The thrust of this paper is to profile the execution phases of applications, which helps optimize the efficiency of the underlying resources. Here we present a novel system-level application-resource-demand phase analysis and prediction approach in support of on-demand resource provisioning. The process we follow is to explore large-scale behavior of applications’ resource consumption, followed by analysis using a set of algorithms based on clustering. The phase profile, which learns from historical runs, is used to classify and predict future phase behavior. This process takes into consideration applications’s resource consumption patterns, phase transition costs and penalties associated with Service-Level Agreements (SLA) violations. Our experimental results with WorldCup98 replay web access logs show that prediction accuracies around 84% or larger for ten-phase cases can be achieved for network performance traces.

The integration of multiple predictors promises higher prediction accuracy than the accuracy that can be obtained with a single predictor. The challenge is how to select the best predictor at any given moment. Traditionally, multiple predictors are run in parallel and the one that generates the best result is selected for prediction. In this paper, we propose a novel approach for predictor integration based on the learning of historical predictions. It uses classification algorithms such as k-Nearest Neighbor (k-NN) based supervised learning to forecast the best predictor for the workload under study. Then only the forecasted best predictor is run for prediction. Our experimental results show that it achieved 20.18% higher best predictor forecasting accuracy than the cumulative MSB based predictor selection approach used in the popular network weather service system. In addition, it outperformed the observed most accurate single predictor in the pool for 44.23% of the performance traces.

Application awareness is an important factor of efficient resource scheduling. This paper introduces a novel approach for application classification based on the principal component analysis (PCA) and the k-nearest neighbor (k-NN) classifier. This approach is used to assist scheduling in heterogeneous computing environments. It helps to reduce the dimensionality of the performance feature space and classify applications based on extracted features. The classification considers four dimensions: CPU-intensive, I/O and paging-intensive, network-intensive, and idle. Application class information and the statistical abstracts of the application behavior are learned over historical runs and used to assist multi-dimensional resource scheduling. This paper describes a prototype classifier for application-centric virtual machines. Experimental results show that scheduling decisions made with the assistance of the application class information, improved system throughput by 22.11% on average, for a set of three benchmark applications.

Virtual machines provide flexible, powerful execution environments for Grid computing, offering isolation and security mechanisms complementary to operating systems, customization and encapsulation of entire application environments, and support for legacy applications. This paper describes a Grid service — VMPlant — that provides for automated configuration and creation of flexible VMs that, once configured to meet application needs, can then subsequently be copied ("cloned") and dynamically instantiated to provide homogeneous execution environments across distributed Grid resources. In combination with complementary middleware for user, data and resource management, the functionality enabled by VMPlant allows for problem-solving environments to deliver Grid applications to users with unprecedented flexibility. VMPlant supports a graph-based model for the definition of customized VM configuration actions; partial graph matching, VM state storage and "cloning" for efficient creation. This paper presents the VMPlant architecture, describes a prototype implementation of the service, and presents an analysis of its performance.