Cloud Computing and Comparison based on Service and Performance between Amazon AWS, Microsoft Azure, and Google Cloud

Kaushik, Prakarsh; Rao, Ashwin Murali; Singh, Devang Pratap; Vashisht, Swati; Gupta, Shubhi

doi:10.1109/ictai53825.2021.9673425

Cited by 22 publications

(4 citation statements)

References 8 publications

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“…They are well-known for their scalability since resources are automatically assigned depending on demand, and developers are only paid for the compute resources utilised during function execution. There are many companies which provide serverless platforms such as AWS Lambda, Microsoft Azure, Google Cloud Platform (GCP), IBM cloud functions etc [51]. In this study, three popular platforms: AWS Lambda, Azure and GCP are considered for empirical analysis.…”

Section: B Serverless Computingmentioning

confidence: 99%

Efficient Security for Resource-Constrained Devices with LPM: A Lightweight Protocol Utilizing Chaotic Map-Based S-Box

Ramesh,

Ramesh

2024

Preprint

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Serverless computing has become a very popular cloud-based solution for designing, testing and deploying the applications as serverless functions. Serverless computing has become a buzzword in both industry and academia as many large IT giants have rolled out their own serverless platforms. Independent design, deployment and auto scalability are its major features with Function-as-a-Service (FaaS) as its popular implementation. On the other hand, microservices are an emerging trend for design of large enterprise applications as many companies like Amazon, Uber and Spotify have already migrated their existing applications to microservices style. However, with the advent of serverless platforms, to design efficient applications with simpler infrastructure management, reduced operational overhead and cost factor, migrating microservices to serverless has become inevitable. Additionally, existing studies report that serverless platforms suffer from cold start latency. Therefore, in this paper, a framework is proposed which has two phases: (i) an empirical investigation to find out the impact of programming language on the cold start of serverless functions and (ii) an approach for migration of microservices to serverless platforms. The evaluation results help us to identify the platform with low cold start latency and also recommend the choice of programming language with lower latency. After migrating the containerized microservices to serverless, a comparison in terms of performance is conducted. The applications designed as serverless functions exhibit better response time and throughput compared to containerized microservices.

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Section: B Serverless Computingmentioning

confidence: 99%

Efficient Security for Resource-Constrained Devices with LPM: A Lightweight Protocol Utilizing Chaotic Map-Based S-Box

Ramesh,

Ramesh

2024

Preprint

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“…It provides users with easy access to a wide range of computing resources that can be scaled up or down based on demand without requiring users to invest in and maintain their own physical infrastructure. Fog computing, on the other hand, is a distributed computing model that brings computing resources closer to the edge of the network, closer to where data is generated and consumed, and provides real-time processing and decision-making capabilities [160].…”

Section: Cloud and Fog Systemsmentioning

confidence: 99%

Early Detection of Earthquakes Using IoT and Cloud Infrastructure: A Survey

Abdalzaher,

Krichen,

Yiltas-Kaplan

et al. 2023

Sustainability

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Earthquake early warning systems (EEWS) are crucial for saving lives in earthquake-prone areas. In this study, we explore the potential of IoT and cloud infrastructure in realizing a sustainable EEWS that is capable of providing early warning to people and coordinating disaster response efforts. To achieve this goal, we provide an overview of the fundamental concepts of seismic waves and associated signal processing. We then present a detailed discussion of the IoT-enabled EEWS, including the use of IoT networks to track the actions taken by various EEWS organizations and the cloud infrastructure to gather data, analyze it, and send alarms when necessary. Furthermore, we present a taxonomy of emerging EEWS approaches using IoT and cloud facilities, which includes the integration of advanced technologies such as machine learning (ML) algorithms, distributed computing, and edge computing. We also elaborate on a generic EEWS architecture that is sustainable and efficient and highlight the importance of considering sustainability in the design of such systems. Additionally, we discuss the role of drones in disaster management and their potential to enhance the effectiveness of EEWS. Furthermore, we provide a summary of the primary verification and validation methods required for the systems under consideration. In addition to the contributions mentioned above, this study also highlights the implications of using IoT and cloud infrastructure in early earthquake detection and disaster management. Our research design involved a comprehensive survey of the existing literature on early earthquake warning systems and the use of IoT and cloud infrastructure. We also conducted a thorough analysis of the taxonomy of emerging EEWS approaches using IoT and cloud facilities and the verification and validation methods required for such systems. Our findings suggest that the use of IoT and cloud infrastructure in early earthquake detection can significantly improve the speed and effectiveness of disaster response efforts, thereby saving lives and reducing the economic impact of earthquakes. Finally, we identify research gaps in this domain and suggest future directions toward achieving a sustainable EEWS. Overall, this study provides valuable insights into the use of IoT and cloud infrastructure in earthquake disaster early detection and emphasizes the importance of sustainability in designing such systems.

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“…Stored data files managed by the database tables, such as images, volumes, DNN models, etc., are saved locally in separate locations and not in the database. Therefore, the operation can be better handled using cloud services such as Amazon AWS and Azure cloud computing [32][33][34];…”

Section: Limitationsmentioning

confidence: 99%

CTIMS: Automated Defect Detection Framework Using Computed Tomography

et al. 2022

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Non-Destructive Testing (NDT) is one of the inspection techniques used in industrial tool inspection for quality and safety control. It is performed mainly using X-ray Computed Tomography (CT) to scan the internal structure of the tools and detect the potential defects. In this paper, we propose a new toolbox called the CT-Based Integrity Monitoring System (CTIMS-Toolbox) for automated inspection of CT images and volumes. It contains three main modules: first, the database management module, which handles the database and reads/writes queries to retrieve or save the CT data; second, the pre-processing module for registration and background subtraction; third, the defect inspection module to detect all the potential defects (missing parts, damaged screws, etc.) based on a hybrid system composed of computer vision and deep learning techniques. This paper explores the different features of the CTIMS-Toolbox, exposes the performance of its modules, compares its features to some existing CT inspection toolboxes, and provides some examples of the obtained results.

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Cloud Computing and Comparison based on Service and Performance between Amazon AWS, Microsoft Azure, and Google Cloud

Cited by 22 publications

References 8 publications

Efficient Security for Resource-Constrained Devices with LPM: A Lightweight Protocol Utilizing Chaotic Map-Based S-Box

Efficient Security for Resource-Constrained Devices with LPM: A Lightweight Protocol Utilizing Chaotic Map-Based S-Box

Early Detection of Earthquakes Using IoT and Cloud Infrastructure: A Survey

CTIMS: Automated Defect Detection Framework Using Computed Tomography

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