Farrukh Nadeem

ASKALON: A Development and Grid Computing Environment for Scientific Workflows

Most existing Grid application development environments provide the application developer with a nontransparent Grid. Commonly, application developers are explicitly involved in tedious tasks such as selecting software components deployed on specific sites, mapping applications onto the Grid, or selecting appropriate computers for their applications. Moreover, many programming interfaces are either implementation-technology-specific (e.g., based on Web services [24]) or force the application developer to program at a low-level middleware abstraction (e.g., start task, transfer data [22, 153]). While a variety of graphical workflow composition tools are currently being proposed, none of them is based on standard modeling techniques such as Unified Modeling Language (UML).

A Hybrid Intelligent Method for Performance Modeling and Prediction of Workflow Activities in Grids

Grid schedulers require individual activity performance predictions to map workflow activities on different Grid sites. The effectiveness of the scheduling systems is hampered by inaccurate predictions due to the inability of existing predictors to effectively model the dynamic and heterogeneous nature of Grid resources, or the wide range of problem sizes and runtime arguments. To address this deficiency, we propose a hybrid Bayesian-neural network approach to dynamically model and predict the execution time of activities in real workflow applications. Bayesian network is used for a high-level representation of activities performance probability distribution against different factors affecting the performance. The important attributes are dynamically selected by the Bayesian network and fed into a radial basis function neural network to make further predictions. Our approach is generic to any type of scientific applications, and flexible to import expert knowledge to further improve accuracies. Experimental results for activities from three realworld workflow applications are presented to show effectivenessof our approach.

Soft Benchmarks-Based Application Performance Prediction Using a Minimum Training Set

Application execution time prediction is of key importance in making decisions about efficient usage of Grid resources. Grid services lack support of a generic application execution time prediction service due to environment specific solutions provided by the existing prediction techniques. To remedy this, we present a generic and comprehensive system to provide execution time predictions of applications on different Grid-sites. Our system is based on a two layered training phase to minimize the training effort, which is our first main contribution. The training phase is driven by a novel experimental design. We also introduce a mechanism of sharing performance measurements across the Grid, on the basis of soft benchmarks, which is our second contribution. Both of these phases support our prediction engine to serve robust predictions. Experiments from the prototype implementation are shown to demonstrate the effectiveness of our proposed system.

Predicting the execution time of grid workflow applications through local learning

Workflow execution time prediction is widely seen as a key service to understand the performance behavior and support the optimization of Grid workflow applications. In this paper, we present a novel approach for estimating the execution time of workflows based on Local Learning. The workflows are characterized in terms of different attributes describing structural and runtime information about workflow activities, control and data flow dependencies, number of Grid sites, problem size, etc. Our local learning framework is complemented by a dynamic weighing scheme that assigns weights to workflow attributes reflecting their impact on the workflow execution time. Predictions are given through intervals bounded by the minimum and maximum predicted values, which are associated with a confidence value indicating the degree of confidence about the prediction accuracy. Evaluation results for three real world workflows on a real Grid are presented to demonstrate the prediction accuracy and overheads of the proposed method.

Using Templates to Predict Execution Time of Scientific Workflow Applications in the Grid

Workflow execution time predictions for Grid infrastructures is of critical importance for optimized workflow executions, advance reservations of resources, and overhead analysis. Predicting workflow execution time is complex due to multeity of workflow structures, involvement of several Grid resources in workflow execution, complex dependencies of workflow activities and dynamic behavior of the Grid. In this paper we present an online workflow execution time prediction system exploiting similarity templates. The workflows are characterized considering the attributes describing their performance at different Grid infrastructural levels. A “supervised exhaustive search” is employed to find suitable templates. We also make a provision of including expert user knowledge about the workflow performance in the procession of our methods. Results for three real world applications are presented to show the effectiveness of our approach.

Using the NS-2 Network Simulator for Evaluating Network on Chips (NoC)

Networks on chips (NoCs) have been introduced as a remedy for the growing problems of current interconnects in VLSI chips. Being a relatively new domain in research, simulation tools for NoCs are scarce. To fill the gap, we use network simulator NS-2 for simulating NoCs, especially at high level chip design. The huge library of network elements along with its flexibility to accommodate customized designs, NS-2 becomes a viable choice for NoCs. We have used NS-2 to simulate our prototype of a fault tolerant protocol for NoCs

A Framework For Resource Availability Characterization And Online Prediction In The Grids

Production grids integrate today thousands of resources into e-Science platforms. However, the current practice of running yearly tens of millions of single-resource, long-running grid jobs with few fault tolerance capabilities is hampered by the highly dynamic grid resource availability. In additional to resource failures, grids introduce a new vector of resource availability dynamics: the resource sharing policy established by the resource owners. As a result, the availability-aware grid resourcemanagement is a challenging problem for today’s researchers. To address this problem, we present in this work GriS-Prophet, an integrated system for resource availability monitoring, analysis, and prediction. Using GriS-Prophet’s analysis tools on a long-term availability trace from the Austrian Grid, we characterize the grid resource availability for three resource availability policies. Notably, we show that the three policies lead to very different capabilities for running the typical grid workloads efficiently. We introduce a new resource availability predictor based on Bayesian inference. Last but not least, using GriS-Prophet’s prediction tools we achieve an accuracy of more than 90% and 75% in our instance and duration availability predictions respectively.

Optimizing execution time predictions of scientific workflow applications in the Grid through evolutionary programming

Planning for execution of scientific workflow applications in the Grid requires in advance prediction of workflow execution time for optimized execution of these applications. However, predicting execution times of such applications is very complex mainly due to different structures of workflows, possible parallel execution of workflow tasks on multiple resources and the dynamic and heterogeneous nature of the Grid. In this paper, we describe an optimized method (in extension to a previous work by Nadeem et al. (2009) [4]) for execution time prediction of workflow applications in the Grid. We characterize workflows in terms of attributes describing their structures and performance during different stages of their execution. Overall, performance of the workflows is modeled through templates of workflow attributes. An optimized method exploiting evolutionary programming is employed to search for suitable templates. Three different induction models are employed to generate predictions and later compared for their accuracy. The results from our experiments for three real-world workflow applications on a real Grid are presented to show the effectiveness of our approach. We also compare the proposed approach with our previous method based on supervised exhaustive search by Nadeem and Fahringer (2009) [4].

An Early Evaluation and Comparison of Three Private Cloud Computing Software Platforms

Cloud computing, after its success as a commercial infrastructure, is now emerging as a private infrastructure. The software platforms available to build private cloud computing infrastructure vary in their performance for management of cloud resources as well as in utilization of local physical resources. Organizations and individuals looking forward to reaping the benefits of private cloud computing need to understand which software platform would provide the efficient services and optimum utilization of cloud resources for their target applications. In this paper, we present our initial study on performance evaluation and comparison of three cloud computing software platforms from the perspective of common cloud users who intend to build their private clouds. We compare the performance of the selected software platforms from several respects describing their suitability for applications from different domains. Our results highlight the critical parameters for performance evaluation of a software platform and the best software platform for different application domains.

Interactive case based learning in teaching decision support systems and business intelligence

An important learning outcome in teaching Decision Support Systems and Business Intelligence is understanding the decision making processes in real-life. Classroom discussion can be an effective tool for understanding how different factors affect the decision making but reality of the real-life business is so complex to be understood with simple discussions. In such a scenario, real-life case studies can be very helpful. The real-life case studies offer experiential learning that can improve the learning outcomes of the course. This paper aims at describing how interactive action oriented case studies, with student participation, aid in the process of learning in the Decision Support Systems and Business Intelligence course for undergraduate students, and make it more dynamic and interesting.