44866557
A Multiple-Criterion Model for Machine Scheduling
We consider a scheduling problem involving a single processor being utilized by two or more customers. Traditionally, such scenarios are modeled by assuming that each customer has the same criterion. In practice, this assumption may not hold. Instead of using a single criterion, we examine the implications of minimizing an aggregate scheduling objective function in which jobs belonging to different customers are evaluated based on their individual criteria. We examine three basic scheduling criteria: minimizing makespan, minimizing maximum lateness, and minimizing total weighted completion time. Although determining a minimum-cost schedule according to any one of these criteria is polynomially solvable, we demonstrate that when minimizing a mix of these criteria, the problem becomes NP-hard.
42617820
The Optimal Deployment of Filters to Limit Forged Address Attacks in Communication Networks
We consider forged address attacks in communication networks, wherein an attacking node forges the address of communication requests sent to a victim node. Unless the victim can ascertain the origin of the attack, it must cease to respond to communication requests until the attack subsides. We examine the problem of identifying a minimum cardinality subset of nodes on a network, such that placing route-based filters on these nodes provides a prescribed level of security. We model the problem as an integer program and illustrate its performance on randomly generated networks. Next, we develop a greedy heuristic to deploy filters in these networks, and compare its solution quality to that of the integer programming approach. These findings are then used to motivate both the importance of addressing this problem by optimal techniques and to motivate future research approaches for solving such problems.
38357274
Algorithms for distributing telecommunication traffic on a multiple-ring SONET-based network
Abstract We examine a problem arising in the assignment of telecommunication traffic to a set of synchronous optical network (SONET) rings. Prior SONET design algorithms determine node-to-ring assignments while concurrently prescribing an assignment of traffic to the rings. However, due to uncertain voice and data demand among client nodes, it may become necessary to revise the planned routing scheme once the true values of this data are realized. We develop a minimum cost flow algorithm for the case in which demands may be split across multiple rings, and provide a transformation to a maximum flow problem for specially structured data. The case in which each demand pair may be routed on only one ring is proven to be NP-hard, and we accordingly provide a powerful heuristic and an effective standard valid inequality generation scheme to optimally solve this problem within reasonable computational limits. The effectiveness of our approach is demonstrated on a set of randomly generated test data.
41948538
Enhanced Model Representations for an Intra-Ring Synchronous Optical Network Design Problem Allowing Demand Splitting
In this paper, we consider a network design problem arising in the context of deploying synchronous optical networks (SONET) using a unidirectional path switched ring architecture, a standard of transmission using optical fiber technology. Given several rings of this type, the problem is to find an assignment of nodes to possibly multiple rings, and to determine what portion of demand traffic between node pairs spanned by each ring should be allocated to that ring. The constraints require that the demand traffic between each node pair should be satisfiable given the ring capacities, and that no more than a specified maximum number of nodes should be assigned to each ring. The objective function is to minimize the total number of node-to-ring assignments, and hence, the capital investment in add-drop multiplexer equipments. We formulate the problem as a mixed-integer programming model, and propose several alternative modeling techniques designed to improve the mathematical representation of this problem. We then develop various classes of valid inequalities for the problem along with suitable separation procedures for tightening the representation of the model, and accordingly, prescribe an algorithmic approach that coordinates tailored routines with a commercial solver (CPLEX). We also propose a heuristic procedure which enhances the solvability of the problem and provides bounds within 5--13% of the optimal solution. Promising computational results are presented that exhibit the viability of the overall approach and that lend insights into various modeling and algorithmic constructs.
37305588
A dynamic programming algorithm for the conditional covering problem on tree graphs
In a previous article, we presented algorithms for solving the Conditional Covering Problem (CCP) on path and extended star graphs. The CCP on these graphs can be solved in O(n2) time, where n is the number of nodes in the graph. In this article, we propose a new dynamic programming procedure to solve the CCP on tree graphs. This recursion works from the leaf nodes of the tree up to the root node, using notions of protected and unprotected costs as done for the CCP path algorithm in our previous work. We introduce new preliminary routines and data structures to merge information from subpaths and subtrees, resulting in an O(n4) algorithm to optimally solve the problem. © 2005 Wiley Periodicals, Inc. NETWORKS, Vol. 46(4), 186–197 2005
10698762
Exact and heuristic algorithms for solving the generalized minimum filter placement problem
We consider a problem of placing route-based filters in a communication network to limit the number of forged address attacks to a prescribed level. Nodes in the network communicate by exchanging packets along arcs, and the originating node embeds the origin and destination addresses within each packet that it sends. In the absence of a validation mechanism, one node can send packets to another node using a forged origin address to launch an attack against that node. Route-based filters can be established at various nodes on the communication network to protect against these attacks. A route-based filter examines each packet arriving at a node, and determines whether or not the origin address could be legitimate, based on the arc on which the packet arrives, the routing information, and possibly the destination. The problem we consider seeks to find a minimum cardinality subset of nodes to filter so that the prescribed level of security is achieved. We formulate a mixed-integer programming model for the problem and derive valid inequalities for this model by identifying polynomially-solvable subgraphs of the communication network. We also present three heuristics for solving the filter placement problem and evaluate their performance against the optimal solution provided by the mixed-integer programming model.
9479207
Ontology-based multi-classification learning for video concept detection
In this paper, an ontology-based multi-classification learning algorithm is adopted to detect concepts in the NIST TREC-2003 video retrieval benchmark which defines 133 video concepts, organized hierarchically and each video data can belong to one or more concepts. The algorithm consists of two steps. In the first step, each single concept model is constructed independently. In the second step, ontology-based concept learning improves the accuracy of the individual concept by considering the possible influence relations between concepts based on a predefined ontology hierarchy. The advantage of ontology learning is that its influence path is based on an ontology hierarchy, which has real semantic meanings. Besides semantics, it also considers the data correlation to decide the exact influence assigned to each path, which makes the influence more flexible according to data distribution. This learning algorithm can be used for multiple topic document classification such as Internet documents and video documents. We demonstrate that precision-recall can be significantly improved by taking ontology into account
3042095
Measuring the Robustness of Resource Allocations in a Stochastic Dynamic Environment
Heterogeneous distributed computing systems often must operate in an environment where system parameters are subject to uncertainty. Robustness can be defined as the degree to which a system can function correctly in the presence of parameter values different from those assumed. We present a methodology for quantifying the robustness of resource allocations in a dynamic environment where task execution times are stochastic. The methodology is evaluated through measuring the robustness of three different resource allocation heuristics within the context of a stochastic dynamic environment. A Bayesian regression model is fit to the combined results of the three heuristics to demonstrate the correlation between the stochastic robustness metric and the presented performance metric. The correlation results demonstrated the significant potential of the stochastic robustness metric to predict the relative performance of the three heuristics given a common objective function.
14227060
Visualization of design knowledge component relationships to facilitate reuse
Within the software development process, reuse at the requirements level has become an increasingly more compelling notion. Following a human-centric approach, this work focuses on both requirements and design solution reuse using a design knowledge repository. In recent years, many improvements have been made to increase reuse through design knowledge repositories, but retrieval of knowledge in the context of design activities continues to be a formidable challenge. We propose a new system, called CERVi, to browse a repository through visualization by exploiting relationships between units of knowledge (in our case, claims). These relationships are key to finding the most appropriate reusable knowledge based on design conditions. Testing shows that CERVi enhances the design knowledge selection process and helps users proceed through structured design decision making. Our approach would be most useful to those interested in unlocking the potential of design knowledge reuse.
568831
Evolving fuzzy decision tree structure that adapts in real-time
A fuzzy logic algorithm has been developed that automatically allocates electronic attack (EA) resources distributed over different platforms in real-time. The controller must be able to make decisions based on rules provided by experts. The fuzzy logic approach allows the direct incorporation of expertise. Genetic algorithm based optimization is conducted to determine the form of the membership functions for the fuzzy root concepts. The resource manager is made up of five parts, the isolated platform model, the multi-platform model, the fuzzy EA model, the fuzzy parameter selection tree and the fuzzy strategy tree. Automatic determination of fuzzy decision tree topology using a genetic program, an algorithm that creates other algorithms is discussed. A comparison to subtrees obtained using a genetic program and those constructed by hand from rules is made. Experiments designed to test various concepts in the expert system are discussed, including its ability to allow multiple platforms to self-organize.
5328327
Fault-tolerant parallel applications using queues and actions
There are many techniques supporting execution of large computations over a network of workstations (NOW) but data intensive computations are usually run on high performance parallel machines. A NOW comprising individual user's machines typically has a low performance interconnect and suffers arbitrary changes of availability. Exploiting such resources to execute data intensive computations is difficult but even in a more constrained environment there is an unfulfilled need for fault-tolerance. The structuring approach presented fulfills this need. Performance exceeding 100 Mflop/s is demonstrated for large fault-tolerant out of core examples of matrix multiplication and Cholesky factorisation using five 133 MHz Pentium compute machines.