Floriano Zini

Optorsim: A Grid Simulator for Studying Dynamic Data Replication Strategies

Computational grids process large, computationally intensive problems on small data sets. In contrast, data grids process large computational problems that in turn require evaluating, mining and producing large amounts of data. Replication, creating geographically disparate identical copies of data, is regarded as one of the major optimization techniques for reducing data access costs. In this paper, several replication algorithms are discussed. These algorithms were studied using the Grid simulator: OptorSim. OptorSim provides a modular framework within which optimization strategies can be studied under different Grid configurations. The goal is to explore the stability and transient behaviour of selected optimization techniques. We detail the design and implementation of OptorSim and analyze various replication algorithms based on different Grid workloads.

Evaluation of an economy-based file replication strategy for a data grid

Optimising the use of Grid resources is critical for users to effectively exploit a Data Grid. Data replication is considered a major technique for reducing data access cost to Grid jobs. This paper evaluates a novel replication strategy, based on an economic model, that optimises both the selection of replicas for running jobs and the dynamic creation of replicas in Grid sites. In our model, optimisation agents are located on Grid sites and use an auction protocol for selecting the optimal replica of a data file and a prediction function to make informed decisions about local data replication. We evaluate our replication strategy with OptorSim, a Data Grid simulator developed by the authors. The experiments show that our proposed strategy results in a notable improvement over traditional replication strategies in a Grid environment.

Simulation of Dynamic Grid Replication Strategies in OptorSim

Computational Grids normally deal with large computationally intensive problems on small data sets. In contrast, Data Grids mostly deal with large computational problems that in turn require evaluating and mining large amounts of data. Replication is regarded as one of the major optimisation techniques for providing fast data access.Within this paper, several replication algorithms are studied. This is achieved using the Grid simulator: OptorSim. OptorSim provides a modular framework within which optimisation strategies can be studied under different Grid configurations. The goal is to explore the stability and transient behaviour of selected optimisation techniques.

Evaluating scheduling and replica optimisation strategies in OptorSim

Grid computing is fast emerging as the solution to the problems posed by the massive computational and data handling requirements of many current international scientific projects. Simulation of the grid environment is important to evaluate the impact of potential data handling strategies before being deployed on the grid. We look at the effects of various job scheduling and data replication strategies and compare them in a variety of grid scenarios, evaluating several performance metrics. We use the grid simulator OptorSim, and base our simulations on a world-wide grid testbed for data intensive high energy physics experiments. Our results show that the choice of scheduling and data replication strategies can have a large effect on both job throughput and the overall consumption of grid resources.

Analysis of Scheduling and Replica Optimisation Strategies for Data Grids Using OptorSim

Abstract Many current international scientific projects are based on large scale applications that are both computationally complex and require the management of large amounts of distributed data. Grid computing is fast emerging as the solution to the problems posed by these applications. To evaluate the impact of resource optimisation algorithms, simulation of the Grid environment can be used to achieve important performance results before any algorithms are deployed on the Grid. In this paper, we study the effects of various job scheduling and data replication strategies and compare them in a variety of Grid scenarios using several performance metrics. We use the Grid simulator \textsf{OptorSim} , and base our simulations on a world-wide Grid testbed for data intensive high energy physics experiments. Our results show that scheduling algorithms which take into account both the file access cost of jobs and the workload of computing resources are the most effective at optimising computing and storage resources as well as improving the job throughput. The results also show that, in most cases, the economy-based replication strategies which we have developed improve the Grid performance under changing network loads.

Towards an Economy-Based Optimisation of File Access and Replication on a Data Grid

We are working on a system for the optimised access and replication of data on a Data Grid. Our approach is based on the use of an economic model that includes the actors and the resources in the Grid. Optimisation is obtained via interaction of the actors in the model, whose goals are maximising the profits and minimising the costs of data resource management. In the system, local optimisation results in global optimisation through emergent marketplace behaviour. In this paper we give an overview of our model and present part of the complex economic reasoning required to support this desired marketplace interaction model.

OptorSim : a Simulation Tool for Scheduling and Replica Optimisation in Data Grids

In large-scale Grids, the replication of files to different sites is an important data management mechanism which can reduce access latencies and give improved usage of resources such as network bandwidth, storage and computing power. In the search for an optimal data replication strategy, the Grid simulator OptorSim was developed as part of the European DataGrid project. Simulations of various HEP Grid scenarios have been undertaken using different job scheduling and file replication algorithms, with the experimental emphasis being on physics analysis use-cases. Previously, the CMS Data Challenge 2002 testbed and UK GridPP testbed were among those simulated; recently, our focus has been on the LCG testbed. A novel economybased strategy has been investigated as well as more traditional methods, with the economic models showing distinct advantages for heavily loaded grids.

Catallaxy-based Grid markets

Grid computing has recently become an important paradigm for managing computationally demanding applications, composed of a collection of services. The dynamic discovery of services, and the selection of a particular service instance providing the best value out of the discovered alternatives, poses a complex multi-attribute n:m allocation decision problem, which is often solved using a centralized resource broker. To manage complexity, this article proposes a two-layer architecture for service discovery in such Application Layer Networks (ALN). The first layer consists of a service market in which complex services are translated to a set of basic services, which are distinguished by price and availability. The second layer provides an allocation of services to appropriate resources in order to enact the specified services. This framework comprises the foundations for a later comparison of centralized and decentralized market mechanisms for allocation of services and resources in ALNs and Grids.

Extending Multi-agent Cooperation by Overhearing

Much cooperation among humans happens following a common pattern: by chance or deliberately, a person overhears a conversation between two or more parties and steps in to help, for instance by suggesting answers to questions, by volunteering to perform actions, by making observations or adding information. We describe an abstract architecture to support a similar pattern in societies of artificial agents. Our architecture involves pairs of so-called service agents (or services) engaged in some tasks, and unlimited number of suggestive agents (or suggesters). The latter have an understanding of the work behaviours of the former through a publicly available model, and are able to observe the messages they exchange. Depending on their own objectives, the understanding they have available, and the observed communication, the suggesters try to cooperate with the services, by initiating assisting actions, and by sending suggestions to the services. These in effect may induce a change in services behaviour. Our architecture has been applied in a few industrial and research projects; a simple demonstrator, implemented by means of a BDI toolkit, JACK Intelligent Agents, is discussed in detail.

Logic Programming and Multi-Agent Systems: A Synergic Combination for Applications and Semantics

The paper presents an ongoing research project that uses Logic Programming, Linear Logic Programming, and their related techniques for executable specifications and rapid prototyping of Multi-Agent Systems. The MAS paradigm is an extremely rich one and we believe that Logic Programming will play a very effective role in this area, both as a tool for developing real applications and as a semantically well founded language for basing program analysis and proof of properties on.