PABO: Mitigating Congestion via Packet Bounce in Data Center Networks

In today's data center, a diverse mix of throughput-sensitive long flows and delay-sensitive short flows are commonly presented in shallow-buffered switches. Long flows could potentially block the transmission of delay-sensitive short flows, leading to degraded performance. Congestion can also be caused by the synchronization of multiple TCP connections for short flows, as typically seen in the partition/aggregate traffic pattern. While multiple end-to-end transport-layer solutions have been proposed, none of them have tackled the real challenge: reliable transmission in the network. In this paper, we fill this gap by presenting PABO -- a novel link-layer design that can mitigate congestion by temporarily bouncing packets to upstream switches. PABO's design fulfills the following goals: i) providing per-flow based flow control on the link layer, ii) handling transient congestion without the intervention of end devices, and iii) gradually back propagating the congestion signal to the source when the network is not capable to handle the congestion.Experiment results show that PABO can provide prominent advantage of mitigating transient congestions and can achieve significant gain on end-to-end delay

Divergence analysis and processing for Mandarin-English parallel text exploitation

Previous work shows that the process of parallel text exploitation to extract mappings between language pairs raises the capability of language translation. However, while this process can be fully automated, one thorny problem called “divergence” causes indisposed mapping extraction. Therefore, this paper discuss the issues of parallel text exploitation, in general, with special emphasis on divergence analysis and processing. In the experiments on a Mandarin-English travel conversation corpus of 11,885 sentence pairs, the perplexity with the alignments in IBM translation model is reduced averagely from 13.65 to 4.18

Realizing value from project implementation under uncertainty : an exploratory study using system dynamics

Project Implementation is not a trivial task even after careful planning and scheduling. One of the reasons is the existence of unexpected events at strategic and operational levels during the project execution process. This paper presents a system dynamics model of a project monitoring and control system. Embedded with both strategic and tactical uncertainties, the model experiments with typical remedial actions to disturbances during the implementation of a project under a behavioral paradigm. Simple proportional adjustment seems to work well under low levels of unexpected disturbances but prospect theory-based behavior works better under extreme situations. Our findings indicate over-reacting behavior, which is influenced by biases and reporting errors, can generate project escalation. Thus, thresholds for remedial actions should be implemented in project control and monitoring systems to avoid over-reacting behavior leading to escalation and waste of resources

Energy-Efficient Flow Scheduling and Routing with Hard Deadlines in Data Center Networks

The power consumption of enormous network devices in data centers has emerged as a big concern to data center operators. Despite many traffic-engineering-based solutions, very little attention has been paid on performance-guaranteed energy saving schemes. In this paper, we propose a novel energy-saving model for data center networks by scheduling and routing "deadline-constrained flows" where the transmission of every flow has to be accomplished before a rigorous deadline, being the most critical requirement in production data center networks. Based on speed scaling and power-down energy saving strategies for network devices, we aim to explore the most energy efficient way of scheduling and routing flows on the network, as well as determining the transmission speed for every flow. We consider two general versions of the problem. For the version of only flow scheduling where routes of flows are pre-given, we show that it can be solved polynomially and we develop an optimal combinatorial algorithm for it. For the version of joint flow scheduling and routing, we prove that it is strongly NP-hard and cannot have a Fully Polynomial-Time Approximation Scheme (FPTAS) unless P=NP. Based on a relaxation and randomized rounding technique, we provide an efficient approximation algorithm which can guarantee a provable performance ratio with respect to a polynomial of the total number of flows.Comment: 11 pages, accepted by ICDCS'1

GreenDCN: a General Framework for Achieving Energy Efficiency in Data Center Networks

The popularization of cloud computing has raised concerns over the energy consumption that takes place in data centers. In addition to the energy consumed by servers, the energy consumed by large numbers of network devices emerges as a significant problem. Existing work on energy-efficient data center networking primarily focuses on traffic engineering, which is usually adapted from traditional networks. We propose a new framework to embrace the new opportunities brought by combining some special features of data centers with traffic engineering. Based on this framework, we characterize the problem of achieving energy efficiency with a time-aware model, and we prove its NP-hardness with a solution that has two steps. First, we solve the problem of assigning virtual machines (VM) to servers to reduce the amount of traffic and to generate favorable conditions for traffic engineering. The solution reached for this problem is based on three essential principles that we propose. Second, we reduce the number of active switches and balance traffic flows, depending on the relation between power consumption and routing, to achieve energy conservation. Experimental results confirm that, by using this framework, we can achieve up to 50 percent energy savings. We also provide a comprehensive discussion on the scalability and practicability of the framework.Comment: 14 pages, accepted by IEEE JSA

Subclinical infection without encephalitis in mice following intranasal exposure to Nipah virus-Malaysia and Nipah virus-Bangladesh

BACKGROUND: Nipah virus and Hendra virus are closely related and following natural or experimental exposure induce similar clinical disease. In humans, encephalitis is the most serious outcome of infection and, hitherto, research into the pathogenesis of henipavirus encephalitis has been limited by the lack of a suitable model. Recently we reported a wild-type mouse model of Hendra virus (HeV) encephalitis that should facilitate detailed investigations of its neuropathogenesis, including mechanisms of disease recrudescence. In this study we investigated the possibility of developing a similar model of Nipah virus encephalitis. FINDINGS: Aged and young adult wild type mice did not develop clinical disease including encephalitis following intranasal exposure to either the Malaysia (NiV-MY) or Bangladesh (NiV-BD) strains of Nipah virus. However viral RNA was detected in lung tissue of mice at euthanasia (21 days following exposure) accompanied by a non-neutralizing antibody response. In a subsequent time course trial this viral RNA was shown to be reflective of an earlier self-limiting and subclinical lower respiratory tract infection through successful virus re-isolation and antigen detection in lung. There was no evidence for viremia or infection of other organs, including brain. CONCLUSIONS: Mice develop a subclinical self-limiting lower respiratory tract infection but not encephalitis following intranasal exposure to NiV-BD or NiV-MY. These results contrast with those reported for HeV under similar exposure conditions in mice, demonstrating a significant biological difference in host clinical response to exposure with these viruses. This finding provides a new platform from which to explore the viral and/or host factors that determine the neuroinvasive ability of henipaviruses