A new paradigm: Data-aware scheduling in grid computing

Kosar, Tevfik; Balman, Mehmet

doi:10.1016/j.future.2008.09.006

Cited by 117 publications

(54 citation statements)

References 7 publications

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“…Algorithm was capable to adjust the resource allocation dynamically, based on the updated information of the actual task executions. Efficient and reliable access limitations of traditional CPU-oriented batch schedulers were discussed by [21]. To improve the performance, authors proposed Stork data placement scheduler in data intensive computing.…”

Section: B Study On Performance Toolsmentioning

confidence: 99%

Study of Response Time in Cloud Computing

Singh¹

2014

IJIEEB

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Abstract-Performance of a cloud is a critical issue and dependent on various factors that include load balancing used to distribute the load at data center. Proliferation of the cloud paradigm is profoundly dependent on the performance of the cloud. Therefore, to examine the cloud performance, it is significant to identify the factors that govern cloud performance. This work is focused to identify the factors that may have significant impact on the cloud performance. To identify the performance factors, we have reviewed the major publications of the last five years as well as the major real time cloud performance tools offered. Study revealed that response time is the major factor that has the significant impact on cloud computing performance. Finally, we have conducted an experiment to establish the dependency of response time on various load balancing techniques and number of data center. Experiment revealed that the round robin is directly proportional to the number of data center

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Section: B Study On Performance Toolsmentioning

confidence: 99%

Study of Response Time in Cloud Computing

Singh¹

2014

IJIEEB

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“…STORK (Kosar & Livny 2004;Kosar & Balman 2009) engages with CONDOR (Thain et al 2005) to provide a new data placement scheduler for bulk data transfer jobs that are fault-tolerant batch processes that can be queued, scheduled, monitored, managed and check-pointed. STORK supports transfers between local file systems, GRIDFTP, FTP, HTTP, SRB, NeST and SRM.…”

Section: Existing Technologies and Servicesmentioning

confidence: 99%

“…Nevertheless, as stated by Kosar & Balman (2009), these applications and their successors represent initial steps towards data-intensive computing and data-aware batch scheduling. This is a newly emerging paradigm where many challenges remain, which includes the emergence of standards for describing data transfer/copying activities.…”

Section: Existing Technologies and Servicesmentioning

confidence: 99%

Towards a scalable, open-standards service for brokering cross-protocol data transfers across multiple sources and sinks

Meredith

Crouch

Galang³

et al. 2010

Phil. Trans. R. Soc. A.

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Data Transfer Service (DTS)is an open-source project that is developing a documentcentric message model for describing a bulk data transfer activity, with an accompanying set of loosely coupled and platform-independent components for brokering the transfer of data between a wide range of (potentially incompatible) storage resources as scheduled, fault-tolerant batch jobs. The architecture scales from small embedded deployments on a single computer to large distributed deployments through an expandable 'workernode pool' controlled through message-orientated middleware. Data access and transfer efficiency are maximized through the strategic placement of worker nodes at or between particular data sources/sinks. The design is inherently asynchronous, and, when third-party transfer is not available, it side-steps the bandwidth, concurrency and scalability limitations associated with buffering bytes directly through intermediary client applications. It aims to address geographical-topological deployment concerns by allowing service hosting to be either centralized (as part of a shared service) or confined to a single institution or domain. Established design patterns and open-source components are coupled with a proposal for a document-centric and open-standards-based messaging protocol. As part of the development of the message protocol, a bulk data copy activity document is proposed for the first time.

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“…in parameter sweep applications [2,3], the scheduling problems in Computational Grids (CGs) and in Data Grids (DGs) is dealing with in a separated way. Much of the current efforts are focused on scheduling workloads in a data center or schedule movement of data and data placement [42] for efficient resource/storage utilization or energy-effective scheduling in largescale data centers [41], [8], [33], [18], [48], [51], [57], [7], [10], [16], [17]. A recent example is that of GridBatch [44] for large scale data-intensive problems on cloud infrastructures.…”

Section: Introductionmentioning

confidence: 99%

Data Scheduling in Data Grids and Data Centers: A Short Taxonomy of Problems and Intelligent Resolution Techniques

Kołodziej

Khan

2013

Transactions on Computational Collective Intelligence X

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Abstract. Data-aware scheduling in today's large-scale heterogeneous environments has become a major research issue. Data Grids (DGs) and Data Centers arise quite naturally to support needs of scientific communities to share, access, process, and manage large data collections geographically distributed. Data scheduling, although similar in nature with grid scheduling, is given rise to the definition of a new family of optimization problems. New requirements such as data transmission, decoupling of data from processing, data replication, data access and security are to be added to the scheduling problem are the basis for the definition of a whole taxonomy of data scheduling problems. In this paper we briefly survey the state-of-the-art in the domain. We exemplify the model and methodology for the case of data-aware independent job scheduling in computational grid and present several heuristic resolution methods for the problem.

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A new paradigm: Data-aware scheduling in grid computing

Cited by 117 publications

References 7 publications

Study of Response Time in Cloud Computing

Study of Response Time in Cloud Computing

Towards a scalable, open-standards service for brokering cross-protocol data transfers across multiple sources and sinks

Data Scheduling in Data Grids and Data Centers: A Short Taxonomy of Problems and Intelligent Resolution Techniques

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