WDCloud: An end to end system for large-scale watershed delineation on cloud

Public clouds such as Amazon Web Services (AWS) and Microsoft's Azure provide excellent capabilities for scalable Web applications and Hadoop-based processing. Recent additions to public clouds to support connected devices and IoT have the potential to similarly disrupt emerging home-grown and/or proprietary approaches. While early public cloud IoT success stories have focused on smaller-scale scenarios such as connected houses, it is unclear to what extent these new public cloud mechanisms and abstractions are suitable and effective for larger-scale and/or scientific scenarios, which often have a different set of constraints or requirements. In this paper, the design and implementation of a representative cloud-based IoT infrastructure in a specific public cloud - AWS - is presented. The system created is for dynamic vehicle traffic control based on vehicle volumes/patterns and public transport punctuality. We find that constructing server-less, stateful, and data driven IoT applications in AWS that can operate in real-time is non-trivial. The primary challenges span application manageability and design, latency performance, asynchronicity, and scalability.

Double-smoothing for bias reduction in local linear regression

Local linear regression with nonparametrically generated covariates for weakly dependent data

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 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

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.

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.

Cybersecurity training activities require specialized environments, typically called cyber ranges, to make it possible for trainees to acquire not only security knowledge, but also practical security skills. However, the setup of these training environments is a tedious task, which hinders the wider use of cyber ranges for security training. In its turn, this has a negative impact on the development of the cybersecurity workforce that is exceedingly necessary in our network-centric society. In this paper we introduce our approach of using the Amazon Web Services (AWS) Elastic Compute Cloud (EC2) public cloud for cyber range deployment, thus making it possible to conduct cybersecurity training activities at scale and at a relatively low cost. Our system was implemented by extending the functionality of the cyber range instantiation system CyRIS that is available as open source on GitHub. We evaluated our implementation from several perspectives, demonstrating that public cloud deployment can provide similar functionality and performance compared to local server or private cloud deployment, while avoiding the high purchase and management costs associated to those.

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%.

Nonparametric regression is a standard statistical tool with increased importance in the Big Data era. Boundary points pose additional difficulties but local polynomial regression can be used to alleviate them. Local linear regression, for example, is easy to implement and performs quite well both at interior and boundary points. Estimating the conditional distribution function and/or the quantile function at a given regressor point is immediate via standard kernel methods but problems ensue if local linear methods are to be used. In particular, the distribution function estimator is not guaranteed to be monotone increasing, and the quantile curves can “cross.” In the article at hand, a simple method of correcting the local linear distribution estimator for monotonicity is proposed, and its good performance is demonstrated via simulations and real data examples. Supplementary materials for this article are available online.

High Performance Cloud Computing (HPCC), which is mature in many industries, is not a new technology, but rather a new method of delivering two resource components of storage capacity and computational power. Within the upstream area of the oil and gas industry, Cloud Computing is very immature, as the industry as a whole has always been challenged by storage and computational capability. However, there is recent evidence to consider using HPCC due to the promise of several benefits, such as flexibility, accessibility and cost reduction (pay-per-use). HPCC can be categorised into private, public or a hybrid model. Scenario-based modeling, the probabilistic approach for uncertainty analysis, risk quantification for existing brown field reservoirs and new perspective green field reservoirs all require a significant amount of computational power and storage capabilities. HPCC may create an opportunity for small to mid-size upstream companies that do not want to invest in infrastructure needed for evaluating scientific applications. In this paper a pilot project was commenced, where Amazon Web Services was used as a public HPCC, with an industry standard reservoir simulation software, known as Eclipse has been used. Multiple simulation cases have been launched to the cloud while transferring large amounts of data between simulated offices. The project involved designing and developing a secure lean agile technology model, which was software vendor agnostic. It was believed that this would drive efficiencies and reliability by being able to dynamically scale up or down computing clusters depending on needs. This paper presents the finding of the project and presents a recommended development methodology for creating both tactical and strategic roadmaps for leveraging trends in HPCC to further unlock potential in the industry by driving innovation and business value.

Asymptotic normality for a local composite quantile regression estimator of regression function with truncated data

The study focuses on the imputation for the longitudinal survey data which often has nonignorable nonrespondents. Local linear regression is used to impute the missing values and then the estimation of the time-dependent finite populations means. The asymptotic properties (unbiasedness and consistency) of the proposed estimator are investigated. Comparisons between different parametric and nonparametric estimators are performed based on the bootstrap standard deviation, mean square error and percentage relative bias. A simulation study is carried out to determine the best performing estimator of the time-dependent finite population means. The simulation results show that local linear regression estimator yields good properties.

Deadline-constrained cost-aware workflow scheduling in hybrid cloud