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The prediction of software defect-fixing effort is important for strategic resource allocation and software quality management. Machine learning techniques have become very popular in addressing this problem and many related prediction models have been proposed. However, almost every model today faces a challenging issue of demonstrating satisfactory prediction accuracy and meaningful prediction results...
This paper aims to contribute to bridge the gap between existing theoretical results in distributed radio resource allocation policies based on equilibria in games (assuming complete information and rational players) and practical design of signal processing algorithms for self-configuring wireless networks. For this purpose, the framework of learning theory in games is exploited. Here, a new learning...
This paper presents the parallelization of a machine learning method, called the AdaBoost algorithm. The parallel algorithm follows a dynamically load-balanced master-worker strategy, which is parameterized by the granularity of the tasks distributed to workers. We first show the benefits of this version with heterogeneous processors. Then, we study the application in a real, geographically distributed...
Grid has evolved dramatically into the era of service-oriented grid, which facilitates building of large-scale systems in standard fashions, reusability of essential functions, and interoperability among components. However, grid resource allocation is still a challenging problem for which a grid scheduler has to be operating in a dynamic and uncertain environment. Conventional scheduling algorithms...
It is believed that optimal workload allocation cannot be achieved without considering the cost of parallelism in a given environment. This paper presents a machine learning approach to allocate parallel workload in a cost-aware manner. This instance-based learning approach uses static program features to classify programs, before deciding the best workload allocation scheme based on its prior experience...
Grid environments enable users to share nondedicated resources that lack performance guarantees. This paper describes the design of application-centric middleware components to automatically recover from failures and dynamically adapt to grid environments with changing resource availabilities, improving fault-tolerance and performance. The key components of the application-centric approach are a global...
High-end servers that can be partitioned into logical subsystems and repartitioned on the fly are now becoming available. This development raises the possibility of reconfiguring distributed systems online to optimize for dynamically changing workloads. This paper presents the initial steps towards a system that can learn to alter its current configuration in reaction to the current workload. In particular,...
We study autonomic resource allocation among multiple applications based on optimizing the sum of utility for each application. We compare two methodologies for estimating the utility of resources: a queuing-theoretic performance model and model-free reinforcement learning. We evaluate them empirically in a distributed prototype data center and highlight tradeoffs between the two methods
This paper addresses the problem of dynamic resource allocation among multiple entities sharing a common set of resources. A solution approach is presented based on combining the reinforcement learning methodology with function approximation architectures. An implementation of this approach in Solaris 10 demonstrated a robust near-optimal performance on a simple problem of transferring CPUs among...
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