefficients, we evaluate the tightest possible bound on the expected optimal value for joint distributions consistent with the given multivariate marginals of the subsets in the partition. For univ ..."
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Given a combinatorial optimization problem with an arbitrary partition of the set of random objective coefficients, we evaluate the tightest possible bound on the expected optimal value for joint distributions consistent with the given multivariate marginals of the subsets in the partition. For univariate marginals, this bound was first proposed by Meilijson and Nadas (Journal of Applied Probability, 1979). We generalize the bound to nonoverlapping multivariate marginals using multiple choice integer programming. For discrete distributions, new instances of polynomial time computable multivariate marginal bounds are identified. For the problem of selecting up to M items out a set of N items of maximum total weight, the bound is shown to be computable in polynomial time, when the size of each subset in the partition is O(log N). For an activityonarc PERT network, the partition is naturally defined by subsets of incoming arcs into nodes. The worstcase expected project duration is shown to be computable in time polynomial in the maximum number of scenarios for any subset and the size of the network. An instance of a polynomial time solvable two stage stochastic program arising from project crashing is identified. An important feature of the bound is that it is exactly achievable by a joint distribution, unlike many of the existing bounds.