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Author

G. Kousalya

Bio: G. Kousalya is an academic researcher from Coimbatore Institute of Technology. The author has contributed to research in topics: Workflow & Cloud computing. The author has an hindex of 5, co-authored 24 publications receiving 54 citations.

Papers
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Book ChapterDOI
01 Jan 2017
TL;DR: There are many cloud-related aspects yearning for technologically sound automation, acceleration, and augmentation capabilities, and there are pathbreaking work-around approaches, algorithms, and architectures for workload consolidation.
Abstract: Cloud infrastructures typically offer access to boundless virtual resources dynamically provisioned on demand for hosting, running, and managing a variety of mission-critical applications like scientific workflows, big data processing application, business intelligence-based applications, high-performance computing (HTC), and high transaction computing (HTC) Due to the surging popularity of the irresistible cloud idea, there are cloud datacenters spreading across the globe comprising heterogeneous cloud platforms and infrastructures catering to fast-evolving demands of worldwide businesses The pervasive connectivity has enabled for the unprecedented success of the cloud concept However, intensive automation is the key to the originally intended success of the cloud paradigm Researchers across the world are focusing on unearthing powerful and pioneering tools and techniques for automated infrastructure life-cycle management Similarly there are pathbreaking work-around approaches, algorithms, and architectures for workload consolidation In short, there are many cloud-related aspects yearning for technologically sound automation, acceleration, and augmentation capabilities

9 citations

Book ChapterDOI
10 Aug 2015
TL;DR: This paper is proposing a secure partitioning of application so that the most sensitive or vulnerable part of the application can be kept in the mobile and rest of the applications can be offloaded to the cloud.
Abstract: Smart phones are capable of providing smart services to the users very similar to laptops and desktop computers. Despite of all these capabilities battery life and computational capabilities are still lacking. By combining mobiles with cloud will reduce all these disadvantages because cloud is having infinite resources for processing. But in cloud security is a major concern. Since mobile devices contain private data a secure offloading of application is necessary. In this paper we are proposing a secure partitioning of application so that the most sensitive or vulnerable part of the application can be kept in the mobile and rest of the application can be offloaded to the cloud.

9 citations


Cited by
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Journal ArticleDOI
TL;DR: The main goal of this study is to holistically analyze the security threats, challenges, and mechanisms inherent in all edge paradigms, while highlighting potential synergies and venues of collaboration.

1,045 citations

Journal ArticleDOI
TL;DR: The main security and privacy challenges in this field which have grown much interest among the academia and research community are presented and corresponding security solutions have been proposed and identified in literature by many researchers to counter the challenges.

221 citations

Book ChapterDOI
01 Jan 2020
TL;DR: In this article, an energy-efficient smart edge based health care support system (EESE-HSS) is proposed for diabetic patients with cardiovascular disease, which makes use of a hierarchical computing architecture for exerting expeditious diagnosis during emergencies.
Abstract: The ubiquitous usage of wearable IoT (wIoT) devices has created a formidable opportunity for remote health monitoring system to provide paramount services such as preventive care and early intervention for populations at risk. The cloud-edge paradigm can efficiently manifest the complex computations required in providing these services. But the challenge in its exertion lies in incorporating intelligence at the edge devices. With the deluge of data availability, deep learning methods are very promising to obtain sufficient performance in healthcare applications. As the edge devices are resource-constrained in terms of compute capability and energy consumption, unleashing deep learning services from the cloud to the edge requires efficient tackling of the exorbitant computational and energy requirements of deep learning frameworks. In this chapter, an energy-efficient smart edge based health care support system (EESE-HSS) is proposed for diabetic patients with cardiovascular disease. The proposed cloud-edge paradigm makes use of a hierarchical computing architecture for exerting expeditious diagnosis during emergencies. The intelligence framework incorporated at the edge is also built in an energy-efficient manner. Thus, the proposed healthcare support system has better efficacy in terms of energy efficiency and reduced latency. This makes it very supportive for fall detection in diabetic patients with cardiovascular disease who are susceptible to the risk of heart attack, stroke, heart failure and other vicious diseases.

98 citations

Journal ArticleDOI
TL;DR: A critical literature survey on the utilization of IoT technology in the automotive industry, emphasizing the evolution of technology-enabling connectivity and applications and assessing various connectivity types embedded in the sensor node functionalities to reveal technical challenges for future automotive IoT advancement.

69 citations

Journal ArticleDOI
TL;DR: A Completion Time Driven Hyper-Heuristic (CTDHH) approach for cost optimisation of SWFS in a cloud environment is proposed and has proven to yield the most effective performance results for all considered experimental scenarios.

58 citations