Predictive variables for agile development merging cloud computing services

Cloud Technology is a most challenging modern area in the field of modern technologies in whichassets(e.g.,CPUandcapacity)canberentedanddischargedbytheclientsthroughinterneton-demand basis. The cloud computing has been giving virtual computing services to a little, medium and extensive industries, and services, for example, infrastructure as a service (IaaS), platform as a service (PaaS), and softwareasaservice(SaaS).Cloudcomputinghasagreatcombinationwiththeagilesoftwaredevelopment as a research area. Many researchers worked in Agile Cloud field. The software industries are using the agile methodology for efficient software development need some platform to get quick feedback from the client. Therefore, the agile-cloud is a great combination for it but due to security reasons that directly influence the less adoption of cloud in software industries. This paper reports the survey results of software industries. The total of seven IT industries and many professionals was involved in this paper. However, this paper also contributes and reveals how existing issues can affect agile-cloud adoption for efficient software development. Furthermore, we do not find any type of survey conducted in Pakistan’s software industries-related to cloud-agile adoption

Microservice Transition and its Granularity Problem: A Systematic Mapping Study

Microservices have gained wide recognition and acceptance in software industries as an emerging architectural style for autonomic, scalable, and more reliable computing. The transition to microservices has been highly motivated by the need for better alignment of technical design decisions with improving value potentials of architectures. Despite microservices' popularity, research still lacks disciplined understanding of transition and consensus on the principles and activities underlying "micro-ing" architectures. In this paper, we report on a systematic mapping study that consolidates various views, approaches and activities that commonly assist in the transition to microservices. The study aims to provide a better understanding of the transition; it also contributes a working definition of the transition and technical activities underlying it. We term the transition and technical activities leading to microservice architectures as microservitization. We then shed light on a fundamental problem of microservitization: microservice granularity and reasoning about its adaptation as first-class entities. This study reviews state-of-the-art and -practice related to reasoning about microservice granularity; it reviews modelling approaches, aspects considered, guidelines and processes used to reason about microservice granularity. This study identifies opportunities for future research and development related to reasoning about microservice granularity.Comment: 36 pages including references, 6 figures, and 3 table

IoT analytics and agile optimization for solving dynamic team orienteering problems with mandatory visits

Transport activities and citizen mobility have a deep impact on enlarged smart cities. By analyzing Big Data streams generated through Internet of Things (IoT) devices, this paper aims to show the efficiency of using IoT analytics, as an agile optimization input for solving real-time problems in smart cities. IoT analytics has become the main core of large-scale Internet applications, however, its utilization in optimization approaches for real-time configuration and dynamic conditions of a smart city has been less discussed. The challenging research topic is how to reach real-time IoT analytics for use in optimization approaches. In this paper, we consider integrating IoT analytics into agile optimization problems. A realistic waste collection problem is modeled as a dynamic team orienteering problem with mandatory visits. Open data repositories from smart cities are used for extracting the IoT analytics to achieve maximum advantage under the city environment condition. Our developed methodology allows us to process real-time information gathered from IoT systems in order to optimize the vehicle routing decision under dynamic changes of the traffic environments. A series of computational experiments is provided in order to illustrate our approach and discuss its effectiveness. In these experiments, a traditional static approach is compared against a dynamic one. In the former, the solution is calculated only once at the beginning, while in the latter, the solution is re-calculated periodically as new data are obtained. The results of the experiments clearly show that our proposed dynamic approach outperforms the static one in terms of rewardsThis project has received the support of the Ajuntament of Barcelona and the Fundació “la Caixa” under the framework of the Barcelona Science Plan 2020-2023 (grant 21S09355-001)Peer ReviewedPostprint (published version

Issues of WLAN

In communications the area of coverage is very important, such that personal space or long range to send information. The distance refers to class of networks such as per-sonal range or wide area, while the protocols of communications refer to mode or type of networks, such as ad-hoc or self organization etc. in this report we refer to the for-mer part and analyze some of the used cases for students as tutorials

Issues of WLAN

In communications the area of coverage is very important, such that personal space or long range to send information. The distance refers to class of networks such as per-sonal range or wide area, while the protocols of communications refer to mode or type of networks, such as ad-hoc or self organization etc. in this report we refer to the for-mer part and analyze some of the used cases for students as tutorials

Artificial iIntelligence for Big Data: issues and challenges

Artificial intelligence (AI) concerns the study and development of intelligent ma-chines and software. The associated ICT research is highly technical and specialized, and its focal problems include the developments of software that can reason, gather knowledge, plan intelligently, learn, communicate, perceive and manipulate objects. AI also allows users of big data to automate and enhance complex descriptive and predictive analytical tasks that, when performed by humans, would be extremely la-bour intensive and time consuming. Thus, unleashing AI on big data can have a sig-nificant impact on the role data plays in deciding how we work, how we travel and how we conduct business. This paper explores how Artificial Intelligence, in conjunc-tion with Big Data technologies, can help organizations to bring about operational and business transformation.Deep learning will also be connected to other major learning frameworks such as reinforcement learning and transfer learning. A thorough survey of the literature on deep learning for wireless communication networks is provided, followed by a detailed description of several novel case-studies wherein the use of deep learning proves extremely useful for network design. For each case-study, it will be shown how the use of (even approximate) mathematical models can significantly reduce the amount of live data that needs to be acquired/measured to implement data-driven approaches

WBAN Applications and Issues

In communications the area of coverage is very important, such that personal space or long range to send information. The distance refers to class of networks such as per-sonal range or wide area, while the protocols of communications refer to mode or type of networks, such as ad-hoc or self organization etc. Our aim is to provide a tutorial to introduce WBAN and its working knowledge as well as architecture. We will address Emergency health issues and suggest how it can be improved

WBAN Routing Protocols in Health care

The emergence of wireless body area network (WBAN) technology has brought hope and dawn to solve the problems of population aging, various chronic diseases, and medical facility shortage. The increasing demand for real-time applications in such networks stimulates many research activities. Our aim is to provide a tutorial to introduce WBAN routing protocols. We classify, and compare the advantages and disadvantages of various routing protocols. We also address Emergency health issues and suggest how it can be improved

WBAN Applications and Issues

In communications the area of coverage is very important, such that personal space or long range to send information. The distance refers to class of networks such as per-sonal range or wide area, while the protocols of communications refer to mode or type of networks, such as ad-hoc or self organization etc. Our aim is to provide a tutorial to introduce WBAN and its working knowledge as well as architecture. We will address Emergency health issues and suggest how it can be improved

Deep Reinforcement Learning in Health care systems

In computational science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. We classify, and compare the advantages and disadvantages of various routing proto-cols. We also address Emergency health issues and suggest how it can be improved