Supporting Quality of Service in Scientific Workflows

While workflow management systems have been utilized in enterprises to support businesses for almost two decades, the use of workflows in scientific environments was fairly uncommon until recently. Nowadays, scientists use workflow systems to conduct scientific experiments, simulations, and distributed computations. However, most scientific workflow management systems have not been built using existing workflow technology; rather they have been designed and developed from scratch. Due to the lack of generality of early scientific workflow systems, many domain-specific workflow systems have been developed. Generally speaking, those domain-specific approaches lack common acceptance and tool support and offer lower robustness compared to business workflow systems. In this thesis, the use of the industry standard BPEL, a workflow language for modeling business processes, is proposed for the modeling and the execution of scientific workflows. Due to the widespread use of BPEL in enterprises, a number of stable and mature software products exist. The language is expressive (Turingcomplete) and not restricted to specific applications. BPEL is well suited for the modeling of scientific workflows, but existing implementations of the standard lack important features that are necessary for the execution of scientific workflows. This work presents components that extend an existing implementation of the BPEL standard and eliminate the identified weaknesses. The components thus provide the technical basis for use of BPEL in academia. The particular focus is on so-called non-functional (Quality of Service) requirements. These requirements include scalability, reliability (fault tolerance), data security, and cost (of executing a workflow). From a technical perspective, the workflow system must be able to interface with the middleware systems that are commonly used by the scientific workflow community to allow access to heterogeneous, distributed resources (especially Grid and Cloud resources). The major components cover exactly these requirements: Cloud Resource Provisioner Scalability of the workflow system is achieved by automatically adding additional (Cloud) resources to the workflow system’s resource pool when the workflow system is heavily loaded. Fault Tolerance Module High reliability is achieved via continuous monitoring of workflow execution and corrective interventions, such as re-execution of a failed workflow step or replacement of the faulty resource. Cost Aware Data Flow Aware Scheduler The majority of scientific workflow systems only take the performance and utilization of resources for the execution of workflow steps into account when making scheduling decisions. The presented workflow system goes beyond that. By defining preference values for the weighting of costs and the anticipated workflow execution time, workflow users may influence the resource selection process. The developed multiobjective scheduling algorithm respects the defined weighting and makes both efficient and advantageous decisions using a heuristic approach. Security Extensions Because it supports various encryption, signature and authentication mechanisms (e.g., Grid Security Infrastructure), the workflow system guarantees data security in the transfer of workflow data. Furthermore, this work identifies the need to equip workflow developers with workflow modeling tools that can be used intuitively. This dissertation presents two modeling tools that support users with different needs. The first tool, DAVO (domain-adaptable, Visual BPEL Orchestrator), operates at a low level of abstraction and allows users with knowledge of BPEL to use the full extent of the language. DAVO is a software that offers extensibility and customizability for different application domains. These features are used in the implementation of the second tool, SimpleBPEL Composer. SimpleBPEL is aimed at users with little or no background in computer science and allows for quick and intuitive development of BPEL workflows based on predefined components

Runtime Adaptation of Scientific Service Workflows

Software landscapes are rather subject to change than being complete after having been built. Changes may be caused by a modified customer behavior, the shift to new hardware resources, or otherwise changed requirements. In such situations, several challenges arise. New architectural models have to be designed and implemented, existing software has to be integrated, and, finally, the new software has to be deployed, monitored, and, where appropriate, optimized during runtime under realistic usage scenarios. All of these situations often demand manual intervention, which causes them to be error-prone. This thesis addresses these types of runtime adaptation. Based on service-oriented architectures, an environment is developed that enables the integration of existing software (i.e., the wrapping of legacy software as web services). A workflow modeling tool that aims at an easy-to-use approach by separating the role of the workflow expert and the role of the domain expert. After the development of workflows, tools that observe the executing infrastructure and perform automatic scale-in and scale-out operations are presented. Infrastructure-as-a-Service providers are used to scale the infrastructure in a transparent and cost-efficient way. The deployment of necessary middleware tools is automatically done. The use of a distributed infrastructure can lead to communication problems. In order to keep workflows robust, these exceptional cases need to treated. But, in this way, the process logic of a workflow gets mixed up and bloated with infrastructural details, which yields an increase in its complexity. In this work, a module is presented that can deal automatically with infrastructural faults and that thereby allows to keep the separation of these two layers. When services or their components are hosted in a distributed environment, some requirements need to be addressed at each service separately. Although techniques as object-oriented programming or the usage of design patterns like the interceptor pattern ease the adaptation of service behavior or structures. Still, these methods require to modify the configuration or the implementation of each individual service. On the other side, aspect-oriented programming allows to weave functionality into existing code even without having its source. Since the functionality needs to be woven into the code, it depends on the specific implementation. In a service-oriented architecture, where the implementation of a service is unknown, this approach clearly has its limitations. The request/response aspects presented in this thesis overcome this obstacle and provide a SOA-compliant and new methods to weave functionality into the communication layer of web services. The main contributions of this thesis are the following: Shifting towards a service-oriented architecture: The generic and extensible Legacy Code Description Language and the corresponding framework allow to wrap existing software, e.g., as web services, which afterwards can be composed into a workflow by SimpleBPEL without overburdening the domain expert with technical details that are indeed handled by a workflow expert. Runtime adaption: Based on the standardized Business Process Execution Language an automatic scheduling approach is presented that monitors all used resources and is able to automatically provision new machines in case a scale-out becomes necessary. If the resource's load drops, e.g., because of less workflow executions, a scale-in is also automatically performed. The scheduling algorithm takes the data transfer between the services into account in order to prevent scheduling allocations that eventually increase the workflow's makespan due to unnecessary or disadvantageous data transfers. Furthermore, a multi-objective scheduling algorithm that is based on a genetic algorithm is able to additionally consider cost, in a way that a user can define her own preferences rising from optimized execution times of a workflow and minimized costs. Possible communication errors are automatically detected and, according to certain constraints, corrected. Adaptation of communication: The presented request/response aspects allow to weave functionality into the communication of web services. By defining a pointcut language that only relies on the exchanged documents, the implementation of services must neither be known nor be available. The weaving process itself is modeled using web services. In this way, the concept of request/response aspects is naturally embedded into a service-oriented architecture

Integrating multiple clusters for compute-intensive applications

Multicluster grids provide one promising solution to satisfying the growing computational demands of compute-intensive applications. However, it is challenging to seamlessly integrate all participating clusters in different domains into a single virtual computational platform. In order to fully utilize the capabilities of multicluster grids, computer scientists need to deal with the issue of joining together participating autonomic systems practically and efficiently to execute grid-enabled applications. Driven by several compute-intensive applications, this theses develops a multicluster grid management toolkit called Pelecanus to bridge the gap between user\u27s needs and the system\u27s heterogeneity. Application scientists will be able to conduct very large-scale execution across multiclusters with transparent QoS assurance. A novel model called DA-TC (Dynamic Assignment with Task Containers) is developed and is integrated into Pelecanus. This model uses the concept of a task container that allows one to decouple resource allocation from resource binding. It employs static load balancing for task container distribution and dynamic load balancing for task assignment. The slowest resources become useful rather than be bottlenecks in this manner. A cluster abstraction is implemented, which not only provides various cluster information for the DA-TC execution model, but also can be used as a standalone toolkit to monitor and evaluate the clusters\u27 functionality and performance. The performance of the proposed DA-TC model is evaluated both theoretically and experimentally. Results demonstrate the importance of reducing queuing time in decreasing the total turnaround time for an application. Experiments were conducted to understand the performance of various aspects of the DA-TC model. Experiments showed that our model could significantly reduce turnaround time and increase resource utilization for our targeted application scenarios. Four applications are implemented as case studies to determine the applicability of the DA-TC model. In each case the turnaround time is greatly reduced, which demonstrates that the DA-TC model is efficient for assisting application scientists in conducting their research. In addition, virtual resources were integrated into the DA-TC model for application execution. Experiments show that the execution model proposed in this thesis can work seamlessly with multiple hybrid grid/cloud resources to achieve reduced turnaround time

Failure-awareness and dynamic adaptation in data scheduling

Over the years, scientific applications have become more complex and more data intensive. Especially large scale simulations and scientific experiments in areas such as physics, biology, astronomy and earth sciences demand highly distributed resources to satisfy excessive computational requirements. Increasing data requirements and the distributed nature of the resources made I/O the major bottleneck for end-to-end application performance. Existing systems fail to address issues such as reliability, scalability, and efficiency in dealing with wide area data access, retrieval and processing. In this study, we explore data-intensive distributed computing and study challenges in data placement in distributed environments. After analyzing different application scenarios, we develop new data scheduling methodologies and the key attributes for reliability, adaptability and performance optimization of distributed data placement tasks. Inspired by techniques used in microprocessor and operating system architectures, we extend and adapt some of the known low-level data handling and optimization techniques to distributed computing. Two major contributions of this work include (i) a failure-aware data placement paradigm for increased fault-tolerance, and (ii) adaptive scheduling of data placement tasks for improved end-to-end performance. The failure-aware data placement includes early error detection, error classification, and use of this information in scheduling decisions for the prevention of and recovery from possible future errors. The adaptive scheduling approach includes dynamically tuning data transfer parameters over wide area networks for efficient utilization of available network capacity and optimized end-to-end data transfer performance

Towards a Mini-App for Smoothed Particle Hydrodynamics at Exascale

The smoothed particle hydrodynamics (SPH) technique is a purely Lagrangian method, used in numerical simulations of fluids in astrophysics and computational fluid dynamics, among many other fields. SPH simulations with detailed physics represent computationally-demanding calculations. The parallelization of SPH codes is not trivial due to the absence of a structured grid. Additionally, the performance of the SPH codes can be, in general, adversely impacted by several factors, such as multiple time-stepping, long-range interactions, and/or boundary conditions. This work presents insights into the current performance and functionalities of three SPH codes: SPHYNX, ChaNGa, and SPH-flow. These codes are the starting point of an interdisciplinary co-design project, SPH-EXA, for the development of an Exascale-ready SPH mini-app. To gain such insights, a rotating square patch test was implemented as a common test simulation for the three SPH codes and analyzed on two modern HPC systems. Furthermore, to stress the differences with the codes stemming from the astrophysics community (SPHYNX and ChaNGa), an additional test case, the Evrard collapse, has also been carried out. This work extrapolates the common basic SPH features in the three codes for the purpose of consolidating them into a pure-SPH, Exascale-ready, optimized, mini-app. Moreover, the outcome of this serves as direct feedback to the parent codes, to improve their performance and overall scalability.Comment: 18 pages, 4 figures, 5 tables, 2018 IEEE International Conference on Cluster Computing proceedings for WRAp1

Data Replication and Its Alignment with Fault Management in the Cloud Environment

Nowadays, the exponential data growth becomes one of the major challenges all over the world. It may cause a series of negative impacts such as network overloading, high system complexity, and inadequate data security, etc. Cloud computing is developed to construct a novel paradigm to alleviate massive data processing challenges with its on-demand services and distributed architecture. Data replication has been proposed to strategically distribute the data access load to multiple cloud data centres by creating multiple data copies at multiple cloud data centres. A replica-applied cloud environment not only achieves a decrease in response time, an increase in data availability, and more balanced resource load but also protects the cloud environment against the upcoming faults. The reactive fault tolerance strategy is also required to handle the faults when the faults already occurred. As a result, the data replication strategies should be aligned with the reactive fault tolerance strategies to achieve a complete management chain in the cloud environment. In this thesis, a data replication and fault management framework is proposed to establish a decentralised overarching management to the cloud environment. Three data replication strategies are firstly proposed based on this framework. A replica creation strategy is proposed to reduce the total cost by jointly considering the data dependency and the access frequency in the replica creation decision making process. Besides, a cloud map oriented and cost efficiency driven replica creation strategy is proposed to achieve the optimal cost reduction per replica in the cloud environment. The local data relationship and the remote data relationship are further analysed by creating two novel data dependency types, Within-DataCentre Data Dependency and Between-DataCentre Data Dependency, according to the data location. Furthermore, a network performance based replica selection strategy is proposed to avoid potential network overloading problems and to increase the number of concurrent-running instances at the same time

Dynamic workflow management for large scale scientific applications

The increasing computational and data requirements of scientific applications have made the usage of large clustered systems as well as distributed resources inevitable. Although executing large applications in these environments brings increased performance, the automation of the process becomes more and more challenging. The use of complex workflow management systems has been a viable solution for this automation process. In this thesis, we study a broad range of workflow management tools and compare their capabilities especially in terms of dynamic and conditional structures they support, which are crucial for the automation of complex applications. We then apply some of these tools to two real-life scientific applications: i) simulation of DNA folding, and ii) reservoir uncertainty analysis. Our implementation is based on Pegasus workflow planning tool, DAGMan workflow execution system, Condor-G computational scheduler, and Stork data scheduler. The designed abstract workflows are converted to concrete workflows using Pegasus where jobs are matched to resources; DAGMan makes sure these jobs execute reliably and in the correct order on the remote resources; Condor-G performs the scheduling for the computational tasks and Stork optimizes the data movement between different components. Integrated solution with these tools allows automation of large scale applications, as well as providing complete reliability and efficiency in executing complex workflows. We have also developed a new site selection mechanism on top of these systems, which can choose the most available computing resources for the submission of the tasks. The details of our design and implementation, as well as experimental results are presented