arXiv subjects

Smruti Rekha Swain

Publications and source records attributed to Smruti Rekha Swain.

4 recordsLinked to original sources

arXiv2026

Cloud Computing Review: A Decade of Research

The popularity and rapid development of Cloud Computing in recent years has led to a vast number of publications capturing the accumulated knowledge in this field. Due to the interdisciplinary nature and significant relevance of cloud computing research, it has become increasingly challenging to comprehend the overall structure and progress of this field without employing analytical methods. While the evaluation of scientific research has a long tradition in many fields, we have identified a lack of comprehensive scientometric studies specifically focused on cloud computing. This study applies scientometric techniques to empirically examine cloud computing research evolution and current state from a macroscopic perspective. We employed the CiteSpace tool for visual analysis, exploring topics related to cloud computing by retrieving papers published between 2014 and 2023 from the Web of Science Core database. Our approach involved constructing collaboration networks among authors, institutions, and countries to pinpoint the most prolific contributors in each category. Through the analysis of core journal distributions via journal co-citations, document co-citation networks, and clustering analysis, we uncovered the underlying research topics and knowledge structure. The results of this study enhance our understanding of patterns, trends, and other critical factors, offering a foundation for guiding research activities, sharing knowledge, and fostering collaboration in the field of cloud computing research.

cs.GT↗

arXiv2025

Secure Resource Management in Cloud Computing: Challenges, Strategies and Meta-Analysis

Secure resource management (SRM) within a cloud computing environment is a critical yet infrequently studied research topic. This paper provides a comprehensive survey and comparative performance evaluation of potential cyber threat countermeasure strategies that address security challenges during cloud workload execution and resource management. Cybersecurity is explored specifically in the context of cloud resource management, with an emphasis on identifying the associated challenges. The cyber threat countermeasure methods are categorized into three classes: defensive strategies, mitigating strategies, and hybrid strategies. The existing countermeasure strategies belonging to each class are thoroughly discussed and compared. In addition to conceptual and theoretical analysis, the leading countermeasure strategies within these categories are implemented on a common platform and examined using two real-world virtual machine (VM) data traces. Based on this comprehensive study and performance evaluation, the paper discusses the trade-offs among these countermeasure strategies and their utility, providing imperative concluding remarks on the holistic study of cloud cyber threat countermeasures and secure resource management. Furthermore, the study suggests future methodologies that could effectively address the emerging challenges of secure cloud resource management.

cs.CR↗

arXiv2023

An AI-driven intelligent traffic management model for 6G cloud radio access networks

This letter proposes a novel Cloud Radio Access Network (C-RAN) traffic analysis and management model that estimates probable RAN traffic congestion and mitigate its effect by adopting a suitable handling mechanism. A computation approach is introduced to classify heterogeneous RAN traffic into distinct traffic states based on bandwidth consumption and execution time of various job requests. Further, a cloud-based traffic management is employed to schedule and allocate resources among user job requests according to the associated traffic states to minimize latency and maximize bandwidth utilization. The experimental evaluation and comparison of the proposed model with state-of-the-art methods reveal that it is effective in minimizing the worse effect of traffic congestion and improves bandwidth utilization and reduces job execution latency up to 17.07% and 18%, respectively.

cs.DC↗

arXiv2022

Efficient Resource Management in Cloud Environment

In cloud computing resource management plays a significant role in data centres and it is directly dependent on the application workload. Various services such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) are offered by cloud computing to provide compute, network, and storage capabilities to the cloud users utilizing the pay-per-usage approach. Resource allocation is a prior solution to address various demanding situations like the under/overload handling, resource wastage, load balancing, Quality-of-Services (QoS) violations, VM migration and many more. The primary aim of Virtual Machine Placement (VMP) is mapping of Virtual Machines (VMs) to physical machines (PMs), such that the PMs may be utilized to their maximum efficiency, where the already active VMs are not to be interrupted. It provides a list of live VM migrations that must be accomplished to get the optimum solution and reduces energy consumption to a larger extent. The inefficient VMP leads to wastage of resources, excessive energy consumption and also increase overall operational cost of the data center. On this context, this article provides an extensive survey of resource management schemes in cloud environment. A conceptual scheme for resource management, grouping of current machine learning based resource allocation strategies, and fundamental problems of ineffective distribution of physical resources are analyzed. Thereafter, a complete survey of existing techniques in machine learning based mechanisms in the field of cloud resource management are explained. Ultimately, the paper explores and concludes distinct approaching challenges and future research guidelines associated to resource management in cloud environment.

cs.DC↗