Sphere Packings, Lattices and Groups

The scalability of modern data centers has become a practical concern and has attracted significant attention in recent years. In contrast to existing solutions that require changes in the network architecture and the routing protocols, this paper proposes using traffic-aware virtual machine (VM) placement to improve the network scalability. By optimizing the placement of VMs on host machines, traffic patterns among VMs can be better aligned with the communication distance between them, e.g. VMs with large mutual bandwidth usage are assigned to host machines in close proximity. We formulate the VM placement as an optimization problem and prove its hardness. We design a two-tier approximate algorithm that efficiently solves the VM placement problem for very large problem sizes. Given the significant difference in the traffic patterns seen in current data centers and the structural differences of the recently proposed data center architectures, we further conduct a comparative analysis on the impact of the traffic patterns and the network architectures on the potential performance gain of traffic-aware VM placement. We use traffic traces collected from production data centers to evaluate our proposed VM placement algorithm, and we show a significant performance improvement compared to existing general methods that do not take advantage of traffic patterns and data center network characteristics.

/pdf/improving-the-scalability-of-data-center-networks-with-2orx1c99p1.pdf

Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement

The growing commercial interest in indoor location-based services (ILBS) has spurred recent development of many indoor positioning techniques. Due to the absence of global positioning system (GPS) signal, many other signals have been proposed for indoor usage. Among them, Wi-Fi (802.11) emerges as a promising one due to the pervasive deployment of wireless LANs (WLANs). In particular, Wi-Fi fingerprinting has been attracting much attention recently because it does not require line-of-sight measurement of access points (APs) and achieves high applicability in complex indoor environment. This survey overviews recent advances on two major areas of Wi-Fi fingerprint localization: advanced localization techniques and efficient system deployment. Regarding advanced techniques to localize users, we present how to make use of temporal or spatial signal patterns, user collaboration, and motion sensors. Regarding efficient system deployment, we discuss recent advances on reducing offline labor-intensive survey, adapting to fingerprint changes, calibrating heterogeneous devices for signal collection, and achieving energy efficiency for smartphones. We study and compare the approaches through our deployment experiences, and discuss some future directions.

/pdf/wi-fi-fingerprint-based-indoor-positioning-recent-advances-1sxd8ckwi0.pdf

Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons

Complexity and Approximation

Discrete optimization problems are everywhere, from traditional operations research planning problems, such as scheduling, facility location, and network design; to computer science problems in databases; to advertising issues in viral marketing. Yet most such problems are NP-hard. Thus unless P = NP, there are no efficient algorithms to find optimal solutions to such problems. This book shows how to design approximation algorithms: efficient algorithms that find provably near-optimal solutions. The book is organized around central algorithmic techniques for designing approximation algorithms, including greedy and local search algorithms, dynamic programming, linear and semidefinite programming, and randomization. Each chapter in the first part of the book is devoted to a single algorithmic technique, which is then applied to several different problems. The second part revisits the techniques but offers more sophisticated treatments of them. The book also covers methods for proving that optimization problems are hard to approximate. Designed as a textbook for graduate-level algorithms courses, the book will also serve as a reference for researchers interested in the heuristic solution of discrete optimization problems.

https://www.designofapproxalgs.com/book.pdf

The Design of Approximation Algorithms

Two simple approximation algorithms for the minimum $k$-cut problem are presented. Each algorithm finds a $k$ cut having weight within a factor of $(2-2/k)$ of the optimal. One of our algorithms is particularly efficient---it requires a total of only $n-1$ maximum flow computations for finding a set of near-optimal $k$ cuts, one for each value of $k$ between 2 and $n$.

Finding $k$ Cuts within Twice the Optimal

Two simple approximation algorithms are presented for the minimum k-cut problem. Each algorithm finds a k-cut having weight within a factor of (2-2/k) of the optimal. One of the algorithms is particularly efficient, requiring a total of only n-1 maximum flow computations for finding a set of near-optimal k-cuts, one for each value of k between 2 and n. >

Finding k-cuts within twice the optimal

Emerging technologies such as Bluetooth are expected to become a ubiquitous solution for providing short range, low power, low cost, pico-cellular wireless connectivity. Bluetooth is a master driven time division duplex (TDD) system that supports an asynchronous channel for data traffic as well as synchronous channels for voice traffic. Data applications running over Bluetooth such as http, ftp and real audio will need transport layer protocols such as TCP and UDP to send packets over the wireless links. In this paper we study several schemes designed to improve the performance of asynchronous data traffic over a Bluetooth piconet that supports multiple active slaves. We propose and compare a number of segmentation and reassembly policies and MAC scheduling algorithms with a view towards enhancing the performance of transport layer sessions. We investigate the effect of different FEC and ARQ schemes at the baseband level, using a two-state Markov channel model for the Bluetooth RF link. We also study how the presence of circuit-switched voice impacts the performance of data traffic.

/pdf/enhancing-performance-of-asynchronous-data-traffic-over-the-3ytev02ph2.pdf

Enhancing performance of asynchronous data traffic over the Bluetooth wireless ad-hoc network

This paper presents a waist-worn Pedestrian Dead Reckoning (PDR) System that requires minimal end-user calibration. The PDR system is based on an Inertial Measurement Unit (IMU) comprising of a tri-axial accelerometer, a tri-axial magnetometer and a tri-axial gyroscope. We propose a novel heading estimation scheme using a quaternion-based extended Kalman filter (EKF) that estimates magnetic disturbances and corrects for them. Accelerometer measurements are used to detect step events and to estimate step lengths. Experimental results show that a relative distance error of about 3% to 8% can be obtained using our methods.

/pdf/strap-down-pedestrian-dead-reckoning-system-4vh3esv046.pdf

Huzur Saran

Papers

Finding $k$ Cuts within Twice the Optimal

Finding k-cuts within twice the optimal

Enhancing performance of asynchronous data traffic over the Bluetooth wireless ad-hoc network

Strap-down Pedestrian Dead-Reckoning system

Covering orthogonal polygons with star polygons: the perfect graph approach