Abstract — Real-world wireless sensing applications demand system platforms with a wide range of size, cost, power consumption, connectivity, performance, and flexibility requirements. These goals cannot be achieved without understanding the nature of the sensing functions in the first place, which

on applications where computation costs are dominant, Shredder is designed to operate in both compute-and dataintensive environments. To allow this, Shredder provides several novel optimizations aimed at reducing the cost of transferring data between host (CPU) and GPU, fully utilizing the multicore architecture

Scheduling in an input buffered switch can be viewed as repeated matching (corresponding to once every time slot) in a bipartite graph. It has been shown that scheduling algorithms based on maximum weight matching (MWM) with queue-lengths as the weights, leads to excellent performance in terms

Recently, machine learning algorithms have successfully entered large-scale real-world industrial applications (e.g. search engines and email spam filters). Here, the CPU cost during testtime must be budgeted and accounted for. In this paper, we address the challenge of balancing the test-time cost

of determining the Euclidean distance from a point to the perfect bipartite matching polytope and the feasible flows polyhedron. We first show that the idea of minimum mean cycle canceling originally worked out for linear cost circulations by Goldberg and Tarjan [5] and extended to some other problems [2, 4, 12

Abstract—With the ever-increasing number and complexity of applications deployed in data centers, the underlying network infrastructure can no longer sustain such a trend and exhibits several problems, such as resource fragmentation and low bi-section bandwidth. In pursuit of a real-world

Abstract—With the ever-increasing number and complexity of applications deployed in data centers, the underlying network infrastructure can no longer sustain such a trend and exhibits several problems, such as resource fragmentation and low bisection bandwidth. In pursuit of a real-world applicable

networks, which provides a flexible and promising solution for real-world applications.

efficient algorithms for optimal assignment that employ novel edge-pruning strategies, based on the spatial properties of the problem. Additionally, we develop incremental techniques that maintain an optimal assignment (in the presence of updates) with a processing cost several times lower than CCA re-computation

April 29, 2011Für UnsShe had never before seen a rabbit with either a waistcoat-pocket, or a watch to take out of it, and burning with curiosity, she ran across the field after it Lewis CarrollThe following document, written under the supervision of Dr. reviewed by: