A worldwide flock of Condors: Load sharing among workstation clusters

Epema, Dick; Livny, Miron; Dantzig, R. van; Evers, X.; Pruyne, Jim

doi:10.1016/0167-739x(95)00035-q

Cited by 217 publications

(114 citation statements)

References 2 publications

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“…We evaluated the performance of the original and enhanced scheduling and provisioning services to address peak loads and accomplish application execution within the deadline. 4 and 1.7 GB of memory, at the cost of US$0.085 per instance per hour. The operating system of Desktop Grid nodes is Windows Server while operating system of public Cloud resources is Fedora Linux.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Condor [4] is a Desktop Grid system which was later expanded to support Grid applications. It allows formation of local pools of resources that can exchange resources with other pools, likewise typical Grid middleware.…”

Section: Related Workmentioning

confidence: 99%

“…Unlike earlier frameworks, such as Condor [4] and ProActive [5], Aneka is the first middleware technology to provide a service-oriented container framework that allows realization of different programming models and enables seamless integration of enterprise computing resources (e.g., Desktop Grids and servers) with public Cloud resources. The Aneka framework has been used to implement various programming models including Thread, Task, MapReduce, and deployment of their applications on private and public Clouds seamlessly.…”

Section: Introductionmentioning

confidence: 99%

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The Aneka platform and QoS-driven resource provisioning for elastic applications on hybrid Clouds

Calheiros¹,

Vecchiola²,

Karunamoorthy³

et al. 2012

Future Generation Computer Systems

153

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a b s t r a c tCloud computing alters the way traditional software systems are built and run by introducing a utilitybased model for delivering IT infrastructure, platforms, applications, and services. The consolidation of this new paradigm in both enterprises and academia demanded reconsideration in the way IT resources are used, so Cloud computing can be used together with available resources. A case for the utilization of Clouds for increasing the capacity of computing infrastructures is Desktop Grids: these infrastructures typically provide best effort execution of high throughput jobs and other workloads that fit the model of the platform. By enhancing Desktop Grid infrastructures with Cloud resources, it is possible to offer QoS to users, motivating the adoption of Desktop Grids as a viable platform for application execution. In this paper, we describe how Aneka, a platform for developing scalable applications on the Cloud, supports such a vision by provisioning resources from different sources and supporting different application models. We highlight the key concepts and features of Aneka that support the integration between Desktop Grids and Clouds and present an experiment showing the performance of this integration.

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Section: Performance Evaluationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Aneka platform and QoS-driven resource provisioning for elastic applications on hybrid Clouds

Calheiros¹,

Vecchiola²,

Karunamoorthy³

et al. 2012

Future Generation Computer Systems

153

View full text Add to dashboard Cite

show abstract

“…The particular grid engine that we use is provided by the Condor resource manager (Epema et al, 1996;Litzkow et al, 1988), and involves a large collection of machines all running the Linux operating system. Our implementation does not require that the workers share a file system with the master since this would significantly restrict the number of workers that could be used.…”

Section: Grid Computingmentioning

confidence: 99%

Simultaneous Batching and Scheduling Using Dynamic Decomposition on a Grid

Ferris

Maravelias

Sundaramoorthy

2009

INFORMS Journal on Computing

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Scheduling problems arise in many applications in process industries. However, despite various efforts to develop efficient scheduling methods, current approaches cannot be used to solve instances of industrial importance in reasonable time frames. The goal of this paper is the development of a dynamic decomposition scheme that exploits the structure of the problem and is well suited for grid computing. The problem we study is the simultaneous batching and scheduling of multi-stage batch processes. The algorithm first decomposes the original problem into 1 st -level subproblems based on batching decisions. The subproblems that remain unsolved within a resource limit are then dynamically decomposed into 2 nd -level subproblems based on batch-unit assignment decisions in one stage. The process can be repeated in a dynamic fashion by identifying subproblems that cannot be solved within a given resource limit and decomposing them by batch-unit assignment, until all subproblems are solved. Alternatively, a problem can be decomposed into a number of promising subproblems using an automatic strong branching scheme. Our results show that the proposed method can be used on a grid computer to solve large problems to optimality in reasonable computational time.

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“…An area to investigate in the future is the interface to the compute grid. We currently use the shell to spawn computation, but are targeting Condor [9] and GIPSE [23]. These topics will be the subject of other papers.…”

mentioning

confidence: 99%

Generosity and gluttony in GEMS: grid enabled molecular simulations

Wozniak

Thain

Striegel

et al.

HPDC-14. Proceedings. 14th IEEE International Symposium on High Performance Distributed Computing, 2005.

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Biomolecular simulations produce more output data than can be managed effectively by traditional computing systems. Researchers need distributed systems that allow the pooling of resources, the sharing of simulation data, and the reliable publication of both tentative and final results. To address this need, we have designed GEMS, a system that enables biomolecular researchers to store, search, and share large scale simulation data. The primary design problem is striking a balance between generosity and gluttony. On one hand, storage providers wish to be generous and share resources with their collaborators. On the other hand, an unchecked data producer can be gluttonous and easily replicate data unnecessarily until it fills all available space. To balance generosity and gluttony, GEMS allows both storage providers and data producers to state and enforce policies on the consumption of storage and the replication of data. By taking advantage of known properties of simulation data, the system is able to distinguish between high value final results that must be preserved and low value intermediate results that can be deleted and regenerated if necessary. We have built a prototype of GEMS on a cluster of workstations and demonstrate its ability to store new data, to replicate within policy limits, and to recover from failures.

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A worldwide flock of Condors: Load sharing among workstation clusters

Cited by 217 publications

References 2 publications

The Aneka platform and QoS-driven resource provisioning for elastic applications on hybrid Clouds

The Aneka platform and QoS-driven resource provisioning for elastic applications on hybrid Clouds

Simultaneous Batching and Scheduling Using Dynamic Decomposition on a Grid

Generosity and gluttony in GEMS: grid enabled molecular simulations

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