Export Citations
Save this search
Please login to be able to save your searches and receive alerts for new content matching your search criteria.
- research-articleJuly 2022
Developing and Comparing Cloud-based Fuzzy Systems for Monitoring Health Related Signals in Assistive Environments
PETRA '22: Proceedings of the 15th International Conference on PErvasive Technologies Related to Assistive EnvironmentsPages 407–413https://doi.org/10.1145/3529190.3534742Fuzzy logic system (FLS) based applications in Ambient Intelligence (AmI) have exhibited their capabilities in realizing intelligent environments. FLS deployment is usually associated with non-scalable hardware and software platforms. For intensive ...
- tutorialAugust 2020
Put Deep Learning to Work: Accelerate Deep Learning through Amazon SageMaker and ML Services
KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data MiningPage 3496https://doi.org/10.1145/3394486.3406698Deploying deep learning (DL) projects are becoming increasingly more pervasive at enterprises and startups alike. At Amazon, Machine Learning University (MLU)-trained engineers are taking DL to every aspect of Amazon's businesses, beyond just Amazon Go, ...
- short-paperJune 2021
Distributed Parallel Analysis Engine for High Energy Physics Using AWS Lambda
HiPS '21: Proceedings of the 1st Workshop on High Performance Serverless ComputingPages 13–16https://doi.org/10.1145/3452413.3464788The High-Energy Physics experiments at CERN produce a high volume of data. It is not possible to analyze big chunks of it within a reasonable time by any single machine. The ROOT framework was recently extended with the distributed computing ...
- research-articleDecember 2019
Exploring the Cost-benefit of AWS EC2 GPU Instances for Deep Learning Applications
UCC'19: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud ComputingPages 21–29https://doi.org/10.1145/3344341.3368814Deep Learning is a subfield of machine learning methods based on artificial neural networks. Thanks to the increased data availability and computational power, such as Graphic Process Units (GPU), training deep networks - a time-consuming process - ...
- short-paperApril 2019
Performance Modeling for Cloud Microservice Applications
ICPE '19: Proceedings of the 2019 ACM/SPEC International Conference on Performance EngineeringPages 25–32https://doi.org/10.1145/3297663.3310309Microservices enable a fine-grained control over the cloud applications that they constitute and thus became widely-used in the industry. Each microservice implements its own functionality and communicates with other microservices through language- and ...
- research-articleOctober 2017
New Educational ICT Environment with Cloud in Kyushu University
SIGUCCS '17: Proceedings of the 2017 ACM SIGUCCS Annual ConferencePages 105–108https://doi.org/10.1145/3123458.3123490We reported the status of the educational ICT (Information and Communication Technology) environment in Kyushu University in SIGUCCS 2009. At that time, we provided the host computer and many iMacs as terminals for students. We have been using BYOD in ...
- research-articleMay 2017
Azure Data Lake Store: A Hyperscale Distributed File Service for Big Data Analytics
- Raghu Ramakrishnan,
- Baskar Sridharan,
- John R. Douceur,
- Pavan Kasturi,
- Balaji Krishnamachari-Sampath,
- Karthick Krishnamoorthy,
- Peng Li,
- Mitica Manu,
- Spiro Michaylov,
- Rogério Ramos,
- Neil Sharman,
- Zee Xu,
- Youssef Barakat,
- Chris Douglas,
- Richard Draves,
- Shrikant S. Naidu,
- Shankar Shastry,
- Atul Sikaria,
- Simon Sun,
- Ramarathnam Venkatesan
SIGMOD '17: Proceedings of the 2017 ACM International Conference on Management of DataPages 51–63https://doi.org/10.1145/3035918.3056100Azure Data Lake Store (ADLS) is a fully-managed, elastic, scalable, and secure file system that supports Hadoop distributed file system (HDFS) and Cosmos semantics. It is specifically designed and optimized for a broad spectrum of Big Data analytics ...
- invited-talkJanuary 2015
Cloud Native Cost Optimization
ICPE '15: Proceedings of the 6th ACM/SPEC International Conference on Performance EngineeringPage 109https://doi.org/10.1145/2668930.2693197For traditional datacenter applications capacity is a fixed upfront cost, so there is little incentive to stop using it once it's been allocated, and it has to be over-provisioned most of the time so there is enough capacity for peak loads. When ...
- demonstrationOctober 2012
AMADA: web data repositories in the amazon cloud
- Andrés Aranda-Andújar,
- Francesca Bugiotti,
- Jesús Camacho-Rodríguez,
- Dario Colazzo,
- François Goasdoué,
- Zoi Kaoudi,
- Ioana Manolescu
CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge managementPages 2749–2751https://doi.org/10.1145/2396761.2398749We present AMADA, a platform for storing Web data (in particular, XML documents and RDF graphs) based on the Amazon Web Services (AWS) cloud infrastructure. AMADA operates in a Software as a Service (SaaS) approach, allowing users to upload, index, ...
- research-articleJuly 2012
Integrating data-intensive cloud computing with multicores and clusters in an HPC course
ITiCSE '12: Proceedings of the 17th ACM annual conference on Innovation and technology in computer science educationPages 69–74https://doi.org/10.1145/2325296.2325316This paper presents the design and implementation of a new High-Performance Computing (HPC) course. This course amalgamates the emerging trend of data-intensive cloud computing with the dominant innovation of multicore computing and the important legacy ...
- posterMarch 2012
Double dip map-reduce for processing cross validation jobs
SAC '12: Proceedings of the 27th Annual ACM Symposium on Applied ComputingPages 473–477https://doi.org/10.1145/2245276.2245367Cross validation is fundamental to machine learning as it provides a reliable way in which to evaluate algorithms and the overall quality of the corpora in use. In typical cross validation, the corpus is initially divided into learning and training ...
- posterNovember 2011
Poster: a framework for data-intensive computing with cloud bursting
SC '11 Companion: Proceedings of the 2011 companion on High Performance Computing Networking, Storage and Analysis CompanionPages 5–6https://doi.org/10.1145/2148600.2148604In this work, we consider the challenge of data analysis in a scenario where data is stored across a local cluster and cloud resources. We describe a software framework to enable data-intensive computing with cloud bursting, i.e., using a combination of ...
- research-articleJune 2008
Building a database on S3
SIGMOD '08: Proceedings of the 2008 ACM SIGMOD international conference on Management of dataPages 251–264https://doi.org/10.1145/1376616.1376645There has been a great deal of hype about Amazon's simple storage service (S3). S3 provides infinite scalability and high availability at low cost. Currently, S3 is used mostly to store multi-media documents (videos, photos, audio) which are shared by a ...