A Taxonomy of Workflow Management Systems for Grid Computing

With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing complex workflows. Therefore, many efforts have been made towards the development of workflow management systems for Grid computing. In this paper, we propose a taxonomy that characterizes and classifies various approaches for building and executing workflows on Grids. We also survey several representative Grid workflow systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the design and engineering similarities and differences of state-of-the-art in Grid workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure

A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing

Data Grids have been adopted as the platform for scientific communities that need to share, access, transport, process and manage large data collections distributed worldwide. They combine high-end computing technologies with high-performance networking and wide-area storage management techniques. In this paper, we discuss the key concepts behind Data Grids and compare them with other data sharing and distribution paradigms such as content delivery networks, peer-to-peer networks and distributed databases. We then provide comprehensive taxonomies that cover various aspects of architecture, data transportation, data replication and resource allocation and scheduling. Finally, we map the proposed taxonomy to various Data Grid systems not only to validate the taxonomy but also to identify areas for future exploration. Through this taxonomy, we aim to categorise existing systems to better understand their goals and their methodology. This would help evaluate their applicability for solving similar problems. This taxonomy also provides a "gap analysis" of this area through which researchers can potentially identify new issues for investigation. Finally, we hope that the proposed taxonomy and mapping also helps to provide an easy way for new practitioners to understand this complex area of research.Comment: 46 pages, 16 figures, Technical Repor

Effective Scheduling of Grid Resources Using Failure Prediction

In large-scale grid environments, accurate failure prediction is critical to achieve effective resource allocation while assuring specified QoS levels, such as reliability. Traditional methods, such as statistical estimation techniques, can be considered to predict the reliability of resources. However, naive statistical methods often ignore critical characteristic behavior of the resources. In particular, periodic behaviors of grid resources are not captured well by statistical methods. In this paper, we present an alternative mechanism for failure prediction. In our approach, the periodic pattern of resource failures are determined and actively exploited for resource allocation with better QoS guarantees. The proposed scheme is evaluated under a realistic simulation environment of computational grids. The availability of computing resources are simulated according to real trace that was collected from our large-scale monitoring experiment on campus computers. Our evaluation results show that the proposed approach enables significantly higher resource scheduling effectiveness under a variety of workloads compared to baseline approaches

Reliable Provisioning of Spot Instances for Compute-intensive Applications

Cloud computing providers are now offering their unused resources for leasing in the spot market, which has been considered the first step towards a full-fledged market economy for computational resources. Spot instances are virtual machines (VMs) available at lower prices than their standard on-demand counterparts. These VMs will run for as long as the current price is lower than the maximum bid price users are willing to pay per hour. Spot instances have been increasingly used for executing compute-intensive applications. In spite of an apparent economical advantage, due to an intermittent nature of biddable resources, application execution times may be prolonged or they may not finish at all. This paper proposes a resource allocation strategy that addresses the problem of running compute-intensive jobs on a pool of intermittent virtual machines, while also aiming to run applications in a fast and economical way. To mitigate potential unavailability periods, a multifaceted fault-aware resource provisioning policy is proposed. Our solution employs price and runtime estimation mechanisms, as well as three fault tolerance techniques, namely checkpointing, task duplication and migration. We evaluate our strategies using trace-driven simulations, which take as input real price variation traces, as well as an application trace from the Parallel Workload Archive. Our results demonstrate the effectiveness of executing applications on spot instances, respecting QoS constraints, despite occasional failures.Comment: 8 pages, 4 figure

Analysis of Effective Load Balancing Techniques in Distributed Environment

Computational approaches contribute a significance role in various fields such as medical applications, astronomy, and weather science, to perform complex calculations in speedy manner. Today, personal computers are very powerful but underutilized. Most of the computer resources are idle; 75% of the time and server are often unproductive. This brings the sense of distributed computing, in which the idea is to use the geographically distributed resources to meet the demand of high-performance computing. The Internet facilitates users to access heterogeneous services and run applications over a distributed environment. Due to openness and heterogeneous nature of distributed computing, the developer must deal with several issues like load balancing, interoperability, fault occurrence, resource selection, and task scheduling. Load balancing is the mechanism to distribute the load among resources optimally. The objective of this chapter is to discuss need and issues of load balancing that evolves the research scope. Various load balancing algorithms and scheduling methods are analyzed that are used for performance optimization of web resources. A systematic literature with their solutions and limitations has been presented. The chapter provides a concise narrative of the problems encountered and dimensions for future extension

Découverte et allocation des ressources pour le traitement de requêtes dans les systèmes grilles

De nos jours, les systèmes Grille, grâce à leur importante capacité de calcul et de stockage ainsi que leur disponibilité, constituent l'un des plus intéressants environnements informatiques. Dans beaucoup de différents domaines, on constate l'utilisation fréquente des facilités que les environnements Grille procurent. Le traitement des requêtes distribuées est l'un de ces domaines où il existe de grandes activités de recherche en cours, pour transférer l'environnement sous-jacent des systèmes distribués et parallèles à l'environnement Grille. Dans le cadre de cette thèse, nous nous concentrons sur la découverte des ressources et des algorithmes d'allocation de ressources pour le traitement des requêtes dans les environnements Grille. Pour ce faire, nous proposons un algorithme de découverte des ressources pour le traitement des requêtes dans les systèmes Grille en introduisant le contrôle de topologie auto-stabilisant et l'algorithme de découverte des ressources dirigé par l'élection convergente. Ensuite, nous présentons un algorithme d'allocation des ressources, qui réalise l'allocation des ressources pour les requêtes d'opérateur de jointure simple par la génération d'un espace de recherche réduit pour les nœuds candidats et en tenant compte des proximités des candidats aux sources de données. Nous présentons également un autre algorithme d'allocation des ressources pour les requêtes d'opérateurs de jointure multiple. Enfin, on propose un algorithme d'allocation de ressources, qui apporte une tolérance aux pannes lors de l'exécution de la requête par l'utilisation de la réplication passive d'opérateurs à état. La contribution générale de cette thèse est double. Premièrement, nous proposons un nouvel algorithme de découverte de ressource en tenant compte des caractéristiques des environnements Grille. Nous nous adressons également aux problèmes d'extensibilité et de dynamicité en construisant une topologie efficace sur l'environnement Grille et en utilisant le concept d'auto-stabilisation, et par la suite nous adressons le problème de l'hétérogénéité en proposant l'algorithme de découverte de ressources dirigé par l'élection convergente. La deuxième contribution de cette thèse est la proposition d'un nouvel algorithme d'allocation des ressources en tenant compte des caractéristiques de l'environnement Grille. Nous abordons les problèmes causés par la grande échelle caractéristique en réduisant l'espace de recherche pour les ressources candidats. De ce fait nous réduisons les coûts de communication au cours de l'exécution de la requête en allouant des nœuds au plus près des sources de données. Et enfin nous traitons la dynamicité des nœuds, du point de vue de leur existence dans le système, en proposant un algorithme d'affectation des ressources avec une tolérance aux pannes.Grid systems are today's one of the most interesting computing environments because of their large computing and storage capabilities and their availability. Many different domains profit the facilities of grid environments. Distributed query processing is one of these domains in which there exists large amounts of ongoing research to port the underlying environment from distributed and parallel systems to the grid environment. In this thesis, we focus on resource discovery and resource allocation algorithms for query processing in grid environments. For this, we propose resource discovery algorithm for query processing in grid systems by introducing self-stabilizing topology control and converge-cast based resource discovery algorithms. Then, we propose a resource allocation algorithm, which realizes allocation of resources for single join operator queries by generating a reduced search space for the candidate nodes and by considering proximities of candidates to the data sources. We also propose another resource allocation algorithm for queries with multiple join operators. Lastly, we propose a fault-tolerant resource allocation algorithm, which provides fault-tolerance during the execution of the query by the use of passive replication of stateful operators. The general contribution of this thesis is twofold. First, we propose a new resource discovery algorithm by considering the characteristics of the grid environments. We address scalability and dynamicity problems by constructing an efficient topology over the grid environment using the self-stabilization concept; and we deal with the heterogeneity problem by proposing the converge-cast based resource discovery algorithm. The second main contribution of this thesis is the proposition of a new resource allocation algorithm considering the characteristics of the grid environment. We tackle the scalability problem by reducing the search space for candidate resources. We decrease the communication costs during the query execution by allocating nodes closer to the data sources. And finally we deal with the dynamicity of nodes, in terms of their existence in the system, by proposing the fault-tolerant resource allocation algorithm