Abstractions and optimizations for data-driven applications across edge and cloud (Record no. 429616)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02065nam a22002297a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 230810b |||||||| |||| 00| 0 eng d |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | en |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 600 |
| Item number | AKA |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Khochare, Aakash |
| 245 ## - TITLE STATEMENT | |
| Title | Abstractions and optimizations for data-driven applications across edge and cloud |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc | Bangalore : |
| Name of publisher, distributor, etc | IISc , |
| Date of publication, distribution, etc | 2023 . |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xvi, 223p. |
| Other physical details | col. ill. ; |
| Dimensions | 29.1 cm * 20.5 cm |
| Accompanying material | e-Thesis |
| Size of unit | 6.252Mb |
| 500 ## - GENERAL NOTE | |
| General note | include bibliographic reference and index |
| 502 ## - DISSERTATION NOTE | |
| Dissertation note | PhD; 2023; Computational and data sciences |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Modern data driven applications have a novel set of requirements. Advances in deep neural networks (DNN) and computer vision (CV) algorithms have made it feasible to extract meaningful insights from large-scale deployments of urban cameras and drone video feeds. These data driven applications, usually composed as workflows, tend to have high bandwidth and low latency requirements in order to extract timely results from large data sources. Other applications may necessitate the use of multiple geographically distributed resources. Such requirements may be driven by data privacy regulations such as the General Data Protection Regulation (GDPR) of the European Union, need for specialized hardware, or as a means of avoiding vendor lock-ins. To support these modern applications, a diverse computing landscape has emerged over the last decade. We have witnessed increasingly powerful Edge computing resources be available in network proximity to the data sources for these applications. The number of Cloud Service Providers (CSPs) has increased along with the regions in which they operate. And finally, the CSPs have supplemented Infrastructure as a Service (IaaS) offerings with modern serverless compute offerings which promise cost benefits as well as lower operational overheads. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Distributed Computing |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Systems for Machine Learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Serverless Computing |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Simmhan, Yogesh advised |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://etd.iisc.ac.in/handle/2005/6182 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Thesis |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Date acquired | Total Checkouts | Full call number | Barcode | Date last seen | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | JRD Tata Memorial Library | JRD Tata Memorial Library | 10/08/2023 | 600 AKA | ET00195 | 10/08/2023 | E-BOOKS |
