Predictable execution of scientific workflows using advance resource reservations

Scientific Workflows are long-running and data intensive, and may encompass operations provided by multiple physically distributed service providers. The traditional approach to execute such workflows is to employ a single workflow engine which orchestrates the entire execution of a workflow instance, while being mostly agnostic about the state of the infrastructure it operates in (e.g., host or network load). Therefore, such centralized best-effort execution may use resources inefficiently -- for instance, repeatedly shipping large data volumes over slow network connections -- and cannot provide Quality of Service (QoS) guarantees. In particular, independent parallel executions might cause an overload of some resources, resulting in a performance degradation affecting all involved parties. In order to provide predictable behavior, we propose an approach where resources are managed proactively (i.e., reserved before being used), and where workflow execution is handled by multiple distributed and cooperating workflow engines. This allows to efficiently use the existing resources (for instance, using the most suitable provider for operations, and considering network locality for large data transfers) without overloading them, while at the same time providing predictability -- in terms of resource usage, execution timing, and cost -- for both service providers and customers. The contributions of this thesis are as follows. First, we present a system model which defines the concepts and operations required to formally represent a system where service providers are aware of the resource requirements of the operations they make available, and where (planned) workflow executions are adapted to the state of the infrastructure. Second, we describe our prototypical implementation of such a system, where a workflow execution comprises two main phases. In the planning phase, the resources to reserve for an upcoming workflow execution must be determined; this is realized using a Genetic Algorithm. We present conceptual and implementation details of the chromosome layout, and the fitness functions employed to plan executions according to one or more user-defined optimization goals. During the execution phase, the system must ensure that the actual resource usages abide to the reservations made. We present details on how such enforcement can be performed for various resource types. Third, we describe how these parts work together, and how the entire prototype system is deployed on an infrastructure based on WSDL/SOAP Web Services, UDDI Registries, and Glassfish Application Servers. Finally, we discuss the results of various evaluations, encompassing both the planning and runtime enforcement

Exploring resource/performance trade-offs for streaming applications on embedded multiprocessors

Embedded system design is challenged by the gap between the ever-increasing customer demands and the limited resource budgets. The tough competition demands ever-shortening time-to-market and product lifecycles. To solve or, at least to alleviate, the aforementioned issues, designers and manufacturers need model-based quantitative analysis techniques for early design-space exploration to study trade-offs of different implementation candidates. Moreover, modern embedded applications, especially the streaming applications addressed in this thesis, face more and more dynamic input contents, and the platforms that they are running on are more flexible and allow runtime configuration. Quantitative analysis techniques for embedded system design have to be able to handle such dynamic adaptable systems. This thesis has the following contributions: - A resource-aware extension to the Synchronous Dataflow (SDF) model of computation. - Trade-off analysis techniques, both in the time-domain and in the iterationdomain (i.e., on an SDF iteration basis), with support for resource sharing. - Bottleneck-driven design-space exploration techniques for resource-aware SDF. - A game-theoretic approach to controller synthesis, guaranteeing performance under dynamic input. As a first contribution, we propose a new model, as an extension of static synchronous dataflow graphs (SDF) that allows the explicit modeling of resources with consistency checking. The model is called resource-aware SDF (RASDF). The extension enables us to investigate resource sharing and to explore different scheduling options (ways to allocate the resources to the different tasks) using state-space exploration techniques. Consistent SDF and RASDF graphs have the property that an execution occurs in so-called iterations. An iteration typically corresponds to the processing of a meaningful piece of data, and it returns the graph to its initial state. On multiprocessor platforms, iterations may be executed in a pipelined fashion, which makes performance analysis challenging. As the second contribution, this thesis develops trade-off analysis techniques for RASDF, both in the time-domain and in the iteration-domain (i.e., on an SDF iteration basis), to dimension resources on platforms. The time-domain analysis allows interleaving of different iterations, but the size of the explored state space grows quickly. The iteration-based technique trades the potential of interleaving of iterations for a compact size of the iteration state space. An efficient bottleneck-driven designspace exploration technique for streaming applications, the third main contribution in this thesis, is derived from analysis of the critical cycle of the state space, to reveal bottleneck resources that are limiting the throughput. All techniques are based on state-based exploration. They enable system designers to tailor their platform to the required applications, based on their own specific performance requirements. Pruning techniques for efficient exploration of the state space have been developed. Pareto dominance in terms of performance and resource usage is used for exact pruning, and approximation techniques are used for heuristic pruning. Finally, the thesis investigates dynamic scheduling techniques to respond to dynamic changes in input streams. The fourth contribution in this thesis is a game-theoretic approach to tackle controller synthesis to select the appropriate schedules in response to dynamic inputs from the environment. The approach transforms the explored iteration state space of a scenario- and resource-aware SDF (SARA SDF) graph to a bipartite game graph, and maps the controller synthesis problem to the problem of finding a winning positional strategy in a classical mean payoff game. A winning strategy of the game can be used to synthesize the controller of schedules for the system that is guaranteed to satisfy the throughput requirement given by the designer

A Survey on Automatic Parameter Tuning for Big Data Processing Systems

Big data processing systems (e.g., Hadoop, Spark, Storm) contain a vast number of configuration parameters controlling parallelism, I/O behavior, memory settings, and compression. Improper parameter settings can cause significant performance degradation and stability issues. However, regular users and even expert administrators grapple with understanding and tuning them to achieve good performance. We investigate existing approaches on parameter tuning for both batch and stream data processing systems and classify them into six categories: rule-based, cost modeling, simulation-based, experiment-driven, machine learning, and adaptive tuning. We summarize the pros and cons of each approach and raise some open research problems for automatic parameter tuning.Peer reviewe

QoS-guaranteed resource provisioning for cloud-based MapReduce

This PhD project has investigated how to guarantee the quality of MapReduce services in cloud computing while minimizing the operational cost of the MapReduce services through dynamic resource provisioning. In this PhD project, a framework for the dynamic resource provisioning has been developed. Meanwhile, theoretical results for the dynamic resource provisioning have been derived, and a set of efficient and effective algorithms used in the framework have been proposed

Prescriptive Process Analytics using Counterfacts

openIn the age of technology integration and big data, the capability to extract value from abundant data has become paramount. Business Process Management (BPM), a field focused on the management of complex business processes, gives rise to a vast amount of event data. The subfield of “process mining” harnesses this data, acting as a conduit between BPM and data mining. Prescriptive analytics are a technique that uses data science techniques to provide actionable steps to improve a running process instance. This thesis delves into the realm of prescriptive process analytics, spotlighting the use of counterfactuals. Building upon established predictive frameworks, it seeks to develop a domain-agnostic prescriptive analysis mechanism. The primary aim is to generate recommendations that not only suggest the next-best activity, but also pinpoint the optimal resource to undertake it, all in a bid to optimize a predefined Key Performance Indicator (KPI). By comparing the efficacy of our proposed framework with existing methodologies on real-world datasets, we aim to underscore the significance and potential of our approach. The research unfolds through multiple chapters that elucidate foundational principles, state-of-the-art methods, our unique framework, and its evaluation through two distinct case studies. The hope is to chart a course for future endeavors in the domain, cementing the importance of prescriptive process analytics and its transformative impact on the business landscape