An efficient scheduling method for grid systems based on a hierarchical stochastic petri net

This paper addresses the problem of resource scheduling in a grid computing environment. One of the main goals of grid computing is to share system resources among geographically dispersed users, and schedule resource requests in an efficient manner. Grid computing resources are distributed, heterogeneous, dynamic, and autonomous, which makes resource scheduling a complex problem. This paper proposes a new approach to resource scheduling in grid computing environments, the hierarchical stochastic Petri net (HSPN). The HSPN optimizes grid resource sharing, by categorizing resource requests in three layers, where each layer has special functions for receiving subtasks from, and delivering data to, the layer above or below. We compare the HSPN performance with the Min-min and Max-min resource scheduling algorithms. Our results show that the HSPN performs better than Max-min, but slightly underperforms Min-min

Modeling and Simulation of Task Allocation with Colored Petri Nets

The task allocation problem is a key element in the solution of several applications from different engineering fields. With the explosion of the amount of information produced by the today Internet-connected solutions, scheduling techniques for the allocation of tasks relying on grids, clusters of computers, or in the cloud computing, is at the core of efficient solutions. The task allocation is an important problem within some branch of the computer sciences and operations research, where it is usually modeled as an optimization of a combinatorial problem with the inconvenience of a state explosion problem. This chapter proposes the modeling of the task allocation problem by the use of Colored Petri nets. The proposed methodology allows the construction of compact models for task scheduling problems. Moreover, a simulation process is possible within the constructed model, which allows the study of some performance aspects of the task allocation problem before any implementation stage

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

Distributed Handler Architecture

Thesis (PhD) - Indiana University, Computer Sciences, 2007Over the last couple of decades, distributed systems have been demonstrated an architectural evolvement based on models including client/server, multi-tier, distributed objects, messaging and peer-to-peer. One recent evolutionary step is Service Oriented Architecture (SOA), whose goal is to achieve loose-coupling among the interacting software applications for scalability and interoperability. The SOA model is engendered in Web Services, which provide software platforms to build applications as services and to create seamless and loosely-coupled interactions. Web Services utilize supportive functionalities such as security, reliability, monitoring, logging and so forth. These functionalities are typically provisioned as handlers, which incrementally add new capabilities to the services by building an execution chain. Even though handlers are very important to the service, the way of utilization is very crucial to attain the potential benefits. Every attempt to support a service with an additive functionality increases the chance of having an overwhelmingly crowded chain: this makes Web Service fat. Moreover, a handler may become a bottleneck because of having a comparably higher processing time. In this dissertation, we present Distributed Handler Architecture (DHArch) to provide an efficient, scalable and modular architecture to manage the execution of the handlers. The system distributes the handlers by utilizing a Message Oriented Middleware and orchestrates their execution in an efficient fashion. We also present an empirical evaluation of the system to demonstrate the suitability of this architecture to cope with the issues that exist in the conventional Web Service handler structures

Active Data: A Data-Centric Approach to Data Life-Cycle Management

International audienceData-intensive science offers new opportunities for innovation and discoveries, provided that large datasets can be handled efficiently. Data management for data-intensive science applications is challenging; requiring support for complex data life cycles, coordination across multiple sites, fault tolerance, and scalability to support tens of sites and petabytes of data. In this paper, we argue that data management for data-intensive science applications requires a fundamentally different management approach than the current ad-hoc task centric approach. We propose Active Data, a fundamentally novel paradigm for data life cycle management. Active Data follows two principles: data-centric and event-driven. We report on the Active Data programming model and its preliminary implementation, and discuss the benefits and limitations of the approach on recognized challenging data-intensive science use-cases.Les importants volumes de données produits par la science présentent de nouvelles opportunités d'innovation et de découvertes. Cependant ceci sera conditionné par notre capacité à gérer efficacement de très grands jeux de données. La gestion de données pour les applications scientifiques data-intensive présente un véritable défi~; elle requière le support de cycles de vie très complexes, la coordination de plusieurs sites, de la tolérance aux pannes et de passer à l'échelle sur des dizaines de sites avec plusieurs péta-octets de données. Dans cet article nous argumentons que la gestion des données pour les applications scientifiques data-intensive nécessite une approche fondamentalement différente de l'actuel paradigme centré sur les tâches. Nous proposons Active Data, un nouveau paradigme pour la gestion du cycle de vie des données. Active Data suit deux principes~: il est centré sur les données et à base d'événements. Nous présentons le modèle de programmation Active Data, un prototype d'implémentation et discutons des avantages et limites de notre approche à partir d'étude de cas d'applications scientifiques

Executable system architecting using systems modeling language in conjunction with Colored Petri Nets - a demonstration using the GEOSS network centric system

Models and simulation furnish abstractions to manage complexities allowing engineers to visualize the proposed system and to analyze and validate system behavior before constructing it. Unified Modeling Language (UML) and its systems engineering extension, Systems Modeling Language (SysML), provide a rich set of diagrams for systems specification. However, the lack of executable semantics of such notations limits the capability of analyzing and verifying defined specifications. This research has developed an executable system architecting framework based on SysML-CPN transformation, which introduces dynamic model analysis into SysML modeling by mapping SysML notations to Colored Petri Net (CPN), a graphical language for system design, specification, simulation, and verification. A graphic user interface was also integrated into the CPN model to enhance the model-based simulation. A set of methodologies has been developed to achieve this framework. The aim is to investigate system wide properties of the proposed system, which in turn provides a basis for system reconfiguration --Abstract, page iii