ASKALON: a tool set for cluster and Grid computing

Abstract

SUMMARYPerformance engineering of parallel and distributed applications is a complex task that iterates through various phases, ranging from modeling and prediction, to performance measurement, experiment management, data collection, and bottleneck analysis. There is no evidence so far that all of these phases should/can be integrated into a single monolithic tool. Moreover, the emergence of computational Grids as a common single wide-area platform for high-performance computing raises the idea to provide tools as interacting Grid services that share resources, support interoperability among different users and tools, and, most importantly, provide omnipresent services over the Grid. We have developed the ASKALON tool set to support performance-oriented development of parallel and distributed (Grid) applications. ASKALON comprises four tools, coherently integrated into a service-oriented architecture. SCALEA is a performance instrumentation, measurement, and analysis tool of parallel and distributed applications. ZENTURIO is a general purpose experiment management tool with advanced support for multi-experiment performance analysis and parameter studies. AKSUM provides semi-automatic highlevel performance bottleneck detection through a special-purpose performance property specification language. The PerformanceProphet enables the user to model and predict the performance of parallel applications at the early stages of development. In this paper we describe the overall architecture of the ASKALON tool set and outline the basic functionality of the four constituent tools. The structure of each tool is based on the composition and sharing of remote Grid services, thus enabling tool interoperability. In addition, a data repository allows the tools to share the common application performance and output data that have been derived by the individual tools. A service repository is used to store common portable Grid service implementations. A general-purpose Factory service is employed to create service instances on arbitrary remote Grid sites. Discovering and dynamically binding to existing remote services is achieved through registry services. The ASKALON visualization diagrams support both online and postmortem visualization of performance and output data. We demonstrate the usefulness and effectiveness of ASKALON by applying the tools to real-world applications.

Dynamic Instrumentation, Performance Monitoring and Analysis of Grid Scientific Workflows

“…Workflows and their relevant performance metrics are stored and utilized for comparing the performance of subgraphs of workflows and supporting multi-workflow analysis. We are currently working towards the full implementation of our prototype, and are in the process to integrate the prototype into the ASKALON toolset [14].…”

Clear and Precise Specification of Ecological Data Management Processes and Dataset Provenance

“…JOpera workflows also are based upon the use of a DFG formalism to represent scientific processes. Teuta [13], [14] represents scientific processes through UML diagrams that offer some features, such as limited forms of concurrency, that go beyond the semantic features of a basic DFG. We believe that the Water Budget process described here illustrates aspects of scientific processes that cannot be easily captured using DFGs.…”

A Compromised-Time-Cost Scheduling Algorithm in SwinDeW-C for Instance-Intensive Cost-Constrained Workflows on a Cloud Computing Platform

“…Best-effort based scheduling attempts to minimise the execution time without considering other factors such as the monetary cost of accessing resources and various users' QoS satisfaction levels. Some examples are the Heterogeneous EarliestFinish-Time algorithm (Tannenbaum, Wright, Miller and Livny 2002) used by ASKALON (Fahringer, Jugravu , Pllana , Prodan, Slovis and Truong 2005), the Min-Min algorithm (Maheswaran, Ali , Siegel , Hensgen and Freund 1999) used by GrADS (Berman, Chien, Cooper, Dongarra, Foster, Gannon, Johnsson, Kennedy, Kesselman, MellorCrummey, Reed, Torczon and Wolski 2001) and a throughput maximisation strategy used by SwinDeW-G (Yang, Liu, Chen, Lignier and Jin 2007) and SwinDeW-C (Yang, Liu, Chen, Liu, Yuan and Jin 2008). In contrast, QoS constraint based scheduling attempts to maximise the performance under QoS constraints of which cost is one major constraint that we focus on in this paper.…”