Deb Ghosh

File allocation problem: comparison of models with worst case and average communication delays

A major design issue facing the designer of a distributed computing system involves the determination of the number of file copies and their locations in the distributed environment. This problem is commonly referred to as the file allocation problem (FAP). This paper considers two FAP models that seek to minimize operating costs (i.e., the total cost of file storage and query/update communication). The first model ensures the attainment of acceptable levels of communication delay during peak network traffic periods (worst-case scenario). The second model considers average communication delay. Unlike previous FAP research, the proposed models treat communication delay on a query-by-query basis, and not as a single, system-wide average delay constraint. For both models, a Lagrangian relaxation-based solution procedure is proposed for the resulting 0/1 integer programming problem. In the case of average delays, we utilize a hybrid model combining analytic and simulation procedures. The results of computatio...

Perfect and Ideal 0, ±1 Matrices

A 0, ±1 matrix A is said to be perfect (resp. ideal) if the corresponding generalized packing ( resp. covering) polytope is integral. Given a 0, ±1 matrix A, we construct a 0, 1 matrix that is perfect if and only if A is perfect. A similar result is obtained for the generalized covering problem. We also extend some known results on perfect 0, 1 matrices to the 0, ±1 case.

Configuring express pipes in emerging telecommunication networks

Over the past few decades, telecommunication networks have been designed and implemented to support a wide variety of services, including the integration of a customers voice, data and video communication requirements. Rapid advances in VLSI technology, coupled with declining costs of fiber-based transmission systems, have resulted in high speed, broadband digital networks capable of transporting hundreds of megabits per second. At the same time, a growing number of business customers have expressed the need for controlling and re-configuring their private networks themselves, to better match their business needs. In digital networks, a key component in allowing this flexibility is the establishment of intelligent Digital Cross-Connect Systems (DCS). With DCS, network re-configuration, including bandwidth allocation and topology changes, can be made on a near real time basis, or be scheduled on a reservation basis to take place once (or multiple times) in the future. In this paper, we study the application of digital cross-connect systems in packet switched networks. These systems allow the designer to configure express pipes or direct links between source—destination pairs that have high communication traffic requirements. Since express pipes are configured by borrowing channels from the underlying, leased, backbone network, establishing a direct link reduces the number of “free” channels available for regular switched traffic, and consequently have the potential of increasing queueing delays. We formulate the problem of judiciously selecting express pipes as a 0/1 nonlinear integer programming problem, that seeks to minimize overall network delay. An efficient Lagrangian relaxation based solution procedure is proposed. Results of computational experiments with the proposed solution technique are also reported.

Partitioning the Attribute Set for a Probabilistic Reasoning System

The ability of a computerized system to model the reasoning process of humans has become an important area of research. This research considers a probabilistic reasoning system for applications that require decision making under uncertain conditions. The reasoning system captures the uncertainty associated with different feasible outcomes, and based on historical data, provides users with a measure of this uncertainty. To make accurate predictions, the scheme requires that predictive variables that are not conditionally independent of each other given the outcome be grouped into compound attributes for the purpose of estimating probabilities. These compound attributes partition the entire set of predictive attributes into disjoint sets. An important design issue, then, is that the appropriate partitioning scheme be obtained before the reasoning scheme is used in practice. We formulate the problem of finding the optimal partitioning scheme, and present five different (although related) heuristic techniques to induce partitions from historical cases. Using simulated data, all five techniques are shown to capture accurately underlying dependencies across attributes when a reasonable amount of historical data is available for analysis. In situations where few historical cases are available, the induced structures are less accurate. In such situations, the performance of induced structures for making probability predictions is nevertheless found to be as good as that when using the true structure. Finally, we test for external validity by applying the techniques on a real-world dataset on credit applications for a bank. We show, using this dataset, that (i) the classificatory performance of the reasoning system using structures generated by the heuristic techniques is as good as the performance of the widely used decision tree induction algorithm C4.5, and (ii) the induced structures are able to provide reliable probability estimates for making decisions in environments with asymmetric misclassification costs.

File allocation involving worst case response times and link capacities: Model and solution procedure

Abstract A major design issue facing the designer of a distributed computing system involves the determination of the number of file copies and their locations in the distributed environment. This problem is commonly referred to as the file allocation problem (FAP). In this paper, a FAP model is formulated that seeks to obtain the lowest cost file allocation strategy, that ensures the attainment of acceptable levels of response times during peak demand periods, for all on-line queries. Unlike previous FAP research, the proposed model treats response time on a query-by-query basis, and not as a single, system wide average delay constraint . A Laggrangian relaxation based solution procedure is proposed for the resulting 0/1 integer programming problem. Results of computational experiments with the proposed solution procedure are reported.

A solution procedure for the file allocation problem with file availability and response time

A major design issue facing the designer of a distributed computing system involves the dertermination of the number of file copies and their locations in the distributed environment. This problem is commonly referred to as the file allocation problem (FAP). In this paper, a FAP model is formulated that seeks to obtain the lowest cost file allocation strategy. The model ensures, for all on-line queries, the attainment of acceptable levels of (i) response times during peak demand periods, and (ii) file availability. Unlike previous FAP research, the proposed model treats response time on a query-by-query basis, and not as a single, system-wide average delay constraint. Similarly, file availability is treated on a file-by-file basis. A branch-and-bound solution procedure is proposed for solving the resulting 01 integer programming problem to optimality. Results of computational experiment with the proposed solution procedure are reported.

A probabilistic reasoning model: formulation and control strategy

Abstract It has been recognized that past experiences of a decision maker often plays a pivotal role in solving new problem instances. Therefore, the ability to model human reasoning processes has become an important subject of research in recent years. In many applications, the reasoning process must deal with uncertainty inherent in the problem domain. This research addresses the issue of supporting the model formulation and data acquisition processes for situations that (i) operate under uncertain conditions, and (ii) utilize evidential information that is gathered in stages. A theoretical framework is presented for the probabilistic formulation of the reasoning process that incorporates past experiences. The model is validated by testing its performance on simulated data, and is shown to work well when a sufficiently large number of cases are available for estimating probabilities. The probabilistic reasoning system can revise beliefs in an intuitively appealing and theoretically sound manner when information is acquired in an incremental fashion. Two dynamic information gathering strategies are discussed for such a reasoning system, one using information theoretic techniques, and the other using decision theoretic techniques.

Allocating modelling resources in distributed model management systems

Due to the growing popularity of distributed computing systems and the increased level of modelling activity in most organizations, significant benefits can be realized through the implementation of distributed model management systems (DMMS). These systems can be defined as a collection of logically related modelling resources distributed over a computer network. In several ways, functions of DMMS are isomorphic to those of distributed database systems. In general, this paper examines issues viewed as central to the development of distributed model bases (DMB). Several criteria relevant to the overall DMB design problem are discussed. Specifically, this paper focuses on the problem of distributing decision models and tools (solvers), henceforth referred to as theModel Allocation Problem (MAP), to individual computing sites in a geographically dispersed organization. In this research, a 0/1 integer programming model is formulated for the MAP, and an efficient dual ascent heuristic is proposed. Our extensive computational study shows in most instances heuristic-generated solutions which are guaranteed to be within 1.5–7% of optimality. Further, even problems with 420 integer and 160,000 continuous variables took no more than 60 seconds on an IBM 3090-600E computer.

A decision support methodology for the file allocation problem in distributed computing systems

A methodology using the decision-support-systems approach for solving realistically sized file-allocation problems in distributed computing systems is developed. The methodology allows the designer: (1) to deal with the operating-cost and response-time considerations simultaneously; (2) to adapt to changes in the strategic emphasis between the two objectives; and (3) to incorporate time-varying file-access patterns and cost structures. A solution procedure which integrates combinatorial optimization and simulation techniques is developed and validated by numerical experiments.<<ETX>>

Knowledge‐Based Simulation for Business Process Redesign

This research focuses on knowledge-based simulation modeling for process redesign. Though the proposed technique can be utilized for ‘starting with a clean slate’, it is particularly well suited for situations where an existing process is already documented, and an attempt is being made to improve or redesign this process. We present a methodology that utilizes the basic process structure (represented in a matrix form), and using a rule-based knowledge acquisition system, interacts with the analyst to construct the process knowledge base. Once all the knowledge has been acquired, the system automatically generates an executable simulation model. Major benefits of this algorithmic approach include (1) reduced model building time, (2) increased analyst productivity, and (3) the assurance that basic process characteristics are not accidentally omitted in the simulation model. To test the validity and applicability of the proposed technique a prototype system has been developed that generates simulation programs in SLAM.© 1997 John Wiley & Sons, Ltd.