RoBuSt: A Crash-Failure-Resistant Distributed Storage System

In this work we present the first distributed storage system that is provably robust against crash failures issued by an adaptive adversary, i.e., for each batch of requests the adversary can decide based on the entire system state which servers will be unavailable for that batch of requests. Despite up to $\gamma n^{1/\log\log n}$ crashed servers, with $\gamma>0$ constant and $n$ denoting the number of servers, our system can correctly process any batch of lookup and write requests (with at most a polylogarithmic number of requests issued at each non-crashed server) in at most a polylogarithmic number of communication rounds, with at most polylogarithmic time and work at each server and only a logarithmic storage overhead. Our system is based on previous work by Eikel and Scheideler (SPAA 2013), who presented IRIS, a distributed information system that is provably robust against the same kind of crash failures. However, IRIS is only able to serve lookup requests. Handling both lookup and write requests has turned out to require major changes in the design of IRIS.Comment: Revised full versio

RMD-QOSM - The Resource Management in Diffserv QoS model

This document describes an NSIS QoS Model for networks that use the Resource Management in Diffserv (RMD) concept. RMD is a technique for adding admission control and preemption function to Differentiated Services (Diffserv) networks. The RMD QoS Model allows devices external to the RMD network to signal reservation requests to edge nodes in the RMD network. The RMD Ingress edge nodes classify the incoming flows into traffic classes and signals resource requests for the corresponding traffic class along the data path to the Egress edge nodes for each flow. Egress nodes reconstitute the original requests and continue forwarding them along the data path towards the final destination. In addition, RMD defines notification functions to indicate overload situations within the domain to the edge nodes

RMD-QOSM: The NSIS Quality-of-Service Model for Resource Management in Diffserv

This document describes a Next Steps in Signaling (NSIS) Quality-of- Service (QoS) Model for networks that use the Resource Management in Diffserv (RMD) concept. RMD is a technique for adding admission control and preemption function to Differentiated Services (Diffserv) networks. The RMD QoS Model allows devices external to the RMD network to signal reservation requests to Edge nodes in the RMD network. The RMD Ingress Edge nodes classify the incoming flows into traffic classes and signals resource requests for the corresponding traffic class along the data path to the Egress Edge nodes for each flow. Egress nodes reconstitute the original requests and continue forwarding them along the data path towards the final destination. In addition, RMD defines notification functions to indicate overload situations within the domain to the Edge nodes

System Support for Bandwidth Management and Content Adaptation in Internet Applications

This paper describes the implementation and evaluation of an operating system module, the Congestion Manager (CM), which provides integrated network flow management and exports a convenient programming interface that allows applications to be notified of, and adapt to, changing network conditions. We describe the API by which applications interface with the CM, and the architectural considerations that factored into the design. To evaluate the architecture and API, we describe our implementations of TCP; a streaming layered audio/video application; and an interactive audio application using the CM, and show that they achieve adaptive behavior without incurring much end-system overhead. All flows including TCP benefit from the sharing of congestion information, and applications are able to incorporate new functionality such as congestion control and adaptive behavior.Comment: 14 pages, appeared in OSDI 200

Overcoming Bandwidth Fluctuations in Hybrid Networks with QoS-Aware Adaptive Routing

With an escalating reliance on sensor-driven scientific endeavors in challenging terrains, the significance of robust hybrid networks, formed by a combination of wireless and wired links, is more noticeable than ever. These networks serve as essential channels for data streaming to centralized data centers, but their efficiency is often degraded by bandwidth fluctuations and network congestion. Especially in bandwidth-sensitive hybrid networks, these issues present demanding challenges to Quality of Service (QoS). Traditional network management solutions fail to provide an adaptive response to these dynamic challenges, thereby underscoring the need for innovative solutions. This thesis introduces a novel approach leveraging the concept of Software-Defined Networking (SDN) to establish a dynamic, congestion-aware routing mechanism. This proposed mechanism stands out by comprising a unique strategy of using bandwidth-based measurements, which help accurately detect and localize network congestion. Unlike traditional methodologies that rely on rigid route management, our approach demonstrates dynamic data flow route adjustment. Experimental data indicate promising outcomes with clear improvements in network utilization and application performance. Furthermore, the proposed algorithm exhibits remarkable scalability, providing quick route-finding solutions for various data flows, without impacting system performance. Thus, this thesis contributes to the ongoing discourse on enhancing hybrid network efficiency in challenging conditions, setting the stage for future explorations in this area

Towards Cost-effective On-demand continuous Media Service: A Peer-to- Peer Approach

To overcome the limited bandwidth of streaming servers, Content Distribution Networks (CDNs) are deployed on the edge of the Internet. A large number of such servers have to be installed to make the whole system scalable, making CDN a very expensive way to distribute media. The primary concern of this research is to find an inexpensive way to alleviate the traffic load for media streaming on the servers in a continuous media service infrastructure. Our approach to solve the above problem is motivated by the emerging concept of peer-to-peer computing. Specifically, we let clients that obtained a media object act as streaming servers for following requests to that media object. Unlike the server/client scheme, peers are heterogeneous in the storage capacity and out-bound bandwidth they can contribute. Secondly, peers are heterogeneous in the duration of their commitment to the community. In our research, we identified the following problems in the context of peer-to-peer streaming: 1) How does the come-and-go behaviors of peers affect the system performance? 2). How do we manage the limited resources contributed by each peer? 3). The design of a peer-to-peer streaming protocol that handles peer failure. Solutions to these problems are described and analyzed. 1

Multi-path Probabilistic Available Bandwidth Estimation through Bayesian Active Learning

Knowing the largest rate at which data can be sent on an end-to-end path such that the egress rate is equal to the ingress rate with high probability can be very practical when choosing transmission rates in video streaming or selecting peers in peer-to-peer applications. We introduce probabilistic available bandwidth, which is defined in terms of ingress rates and egress rates of traffic on a path, rather than in terms of capacity and utilization of the constituent links of the path like the standard available bandwidth metric. In this paper, we describe a distributed algorithm, based on a probabilistic graphical model and Bayesian active learning, for simultaneously estimating the probabilistic available bandwidth of multiple paths through a network. Our procedure exploits the fact that each packet train provides information not only about the path it traverses, but also about any path that shares a link with the monitored path. Simulations and PlanetLab experiments indicate that this process can dramatically reduce the number of probes required to generate accurate estimates