Local Concurrent Error Detection and Correction in Data Structures Using Virtual Backpointers

Coordinated Science Laboratory was formerly known as Control Systems LaboratorySDIO/IST managed by the Office of Naval Research / N00014-86-K-0519National Aeronautics and Space Administration / NASA NAG 1-602Joint Services Electronics Program / N00014-84-C-014

Fault-Tolerant Computing: An Overview

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryNASA / NAG-1-613Semiconductor Research Corporation / 90-DP-109Joint Services Electronics Program / N00014-90-J-127

Checkpoint-based forward recovery using lookahead execution and rollback validation in parallel and distributed systems

This thesis studies a forward recovery strategy using checkpointing and optimistic execution in parallel and distributed systems. The approach uses replicated tasks executing on different processors for forwared recovery and checkpoint comparison for error detection. To reduce overall redundancy, this approach employs a lower static redundancy in the common error-free situation to detect error than the standard N Module Redundancy scheme (NMR) does to mask off errors. For the rare occurrence of an error, this approach uses some extra redundancy for recovery. To reduce the run-time recovery overhead, look-ahead processes are used to advance computation speculatively and a rollback process is used to produce a diagnosis for correct look-ahead processes without rollback of the whole system. Both analytical and experimental evaluation have shown that this strategy can provide a nearly error-free execution time even under faults with a lower average redundancy than NMR

The Measurement Manager: Modular and Efficient End-to-End Measurement Services

End-to-end network measurement is used to improve the precision, efficiency, and fairness for a variety of Internet protocols and applications. Measurement is typically performed in one of three ways: (1) actively, by injecting specially crafted probe packets into the network, (2) passively, by observing existing data traffic, and (3) customized, where applications use their own traffic to perform customized measurements. All current approaches suffer from drawbacks. Passive techniques are efficient but are constrained by the shape of the existing traffic. Active techniques are faster, more accurate and more flexible but impose a significantly higher overhead. And finally, custom techniques combine flexibility with efficiency, but are so tightly coupled with each application that they are not reusable. To address these shortcomings, we present the Measurement Manager, a practical, modular, and efficient service for performing end-to-end network measurements between hosts. Our architecture introduces a new hybrid approach to network measurement, where applications can pool together their data packets to be reused as padding inside network probes in a transparent and systematic way. We achieve this through the Measurement Manager Protocol (MGRP), a new transport protocol for sending probes that combines data packets and probes on the fly. In MGRP, active measurement algorithms specify the probes they wish to send using a Probe API and applications allow MGRP to use data from their own packets to fill the otherwise wasted probe padding. We have implemented the Measurement Manager inside the Linux kernel and have adapted existing applications and active measurement tools to use our system. Through experimentation we provide detailed empirical evidence that piggybacking data packets on measurement probes is not only feasible but improves source and cross traffic as well as the performance of measurement algorithms while not affecting their accuracy. We show that the Measurement Manager is an architecture with broad applications that can be used to build a generic measurement overlay network as well as expanding the solution space for estimation algorithms, since every application packet can now act as a potential probe

Planning automated guided vehicle movements in a factory

This dissertation examines the problems of planning automated guided vehicle (AGV) movement schedules in an automated factory. AGVs are used mainly for material delivery and will have an important role in linking "islands of automation" in automated factories. Their employment in this context requires the plans to be generated in a manner which supports temporal projection so that further planning in other areas is possible. Planning also occurs in a dynamic scenario—while some plans are being executed, planning for new tasks and replanning failing plans occur. Expeditious planning is thus important so that deadlines can be met. Furthermore, dynamic replanning in a multi-agent environment has repercussions—changing one plan may require revision of other plans. Hence the issue of limiting the side effects of dynamic replanning is also considered. In dealing with these issues, the goals of this research are: (1) generate movement plans which can be executed efficiently; (2) develop fast algorithms for the recurrent subproblems viz. task assignment and route planning; and (3) generate robust plans which tolerate execution deviations; this helps to minimize disruptive dynamic replanning with its tendency to initiate a chain reaction of plan revisions. Efficient movement plans mean more productive utilization of the AGV fleet and this objective can be realized by three approaches. First, the tasks are assigned to AGVs optimally using an improved implementation of the Hungarian method. Second, the planner computes shortest routes for the AGVs using a bidirectional heuristic search algorithm which is amenable to parallel implementation for further computational time reduction. Third, whenever AGVs are fortuitously predisposed to assist each other in task execution, the planner will generate gainful collaborative plans. Efficient algorithms have been developed in these areas. The algorithms for task assignment and route planning are also designed to be fast, in keeping with the objective of expeditious planning. Robust plans can be generated using the approach of tolerant planning. Robustness is achieved in two ways: (1) by being tolerant of an AGV's own execution deviations; and (2) by being tolerant of other AGVs' deviant behaviour. Tolerant planning thus defers dynamic replanning until execution errors become excessive. The underlying strategy is to provide more than ample resources (time) for AGVs to achieve various subgoals. Such redundancies aggravate the resource contention problem. To solve this, an iterative negotiation model is proposed. During negotiations, AGVs yield in turn to help eliminate the conflict. The negotiation behaviour of each is governed by how much spare resources each has and tends towards intransigence as the bottom line is approached. In this way, no AGV will jeopardize its own plan while cooperating in the elimination of conflicts. By gradual yielding, an AGV is also able to influence the other party to yield more if it can, therein achieving some fairness. The model has many of the characteristics of negotiation acts in the real world (e.g. skilful negotiation, intransigence, selfishness, willingness to concede, nested negotiations)