Experiences creating a framework for smart traffic control using AWS IOT

Cloud computing has caused a revolution in how companies do business giving them flexibility, scalability, and cost-efficiency like never before. It lets organizations use computing resources through the internet instead of relying on the usual in-house setup making cloud computing a key part of today's IT plans. From new startups to big global companies, businesses are using cloud solutions more and more to boost their ability to adapt cut down on running costs, and come up with new ideas. The cloud ecosystem is big and varied, with different deployment models and service types to suit various business needs. Public, private, and hybrid clouds are the three main deployment models each with its own pros and cons. public clouds, like those from Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform, offer affordable scalability and global reach. Private clouds, on the other hand, are for organizations that have strict security or compliance needs giving them dedicated environments. Hybrid clouds mix the best parts of both letting businesses find a balance between security and scalability by combining private and public cloud resources

Cloud technologies are becoming pervasive and available for private companies or public institutions in different flavors, mostly public cloud or private clouds. Our focus in this work is on the usage of public cloud technologies by the most popular sites in the Internet. While some studies have described the nascent landscape of public cloud computing 5 years ago, surprising little effort has been put to study the recent evolution of this domain.Using DNS data that enables us to map domains (e.g., netflix.com) and their subdomains (e.g., api.netflix.com) with the cloud providers actually used, we refresh our understanding of this ecosystem. We focus on the dominant four cloud providers, namely Amazon Web Services, Microsoft Azure, Google Cloud Computing and IBM Bluemix. We demonstrate that cloud penetration has clearly increased since 2013, reaching almost 50% of the top 1000 domains, from the Alexa list. Furthermore, a significant fraction of domains use multiple cloud providers simultaneously. Still, domain owenrs remain cautious when it comes to choose which subdomain is actually hosted in the cloud and only 17.8% on average of the subdomains are actually hosted in the cloud. In terms of performance, preliminary results indicate that hosting a subdomain in the cloud pays off as compared to private hosting with a decrease of application level latency of 28%.

Recently, more and more companies are using machine learning platforms offered by cloud service providers to build sentiment analysis models that can then be used to analyze public opinions via social media. This paper aims to conduct a comparative analysis of two of the most popular cloud computing platforms, namely Amazon Web Services (AWS) and Microsoft Azure, in terms of their sentiment detection services through the complex analysis of multiple texts. The comparative analysis was carried out by implementing an application that integrates both the sentiment analysis (SA) solutions provided by Amazon Web Services and those offered by Microsoft Azure. To evaluate the services offered by the two platforms, different evaluation metrics were analyzed and compared, such as accuracy, precision, recall, and other relevant characteristics. Also, the paper examines the costs and limitations of the two platforms, Amazon Comprehend and Azure AI Language Text, when they are used to implement solutions for analyzing the sentiments of product reviews. The results obtained highlighted the advantages and disadvantages between the two platforms from several perspectives, such as performance, the quality of the answers provided, or their accuracy. All these aspects help to obtain a clear picture of the advantages and limitations of each service offered by the two cloud platforms.

<title>Abstract</title> Serverless computing reduces management overhead and scales automatically, yet its latency and performance differ significantly across cloud platforms. This study evaluates serverless function execution and infrastructure-level characteristics on Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). We benchmark function invocation latency in edge deployments, conduct regional latency comparisons across AWS and Azure, and use PerfKit Benchmarker to measure network throughput, network latency, and storage I/O on AWS and GCP. Results show that Azure recorded the lowest minimum latency (555 ms) but suffered from high tail delays, while GCP delivered the most consistent average performance (1.14 s). AWS exhibited moderate latency with a relatively stable distribution. At the infrastructure level, GCP nearly doubled single-stream throughput (9.35 Gb/s) compared to AWS (4.97 Gb/s), whereas AWS achieved slightly lower round-trip network latency (60 vs. 67 $\mu$s). Storage I/O performance was nearly identical across both providers. These findings link infrastructure characteristics to observed serverless behavior, providing actionable insights for latency-sensitive and multi-cloud application deployments.

This study delves into the optimization strategies of Amazon Web Services (AWS) cloud computing solutions tailored specifically for businesses. It provides an in-depth exploration of the transformative impact that cloud computing has had on modern business operations, emphasizing the pivotal role played by AWS in delivering scalable, flexible, and cost-effective resources. With projections indicating the global public cloud services market's growth to $332.3 billion in 2021, it becomes evident that cloud solutions are increasingly relied upon, with AWS commanding a significant 32% market share. This paper highlights the importance of optimizing AWS solutions, considering its extensive range of services designed to meet diverse business needs. However, transitioning to cloud environments brings challenges related to data security, compliance, integration, and migration complexities, as evidenced by pertinent studies and literature reviews. This research synthesizes insights gleaned from various studies, emphasizing the adaptability and cost-effectiveness of AWS while stressing the critical role of robust security measures in its implementation across industries and geographic regions. Understanding prevalent business requisites and challenges in cloud adoption, including scalability, cost efficiency, reliability, availability, and compliance, is essential for organizations seeking to harness cloud technology effectively. Furthermore, this paper provides an overview of AWS's services encompassing computing, storage, databases, machine learning, and AI, showcasing how these empower businesses to streamline operations, foster innovation, and scale dynamically. It elucidates successful instances where AWS was strategically implemented in Netflix, Airbnb, Jollibee Group, and Capital One, demonstrating its impact on scalability, innovation, and service enhancement. The paper outlines anticipated trends in AWS and cloud computing, focusing on user-friendly tools, advancements in machine learning, infrastructure improvements, increased automation, and deeper integration of AI and advanced analytics. Ultimately, this research emphasizes business's need to optimize AWS solutions, navigate complexities, foster innovation, and achieve operational excellence in an increasingly competitive market.

Scientific high throughput computing needs are growing dramatically with time and public Clouds have become an attractive option for occasional bursts, due to their ability to be provisioned with minimal advance notice. The available capacity of both compute and networking is however not well understood. This article presents the results of several production runs of the IceCube collaboration that temporarily expanded its batch system environment with GPU-providing compute instances from the three major Cloud providers, namely Amazon Web Services, Microsoft Azure, and the Google Cloud Platform. The aim of these Cloud bursts was to push the limits of Cloud compute, with a particular emphasis on GPU-providing instances. On the compute side, we showed that it is possible to reach peaks of over 380 fp32 PFLOPS using all available GPU-providing instance types and integrate over 1 fp32 EFLOP hour in a single workday by using only the most cost-effective ones. On the network side, we showed intra-Cloud network throughputs of over 1 Tbps, and 100 Gbps throughputs toward on-prem storage both using shared peering arrangements and dedicated network links.

The management of security credentials (e.g., passwords, secret keys) for\ncomputational science workflows is a burden for scientists and information\nsecurity officers. Problems with credentials (e.g., expiration, privilege\nmismatch) cause workflows to fail to fetch needed input data or store valuable\nscientific results, distracting scientists from their research by requiring\nthem to diagnose the problems, re-run their computations, and wait longer for\ntheir results. In this paper, we introduce SciTokens, open source software to\nhelp scientists manage their security credentials more reliably and securely.\nWe describe the SciTokens system architecture, design, and implementation\naddressing use cases from the Laser Interferometer Gravitational-Wave\nObservatory (LIGO) Scientific Collaboration and the Large Synoptic Survey\nTelescope (LSST) projects. We also present our integration with widely-used\nsoftware that supports distributed scientific computing, including HTCondor,\nCVMFS, and XrootD. SciTokens uses IETF-standard OAuth tokens for\ncapability-based secure access to remote scientific data. The access tokens\nconvey the specific authorizations needed by the workflows, rather than\ngeneral-purpose authentication impersonation credentials, to address the risks\nof scientific workflows running on distributed infrastructure including NSF\nresources (e.g., LIGO Data Grid, Open Science Grid, XSEDE) and public clouds\n(e.g., Amazon Web Services, Google Cloud, Microsoft Azure). By improving the\ninteroperability and security of scientific workflows, SciTokens 1) enables use\nof distributed computing for scientific domains that require greater data\nprotection and 2) enables use of more widely distributed computing resources by\nreducing the risk of credential abuse on remote systems.\n

Summary Public cloud providers, such as the Microsoft Azure and the Amazon Web Services (AWS), offer a wide variety of virtual machines, with different specifications, and prices, that are enabling users to run high-performance programs without buying specialized and expensive hardware. Moreover, some providers, such as AWS, allow the user to bid for lower cost virtual machines, called Spot Instances. Nonetheless, these machines may be terminated within a few minutes of warning at the provider discretion. In this work, we leveraged the SPITS programming model to implement a high-performance and fault-tolerant seismic processing application that is proper for execution on Spot instances and analyzed how different virtual machines from the AWS may affect the performance and the price of the computation. Our experimental results indicate that Spot instances have similar performance to regular instances but are roughly three times less expensive. Finally, we show that AWS groups virtual machines in Availability Zones and that selecting virtual machines from different zones may also affect the total execution cost.

Cloud computing is a Service Oriented Architecture which reduced information technology overhead for the end-user and provide great flexibility, reduced total cost of ownership, on-demand services and many other things [1]. Hence it delivered all IT related capability as services rather than product .Services on cloud are divided into three broad categories: Software as a Service, Infrastructure as a Service & Platform as a Service. Same as services cloud is also classified as Private Cloud, Public Cloud & Hybrid Cloud. Private cloud is increasingly become popular between every private organization either large or small wants .To deploy public or private cloud there are many open source software available some are Eucalyptus, Nimbus, OpenStack, Open Nebula Cloud stack and Amazon Web Services .This paper, I provide a study of the main open source software such as Eucalyptus, OpenStack and OpenNebula for cloud implementation. It is believe that the comparison presented in this paper would benefit developers in selecting best open source Software.

By 2021, cloud adoption was quite widespread with many organizations migrating from traditional data-center models to the infrastructure-as-code concept provided by Cloud Service Providers. As part of the Shared Security Responsibility model, customers are expected to monitor their own environments. To this end Amazon Web Services and Microsoft Azure provide a number of capabilities and a plethora of guidance on how to use their platforms and additional security enhancements from their Marketplaces respectively. However, the author asserts that lessons from the Capital One data breach of 2019 needs to be heeded, on the basis that the initial attack vector was a compromised Amazon Web Service instance hosting a web application firewall, thereby enabling thousands upon thousands of personal data records to be extracted from Simple Storage Service assets used by the financial institution. This security breach still occurred even though protective monitoring technologies are made available to customers. But the stark warning here is that, in addition to the aforementioned active security monitoring, there is a need for forensic readiness pertaining to host-based incident response and forensics requirements. This chapter argues that host-based digital forensics principles need to be considered as part of any customer’s portion of the security responsibility. Preparedness to this level is vital for cyber incident response activities, because organizations do not know when the next security incident might occur, or where potentially compromised instances need to be analyzed. This chapter raises awareness so this is implicitly recognized in relation to our use of these public cloud platforms.KeywordsAWSAzureProtective MonitoringDigital ForensicsHost Forensics

The article investigates the impact of modern cloud technologies on the efficiency of DevOps processes, which are key to the automation, flexibility and reliability of software development. With the rapid development of information technology, cloud platforms play an important role in accelerating software product development, testing and deployment. Implementing DevOps in the cloud environment reduces the time to market, increases the stability of software systems and optimizes the use of computing resources, which is critical for modern companies. This paper discusses DevOps teams' main challenges, including the need to scale infrastructure rapidly, ensure continuous integration and delivery (CI/CD), automate testing, monitor performance, and optimize costs. Particular attention is paid to security problems, configuration management and the human factor minimization when implementing program code changes. A comparative analysis of the three leading cloud platforms - Amazon Web Services, Google Cloud Platform, and Microsoft Azure – is carried out in the context of their impact on DevOps processes. The possibilities of automation, support for container technologies, infrastructure scalability, monitoring tools, and integration with CI/CD pipelines are evaluated. AWS was found to offer the broadest set of DevOps tools with a high level of automation, making it an attractive choice for organizations focused on complex enterprise solutions. Google Cloud Platform demonstrates the best support for Kubernetes and containerization, an essential factor for teams working with microservices architecture. Microsoft Azure provides the most profound integration with the Microsoft ecosystem and is the best choice for companies that use Windows and related products. An experimental study has shown that the choice of a cloud platform significantly impacts the speed of release cycles, the stability of the deployed infrastructure, the ability to respond quickly to changes in load, and the productivity of DevOps teams in general. Recommendations for choosing the optimal cloud platform are proposed depending on the specifics of the project, the scale of the organization, the level of system load, and automation requirements.

Cloud Computing has revolutionized the information technology world and the application offering over the last two decades. At the same time recent trends in Network Function Virtualization (NFV) and Software-Defined Wide Area Networks (SD-WAN) and the combination of those with the Cloud paradigm has allowed an unprecedented shift of enterprise networking services towards the Public Cloud. Even though this network evolutionary approach brings many benefits, it still presents many drawbacks as well. The performance stability and service continuity over a black box Public Cloud infrastructure can hinder the formal service guarantees that many new emerging applications may have. To this end, in this paper, we aim to shed light on the overall performance achieved when deploying coast-to-coast and intercontinental Service Function Chains (SFCs) that interconnect geographically distributed enterprise branches over the Amazon Web Services (AWS) infrastructure. In particular, we investigate the impact of region, Virtual Machine (VM) instance, time of the day and day of the week in the overall throughput and delay attained. The obtained results show the strengths and weaknesses of entirely relying on the AWS infrastructure to offer networking services by investigating possible hidden performance bottlenecks.

Serverless computing has fundamentally transformed cloud technology by enabling developers to deploy applications without the burden of managing server infrastructure. This paper delves into the serverless offerings from major cloud providers—AWS (Amazon Web Services), Azure (Microsoft Azure), and GCP (Google Cloud Platform)—and explores how each contributes to this paradigm shift. The discussion highlights the core advantages of serverless computing, including cost efficiency through a pay-as-you-go model, automatic scaling that adjusts to changing demands, and reduced operational complexity, which allows developers to focus more on coding and less on infrastructure concerns. However, serverless computing is not without its challenges. Notable issues include "cold start" delays, where initial function executions may experience latency, impacting overall performance. Additionally, the shared nature of serverless environments raises potential security concerns, and the limited visibility into the execution environment can make debugging and optimization more complex. The paper also considers future trends in serverless computing across AWS, Azure, and GCP. It discusses emerging approaches such as combining serverless with traditional computing models to create hybrid architectures that leverage the strengths of both. Furthermore, it examines ongoing advancements aimed at improving support for complex, stateful applications, seeking to address current limitations and enhance the capabilities of serverless platforms. This exploration provides a detailed look at the current state of serverless computing and anticipates future developments, focusing on how AWS, Azure, and GCP are shaping the evolution of this technology

Cloud computing is one of the paradigms that have undertaken to deliver the utility computing concept. It views computing as a utility similar to water and electricity. We aim in this paper to make an investigation of two highly efficacious Cloud platforms: Microsoft Azure (Azure) and Amazon Web Services (AWS) from users’ perspectives the point of view of users. We highlight and compare in depth the features of Azure and AWS from users’ perspectives. The features which we shall focus on include (1) Pricing, (2) Availability, (3) Confidentiality, (4) Secrecy, (5) Tier Account and (6) Service Level Agreement (SLA). The study shows that Azure is more appropriate when considering Pricing and Availability (Error Rate) while AWS is more appropriate when considering Tier account. Our user survey study and its statistical analysis agreed with the arguments made for each of the six comparisons factors.

The largest Internet of Things (IoT) cloud platform vendors are Microsoft Azure, Amazon Web Services, and Google Cloud. These companies are known as the big three, and have all agreed to join the IoT domain and concentrate on improving the services on their IoT platforms. While these platform descriptions are extensive, users are constantly experiencing difficulties in making the right choice of which platform to use, between the three. This paper presents a comparison of the big three, using the constraints of hubs, analytics, and security. The study also provides some recommendations as to which IoT cloud platform vendor is more ideal, notwithstanding the limitations of the study. In view of these results, users will be able to more confidently select vendors, based on their demands and goals.