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Provisioning fault-tolerant scheduling in computational grid is a challenging task. Most of the existing fault tolerant scheduling schemes are either geared toward proactive or reactive. Proactive schemes emphasize on the reasons responsible for generating faults, whereas reactive mechanisms come into effect after failure detection. Unlike most existing mechanisms, we present a novel, dynamic, adaptive, and hybrid fault-tolerant scheduling scheme based on proactive and reactive approaches. In the proactive approach, the resource filtration algorithm picks resources based on resource location, availability, and reliability. Unlike most existing schemes, which rely on remotely connected resources, the proposed algorithm prefers to employ locally available resources as they might have less failure tendency. To cope with the frequent turnover problem, the proposed scheme calculates resource availability time based on various newly identified parameters (e.g., mean time between availability) and picks highly available nodes for task execution. Resource reliability is an indispensable consideration in the proposed scheme and is calculated based on parameters such as jobs success or failure ratio and the types of failures encountered. We employ an optimal resource identification algorithm to determine and select optimal resources for job execution. The performance of the proposed scheme is validated through the GridSim toolkit. Compared with contemporary approaches, experimental results demonstrate the effectiveness and efficiency of the proposed scheme in terms of various performance metrics, such as wall clock time, throughput, waiting and turnaround time, number of checkpoints, and energy consumption.

Chemical Reaction Networks (CRNs) are a formalism to describe the macroscopic behavior of chemical systems. We introduce COEL, a web- and cloud-based CRN simulation framework, which does not require a local installation, runs simulations on a large computational grid, provides reliable database storage, and offers a visually pleasing and intuitive user interface. We present an overview of the underlying software, the technologies, and the main architectural approaches employed. Some of COEL’s key features include ODE-based simulations of CRNs and multicompartment reaction networks with rich interaction options, a built-in plotting engine, automatic DNA-strand displacement transformation and visualization, SBML/Octave/Matlab export, and a built-in genetic-algorithm-based optimization toolbox for rate constants. COEL is an open-source project hosted on GitHub (doi:10.5281/zenodo.46544), which allows interested research groups to deploy it on their own sever. Regular users can simply use the web instance at no cost at coel-sim.org. The framework is ideally suited for a collaborative use in both research and education.Code available at: 10.5281/zenodo.46544

Scheduling and resource allocation in large scale distributed environments, such as Computational Grids (CGs), arise new requirements and challenges not considered in traditional distributed computing environments. Among these new requirements, task abortion and security become needful criteria for Grid schedulers. The former arises due to the dynamics of the Grid systems, in which resources are expected to enter and leave the system in an unpredictable way. The latter requirement appears crucial in Grid systems mainly due to a multi-domain nature of CGs. The main aim of this paper is to develop a scheduling model that enables the aggregation of task abortion and security requirements as additional, together with makespan and flowtime, scheduling criteria into a cumulative objective function. We demonstrate the high effectiveness of genetic-based schedulers in finding near-optimal solutions for multi-objective scheduling problem, where all criteria (objectives) are simultaneously optimized. The proposed meta-heuristics are experimentally evaluated in static and dynamic Grid scenarios by using a Grid simulator. The obtained results show the fast reduction of the values of basic scheduler performance metrics, especially in the dynamic case, that confirms the usefulness of the proposed approach in real-life scenarios.

Parameter sweep experiments (PSE) involve several issues. In this work, we consider two of them: the generation of the parameters space and the scheduling of the associated tasks. Thus, we propose a general model to generate the parameters space of any PSE applying the Nested Summation Symbol operator. On the other hand, for the scheduling of these kinds of problems, we test an adaptive scheduling approach with fault tolerance. This approach has been implemented, using the DRMAA-C version for GridWay, to allocate tasks in a Grid environment. In the tests, the scheduler shows a good performance. Moreover, the CPU usage of the scheduler is quite low.

In this paper we present the implementation of Genetic Algorithms (GA) for job scheduling on computational grids that optimizes the makespan and the total flowtime. Job scheduling on computational grids is a key problem in large scale grid‐based applications for solving complex problems. The aim is to obtain an efficient scheduler able to allocate a large number of jobs originated from large scale applications to grid resources. Several variations for GA operators are examined in order to identify which works best for the problem. To this end we have developed a grid simulator package to generate large and very large size instances of the problem and have used them to study the performance of GA implementation. Through extensive experimenting and fine tuning of parameters we have identified the configuration of operators and parameters that outperforms the existing implementations in the literature forstaticinstances of the problem. The experimental results show the robustness of the implementation, improved performance of static instances compared to reported results in the literature and, finally, a fast reduction of the makespan making thus the scheduler of practical interest for grid environments. Genetinių algoritmų naudojimas kompiuterių tinkluose ir kalendorinis darbų planavimas Santrauka.Aprašoma, kaip genetinis algoritmas taikomas darbų trukmėms optimizuoti kalendoriniam darbų planavimui, naudojant kompiuterių, sujungtų į tinklą, išteklius. Kalendorinis darbų planavimas, naudojant kompiuterių tinklą, yra aktuali problema, sprendžiant kompleksines, didelio masto problemas. Autorių tikslas ñ sukurti tokį algoritmą, kuris efektyviausiai paskirstytų teikiamų skaičiuoti darbų srautą į kompiuterių tinklą. Ištirti keli algoritmai, išrinktas geriausias. Sukurtas kompiuterių tinklo darbą imituojantis programinis paketas, jis patikrintas, sprendžiant konkrečius uždavinius. Eksperimentuojant rastas geriausias operatorių ir parametrų derinys, o eksperimento rezultatai atskleidė, jog darbų planavimo laikas sutrumpėjo. Reikšminiai žodžiai: genetinis algoritmas, kalendorinis darbų planavimas, kompiuterių tinklas, pavyzdžiai, darbų trukmė, laikas. First Published Online: 21 Oct 2010

The developed DnD Grid system aims to improve building of distributed computer environments by dedicated and non-dedicated resources. The proposed approach supposes overdimensioned static performance and dynamic underloading of non-dedicated resources. Efficiency, availability and autonomy of such distributed heterogeneous environment depend on platform, application, mapping and resource owner/service user (human). The developed resource management system enables creation of a ranking list of owners/users and their resources, monitoring of parameters of the established virtual organization during application execution, as well as improvement of system performance by careful mapping resources and owners/users. The obtained experimental results show that the proposed approach and the corresponding mapping algorithm can significantly improve system performance.

In this chapter, the ProGenGrid (Proteomics and Genomics Grid) research project, which started in 2004, is described. It is a Grid Problem Solving Environment, specialized for the Bioinformatics domain, which aims at providing an integrated environment in order to compose, schedule and monitor biological applications in a Computational Grid. The main feature offered by this environment is the possibility to use a friendly web interface for composing workflow jobs, scheduled on different grid middleware.

Computational grids are useful tools for bringing supercomputing power to users by using idle resources in the network. In the following paper we give a short overview of architecture of the Alchemi grid developed on .Net platform. We created a grid application, which utilizes Rabin-Karp string searching algorithm to test Alchemi grid performances in situation when requests put diverse demands for computing resources to the different grid nodes. Scheduling and dispatching jobs to the computing resources is a critical activity of the grid software. We present a scheduling algorithm which showed performance improvements to the original algorithm shipped with Alchemi grid software.

Fast computer networks and overperformanced computers enable computational grid establishment on non-dedicated and geographically distributed computers. This causes an increase in dynamic heterogeneity which makes resource management much more difficult. An additional problem is represented by a strong dependency of resource availability on a human as their owner. Concession of resources to the grid is basically computer supported cooperative work. The described meeting scheduling model points out properties of a human, application requests, organizational and timing properties of meetings, as well as scheduling. Resource selection is also done based on properties of the owner. Evaluation of the proposed approach is carried out on the basis of results obtained in a simplified testing environment. Extending the mapping procedure by the meeting scheduling model makes success of the application execution in non-dedicated environment comparable to the execution on reserved resources.

Leveraging Grid Computing technology, i.e. the virtualization of distributed computing and data resources such as processing, network bandwidth and storage capacity to create a single system image, we present a Grid Air Quality Forecast System (G-AQFS). The Modeling system consists of meteorological and dispersion models coupled in cascade. The computational workflow of the Modeling system is defined by means of DAGs (Direct Acyclic Graph). A simple system is presented to manage and schedule the computational Grid resources. In particular, the algorithm developed for the Work Flow Scheduler named Depth-First Search Job with Priority (DFSP) is illustrated. As case study the system has been applied over Salento area, in the Apulia region (South-eastern Italy), to simulate ground level ozone concentration. Model predictions have been compared with field measurements, with reasonable results.

Engagement of non-dedicated machines in the computational grid requires special attention by mapping. In addition to application, platform and mapping parameters, a human as a resource owner plays an extremely important role. The problem is easier for the owner/user who also requires grid services at the same time. The grid can provide a powerful support to CSCW. Resource concession can also be treated as cooperation in the grid extension. The proposed resource extension model is based on meeting scheduling. This model puts a group member into a competitive position, since he becomes responsible for his organizational and infrastructure capabilities. He tends to concede the idle capacity to the grid in order to be able to use services provided by that powerful virtual machine.

Summary form only given. This article studies a communication model that aims at extending the scope of computational grids by allowing the execution of parallel and/or distributed applications without imposing any programming constraints or the use of a particular communication layer. Such model leads to the design of a communication framework for grids which allows the use of the appropriate middleware for the application rather than the one dictated by the available resources. Such a framework is able to handle any communication middleware - even several at the same time - on any kind of networking technologies. Our proposed dual-abstraction (parallel and distributed) model is organized into three layers: arbitration, abstraction and personalities which are highlighted. The performance obtained with PadicoTM, our available open source implementation of the proposed framework, show that such functionality can be obtained with still providing very high performance.

This paper describes work in progress to develop a component-based software infrastructure, called Padico, for computational Grids based on the CORBA Component Model from the OMG. The objective of Padico is to offer a component model targeting multi-physics simulations or any applications that require the coupling of several codes (simulation or visualization) within a high-performance environment. This paper addresses mainly two issues we identified as important for a Grid-aware component model. The first issue deals with the encapsulation of parallel codes into components. We propose an extension to the CORBA Component Model called GridCCM. The second issue addresses the problem of the communication between components within a computational Grid. We propose a portable runtime, called Padico/spl trade/, able to efficiently support communication in a heterogeneous networking environment.

International audience; Computational grids are seen as the future emergent computing infrastructures. Their programming requires the use of several paradigms that are implemented through communication middleware and runtimes. However some of these middleware systems and runtimes are unable to take benefit of the presence of specific networking technologies available in grid infrastructures. In this paper, we describe an open integration framework that allows several communication middleware and runtimes to efficiently share the networking resources available in a computational grid. Such framework encourages grid programmers to use the most suited communication paradigms for their applications independently from the underlying networks. Therefore, there is no obstacle to deploy the applications on a specific grid configuration.

Due to the high level of heterogeneity in a computational Grid, designing a runtime system for such computing infrastructure is extremely challenging, for example regarding the ability to exploit transparently and efficiently various networking technologies. Programming a computational Grid often requires the use of several communication paradigms (RPC, RMI, DSM, Message passing) that have to share these networking resources. This paper presents the first step towards a runtime system that allows efficient communication for various communication-oriented middlewares. We introduce a CORBA implementation that reaches 240 MB/s, which is as far as we know the best CORBA performance. Thus, CORBA can be as efficient as MPI on high performance networks. Moreover, we show that different communication middlewares, like CORBA and MPI, can efficiently co-habit within the same runtime system taking full benefit of various networking resources (SAN to WAN).