Technical Rate of Substitution of Spectrum in Future Mobile Broadband Provisioning

Dense deployment of base stations (BSs) and multi-antenna techniques are considered key enablers for future mobile networks. Meanwhile, spectrum sharing techniques and utilization of higher frequency bands make more bandwidth available. An important question for future system design is which element is more effective than others. In this paper, we introduce the concept of technical rate of substitution (TRS) from microeconomics and study the TRS of spectrum in terms of BS density and antenna number per BS. Numerical results show that TRS becomes higher with increasing user data rate requirement, suggesting that spectrum is the most effective means of provisioning extremely fast mobile broadband.Comment: 5 pages, 5 figures, conferenc

A novel approach for the hardware implementation of a PPMC statistical data compressor

This thesis aims to understand how to design high-performance compression algorithms suitable for hardware implementation and to provide hardware support for an efficient compression algorithm. Lossless data compression techniques have been developed to exploit the available bandwidth of applications in data communications and computer systems by reducing the amount of data they transmit or store. As the amount of data to handle is ever increasing, traditional methods for compressing data become· insufficient. To overcome this problem, more powerful methods have been developed. Among those are the so-called statistical data compression methods that compress data based on their statistics. However, their high complexity and space requirements have prevented their hardware implementation and the full exploitation of their potential benefits. This thesis looks into the feasibility of the hardware implementation of one of these statistical data compression methods by exploring the potential for reorganising and restructuring the method for hardware implementation and investigating ways of achieving efficient and effective designs to achieve an efficient and cost-effective algorithm. [Continues.

QuickCast: Fast and Efficient Inter-Datacenter Transfers using Forwarding Tree Cohorts

Large inter-datacenter transfers are crucial for cloud service efficiency and are increasingly used by organizations that have dedicated wide area networks between datacenters. A recent work uses multicast forwarding trees to reduce the bandwidth needs and improve completion times of point-to-multipoint transfers. Using a single forwarding tree per transfer, however, leads to poor performance because the slowest receiver dictates the completion time for all receivers. Using multiple forwarding trees per transfer alleviates this concern--the average receiver could finish early; however, if done naively, bandwidth usage would also increase and it is apriori unclear how best to partition receivers, how to construct the multiple trees and how to determine the rate and schedule of flows on these trees. This paper presents QuickCast, a first solution to these problems. Using simulations on real-world network topologies, we see that QuickCast can speed up the average receiver's completion time by as much as $10\times$ while only using $1.04\times$ more bandwidth; further, the completion time for all receivers also improves by as much as $1.6\times$ faster at high loads.Comment: [Extended Version] Accepted for presentation in IEEE INFOCOM 2018, Honolulu, H

Improving the Performance and Energy Efficiency of GPGPU Computing through Adaptive Cache and Memory Management Techniques

Department of Computer Science and EngineeringAs the performance and energy efficiency requirement of GPGPUs have risen, memory management techniques of GPGPUs have improved to meet the requirements by employing hardware caches and utilizing heterogeneous memory. These techniques can improve GPGPUs by providing lower latency and higher bandwidth of the memory. However, these methods do not always guarantee improved performance and energy efficiency due to the small cache size and heterogeneity of the memory nodes. While prior works have proposed various techniques to address this issue, relatively little work has been done to investigate holistic support for memory management techniques. In this dissertation, we analyze performance pathologies and propose various techniques to improve memory management techniques. First, we investigate the effectiveness of advanced cache indexing (ACI) for high-performance and energy-efficient GPGPU computing. Specifically, we discuss the designs of various static and adaptive cache indexing schemes and present implementation for GPGPUs. We then quantify and analyze the effectiveness of the ACI schemes based on a cycle-accurate GPGPU simulator. Our quantitative evaluation shows that ACI schemes achieve significant performance and energy-efficiency gains over baseline conventional indexing scheme. We also analyze the performance sensitivity of ACI to key architectural parameters (i.e., capacity, associativity, and ICN bandwidth) and the cache indexing latency. We also demonstrate that ACI continues to achieve high performance in various settings. Second, we propose IACM, integrated adaptive cache management for high-performance and energy-efficient GPGPU computing. Based on the performance pathology analysis of GPGPUs, we integrate state-of-the-art adaptive cache management techniques (i.e., cache indexing, bypassing, and warp limiting) in a unified architectural framework to eliminate performance pathologies. Our quantitative evaluation demonstrates that IACM significantly improves the performance and energy efficiency of various GPGPU workloads over the baseline architecture (i.e., 98.1% and 61.9% on average, respectively) and achieves considerably higher performance than the state-of-the-art technique (i.e., 361.4% at maximum and 7.7% on average). Furthermore, IACM delivers significant performance and energy efficiency gains over the baseline GPGPU architecture even when enhanced with advanced architectural technologies (e.g., higher capacity, associativity). Third, we propose bandwidth- and latency-aware page placement (BLPP) for GPGPUs with heterogeneous memory. BLPP analyzes the characteristics of a application and determines the optimal page allocation ratio between the GPU and CPU memory. Based on the optimal page allocation ratio, BLPP dynamically allocate pages across the heterogeneous memory nodes. Our experimental results show that BLPP considerably outperforms the baseline and state-of-the-art technique (i.e., 13.4% and 16.7%) and performs similar to the static-best version (i.e., 1.2% difference), which requires extensive offline profiling.clos

Datacenter Traffic Control: Understanding Techniques and Trade-offs

Datacenters provide cost-effective and flexible access to scalable compute and storage resources necessary for today's cloud computing needs. A typical datacenter is made up of thousands of servers connected with a large network and usually managed by one operator. To provide quality access to the variety of applications and services hosted on datacenters and maximize performance, it deems necessary to use datacenter networks effectively and efficiently. Datacenter traffic is often a mix of several classes with different priorities and requirements. This includes user-generated interactive traffic, traffic with deadlines, and long-running traffic. To this end, custom transport protocols and traffic management techniques have been developed to improve datacenter network performance. In this tutorial paper, we review the general architecture of datacenter networks, various topologies proposed for them, their traffic properties, general traffic control challenges in datacenters and general traffic control objectives. The purpose of this paper is to bring out the important characteristics of traffic control in datacenters and not to survey all existing solutions (as it is virtually impossible due to massive body of existing research). We hope to provide readers with a wide range of options and factors while considering a variety of traffic control mechanisms. We discuss various characteristics of datacenter traffic control including management schemes, transmission control, traffic shaping, prioritization, load balancing, multipathing, and traffic scheduling. Next, we point to several open challenges as well as new and interesting networking paradigms. At the end of this paper, we briefly review inter-datacenter networks that connect geographically dispersed datacenters which have been receiving increasing attention recently and pose interesting and novel research problems.Comment: Accepted for Publication in IEEE Communications Surveys and Tutorial

Development of a dc-ac power conditioner for wind generator by using neural network

This project present of development single phase DC-AC converter for wind generator application. The mathematical model of the wind generator and Artificial Neural Network control for DC-AC converter is derived. The controller is designed to stabilize the output voltage of DC-AC converter. To verify the effectiveness of the proposal controller, both simulation and experimental are developed. The simulation and experimental result show that the amplitude of output voltage of the DC-AC converter can be controlled