With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed r...
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In this work, we present two perspectives of Grid computing by using two different Grid middleware as examples: an Enterprise Grid using Xgrid and a Global Grid with Gridbus. We also present the integration of Enterpr...
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This paper identifies challenges in managing resources in a Grid computing environment and proposes computational economy as a metaphor for effective management of resources and application scheduling. It identifies d...
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We describe several observations regarding the completeness and the complexity of bounded model checking and propose techniques to solve some of the associated computational challenges. We begin by defining the comple...
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We propose a novel probabilistic retrieval model which weights terms according to their contexts in documents. The term weighting function of our model is similar to the language model and the binary independence mode...
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We consider the problem of perpetual traversal by a single agent in an anonymous undirected graph G. Our requirements are: (1) deterministic algorithm, (2) each node is visited within O(n) moves, (3) the agent uses no...
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Current computer applications and user interfaces lack user context and are not successful in learning user preferences to improve user interaction. We present Sycophant, a context learning calendaring application pro...
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Current computer applications and user interfaces lack user context and are not successful in learning user preferences to improve user interaction. We present Sycophant, a context learning calendaring application program which is designed to learn a mapping from user-related contextual features to reminder actions. In this paper, we consider the feasibility of using a genetics-based machine learning technique, XCS, for the purpose of learning this mapping from a set of context features to reminder actions as a predictive data-mining task. We compare XCS's performance with a decision tree algorithm on this learning task and show that XCS outperforms the decision tree learner.
This paper describes an empirical study that investigates the knowledge that Computer science students have about the extent of their own previous learning. The study compares self-generated estimates of performance w...
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ISBN:
(纸本)1595930248
This paper describes an empirical study that investigates the knowledge that Computer science students have about the extent of their own previous learning. The study compares self-generated estimates of performance with actual performance on a data structures quiz taken by undergraduate students in courses requiring data structures as a pre-requisite. The study is contextualized and grounded within a research paradigm in Psychology called calibration of knowledge that suggests that self-knowledge across a range of disciplines is highly unreliable. Such self-knowledge is important because of its role in meta-cognition, particularly in cognitive self-regulation and monitoring. It is also important because of the credence that faculty give to student self-reports. Our results indicate that Computer science student self-estimates correlate moderately with their performance on a quiz, more so for estimates provided after they have taken the quiz than before. The pedagogical implications are that students should be provided with regular opportunities for empirical validation of their knowledge as well as being taught the metacognitive skills of regular self-testing in order to overcome validation bias. Copyright 2005 ACM.
Jobs submitted into a cluster have varying requirements depending on user-specific needs and expectations. Therefore, in utility-driven cluster computing, cluster resource management systems (RMSs) need to be aware of...
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Jobs submitted into a cluster have varying requirements depending on user-specific needs and expectations. Therefore, in utility-driven cluster computing, cluster resource management systems (RMSs) need to be aware of these requirements in order to allocate resources effectively. Service level agreements (SLAs) can be used to differentiate different value of jobs as they define service conditions that the cluster RMS agrees to provide for each different job. The SLA acts as a contract between a user and the cluster whereby the user is entitled to compensation whenever the cluster RMS fails to deliver the required service. In this paper, we present a proportional share allocation technique called LibraSLA that takes into account the utility of accepting new jobs into the cluster based on their SLA. We study how LibraSLA performs with respect to several SLA requirements that include: (i) deadline type whether the job can be delayed, (ii) deadline when the job needs to be finished, (iii) budget to be spent for finishing the job, and (iv) penalty rate for compensating the user for failure to meet the deadline
Parameter-sweep has been widely adopted in large numbers of scientific applications. Parameter-sweep features need to be incorporated into grid workflows so as to increase the scale and scope of such applications. New...
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Parameter-sweep has been widely adopted in large numbers of scientific applications. Parameter-sweep features need to be incorporated into grid workflows so as to increase the scale and scope of such applications. New scheduling mechanisms and algorithms are required to provide optimized policy for resource allocation and task arrangement in such a case. This paper addresses scheduling sequential parameter-sweep tasks in a fine-grained manner. The optimization is produced by pipelining the subtasks and dispatching each of them onto well-selected resources. Two types of scheduling algorithms are discussed and customized to adapt the characteristics of parameter-sweep, as well as their effectiveness has been compared under multifarious scenarios.
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