Abstractions and optimizations for data-driven applications across edge and cloud
- Bangalore : IISc , 2023 .
- xvi, 223p. col. ill. ; 29.1 cm * 20.5 cm e-Thesis 6.252Mb
include bibliographic reference and index
PhD; 2023; Computational and data sciences
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.
Distributed Computing Systems for Machine Learning Serverless Computing