Title: |
Whitepaper on Reusable Hybrid and Multi-Cloud Analytics Service Framework |
Authors: |
von Laszewski, Gregor, Chang, Wo, Reinsch, Russell, Kotevska, Olivera, Karimi, Ali, Sattar, Abdul Rahman, Mazzaferro, Garry, Fox, Geoffrey C. |
Publication Year: |
2023 |
Collection: |
Computer Science |
Subject Terms: |
Computer Science - Distributed, Parallel, and Cluster Computing |
More Details: |
Over the last several years, the computation landscape for conducting data analytics has completely changed. While in the past, a lot of the activities have been undertaken in isolation by companies, and research institutions, today's infrastructure constitutes a wealth of services offered by a variety of providers that offer opportunities for reuse, and interactions while leveraging service collaboration, and service cooperation. This document focuses on expanding analytics services to develop a framework for reusable hybrid multi-service data analytics. It includes (a) a short technology review that explicitly targets the intersection of hybrid multi-provider analytics services, (b) a small motivation based on use cases we looked at, (c) enhancing the concepts of services to showcase how hybrid, as well as multi-provider services can be integrated and reused via the proposed framework, (d) address analytics service composition, and (e) integrate container technologies to achieve state-of-the-art analytics service deployment |
Document Type: |
Working Paper |
Access URL: |
http://arxiv.org/abs/2310.17013 |
Accession Number: |
edsarx.2310.17013 |
Database: |
arXiv |