D
Deval Bhamare
Researcher at Karlstad University
Publications - 40
Citations - 1382
Deval Bhamare is an academic researcher from Karlstad University. The author has contributed to research in topics: Cloud computing & Virtual network. The author has an hindex of 13, co-authored 37 publications receiving 881 citations. Previous affiliations of Deval Bhamare include Indian Institute of Technology Bombay & Monash University.
Papers
More filters
Journal ArticleDOI
A survey on service function chaining
TL;DR: A closer look at the current SFC architecture and a survey of the recent developments in SFC including its relevance with NFV to help determine the future research directions and the standardization efforts of SFC are provided.
Journal ArticleDOI
Network Slicing for 5G: Challenges and Opportunities
TL;DR: The authors discuss this technology’s background and propose a framework for network slicing for 5G, and discuss remaining challenges and future research directions.
Journal ArticleDOI
Optimal virtual network function placement in multi-cloud service function chaining architecture
TL;DR: This work sets up the problem of minimizing inter-cloud traffic and response time in a multi-cloud scenario as an ILP optimization problem, along with important constraints such as total deployment costs and service level agreements (SLAs) and considers link delays and computational delays in the model.
Journal ArticleDOI
Cybersecurity for industrial control systems: A survey
TL;DR: This work discusses the major works, from industry and academia towards the development of the secure ICSs, especially applicability of the machine learning techniques for the ICS cyber-security and may help to address the challenges of securing industrial processes, particularly while migrating them to the cloud environments.
Proceedings ArticleDOI
Machine Learning for Anomaly Detection and Categorization in Multi-Cloud Environments
TL;DR: In this article, the authors investigated both detecting and categorizing anomalies rather than just detecting, which is a common trend in the contemporary research works, and argued that such categorization can be applied to multi-cloud environments using the same machine learning techniques.