arXiv subjects

Ahmad Khonsari

Publications and source records attributed to Ahmad Khonsari.

26 records · Page 2Linked to original sources

arXiv2015

Utility-Optimal Dynamic Rate Allocation under Average End-to-End Delay Requirements

QoS-aware networking applications such as real-time streaming and video surveillance systems require nearly fixed average end-to-end delay over long periods to communicate efficiently, although may tolerate some delay variations in short periods. This variability exhibits complex dynamics that makes rate control of such applications a formidable task. This paper addresses rate allocation for heterogeneous QoS-aware applications that preserves the long-term end-to-end delay constraint while, similar to Dynamic Network Utility Maximization (DNUM), strives to achieve the maximum network utility aggregated over a fixed time interval. Since capturing temporal dynamics in QoS requirements of sources is allowed in our system model, we incorporate a novel time-coupling constraint in which delay-sensitivity of sources is considered such that a certain end-to-end average delay for each source over a pre-specified time interval is satisfied. We propose DA-DNUM algorithm, as a dual-based solution, which allocates source rates for the next time interval in a distributed fashion, given the knowledge of network parameters in advance. Through numerical experiments, we show that DA-DNUM gains higher average link utilization and a wider range of feasible scenarios in comparison with the best, to our knowledge, rate control schemes that may guarantee such constraints on delay.

cs.NI↗

arXiv2015

On the Feasibility of Wireless Interconnects for High-throughput Data Centers

Data Centers (DCs) are required to be scalable to large data sets so as to accommodate ever increasing demands of resource-limited embedded and mobile devices. Thanks to the availability of recent high data rate millimeter-wave frequency spectrum such as 60GHz and due to the favorable attributes of this technology, wireless DC (WDC) exhibits the potentials of being a promising solution especially for small to medium scale DCs. This paper investigates the problem of throughput scalability of WDCs using the established theory of the asymptotic throughput of wireless multi-hop networks that are primarily proposed for homogeneous traffic conditions. The rate-heterogeneous traffic distribution of a data center however, requires the asymptotic heterogeneous throughput knowledge of a wireless network in order to study the performance and feasibility of WDCs for practical purposes. To answer these questions this paper presents a lower bound for the throughput scalability of a multi-hop rate-heterogeneous network when traffic generation rates of all nodes are similar, except one node. We demonstrate that the throughput scalability of conventional multi-hopping and the spatial reuse of the above bi-rate network is inefficient and henceforth develop a speculative 2-partitioning scheme that improves the network throughput scaling potentials. A better lower bound of the throughput is then obtained. Finally, we obtain the throughput scaling of an i.i.d. rate-heterogeneous network and obtain its lower bound. Again we propose a speculative 2-partitioning scheme to achieve a network with higher throughput in terms of improved lower bound. All of the obtained results have been verified using simulation experiments.

cs.NI↗

arXiv2014

On the Construction of Maximum-Quality Aggregation Trees in Deadline-Constrained WSNs

In deadline-constrained data aggregation in wireless sensor networks (WSNs), the imposed sink deadline along with the interference constraint hinders participation of all sensor nodes in data aggregation. Thus, exploiting the wisdom of the crowd paradigm, the total number of participant nodes in data aggregation determines the quality of aggregation ($QoA$). Although the previous studies have proposed optimal algorithms to maximize $QoA$ under an imposed deadline and a given aggregation tree, there is no work on constructing optimal tree in this context. In this paper, we cast an optimization problem to address optimal tree construction for deadline-constrained data aggregation in WSNs. We demonstrate that the ratio between the maximum achievable $QoA$s of the optimal and the worst aggregation trees is as large as $O(2^D)$, where $D$ is the sink deadline and thus makes devising efficient solution of the problem an issue of paramount value. However, the problem is challenging to solve since we prove that it is NP-hard. We apply the recently-proposed Markov approximation framework to devise two distributed algorithms with different computation overheads that converge to a bounded neighborhood of the optimal solution. Extensive simulations in a set of representative randomly-generated scenarios show that the proposed algorithms significantly improve $QoA$ by %101 and %93 in average compared to the best, to our knowledge, existing alternative methods.

cs.NI↗

arXiv2012

Dynamic Warp Resizing in High-Performance SIMT

Modern GPUs synchronize threads grouped in a warp at every instruction. These results in improving SIMD efficiency and makes sharing fetch and decode resources possible. The number of threads included in each warp (or warp size) affects divergence, synchronization overhead and the efficiency of memory access coalescing. Small warps reduce the performance penalty associated with branch and memory divergence at the expense of a reduction in memory coalescing. Large warps enhance memory coalescing significantly but also increase branch and memory divergence. Dynamic workload behavior, including branch/memory divergence and coalescing, is an important factor in determining the warp size returning best performance. Optimal warp size can vary from one workload to another or from one program phase to the next. Based on this observation, we propose Dynamic Warp Resizing (DWR). DWR takes innovative microarchitectural steps to adjust warp size during runtime and according to program characteristics. DWR outperforms static warp size decisions, up to 1.7X to 2.28X, while imposing less than 1% area overhead. We investigate various alternative configurations and show that DWR performs better for narrower SIMD and larger caches.

cs.AR↗

arXiv2012

Quasi-Optimal Network Utility Maximization for Scalable Video Streaming

This paper addresses rate control for transmission of scalable video streams via Network Utility Maximization (NUM) formulation. Due to stringent QoS requirements of video streams and specific characterization of utility experienced by end-users, one has to solve nonconvex and even nonsmooth NUM formulation for such streams, where dual methods often prove incompetent. Convexification plays an important role in this work as it permits the use of existing dual methods to solve an approximate to the NUM problem iteratively and distributively. Hence, to tackle the nonsmoothness and nonconvexity, we aim at reformulating the NUM problem through approximation and transformation of the ideal discretely adaptive utility function for scalable video streams. The reformulated problem is shown to be a D.C. (Difference of Convex) problem. We leveraged Sequential Convex Programming (SCP) approach to replace the nonconvex D.C. problem by a sequence of convex problems that aim to approximate the original D.C. problem. We then solve each convex problem produced by SCP approach using existing dual methods. This procedure is the essence of two distributed iterative rate control algorithms proposed in this paper, for which one can show the convergence to a locally optimal point of the nonconvex D.C. problem and equivalently to a locally optimal point of an approximate to the original nonconvex problem. Our experimental results show that the proposed rate control algorithms converge with tractable convergence behavior.

cs.MM↗

arXiv2012

Content-Aware Rate Control for Video Transmission with Buffer Constraints in Multipath Networks

Being an integral part of the network traffic, nowadays it's vital to design robust mechanisms to provide QoS for multimedia applications. The main goal of this paper is to provide an efficient solution to support content-aware video transmission mechanism with buffer underflow avoidance at the receiver in multipath networks. Towards this, we introduce a content-aware time-varying utility function, where the quality impacts of video content is incorporated into its definition. Using the proposed utility function, we formulate a multipath Dynamic Network Utility Maximization (DNUM) problem for the rate allocation of video streams, where it takes into account QoS demand of video streams in terms of buffer underflow avoidance. Finally, using primal-dual method, we propose a distributed solution that optimally allocates the shared bandwidth to video streams. The numerical examples demonstrate the efficacy of the proposed content-aware rate allocation algorithm for video sources in both single and multiple path network models.

cs.MM↗

arXiv2012

Investigating Warp Size Impact in GPUs

There are a number of design decisions that impact a GPU's performance. Among such decisions deciding the right warp size can deeply influence the rest of the design. Small warps reduce the performance penalty associated with branch divergence at the expense of a reduction in memory coalescing. Large warps enhance memory coalescing significantly but also increase branch divergence. This leaves designers with two choices: use a small warps and invest in finding new solutions to enhance coalescing or use large warps and address branch divergence employing effective control-flow solutions. In this work our goal is to investigate the answer to this question. We analyze warp size impact on memory coalescing and branch divergence. We use our findings to study two machines: a GPU using small warps but equipped with excellent memory coalescing (SW+) and a GPU using large warps but employing an MIMD engine immune from control-flow costs (LW+). Our evaluations show that building coalescing-enhanced small warp GPUs is a better approach compared to pursuing a control-flow enhanced large warp GPU.

cs.AR↗

arXiv2011

NUM-Based Rate Allocation for Streaming Traffic via Sequential Convex Programming

In recent years, there has been an increasing demand for ubiquitous streaming like applications in data networks. In this paper, we concentrate on NUM-based rate allocation for streaming applications with the so-called S-curve utility functions. Due to non-concavity of such utility functions, the underlying NUM problem would be non-convex for which dual methods might become quite useless. To tackle the non-convex problem, using elementary techniques we make the utility of the network concave, however this results in reverse-convex constraints which make the problem non-convex. To deal with such a transformed NUM, we leverage Sequential Convex Programming (SCP) approach to approximate the non-convex problem by a series of convex ones. Based on this approach, we propose a distributed rate allocation algorithm and demonstrate that under mild conditions, it converges to a locally optimal solution of the original NUM. Numerical results validate the effectiveness, in terms of tractable convergence of the proposed rate allocation algorithm.

cs.NI↗