Queueing model based resource optimization for multimedia cloud

Multimedia cloud is a specific cloud computing paradigm, focusing on how cloud can effectively support multimedia services. For multimedia service providers (MSP), there are two fundamental concerns: the quality of service (QoS) and the resource cost. In this paper, we investigate these two fundamental concerns with queueing theory and optimization methods. We introduce a queueing model to characterize the service process in multimedia cloud. Based on the proposed queueing model, we study resource allocation problems in three different scenarios: single-service scenario, multi-service scenario, and priority-service scenario. In each scenario, we formulate and solve the response time minimization problem and the resource cost minimization problem, respectively. We conduct extensive simulations with practical parameters of Windows Azure. Simulation results demonstrate that the proposed resource allocation schemes can optimally allocate cloud resources for each service to achieve the minimal response time under a certain budget or guarantee the QoS provisioning at the minimal resource cost.     

A CHIM-based interactive Tchebycheff procedure for multiple objective decision making

This study presents a new solution procedure for multiple objective programming (MOP). It applies the concept of the normal boundary intersection (NBI) within the framework of the interactive weighted Tchebycheff procedure (IWTP). The proposed procedure is a collaborative approach to overcome the weak points inherent in both the NBI method and IWTP. In order to control the solution procedure of pinpointing a final solution properly, we parameterized the Pareto-frontier via a set of reference point vectors based on the convex hull of individual maxima (CHIM) instead of using the varying weights of each objective. Using well-distributed reference point vectors, we could identify well-distributed Pareto-optimal solutions, thereby eliminating the IWTP filtering stages and reducing the chance of missing the best compromise solution from the decision maker (DM)'s utility point of view. Moreover, by working with a sequence of progressively smaller subsets of reference point vectors, the DM can identify a final solution at earlier stages than with the IWTP.     

Efficient algorithms for minimum-cost flow problems with piecewise-linear convex costs

We present two efficient algorithms for the minimum-cost flow problem in which arc costs are piecewise-linear and convex. Our algorithms are based on novel algorithms of Orlin, which were developed for the case of linear arc costs. Our first algorithm uses the Edmonds-Karp scaling technique. Its complexity isO(M logU(m+n logM)) for a network withn vertices,m arcs, M linear cost segments, and an upper boundU on the supplies and the capacities. The second algorithm is a strongly polynomial version of the first, and it uses Tardos's idea of contraction. Its complexity isO(M logM(m+n logM)). Both algorithms improve by a factor of at leastM/m the complexity of directly applying existing algorithms to a transformed network in which arc costs are linear.The final stage of this work was performed while Ron Shamir was a visitor at DIMACS (Center for Discrete Mathematics and Theoretical Computer Science), Rutgers University. Supported in part by National Science Foundation Grant NSF-STC88-09648, and by Air Force Grants AFOSR-89-0512 and AFOSR-90-0008.     

Optimal parallel algorithms for point-set and polygon problems

In this paper we give parallel algorithms for a number of problems defined on point sets and polygons. All our algorithms have optimalT(n) * P(n) products, whereT(n) is the time complexity andP(n) is the number of processors used, and are for the EREW PRAM or CREW PRAM models. Our algorithms provide parallel analogues to well-known phenomena from sequential computational geometry, such as the fact that problems for polygons can oftentimes be solved more efficiently than point-set problems, and that nearest-neighbor problems can be solved without explicitly constructing a Voronoi diagram.The research of R. Cole was supported in part by NSF Grants CCR-8702271, CCR-8902221, and CCR-8906949, by ONR Grant N00014-85-K-0046, and by a John Simon Guggenheim Memorial Foundation fellowship. M. T. Goodrich's research was supported by the National Science Foundation under Grant CCR-8810568 and by the National Science Foundation and DARPA under Grant CCR-8908092.     

用改进激活集合法优化VLSI互连线

本文介绍离散线宽的单根互连线时延优化的一种算法：改进激活集合法(MASM)。基于Elmore时延模型，单根互连线的优化问题能被表述为凸二次规划，能在多项式时间里求解，得到最优结果。它是一种非常高效的算法．计算结果证明了此算法的有效性。     

Determination and mapping of measurement and design coordinate systems using computational geometric techniques

Automotive industry is one of the most prospective manufacturing industries in the present world. In order to keep up with the challenges of competitors, the automobile industries are taking necessary steps to satisfy the customers by supplying products of good quality at lower cost. The machine vision inspection systems play an important role in quality control with their accurate dimensional measurement capability. The data obtained from these systems have to be analyzed using appropriate algorithms. In the present work, algorithms based on computational geometric techniques have been developed, to set up the coordinate system for the data obtained from the laser vision system for subframe and lower arm assembly of automobile front chassis module. The concept of a convex hull and a modified Voronoi diagram are utilized for establishing the coordinate system for measurement data. The design coordinate system is determined using the Voronoi diagrams. The measurement and design coordinate systems are mapped using 3D transformations. The results obtained for the sample data are also presented in this paper.     

On the conditions for success of sklansky''s convex hull algorithm

The convex hull algorithm for simple polygons, due to Sklansky, fails in some cases, but its extreme simplicity, compared to the later algorithms, revived an interest in this algorithm. A sufficient condition for its success was given recently by Toussaint and Avis. They have proved that the algorithm works for polygons known as weakly externally visible polygons.

In this paper a new notion called external left visibility is introduced and it is shown that this is a necessary and sufficient condition for the success of Sklansky's algorithm. Moreover, algorithms testing simple polygons for external left visibility and weak external visibility are given.     

A Linear-Time Approximation Scheme for Minimum Weight Triangulation of Convex Polygons

A linear-time heuristic for minimum weight triangulation of convex polygons is presented. This heuristic produces a triangulation of length within a factor 1 + ε from the optimum, where ε is an arbitrarily small positive constant. This is the first subcubic algorithm that guarantees such an approximation factor, and it has interesting applications. Received November 21, 1996; revised March 9, 1997.