The cost of doing science on the cloud: The Montage example

By Ewa Deelman, Gurmeet Singh, Miron Livny, B. Berriman, John Good. 2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis, 2008

830 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/df296559baaceb4a5efdb9fb7d6fbaf637fd9ccb

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Citations in the corpus

Evaluating the cost-benefit of using cloud computing to extend the capacity of clusters (2009, background): “[9] evaluated the cost of using Amazon EC2 and S3 services to serve the resource requirements of a scientific application.”
High-Performance Cloud Computing: A View of Scientific Applications (2009, background): “Some studies investigated the benefit of using Cloud computing technologies by analyzing the performances of HPC scientific applications [13] or the cost of performing scientific experiments [14] on the Amazon Cloud infrastructure.”
A Survey of Data-Intensive Scientific Workflow Management (2015, background): “This application is the result of a national virtual observatory project that stitches tiles of images of the sky from diverse sky surveys into a photorealistic single image [40].”
A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing (2009, background): “An important research question arises: Is the performance of clouds sufficient for scientific computing? Though early attempts to characterize clouds and other virtualized services exist [33,10,23,29], this question remains largely unexplored.”
Cost- and deadline-constrained provisioning for scientific workflow ensembles in IaaS clouds (2012, background): “We attribute this to the structure of Montage and CyberShake.”
A data placement strategy in scientific cloud workflows (2010, neutral): “[14] shows that cloud computing offers a cost-effective solution for data-intensive applications, such as scientific workflows [15].”
Characterizing and profiling scientific workflows (2013, methodology): “The characterization is based on novel workflow profiling tools that provide detailed information about the various computational tasks that are present in the workflow.”
On the Use of Cloud Computing for Scientific Workflows (2008, background): “Thus, the benefits of cloud computing to the scientific community are largely unknown and many questions are being asked by domain scientists about the promise and the pitfalls of clouds [14].”
On the Performance Variability of Production Cloud Services (2011, methodology): “Much effort has been put recently in assessing the performance of virtualized resources, in cloud computing environ ments [7]–[11] and in general [21]–[24].”
An Early Performance Analysis of Cloud Computing Services for Scientific Computing (2008, neutral): “There has been a spur of research activity in assessing the performance of virtualized resources, in cloud computing environments and in general [42, 11 , 34, 38, 33, 30, 41, 35, 36].”
CloudAnalyst: A CloudSim-Based Visual Modeller for Analysing Cloud Computing Environments and Applications (2010, methodology): “In addition an experimental brokerage policy for peak load sharing is implemented on CloudAnalyst.”
Cloud auto-scaling with deadline and budget constraints (2010, background): “4 [25][109] and the urgency threshold (u) is set to 0.”
The Cloud Adoption Toolkit: supporting cloud adoption decisions in the enterprise (2010, background): “The cloud computing literature has so far examined the costs of using the cloud via individual case studies [4, 25, 26, 27], and researchers such as Walker [28, 29] have laid down some of the theoretical foundations for cost modeling.”
Fog Computing Micro Datacenter Based Dynamic Resource Estimation and Pricing Model for IoT (2015, neutral): “The corresponding author is Prof. Eui-Nam Huh.”
Auto-scaling to minimize cost and meet application deadlines in cloud workflows (2011, methodology): “For example, [24] addresses the cost of running eScience applications in the cloud, and [25] uses spot instances to accelerate MapReduce job execution with cheaper cost.”
Performance Analysis of High Performance Computing Applications on the Amazon Web Services Cloud (2010, background): “[2].”