Distributed Management of Grid-based Scientific Workflows

Grids and service-oriented technologies are emerging as dominant approaches for distributed systems. With the evolution of these technologies, scientific workflows have been introduced as a tool for scientists to assemble highly specialized applications, and to exchange large heterogeneous datasets in order to automate and accelerate the accomplishment of complex scientific tasks. Several Scientific Workflow Management Systems (SWfMS) have already been designed to support the specification, execution, and monitoring of scientific workflows. Meanwhile, they still face key challenges from two different perspectives: system usability and system efficiency. From the system usability perspective, current SWfMS are not designed to be simple enough for scientists who have quite limited IT knowledge. What’s more, there is no easy mechanism by which scientists can share and re-use scientific experiments that have already been designed and proved by others. From the perspective of system efficiency, existing SWfMS are coordinating and executing workflows in a centralized fashion using a single scheduler and / or a workflow enactor. This creates a single point of failure, forms a scalability bottleneck, and enforces centralized fault handling. In addition, they don’t consider load balancing while mapping abstract jobs onto several computational nodes. Another important challenge exists due to the common nature of scientific workflow applications, that need to exchange a huge amount of data during the execution process. Some available SWfMS use a mediator-based approach for data transfer where data must be transferred first to a centralized data manager, which is completely inefficient. Other SWfMS apply a peer-to-peer approach via data references. Even this approach is not sufficient for scientific workflows as a single complex scientific activity can produce an extensive amount of data. In this thesis, we introduce SWIMS (Scientific Workflow Integration and Management System) framework. It employs the Web Services technology to originate a distributed management system for data-intensive scientific workflows. The purpose of SWIMS is to overcome the previously mentioned challenges through a set of salient features: i) Support for distributed execution and management of workflows, ii) diminution of communication traffic, iii) support for smart re-run, iv) distributed fault handling and load balancing, v) ease of use, and vi) extensive sharing of scientific workflows. We discuss the motivation, design, and implementation of the SWIMS framework. Then, we evaluate it through the Montage application from the astronomy domain

Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

Distributed simulation of city inundation by coupled surface and subsurface porous flow for urban flood decision support system

We present a decision support system for flood early warning and disaster management. It includes the models for data-driven meteorological predictions, for simulation of atmospheric pressure, wind, long sea waves and seiches; a module for optimization of flood barrier gates operation; models for stability assessment of levees and embankments, for simulation of city inundation dynamics and citizens evacuation scenarios. The novelty of this paper is a coupled distributed simulation of surface and subsurface flows that can predict inundation of low-lying inland zones far from the submerged waterfront areas, as observed in St. Petersburg city during the floods. All the models are wrapped as software services in the CLAVIRE platform for urgent computing, which provides workflow management and resource orchestration.Comment: Pre-print submitted to the 2013 International Conference on Computational Scienc

Many-Task Computing and Blue Waters

This report discusses many-task computing (MTC) generically and in the context of the proposed Blue Waters systems, which is planned to be the largest NSF-funded supercomputer when it begins production use in 2012. The aim of this report is to inform the BW project about MTC, including understanding aspects of MTC applications that can be used to characterize the domain and understanding the implications of these aspects to middleware and policies. Many MTC applications do not neatly fit the stereotypes of high-performance computing (HPC) or high-throughput computing (HTC) applications. Like HTC applications, by definition MTC applications are structured as graphs of discrete tasks, with explicit input and output dependencies forming the graph edges. However, MTC applications have significant features that distinguish them from typical HTC applications. In particular, different engineering constraints for hardware and software must be met in order to support these applications. HTC applications have traditionally run on platforms such as grids and clusters, through either workflow systems or parallel programming systems. MTC applications, in contrast, will often demand a short time to solution, may be communication intensive or data intensive, and may comprise very short tasks. Therefore, hardware and software for MTC must be engineered to support the additional communication and I/O and must minimize task dispatch overheads. The hardware of large-scale HPC systems, with its high degree of parallelism and support for intensive communication, is well suited for MTC applications. However, HPC systems often lack a dynamic resource-provisioning feature, are not ideal for task communication via the file system, and have an I/O system that is not optimized for MTC-style applications. Hence, additional software support is likely to be required to gain full benefit from the HPC hardware

Tupleware: Redefining Modern Analytics

There is a fundamental discrepancy between the targeted and actual users of current analytics frameworks. Most systems are designed for the data and infrastructure of the Googles and Facebooks of the world---petabytes of data distributed across large cloud deployments consisting of thousands of cheap commodity machines. Yet, the vast majority of users operate clusters ranging from a few to a few dozen nodes, analyze relatively small datasets of up to a few terabytes, and perform primarily compute-intensive operations. Targeting these users fundamentally changes the way we should build analytics systems. This paper describes the design of Tupleware, a new system specifically aimed at the challenges faced by the typical user. Tupleware's architecture brings together ideas from the database, compiler, and programming languages communities to create a powerful end-to-end solution for data analysis. We propose novel techniques that consider the data, computations, and hardware together to achieve maximum performance on a case-by-case basis. Our experimental evaluation quantifies the impact of our novel techniques and shows orders of magnitude performance improvement over alternative systems