42998438
Performance comparison of checkpoint and recovery protocols
Checkpoint and rollback recovery is a well-known technique for providing fault tolerance to long-running distributed applications. Performance of a checkpoint and recovery protocol depends on the characteristics of the application and the system on which it runs. However, given an application and system environment, there is no easy way to identify which checkpoint and recovery protocol will be most suitable for it. Conventional approaches require implementing the application with all the protocols under consideration, running them on the desired system, and comparing their performances. This process can be very tedious and time consuming. This paper first presents the design and implementation of a simulation environment, distributed process simulation or dPSIM, which enables easy implementation and evaluation of checkpoint and recovery protocols. The tool enables the protocols to be simulated under a wide variety of application, system, and network characteristics. The paper then presents performance evaluation of five checkpoint and recovery protocols. These protocols are implemented and executed in dPSIM under different simulated application, system, and network characteristics. Copyright © 2003 John Wiley & Sons, Ltd.
9811083
Performance enhancement of a contract net protocol based system through instance-based learning
The contract net protocol (CNP) is a widely used coordination mechanism in multiagent systems. It has a lot of communication overhead due to the broadcast of the task announcements. The performance of the CNP degrades drastically when the number of communicating agents and the number of tasks announced increases. Hence, it has problems of scalability. In order to overcome this limitation, an instance-based learning (IBL) mechanism is designed that uses previously stored instances in order to select a target agent. This avoids the expensive bidding process. The scheme is implemented in a simulated distributed hospital system where the CNP is used for resource sharing across hospitals. Experimental results demonstrate that with the incorporation of the IBL, the system performance improves significantly. The system is better scalable with respect to the number of tasks.
1713935
Designing Caching Strategies for Common NFS Workloads
3086074
An Exercise in Fault-Containment: Self-Stabilizing Leader Election
Self-stabilizing algorithms are designed to guarantee convergence to some desired stable state from arbitrary initial states arising out of an arbitrarily large number of faults. However, in a well-designed system, the simultaneous occurrence of a large number of faults is rare. It is therefore desirable to design algorithms that are not only self-stabilizing, but also have the ability to recover very fast from a bounded number of faults. As an illustration, we present a simple self-stabilizing leader election protocol that recovers in O(1) time from a state with a single transient fault on oriented rings. Only the faulty node and its two neighbors change their state during convergence to a stable state. Thus, the effect of a single fault is tightly contained around the fault. The technique for transforming a self-stabilizing algorithm into its fault-contained version is simple and general, and can be applied to other problems as well that satisfy certain properties.
65058359
Group Based Routing in Disconnected Ad Hoc Networks
In this paper, we propose a routing protocol for disconnected ad hoc networks where most nodes tend to move about in groups. To the best of our knowledge, no routing protocol for disconnected ad hoc networks has been designed earlier keeping in mind possible group patterns formed by the movement of nodes. Our protocol works by identifying groups using an efficient distributed group membership protocol, and then routing at the group level, rather than at the node level. The protocol is designed so that existing concepts of routing in disconnected ad hoc networks can be extended to work at the group level. Initial simulations across a broad spectrum of parameters suggest that our protocol performs better in terms of delivery ratio and latency over traditional approaches like AODV [1], and also over disconnected routing approaches like the 2-Hop routing protocol [2].
32945610
A Framework for Automatic Identification of the Best Checkpoint and Recovery Protocol
Fault tolerance is important for a distributed system to increase its reliability and throughput. Checkpoint and recovery protocols have been proposed as fault tolerance for non-critical applications. The performance of checkpoint and recovery protocols plays an important role in the overall performance of a distributed system. The performance of these protocols depends on system characteristics as well as an application characteristics. In this paper, we propose a novel technique to automatically identify the checkpoint and recovery protocol which is likely to perform the best for a given system and an application the system is currently running. We present experimental results to show that the scheme can efficiently determine a suitable checkpoint and recovery protocol for many applications.
17323833
Verification of scheduling in the presence of loops using uninterpreted symbolic simulation
We propose a novel procedure based on uninterpreted symbolic simulation for checking the scheduling step in high-level synthesis. The primary task in scheduling is the assignment of time steps or, equivalently, states to operations. Various transformations like operation reordering and loop unrolling may be performed in the process to meet the optimization criteria. The contribution of our proposal lied in its ability to efficiently handle loops and a wide range of loop transformations performed during scheduling. Our algorithm is based on loop invariant extraction using a combination of uninterpreted symbolic simulation and induction techniques. In spite of its wide scope, our procedure is relatively complete and practical. This work is a part of our effort to provide a suite of techniques for verifying the various steps involved in the high-level synthesis process. It is being implemented in an in-house verification system for checking equivalence of designs generated from high-level specifications through successive refinements. We present case studies to demonstrate the applicability of our approach. These case studies consist of examples where equivalence cannot be established using conventional FSM-based methods. By providing a viable automated equivalence checking technique for such examples, we improve on the state of the art.
2488682
Spatiotemporal annotation graph (STAG): a data model for composite digital objects
In this demonstration, we present a database over complex documents, which, in addition to a structured text content, also has update information, annotations, and embedded objects. We propose a new data model called spatiotemporal annotation graphs (STAG) for a database of composite digital objects and present a system that shows a query language to efficiently and effectively query such database. The particular application to be demonstrated is a database over annotated MS Word and PowerPoint presentations with embedded multimedia objects.
38885821
Embeddings of k-Connected Graphs of Pathwidth k
We present O(n3) embedding algorithms (generalizing subgraph isomorphism) for classes of graphs of bounded pathwidth, where n is the number of vertices in the graph. These include the first polynomialtime algorithm for minor containment and the first O(nc) algorithm (c a constant independent of k) for topological embedding of graphs from subclasses of partial k-trees. Of independent interest are structural properties of k-connected graphs of bounded pathwidth on which our algorithms are based. We also describe special cases which reduce to various generalizations of string matching, permitting more efficient solutions.
18557537
Localization and Register Sharing for Predicate Abstraction
In the domain of software verification, predicate abstraction has emerged to be a powerful and popular technique for extracting finite-state models from often complex source code. In this paper, we report on the application of three techniques for improving the performance of the predicate abstraction refinement loop. The first technique allows faster computation of the abstraction. Instead of maintaining a global set of predicates, we find predicates relevant to various basic blocks of the program by weakest pre-condition propagation along spurious program traces. The second technique enables faster model checking of the abstraction by reducing the number of state variables in the abstraction. This is done by re-using Boolean variables to represent different predicates in the ion. However, some predicates are useful at many program locations and discovering them lazily in various parts of the program leads to a large number of abstraction refinement iterations. The third technique attempts to identify such predicates early in the abstraction refinement loop and handles them separately by introducing dedicated state variables for such predicates. We have incorporated these techniques into NEC's software verification tool F-SOFT, and present promising experimental results for various case studies using these techniques.
17205588
Neurocomputational Mechanisms for Generalization During the Sequential Learning of Multiple Tasks
Traditional artificial neural network models of learning suffer from catastrophic interference. They are commonly trained to perform only one specific task, and when trained on a new task, they forget the original task completely. It has been shown that the foundational neurocomputational principles embodied by the Leabra cognitive modeling framework are sufficient to overcome this limitation significantly. Leabra is consistent with known biological properties of the neural networks of the brain. In particular, this framework includes both fast lateral inhibition and a local synaptic plasticity model that incorporates both correlational and error-based dynamics. It has been shown that the use of sparse internal representation significantly reduces the problem of catastrophic interference during sequential learning of multiple motor skills. We have also provided prelimilary evidence that Leabra is able to generalize the sub-sequences of motor skills, when doing so is appropriate. In this paper, we provide a detailed analysis of the extent of generalization possible with Leabra during sequential learning of multiple tasks. For comparison, we measure the generalization shown by a backpropagation of error learning algorithm based artificial neural network.