iFaaSBus: A Security- and Privacy-Based Lightweight Framework for Serverless Computing Using IoT and Machine Learning

Golec, Muhammed; Ozturac, Ridvan; Pooranian, Zahra; Gill, Sukhpal Singh; Buyya, Rajkumar

doi:10.1109/tii.2021.3095466

Cited by 57 publications

(34 citation statements)

References 25 publications

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“…In future work, we will expand the system so that joining a cloud federation platform is integrated with joining the blockchain network as well as incorporating resource requirements and availability in the HyperLedger model structure. Further, BlockchainBus framework can be integrated with iFaasBus 41 to improve the scalability in the future.…”

Section: Discussionmentioning

confidence: 99%

BlockchainBus: A lightweight framework for secure virtual machine migration in cloud federations using blockchain

Doyle

Golec

Gill

2021

Security and Privacy

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Cloud federations offer numerous advantages such as improved resilience, improved performance, and the prevention of vendor lock-in. In order to function efficiently, however, they must be supported by automated solutions to deploy cloud resources securely and efficiently. In particular, a system which allows virtual machine (VM) migration from one cloud to another while recording this for charging and security purposes is particularly useful. This paper presents a lightweight framework called BlockchainBus which offers secure VM migration in cloud federations using blockchain. This framework is a VM migration Blockchain ledger which can be used to record these migrations. BlockchainBus framework is implemented using the HyperLedger solution and deployed on the Microsoft Azure cloud platform. The overhead of this system is then evaluated. The performance of the BlockchainBus framework was compared with the overall VM migration time and was determined as 2.36 seconds. This result is shorter than the overall VM migration time of 5 seconds and indicates that the BlockchainBus gives better performance.

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Section: Discussionmentioning

confidence: 99%

BlockchainBus: A lightweight framework for secure virtual machine migration in cloud federations using blockchain

Doyle

Golec

Gill

2021

Security and Privacy

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“…In another study, the authors proposed a blockchain-based system to track COVID-19 infected patients globally [18]. Accordingly, while protecting the data security and privacy of the patients, it aimed to keep the spread of the disease under control by following the patients.Golec et al [6] suggested a Covid-19 detection study using IoT and ML in the framework called iFaasBus. In the study, they used a serverless platform for the processing power and storage required for the IoT.…”

Section: Related Workmentioning

confidence: 99%

“…With the accuracy rate in Machine Learning (ML) techniques reaching satisfactory levels, health studies using IoT and ML started to increase [5]. The data received from users through sensors are trained with ML models, and patients are directed to the nearest health institutions in case of a possible pandemic detection [6].…”

Section: Introductionmentioning

confidence: 99%

AIBLOCK: Blockchain based Lightweight Framework for Serverless Computing using AI

Golec

Chowdhury

Jaglan³

et al. 2022

2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid)

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Artificial intelligence (AI)-based studies have been carried out recently for the early detection of COVID-19. The goal is to prevent the spread of the disease and the number of fatal cases. In AI-based COVID-19 diagnostic studies, the integrity of the data is critical to obtain reliable results. In this paper, we propose a Blockchain-based framework called AIBLOCK, to offer the data integrity required for applications such as Industry 4.0, healthcare, and online banking. In addition, the proposed framework is integrated with Google Cloud Platform (GCP)-Cloud Functions, a serverless computing platform that automatically manages resources by offering dynamic scalability. The performance of five different machine learning models is evaluated and compared in terms of Accuracy, Precision, Recall, F-Score and Area under the curve (AUC). The experimental results show that Random Forest gives the best results in terms of accuracy (98.4%). Further, it has been identified that utilization of Blockchain technology can increase the load on memory.

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“…A serverless design, on the other hand, addresses most of the issues that developers experience. Using a serverless architecture, the machine learning models are properly handled and the resources are effectively managed [135]. As a result of this design, developers may spend more time and resources working on AI model training rather than server infrastructure management.…”

Section: Serverless Computingmentioning

confidence: 99%

AI for Next Generation Computing: Emerging Trends and Future Directions

Gill,

Xu,

Ottaviani

et al. 2022

Preprint

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Autonomic computing investigates how systems can achieve (user) specified "control" outcomes on their own, without the intervention of a human operator. Autonomic computing fundamentals have been substantially influenced by those of control theory for closed and open-loop systems. In practice, complex systems may exhibit a number of concurrent and inter-dependent control loops. Despite research into autonomic models for managing computer resources, ranging from individual resources (e.g., web servers) to a resource ensemble (e.g., multiple resources within a data center), research into integrating Artificial Intelligence (AI) and Machine Learning (ML) to improve resource autonomy and performance at scale continues to be a fundamental challenge. The integration of AI/ML to achieve such autonomic and self-management of systems can be achieved at different levels of granularity, from full to human-in-the-loop automation. In this article, leading academics, researchers, practitioners, engineers, and scientists in the fields of cloud computing, AI/ML, and quantum computing join to discuss current research and potential future directions for these fields. Further, we discuss challenges and opportunities for leveraging AI and ML in next generation computing for emerging computing paradigms, including cloud, fog, edge, serverless and quantum computing environments.

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iFaaSBus: A Security- and Privacy-Based Lightweight Framework for Serverless Computing Using IoT and Machine Learning

Cited by 57 publications

References 25 publications

BlockchainBus: A lightweight framework for secure virtual machine migration in cloud federations using blockchain

BlockchainBus: A lightweight framework for secure virtual machine migration in cloud federations using blockchain

AIBLOCK: Blockchain based Lightweight Framework for Serverless Computing using AI

AI for Next Generation Computing: Emerging Trends and Future Directions

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