DeSyRe: on-Demand System Reliability

The DeSyRe project builds on-demand adaptive and reliable Systems-on-Chips (SoCs). As fabrication technology scales down, chips are becoming less reliable, thereby incurring increased power and performance costs for fault tolerance. To make matters worse, power density is becoming a significant limiting factor in SoC design, in general. In the face of such changes in the technological landscape, current solutions for fault tolerance are expected to introduce excessive overheads in future systems. Moreover, attempting to design and manufacture a totally defect and fault-free system, would impact heavily, even prohibitively, the design, manufacturing, and testing costs, as well as the system performance and power consumption. In this context, DeSyRe delivers a new generation of systems that are reliable by design at well-balanced power, performance, and design costs. In our attempt to reduce the overheads of fault-tolerance, only a small fraction of the chip is built to be fault-free. This fault-free part is then employed to manage the remaining fault-prone resources of the SoC. The DeSyRe framework is applied to two medical systems with high safety requirements (measured using the IEC 61508 functional safety standard) and tight power and performance constraints

Fault-tolerant aggregation by flow updating

Data aggregation plays an important role in the design of scalable systems, allowing the determination of meaningful system-wide properties to direct the execution of distributed applications. In the particular case of wireless sensor networks, data collection is often only practicable if aggregation is performed. Several aggregation algorithms have been proposed in the last few years, exhibiting different properties in terms of accuracy, speed and communication tradeoffs. Nonetheless, existing approaches are found lacking in terms of fault tolerance. In this paper, we introduce a novel fault-tolerant averaging based data aggregation algorithm. It tolerates substantial message loss (link failures), while competing algorithms in the same class can be affected by a single lost message. The algorithm is based on manipulating flows (in the graph theoretical sense), that are updated using idempotent messages, providing it with unique robustness capabilities. Furthermore, evaluation results obtained by comparing it with other averaging approaches have revealed that it outperforms them in terms of time and message complexity

Mesh-Mon: a Monitoring and Management System for Wireless Mesh Networks

A mesh network is a network of wireless routers that employ multi-hop routing and can be used to provide network access for mobile clients. Mobile mesh networks can be deployed rapidly to provide an alternate communication infrastructure for emergency response operations in areas with limited or damaged infrastructure. In this dissertation, we present Dart-Mesh: a Linux-based layer-3 dual-radio two-tiered mesh network that provides complete 802.11b coverage in the Sudikoff Lab for Computer Science at Dartmouth College. We faced several challenges in building, testing, monitoring and managing this network. These challenges motivated us to design and implement Mesh-Mon, a network monitoring system to aid system administrators in the management of a mobile mesh network. Mesh-Mon is a scalable, distributed and decentralized management system in which mesh nodes cooperate in a proactive manner to help detect, diagnose and resolve network problems automatically. Mesh-Mon is independent of the routing protocol used by the mesh routing layer and can function even if the routing protocol fails. We demonstrate this feature by running Mesh-Mon on two versions of Dart-Mesh, one running on AODV (a reactive mesh routing protocol) and the second running on OLSR (a proactive mesh routing protocol) in separate experiments. Mobility can cause links to break, leading to disconnected partitions. We identify critical nodes in the network, whose failure may cause a partition. We introduce two new metrics based on social-network analysis: the Localized Bridging Centrality (LBC) metric and the Localized Load-aware Bridging Centrality (LLBC) metric, that can identify critical nodes efficiently and in a fully distributed manner. We run a monitoring component on client nodes, called Mesh-Mon-Ami, which also assists Mesh-Mon nodes in the dissemination of management information between physically disconnected partitions, by acting as carriers for management data. We conclude, from our experimental evaluation on our 16-node Dart-Mesh testbed, that our system solves several management challenges in a scalable manner, and is a useful and effective tool for monitoring and managing real-world mesh networks