Rajdeep Bhowmik

Improving Performance of Web Services Query Matchmaking with Automated Knowledge Acquisition

There is a critical need to design and develop tools that abstract away the fundamental complexity of XML-based Web services specifications and toolkits, and provide an elegant, intuitive, simple, and powerful query-based invocation system to end users. Web services based tools and standards have been designed to facilitate seamless integration and development for application developers. As a result, current implementations require the end user to have intimate knowledge of Web services and related toolkits, and users often play an informed role in the overall Web services execution process. We employ a self-learning mechanism and a set of algorithms and optimizations to match user queries with corresponding operations in Web services. Our system uses Semantic Web concepts and Ontologies in the process of automating Web services matchmaking. We present performance analysis of our system and quantify the exact gains in precision and recall due to the knowledge acquisition algorithms.

A Query-based System for Automatic Invocation of Web Services

There is a critical need to design and develop tools that abstract away the fundamental complexity of XML based Web services specifications and toolkits, and provide an elegant, intuitive, simple, and powerful query based invocation system to end users. Web services based tools and standards have been designed to facilitate seamless integration and development for application developers. As a result, current implementations require the end user to have intimate knowledge of Web services and related toolkits, and users often play an informed role in the overall Web services execution process We employ a set of algorithms and optimizations to match user queries with corresponding operations in Web services, invoke the operations with the correct set of parameters, and present the results to the end user. Our system uses the Semantic Web and Ontologies in the process of automating Web services invocation and execution.

Managing responsiveness of virtual desktops using passive monitoring

Desktop virtualization is a new computing approach to application delivery and management. It leverages OS virtualization and remoting protocols to provide users with remote access to virtual machines running in a centralized data center. It promises significant benefits in terms of improved data security, reduced management complexity, and more efficient and flexible resource usage. However, it brings a lot of management challenges typical for centralized systems, with performance and quality of service management being one of the most important. This paper proposes a management algorithm suitable for efficient resource allocation in virtualized desktop environments and takes application performance QoS features into consideration. It proposes a novel, non-intrusive method for application and remoting protocol agnostic desktop responsiveness monitoring. Moreover, it is based on studies of desktop workload usage which enabled us to discover and leverage workload patterns that can lead to increased efficiency both in terms of desktop responsiveness and resource usage. We have prototyped the system and discuss several case studies validating the approach and illustrating the most important features of the solution.

Semantic framework for free-form search of Grid resources and services

The model of free-form queries has been an enormous success for HTML-based search engines on the web. If the same free-form search is made available for grid services, it will serve as a powerful tool for scientists to retrieve information on resources, monitoring data, replica location sets, and meta-data on scientific data sets, in an intuitive manner. Current implementations of XML-based grid service descriptions require end users to have intimate knowledge of service descriptions, related toolkits, and query languages. We have developed a system that abstracts away these fundamental complexities and provides a simple free-form query based scientific discovery system for grid users. In this paper, we present the design and initial implementation results of our ontological framework that employs matching algorithms, automated extension of ontologies, and semantic Web and ontological concepts to match free-form queries with corresponding grid resource information stored in RDF/OWL format.

Cache Performance Optimization for Processing XML-Based Application Data on Multi-core Processors

There is a critical need to develop new programming paradigms for grid middleware tools and applications to harness the opportunities presented by emerging multi-core processors. Implementations of grid middleware and applications that do not adapt to the programming paradigm when executing on emerging processors can severely impact the overall performance. We focus on the utilization of the L2 cache, which is a critical shared resource on Chip Multiprocessors. The access pattern of the shared L2 cache, which is dependent on how the application schedules and assigns processing work to each thread, can either enhance or undermine the ability to hide memory latency on a multi-core processor. None of the current grid simulators and emulators provides feedback and fine-grained performance data that is essential for a detailed analysis. Using the feedback from an emulation framework, we present performance analysis and provide recommendations on how processing threads can be scheduled on multi-core nodes to enhance the performance of a class of grid applications that requires processing of large-scale XML data. In particular, we discuss the gains associated with the use of the adaptations we have made to the Cache-Affinity and Balanced-Set scheduling algorithms to improve L2 cache performance, and hence the overall application execution time.

Optimizing XML processing for grid applications using an emulation framework

Chip multi-processors (CMPs), commonly referred to as multi-core processors, are being widely adopted for deployment as part of the grid infrastructure. This change in computer architecture requires corresponding design modifications in programming paradigms, including grid middleware tools, to harness the opportunities presented by multi-core processors. Simple and naive implementations of grid middleware on multi-core systems can severely impact performance. This is because programming for CMPs requires special consideration for issues such as limitations of shared bus bandwidth, cache size and coherency, and communication between threads. The goal of developing an optimized multi-threaded grid middleware for emerging multi-core processors will be realized only if researchers and developers have access to an in-depth analysis of the impact of several low level microarchitectural parameters on performance. None of the current grid simulators and emulators provide feedback at the microarchitectural level, which is essential for such an analysis. In earlier work we presented our initial results on the design and implementation of such an emulation framework, Multi- core Grid (McGrid). In this paper we extend that work and present a performance study on the effect of cache coherency, scheduling of processing threads to take advantage of data available in the cache of each core, and read and write access patterns for shared data structures. We present the performance results, analysis, and recommendations based on experiments conducted using the McGrid framework for processing XML-based grid data and documents.

Web Services Operation and Parameter Matchmaking Based on Free-Form User Queries

Service-oriented architectures (SOA), based on Web services as the underlying architecture, have the potential to facilitate dynamic evolution of business processes. However, to fully realize the benefit of the SOA vision, it is critical to shield application developers from the complexity of Web services and related XML-based formats. In the currently available toolkits, users often play an informed role in the mapping of XML based specifications, operation names, message structures, and parameter types. The focus of our work is to design and develop an elegant, intuitive, simple, and powerful free-form query based system that allows interaction with Web services, without requiring the end-user to understand the operation names, parameters types, and other XML-based information in a WSDL document. Our system uses semantic Web concepts, ontologies, and WordNet in the process of automating Web services matchmaking. In this paper we focus on presenting techniques for matching free-form queries with appropriate Web service operations and extracting parameter values from user queries. We quantify the accuracy of our methodologies in terms of precision and recall.

L2 Cache Performance Analysis and Optimizations for Processing HDF5 Data on Multi-core Nodes

It is important to design and develop scientific middleware libraries to harness the opportunities presented by emerging multi-core processors that are available on grid and cloud environments. Scientific middleware libraries not adhering or adapting to this programming paradigm can suffer from severe performance limitations while executing on emerging multi-core processors. In this paper, we focus on the utilization of a critical shared resource on chip multiprocessors (CMPs), the L2 cache. The way in which an application schedules and assigns processing work to each thread determines the access pattern of the shared L2 cache, which may result in either enhancing or diminishing the effects of memory latency on a multi-core processor. Therefore, while processing scientific datasets such as HDF5, it is essential to conduct fine-grained analysis of cache utilization, to make informed processing and scheduling decisions in multi-threaded programming. In this paper, using the TAU toolkit for performance feedback from dual- and quad-core machines, we analyze and recommend methods for effective scheduling of threads on multi-core nodes to augment the performance of scientific applications processing HDF5 data. We discuss the benefits that can be achieved by using L2 Cache-Affinity and L2 Balanced-Set based scheduling algorithms for improving L2 cache performance and effectively the overall execution time.

McGrid: framework for optimizing grid middleware on multi-core processors

The microprocessor industry is rapidly moving towards chip multi-processors (CMPs), commonly referred to as multi-core processors, where multiple cores can independently execute different threads. This change in computer architecture requires corresponding design modifications in programming paradigms, including grid middleware tools, to harness the opportunities presented by multi-core processors. Naive implementations of grid middleware on multi-core systems can severely impact performance because of limitations of shared bus bandwidth, cache size and coherency, and communication between threads. The goal of developing an optimized multi-threaded grid middleware for emerging multi-core processors will be realized only if researchers and developers have access to an in-depth analysis of the impact of several low level microarchitectural parameters on performance. None of the current grid simulators and emulators provide feedback at the microarchitectural level, which is essential for such an analysis. We describe the initial design and implementation of an emulation framework, Multi-core Grid (McGrid), to analyze and provide insightful feedback on the performance limitations, bottlenecks, and optimization opportunities for grid middleware on multi-core systems. We describe early performance results of emulating the processing of some representative XML based grid documents on multi-core nodes using the McGrid framework.

Processing HDF5 Datasets on Multi-core Architectures

In order to make scientific middleware and applications more scalable, there is a need to design them in such a way that they can utilize the evolving multi-core processor architectures available in grid and cloud computing environments. In this paper, we analyze various processing and scheduling techniques on multi-core architectures based on scientific data characteristics and access patterns. More specifically, we conduct fine-grained analysis of scientific datasets such as HDF5 to make effective processing and scheduling decisions in multi-threaded programming. We present performance analysis on how processing threads can be scheduled on multi-core nodes to enhance the performance of scientific applications that process HDF5 data. To accomplish this we introduce a dynamic marking scheme to keep track of the progress of threads on each core. This can be used to help determine work allocation, which results in a decrease in overall application execution time.