Design and Evaluation of Opal2: A Toolkit for Scientific Software as a Service

Abstract—Grid computing provides mechanisms for making large-scale computing environments available to the masses. In recent times, with the advent of Cloud computing, the concepts of Software as a Service (SaaS), where vendors provide key software products as services over the internet that can be accessed by users to perform complex tasks, and Service as Software (SaS), where customizable and repeatable services are packaged as software products that dynamically meet the demands of individual users, have become increasingly popular. Both SaaS and SaS models are highly applicable to scientific software and users alike. Opal2 is a toolkit for wrapping scientific applications as Web services on Grid and cloud computing resources. It provides a mechanism for scientific application developers to expose the functionality of their codes via simple Web service APIs, abstracting out the details of the back-end infrastructure. Services may be combined via cus-tomized workflows for specific research areas and distributed as virtual machine images. In this paper, we describe the overall philosophy and architecture of the Opal2 framework, including its new plug-in architecture and data handling capabilities. We analyze its performance in typical cluster and Grid settings, and in a cloud computing environment within virtual machines

Experimental Methodologies for Large-Scale Systems: a Survey

International audienceThe increasing complexity of available infrastructures with specific features (caches, hyperthreading, dual core, etc.) or with complex architectures (hierarchical, parallel, distributed, etc.) makes it extremely difficult to build analytical models that allow for a satisfying prediction. Hence, it raises the question on how to validate algorithms if a realistic analytic analysis is not possible any longer. As for some many other sciences, the one answer is experimental validation. Nevertheless, experimentation in Computer Science is a difficult subject that today still opens more questions than it solves: What may an experiment validate? What is a ''good experiment''? How to build an experimental environment that allows for "good experiments"? etc. In this paper we will provide some hints on this subject and show how some tools can help in performing ''good experiments'', mainly in the context of parallel and distributed computing. More precisely we will focus on four main experimental methodologies, namely in-situ (real-scale) experiments (with an emphasis on PlanetLab and Grid'5000), Emulation (with an emphasis on Wrekavoc) benchmarking and simulation (with an emphasis on SimGRID and GridSim). We will provide a comparison of these tools and methodologies from a quantitative but also qualitative point of view