Thomas Fahringer

Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing

Cloud computing is an emerging commercial infrastructure paradigm that promises to eliminate the need for maintaining expensive computing facilities by companies and institutes alike. Through the use of virtualization and resource time sharing, clouds serve with a single set of physical resources a large user base with different needs. Thus, clouds have the potential to provide to their owners the benefits of an economy of scale and, at the same time, become an alternative for scientists to clusters, grids, and parallel production environments. However, the current commercial clouds have been built to support web and small database workloads, which are very different from typical scientific computing workloads. Moreover, the use of virtualization and resource time sharing may introduce significant performance penalties for the demanding scientific computing workloads. In this work, we analyze the performance of cloud computing services for scientific computing workloads. We quantify the presence in real scientific computing workloads of Many-Task Computing (MTC) users, that is, of users who employ loosely coupled applications comprising many tasks to achieve their scientific goals. Then, we perform an empirical evaluation of the performance of four commercial cloud computing services including Amazon EC2, which is currently the largest commercial cloud. Last, we compare through trace-based simulation the performance characteristics and cost models of clouds and other scientific computing platforms, for general and MTC-based scientific computing workloads. Our results indicate that the current clouds need an order of magnitude in performance improvement to be useful to the scientific community, and show which improvements should be considered first to address this discrepancy between offer and demand.

Examining the Challenges of Scientific Workflows

Workflows have emerged as a paradigm for representing and managing complex distributed computations and are used to accelerate the pace of scientific progress. A recent National Science Foundation workshop brought together domain, computer, and social scientists to discuss requirements of future scientific applications and the challenges they present to current workflow technologies.

ASKALON: a tool set for cluster and Grid computing

Performance 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 high‐level 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 post‐mortem visualization of performance and output data. We demonstrate the usefulness and effectiveness of ASKALON by applying the tools to real‐world applications. Copyright

ASKALON: a Grid application development and computing environment

We present the ASKALON environment whose goal is to simplify the development and execution of workflow applications on the Grid. ASKALON is centered around a set of high-level services for transparent and effective Grid access, including a Scheduler for optimized mapping of workflows onto the Grid, an Enactment Engine for reliable application execution, a Resource Manager covering both computers and application components, and a Performance Prediction service based on training phase and statistical methods. A sophisticated XML-based programming interface that shields the user from the Grid middleware details allows the high-level composition of workflow applications. ASKALON is used to develop and port scientific applications as workflows in the Austrian Grid project. We present experimental results using two real-world scientific applications to demonstrate the effectiveness of our approach.

Grid capacity planning with negotiation-based advance reservation for optimized QoS

Advance reservation of grid resources can play a key role in enabling grid middleware to deliver on-demand resource provision with significantly improved quality-of-service (QoS). However, in the grid, advance reservation has been largely ignored due to the dynamic grid behavior, underutilization concerns, multi-constrained applications, and lack of support for agreement enforcement. These issues force the grid middleware to make resource allocations at run-time with reduced QoS. To remedy these, we introduce a new, 3-layered negotiation protocol for advance reservation of the grid resources. We model resource allocation as an online strip packing problem and introduce a new mechanism that optimizes resource utilization and QoS constraints while generating the contention-free solutions. The mechanism supports open reservations to deal with the dynamic grid and provides a practical solution for agreement enforcement. We have implemented a prototype and performed experiments to demonstrate the effectiveness of our approach

Specification of grid workflow applications with AGWL: an Abstract Grid Workflow Language

Currently grid application developers often configure available application components into a workflow of tasks that they can submit for executing on the grid. In this paper, we present an abstract grid workflow language (AGWL) for describing grid workflow applications at a high level of abstraction. AGWL has been designed such that the user can concentrate on specifying grid applications without dealing with either the complexity of the grid or any specific implementation technology (e.g. Web service). AGWL is an XML-based language which allows a programmer to define a graph of activities that refer mostly to computational tasks. Activities are connected by control and data flow links. A rich set of constructs (compound activities) is provided to simplify the specification of grid workflow applications which includes compound activities such as if, forEach and while loops as well as advanced compound activities including parallel sections, parallel loops and collection iterators. Moreover, AGWL supports a generic high level access mechanism to data repositories. AGWL is the main interface to the ASKALON grid application development environment and has been applied to numerous real world applications. We describe a material science workflow that has been successfully ported to a grid infrastructure based on an AGWL specification. Only a dozen AGWL activities are needed to describe a workflow with several hundred activity instances.

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 Multi-objective Approach for Workflow Scheduling in Heterogeneous Environments

Traditional scheduling research usually targets make span as the only optimization goal, while several isolated efforts addressed the problem by considering at most two objectives. In this paper we propose a general framework and heuristic algorithm for multi-objective static scheduling of scientific workflows in heterogeneous computing environments. The algorithm uses constraints specified by the user for each objective and approximates the optimal solution by applying a double strategy: maximizing the distance to the constraint vector for dominant solutions and minimizing it otherwise. We analyze and classify different objectives with respect to their impact on the optimization process and present a four-objective case study comprising make span, economic cost, energy consumption, and reliability. We implemented the algorithm as part of the ASKALON environment for Grid and Cloud computing. Results for two real-world applications demonstrate that the solutions generated by our algorithm are superior to user-defined constraints most of the time. Moreover, the algorithm outperforms a related bi-criteria heuristic and a bi-criteria genetic algorithm.

A static parameter based performance prediction tool for parallel programs

This paper presents a Parameter based Performance Prediction Tool (PPPT) which is part of the Vienna Fortran Compilation System (VFCS), a compiler that automatically translates Fortran programs into message passing programs for massively parallel architectures. The PPPT is applied to an explicitly parallel program generated by the VFCS, which may contain synchronous as well as asynchronous communication and is attributed with parameters computed in a previous profiling run. It statically computes a set of optional parameters that characterize the behavior of the parallel program. This includes work distribution, the number of data transfers, the amount of data transferred, transfer times, network contention, and the number of cache misses. These parameters can be selectively determined for statements, loops, procedures, and the entire program; furthermore, their effect with respect to individual processors can be examined. The tool plays an important role in the VFCS by providing the system as well as the user with vital performance information about the program. In particular, it supports automatic data distribution generation and the intelligent selection of transformation strategies, based on properties of the algorithm and characteristics of the target architecture. The tool has been implemented. Experiments show a strong correlation between the statically computed parameters and actual measurements; furthermore it turns out that the predicted parameter values allow a realistic ranking of different program versions with respect to the actual runtime.

Dynamic scheduling of scientific workflow applications on the grid: a case study

The existing Grid workflow scheduling projects do not handle recursive loops which are characteristic to many scientific problems. We propose a hybrid approach for scheduling Directed Graph (DG)-based workflows in a Grid environment with dynamically changing computational and network resources. Our dynamic scheduling algorithm is based on the iterative invocation of classical static Directed Acyclic Graphs (DAGs) scheduling heuristics generated using well-defined cycle elimination and task migration techniques. We approach the static scheduling problem as an application of a modular optimisation tool using genetic algorithms. We report successful implementation and experimental results on a pilot real-world material science workflow application.