AIBLOCK: Blockchain based Lightweight Framework for Serverless Computing using AI

Abstract

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.

Edge AI: A survey

“…Vehicular Edge Computing introduces a computation offloading mechanism with embedded devices in cars offloading data to MEC servers [46]. Implementing offloading and computation resource mechanisms will improve the utilization of MEC servers [48]. Client Edge Computing, including CCTV cameras, mobile phones and smart sensors, can perform some processing rather than sending all data to the edge network; therefore, this model can be helpful to service providers [49].…”

“…However, this technology imposes a high risk to privacy because single user data is transferred to a central cloud for storage and processing [157]. Edge Computing eliminates the need for such long-reach data transmission if more local computational resources are available [48]. Pre-processing can be performed at the edge node in this case as with the smart parking scenario discussed above.…”

“…While previous surveys have focused on individual Edge Computing paradigms, a detailed comparative analysis of the various approaches for implementing Edge Computing paradigms in terms of definitions, architectures, paradigm evolution and application use cases, viewed from the standpoints of applications, functionalities and technologies for 5G and the IoT [46]. Distinct sectors of Edge Computing can be clearly defined that address different markets with different business models and technologies [47]: Fog Computing as the optimal solution for IoT applications to minimize the time required for time-critical processing and Big Data analytics; Mobile Edge Computing as centred on applications intended for large numbers of mobile devices, Micro Data Centres as the optimum for industrial applications in specific locations or at temporary sites while cloudlets are dedicated either (as originally envisioned) to small-enterprise scenarios or can be expanded to publicly funded schemes with free access and open source software [48]. In 2017, Mobile Edge Computing was retitled Multi Access Edge Computing to recognize the large IoT component in proof-of-concept studies and to harmonize Edge Computing systems and applications in highly heterogeneous wireless networks [49].…”

ATOM: AI-Powered Sustainable Resource Management for Serverless Edge Computing Environments

“…Serverless creates new containers to handle these requests; thus, new cold start latencies occur. In addition, it has been observed that the amount of CPU and RAM of the system and the software languages used to execute the function affect cold start latency [10].…”

“…T HE proliferation of Internet of Things (IoT) applications has led to a substantial increase in the volume of data that requires processing [1]. IoT devices are inherently resource-constrained, and as such, cloud systems can address this limitation by providing the resources and processing power they offer.…”