Experiences with the KOALA co-allocating scheduler in multiclusters

In multicluster systems, and more generally, in grids, jobs may require co-allocation, i.e., the simultaneous allocation of resources such as processors and input files in multiple clusters. While such jobs may have reduced runtimes because they have access to more resources, waiting for processors in multiple clusters and for the input files to become available in the right locations, may introduce inefficiencies. Moreover, as single jobs now have to rely on multiple resource managers, co-allocation introduces reliability problems. In this paper, we present two additions to the original design of our KOALA co-allocating scheduler (different priority levels of jobs and incrementally claiming processors), and we report on our experiences with KOALA in our multicluster testbed while it was unstable

Bid-Centric Cloud Service Provisioning

Bid-centric service descriptions have the potential to offer a new cloud service provisioning model that promotes portability, diversity of choice and differentiation between providers. A bid matching model based on requirements and capabilities is presented that provides the basis for such an approach. In order to facilitate the bidding process, tenders should be specified as abstractly as possible so that the solution space is not needlessly restricted. To this end, we describe how partial TOSCA service descriptions allow for a range of diverse solutions to be proposed by multiple providers in response to tenders. Rather than adopting a lowest common denominator approach, true portability should allow for the relative strengths and differentiating features of cloud service providers to be applied to bids. With this in mind, we describe how TOSCA service descriptions could be augmented with additional information in order to facilitate heterogeneity in proposed solutions, such as the use of coprocessors and provider-specific services

Uma interface para refinamento de pesquisas de políticas de segurança em ambientes de grid services

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Ciência da Computação.A computação em grid (ou computação em grade) consiste em uma forma de computação distribuída onde o foco principal é o compartilhamento coordenado de recursos em larga escala e resolução de problemas em organizações virtuais dinâmicas e multi-institucionais. Porém, tal compartilhamento deve ser altamente controlado, para garantir a segurança dos recursos envolvidos. Este trabalho se baseia na especificação OGSA (Open Grid Service Architecture) proposta pelo GGF (Global Grid Forum), em particular no Globus Toolkit 3 que a implementa, e apresenta uma proposta de extensão ao módulo de monitoramento e descoberta de recursos (MDS), para filtrar os resultados retornados baseado nos atributos do usuário e nas políticas do recurso

A REST Model for High Throughput Scheduling in Computational Grids

Current grid computing architectures have been based on cluster management and batch queuing systems, extended to a distributed, federated domain. These have shown shortcomings in terms of scalability, stability, and modularity. To address these problems, this dissertation applies architectural styles from the Internet and Web to the domain of generic computational grids. Using the REST style, a flexible model for grid resource interaction is developed which removes the need for any centralised services or specific protocols, thereby allowing a range of implementations and layering of further functionality. The context for resource interaction is a generalisation and formalisation of the Condor ClassAd match-making mechanism. This set theoretic model is described in depth, including the advantages and features which it realises. This RESTful style is also motivated by operational experience with existing grid infrastructures, and the design, operation, and performance of a proto-RESTful grid middleware package named DIRAC. This package was designed to provide for the LHCb particle physics experiment's âﾜoff-lineâ computational infrastructure, and was first exercised during a 6 month data challenge which utilised over 670 years of CPU time and produced 98 TB of data through 300,000 tasks executed at computing centres around the world. The design of DIRAC and performance measures from the data challenge are reported. The main contribution of this work is the development of a REST model for grid resource interaction. In particular, it allows resource templating for scheduling queues which provide a novel distributed and scalable approach to resource scheduling on the grid