TCP over High Speed Variable Capacity Links: A Simulation Study for Bandwidth Allocation

New optical network technologies provide opportunities for fast, controllable bandwidth management. These technologies can now explicitly provide resources to data paths, creating demand driven bandwidth reservation across networks where an applications bandwidth needs can be meet almost exactly. Dynamic synchronous Transfer Mode (DTM) is a gigabit network technology that provides channels with dynamically adjustable capacity. TCP is a reliable end-to-end transport protocol that adapts its rate to the available capacity. Both TCP and the DTM bandwidth can react to changes in the network load, creating a complex system with inter-dependent feedback mechanisms. The contribution of this work is an assessment of a bandwidth allocation scheme for TCP flows on variable capacity technologies. We have created a simulation environment using ns-2 and our results indicate that the allocation of bandwidth maximises TCP throughput for most flows, thus saving valuable capacity when compared to a scheme such as link over-provisioning. We highlight one situation where the allocation scheme might have some deficiencies against the static reservation of resources, and describe its causes. This type of situation warrants further investigation to understand how the algorithm can be modified to achieve performance similar to that of the fixed bandwidth case

DTMsim - DTM channel simulation in ns

Dynamic Transfer Mode (DTM) is a ring based MAN technology that provides a channel abstraction with a dynamically adjustable capacity. TCP is a reliable end to end transport protocol capable of adjusting its rate. The primary goal of this work is investigate the coupling of dynamically allocating bandwidth to TCP flows with the affect this has on the congestion control mechanism of TCP. In particular we wanted to find scenerios where this scheme does not work, where either all the link capacity is allocated to TCP or congestion collapse occurs and no capacity is allocated to TCP. We have created a simulation environment using ns-2 to investigate TCP over networks which have a variable capacity link. We begin with a single TCP Tahoe flow over a fixed bandwidth link and progressively add more complexity to understand the behaviour of dynamically adjusting link capacity to TCP and vice versa

DTM: a service for managing data persistency and data replication in network-enabled server environments

International audienceNetwork-Enabled Servers (NES) environments are valuable candidates to provide simple computing grid access. These environments allow transparent access to a set of computational servers via Remote Procedure Call mechanisms. In this context, a challenge is to increase performances by decreasing data tra?c. This paper presents DTM (Data Tree Manager) a data management service for NES environments. Based on the notions of data persistency and data replication, DTM proposes a set of e?cient policies which minimise computation times by decreasing data transfers between the clients and the platform. From the end-user point of view, DTM is accessible through a simple and transparent API. In the remainder, we describe DTM and its implementation in the DIET platform. We also present a set of experimental results which exhibit the feasibility and the e?ciency of our approach

BifurKTM: Approximately Consistent Distributed Transactional Memory for GPUs

We present BifurKTM, the first read-optimized Distributed Transactional Memory system for GPU clusters. The BifurKTM design includes: GPU KoSTM, a new software transactional memory conflict detection scheme that exploits relaxed consistency to increase throughput; and KoDTM, a Distributed Transactional Memory model that combines the Data- and Control- flow models to greatly reduce communication overheads. Despite the allure of huge speedups, GPUs are limited in use due to their programmability and extreme sensitivity to workload characteristics. These become daunting concerns when considering a distributed GPU cluster, wherein a programmer must design algorithms to hide communication latency by exploiting data regularity, high compute intensity, etc. The BifurKTM design allows GPU programmers to exploit a new workload characteristic: the percentage of the workload that is Read-Only (e.g. reads but does not modify shared memory), even when this percentage is not known in advance. Programmers designate transactions that are suitable for Approximate Consistency, in which transactions "appear" to execute at the most convenient time for preventing conflicts. By leveraging Approximate Consistency for Read-Only transactions, the BifurKTM runtime system offers improved performance, application flexibility, and programmability without introducing any errors into shared memory. Our experiments show that Approximate Consistency can improve BkTM performance by up to 34x in applications with moderate network communication utilization and a read-intensive workload. Using Approximate Consistency, BkTM can reduce GPU-to-GPU network communication by 99%, reduce the number of aborts by up to 100%, and achieve an average speedup of 18x over a similarly sized CPU cluster while requiring minimal effort from the programmer

Combined Dynamic Thermal Management Exploiting Broadcast-Capable Wireless Network-on-Chip Architecture

With the continuous scaling of device dimensions, the number of cores on a single die is constantly increasing. This integration of hundreds of cores on a single die leads to high power dissipation and thermal issues in modern Integrated Circuits (ICs). This causes problems related to reliability, timing violations and lifetime of electronic devices. Dynamic Thermal Management (DTM) techniques have emerged as potential solutions that mitigate the increasing temperatures on a die. However, considering the scaling of system sizes and the adoption of the Network-on-Chip (NoC) paradigm to serve as the interconnection fabric exacerbates the problem as both cores and NoC elements contribute to the increased heat dissipation on the chip. Typically, DTM techniques can either be proactive or reactive. Proactive DTM techniques, where the system has the ability to predict the thermal profile of the chip ahead of time are more desirable than reactive DTM techniques where the system utilizes thermal sensors to determine the current temperature of the chip. Moreover, DTM techniques either address core or NoC level thermal issues separately. Hence, this thesis proposes a combined proactive DTM technique that integrates both core level and NoC level DTM techniques. The combined DTM mechanism includes a dynamic temperature-aware routing approach for the NoC level elements, and includes task reallocation heuristics for the core level elements. On-chip wireless interconnects recently envisioned to enable energy-efficient data exchange between cores in a multicore chip will be used to provide a broadcast-capable medium to efficiently distribute thermal control messages to trigger and manage the DTM. Combining the proactive DTM technique with on-chip wireless interconnects, the on-chip temperature is restricted within target temperatures without significantly affecting the performance of the NoC based interconnection fabric of the multicore chip

Signal Detection and Estimation for MIMO radar and Network Time Synchronization

The theory of signal detection and estimation concerns the recovery of useful information from signals corrupted by random perturbations. This dissertation discusses the application of signal detection and estimation principles to two problems of significant practical interest: MIMO (multiple-input multiple output) radar, and time synchronization over packet switched networks. Under the first topic, we study the extension of several conventional radar analysis techniques to recently developed MIMO radars. Under the second topic, we develop new estimation techniques to improve the performance of widely used packet-based time synchronization algorithms. The ambiguity function is a popular mathematical tool for designing and optimizing the performance of radar detectors. Motivated by Neyman-Pearson testing principles, an alternative definition of the ambiguity function is proposed under the first topic. This definition directly associates with each pair of true and assumed target parameters the probability that the radar will declare a target present. We demonstrate that the new definition is better suited for the analysis of MIMO radars that perform non-coherent processing, while being equivalent to the original ambiguity function when applied to conventional radars. Based on the nature of antenna placements, transmit waveforms and the observed clutter and noise, several types of MIMO radar detectors have been individually studied in literature. A second investigation into MIMO radar presents a general method to model and analyze the detection performance of such systems. We develop closed-form expressions for a Neyman-Pearson optimum detector that is valid for a wide class of radars. Further, general closed-form expressions for the detector SNR, another tool used to quantify radar performance, are derived. Theoretical and numerical results demonstrating the value of the proposed techniques to optimize and predict the performance of arbitrary radar configurations are presented.There has been renewed recent interest in the application of packet-based time synchronization algorithms such as the IEEE 1588 Precision Time Protocol (PTP), to meet challenges posed by next-generation mobile telecommunication networks. In packet based time synchronization protocols, clock phase offsets are determined via two-way message exchanges between a master and a slave. Since the end-to-end delays in packet networks are inherently stochastic in nature, the recovery of phase offsets from message exchanges must be treated as a statistical estimation problem. While many simple intuitively motivated estimators for this problem exist in the literature, in the second part of this dissertation we use estimation theoretic principles to develop new estimators that offer significant performance benefits. To this end, we first describe new lower bounds on the error variance of phase offset estimation schemes. These bounds are obtained by re-deriving two Bayesian estimation bounds, namely the Ziv-Zakai and Weiss-Weinstien bounds, for use under a non-Bayesian formulation. Next, we describe new minimax estimators for the problem of phase offset estimation, that are optimum in terms of minimizing the maximum mean squared error over all possible values of the unknown parameters.Minimax estimators that utilize information from past timestamps to improve accuracy are also introduced. These minimax estimators provide fundamental limits on the performance of phase offset estimation schemes.Finally, a restricted class of estimators referred to as L-estimators are considered, that are linear functions of order statistics. The problem of designing optimum L-estimators is studied under several hitherto unconsidered criteria of optimality. We address the case where the queuing delay distributions are fully known, as well as the case where network model uncertainty exists.Optimum L-estimators that utilize information from past observation windows to improve performance are also described.Simulation results indicate that significant performance gains over conventional estimators can be obtained via the proposed optimum processing techniques