7 research outputs found
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)