An Early Performance Analysis of Cloud Computing Services for Scientific Computing

By S. Ostermann, Alexandru Iosup, Nezih Yigitbasi, Radu Prodan, Thomas Fahringer, Dick H. J. Epema. 2008

978 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/56f42e248a53b206f03bcaa78fb9e7ecbc8a6c62

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1. Fei Tao
2. Lin Zhang
3. Zibin Zheng
4. Lida Xu
5. Lihui Wang
6. Yilei Zhang
7. Michael R. Lyu
8. Robert X. Gao

Citations in the corpus

• FC-PACO-RM: A Parallel Method for Service Composition Optimal-Selection in Cloud Manufacturing System (2013, background): “, cloud manufacturing (CMfg), is proposed [5] with the combination of advanced computing models and technologies (such as cloud computing [6], high performance computing, service-oriented technologies [7], internet of things (IoT) [8], and so forth).”
• Cloud-enabled prognosis for manufacturing (2015, background): “Previous studies [207-208] have demonstrated the power of cloud computing on executing multi-task work.”
• Performance Analysis of High Performance Computing Applications on the Amazon Web Services Cloud (2010, background): “Various groups have run both standard benchmark suites such as Linpack and NAS [5], [6], [7], [8], [9], and network performance tests [10].”
• QoS Ranking Prediction for Cloud Services (2013, background): “A number of works have been carried out on cloud computing [8], [10], including performance analysis, market-oriented cloud computing, management tool, workload balance, dynamic selection, etc. Quality-of-service has been widely employed for presenting the nonfunctional characteristics of the…”
• Challenges and Opportunities in Edge Computing (2016, background): “those by the Standard Performance Evaluation Corporation (SPEC)26 and by numerous academic researchers [40], [41], [42], [43].”
• Privacy Preserving Deep Computation Model on Cloud for Big Data Feature Learning (2016, background): “Cloud computing provides us with strong computing power and massive storage space [16], [17], [18].”
• The making of cloud applications: an empirical study on software development for the cloud (2014, background): “, relating to server management [4, 5] or performance measurement [6]).”
• Applying reinforcement learning towards automating resource allocation and application scalability in the cloud (2013, background): “Further analyses of performance [9] and performance interference [10] on I/O workloads have previously been published.”
• Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications (2018, background): “[15] compares four different cloud services for processing scientific computing workloads: Amazon EC2, GoGrid, ElasticHosts, and Mosso.”