11,350 research outputs found
System Description for a Scalable, Fault-Tolerant, Distributed Garbage Collector
We describe an efficient and fault-tolerant algorithm for distributed cyclic
garbage collection. The algorithm imposes few requirements on the local
machines and allows for flexibility in the choice of local collector and
distributed acyclic garbage collector to use with it. We have emphasized
reducing the number and size of network messages without sacrificing the
promptness of collection throughout the algorithm. Our proposed collector is a
variant of back tracing to avoid extensive synchronization between machines. We
have added an explicit forward tracing stage to the standard back tracing stage
and designed a tuned heuristic to reduce the total amount of work done by the
collector. Of particular note is the development of fault-tolerant cooperation
between traces and a heuristic that aggressively reduces the set of suspect
objects.Comment: 47 pages, LaTe
Self-Healing Computation
In the problem of reliable multiparty computation (RC), there are
parties, each with an individual input, and the parties want to jointly compute
a function over inputs. The problem is complicated by the fact that an
omniscient adversary controls a hidden fraction of the parties.
We describe a self-healing algorithm for this problem. In particular, for a
fixed function , with parties and gates, we describe how to perform
RC repeatedly as the inputs to change. Our algorithm maintains the
following properties, even when an adversary controls up to parties, for any constant . First, our
algorithm performs each reliable computation with the following amortized
resource costs: messages, computational
operations, and latency, where is the depth of the circuit
that computes . Second, the expected total number of corruptions is , after which the adversarially controlled parties are
effectively quarantined so that they cause no more corruptions.Comment: 17 pages and 1 figure. It is submitted to SSS'1
Complexity of Multi-Value Byzantine Agreement
In this paper, we consider the problem of maximizing the throughput of
Byzantine agreement, given that the sum capacity of all links in between nodes
in the system is finite. We have proposed a highly efficient Byzantine
agreement algorithm on values of length l>1 bits. This algorithm uses error
detecting network codes to ensure that fault-free nodes will never disagree,
and routing scheme that is adaptive to the result of error detection. Our
algorithm has a bit complexity of n(n-1)l/(n-t), which leads to a linear cost
(O(n)) per bit agreed upon, and overcomes the quadratic lower bound
(Omega(n^2)) in the literature. Such linear per bit complexity has only been
achieved in the literature by allowing a positive probability of error. Our
algorithm achieves the linear per bit complexity while guaranteeing agreement
is achieved correctly even in the worst case. We also conjecture that our
algorithm can be used to achieve agreement throughput arbitrarily close to the
agreement capacity of a network, when the sum capacity is given
Parameter estimation for stochastic hybrid model applied to urban traffic flow estimation
This study proposes a novel data-based approach for estimating the parameters of a stochastic hybrid model describing the traffic flow in an urban traffic network with signalized intersections. The model represents the evolution of the traffic flow rate, measuring the number of vehicles passing a given location per time unit. This traffic flow rate is described using a mode-dependent first-order autoregressive (AR) stochastic process. The parameters of the AR process take different values depending on the mode of traffic operation – free flowing, congested or faulty – making this a hybrid stochastic process. Mode switching occurs according to a first-order Markov chain. This study proposes an expectation-maximization (EM) technique for estimating the transition matrix of this Markovian mode process and the parameters of the AR models for each mode. The technique is applied to actual traffic flow data from the city of Jakarta, Indonesia. The model thus obtained is validated by using the smoothed inference algorithms and an online particle filter. The authors also develop an EM parameter estimation that, in combination with a time-window shift technique, can be useful and practical for periodically updating the parameters of hybrid model leading to an adaptive traffic flow state estimator
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