Computing and internet

KEYWORDS: utility computing, cloud computing, grid computing 1 Introduction From the early days of the World Wide Web (WWW) to the present day, the number of people surfing on the Web has grown from a handful to hundreds of millions. The main reasons for this massive growth are the online services and the ease of connecting to the web. In the early 1990s, Internet connections were hard to come by. Home connections were almost non-existent and the lucky few who had a connection at home usually routed it through their universities. Today, the Internet is everywhere. Companies, universities, libraries and even coffee shops have Internet connections, which the employees, visitors and customers can use to surf the Web.

Cloud-based services have evolved significantly over the years. Cloud computing models such as IaaS, PaaS and SaaS are serving as an alternative to traditional in-house infrastructure-based approach. Furthermore, serverless computing is a cloud computing model for ephemeral, stateless and event-driven applications that scale up and down instantly. In contrast to the infinite resources of cloud computing, the Internet of Things is the network of resource-constrained, hetero- geneous and intelligent devices that generate a significant amount of data. Due to the resource-constrained nature of IoT devices, cloud resources are used to pro- cess data generated by IoT devices. However, data processing in the cloud also has few limitations such as latency and privacy concerns. These limitations arise a requirement of local processing of data generated by IoT devices. A serverless platform can be deployed on a cluster of IoT devices using software containers to enable local processing of the sensor data. This work proposes a hybrid multi- layered architecture that not only establishes the possibility of local processing of sensor data but also considers the issues such as heterogeneity, resource constraint nature of IoT devices. We use software containers, and multi-layered architecture to provide the high availability and fault tolerance in our proposed solution.

Abstract With the growing demand for computing resources and network capacity, pro- viding scalable and reliable computing service on the Internet becomes a chal- lenging problem. Recently, much attention has been paid to the “utility comput- ing” concept that aims to provide computing as a utility service similar to water and electricity. While the concept is very challenging in general, we focus our attention in this chapter to a restrictive environment - Web applications. Given the ubiquitous use of Web applications on the Internet, this environment is rich and important enough to warrant careful research. This chapter describes the approaches and challenges related to the architecture and algorithm design in building such a computing platform.

In addition to ordinary users, Internet accessibility has also significantly changed information technology industry and services. Cloud computing is one of such services which is gaining wide popularity. Cloud computing is a model where computing services such as storage, compute servers and database are provided to end users over the Internet by cloud providers. Users are charged per usage for availing these services. This billing model is similar to utilities such as electricity, gas, and water. Cloud computing not only significantly reduced the potential investment for the users but has also reduced the time to access infrastructure and services [4]. As a consequences, businesses of different sizes are moving from traditional in-house data centres to cloud-based services. Netflix is a prime example of the migration of an overgrowing business to the cloud. They moved all of their in-house services to Amazon Web Services in January 2016 to overcome the problem of vertical scaling of their vast databases at their static data centres [5]. Indeed, features such as auto-scaling, redundancy, low initial investment and operational cost metered service make cloud-based models a leading option for organisations with large and increasing data volumes.

1 Middleware challenge 1: the Internet of Things The IoT is a key step toward the ubiquitous and disappear- ing computing world envisioned by Marc Weiser [1] in the nineties. The IoT represents a significant extension of the current Internet to no longer be just a collection of conven- tional smart phones, laptops, desktops, and servers, but to greatly expand the Internet to embrace a wide range of phys- ical objects and devices that exhibit pervasive sensing, com- putational, and actuation capabilities. Through the Internet, such connected Things become globally accessible—and in some cases controllable. More importantly, IoT creates opportunities to develop new types of distributed services and mass market applications in multiple domains, includ- ing smart cities, health care, transport, energy, and new forms of eCommerce.

Customized hardware support has been recognized as a solution for trusted architecture but this introduces more complications to the system design such as interoperability problem between equipment from different vendor. With initiative from Trusted Computing Group to research and develop the trusted platform technology, it is now ready to be applied to a multitude of applications, including Internet voting system. The challenge now is to successfully make the available studies and design of electronic voting system to integrate with Trusted Platform technology and accurately measure the security level and functionality of the resulting hybrid.

Jun Lu Software Engineering College, Chengdu University of Information Technology (CUIT), Chengdu 610225, China Email: jlu@cuit.edu.cn Abstract— Pervasive computing is considered as a new computing model providing computing and services, representing developing direction of the coming generation of information. The emergence of The Network of Things makes changes to the environment of pervasive computing， and the internet of users expanded the spaces and information. It also sets an even higher demand on pervasive computing and upgrade support service and promotes pervasive computing becoming the basic computing model of internet of things. Basing on the new hierarchy computing model of pervasive computing, a new control system model of computing area network (called CAN-CSM) is presented in this paper, regarding as core implement and virtual computing nodes. The internet of things is composition of a large number of CAN. The main function of CAN is reconstruction of virtual equipments dynamically, and it can describes ability, execute estimation, delivering work, providing Data+Code+Status service migrations. In the context of internet of things, things part of "computing area network" can be seen as an abstract

Many forecasts and predictions have been made about the impact of the increases of computing capacity and the growth of the Internet and the world wide web. In this talk we will introduce some of the favorite predictions and will analyze the possibilities for their realization in the long run. The analysis shows that there exist hard limits on the growth of the Internet and the increase in computing capacity. They prove that it is unlikely that some of the predictions will hold in the long run. The restrictions are based on basic physical and economic limitations, which generate tight bounds on the realization of such predictions. The bounds will occur much faster than expected by the simple forecasters.

© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1100 4. RELATED WORKS: 4.1. Integration of Cloud Computing with Internet of Things: Challenges and Open Issues [4]: In this paper author says that the Internet of Things (IoT) is becoming the next Internet-related revolution. It allows billions of devices to be connected and communicate with each other to share information that improves the quality of our daily lives. On the other hand, Cloud Computing provides on-demand, convenient and scalable network access which makes it possible to share computing resources; indeed, this, in turn, enables dynamic data integration from various data sources. There are many issues standing in the way of the successful implementation of both Cloud and IoT. The integration of Cloud Computing with the IoT is the most effective way on which to overcome these issues. The vast number of resources available on the Cloud can be extremely beneficial for the IoT, while the Cloud can gain more publicity to improve its limitations with real world objects in a more dynamic and distributed manner. This paper provides an overview of the integration of the Cloud into the IoT by highlighting the integration benefits and implementation challenges. Discussion will also focus on the architecture of the resultant Cloud- based IoT paradigm and its new applications scenarios. Finally, open issues and future research directions are also suggested.

A32. Be aware of the issues and comply with the rules concerning, for instance: searching for and criteria for checking the validity of information; IT security; internet filtering. A33. Being aware of the legislation and requirements concerning professional use of ICTE, particularly with respect to: Protection of individual and public freedoms, personal safety, child protection, data confidentiality, intellectual property, image rights.

1.4. Related Work There are several approaches to integrate the computational resources available over the Internet into a global comput- ing resource. The closest approach to the WOS is the Jini architecture proposed by SUN Microsystems [9]. Jini al- lows one to build federations of nodes or distributed ob- jects offering different services each relying on its own ser- vice protocol. Lookup services provide localization and discovery functions. These lookup services, however, re- quire the knowledge of all lookup attributes. Moreover, what is looked for must be exactly specified, which means that only attributes to be exactly matched may be speci- fied. For example, a search for the nearest printer can- not be realized. The WOS approach is qualitatively dif- ferent and more general in that communities, i.e. subsets

ASU IoT and RaaS Version 2014 System Sensor Service Controller Service Core Service Network Service Web Service Broker. Simulator Embedded software services Running in Web Browser Runnin[r]

Andheri, Mumbai Abstract- A fundamental understanding of the Internet of Things is that the data collection landscape has changed dramatically. Initially, ingested data in devices and storage caches were completely within the walls of the IT data center. At first, those walls gradually broke down as people started working from homes on their laptop and mobile devices which are connected to enterprises. Now we are seeing an explosion of devices in the field that need to connect and share data with the organizations. These devices, or things, need to share data with the same quality and performance regardless of whether they are in a remote corner of the world or in a metropolitan area. Enterprise data centers can’t be everywhere. Thus, the Internet of Things is the primary application for cloud computing. Cloud computing and Internet of Things are two very different technologies that are both already part of our day to day life. Their adoption and use are expected to be more and more pervasive, making them important components of the Future Internet. A novel paradigm where Cloud and IoT are merged together is foreseen as disruptive and as an enabler of a large number of application scenarios. This paper provides a brief overview of IoT, importance of reference model for IoT, challenges faced by IoT and IoT and cloud convergence to resolve those challenges.

2.3: Performance of Grid Messaging Systems Now let us discuss the performance of the Grid messaging system. As discussed in section 1, the Grid messaging latency is very different from that for MPI as it can take several 100 milliseconds for data to travel between two geographically distributed Grid nodes; in fact the transit time becomes seconds if one must communicate be- tween the nodes via a geosynchronous satellite. One deduction from this is that the Grid is often not a good environment for traditional parallel computing. Grids are not dealing with the fine grain synchronization needed in parallel computing that requires the few microsecond latency seen in MPI for MPP’s. For us here, another more inter- esting deduction is that very different messaging strategies can be used in Grid com- pared to parallel computing. In particular we can perhaps afford to invoke an XML parser for the message and in general invoke high level processing of the message.

b. The Internet is a network that hosts the linked pages which form the World Wide Web, and these pages are viewed through browsers. c. The World Wide Web is a network, and browsers are another name for the Internet. d. Any of the previous e. a or b

Analysis: In this section, we presented the evaluation of GiGi-MR. We summarize briefly its key aspects. First, we experienced performance improvements (in the execution and turnaround times) on a set of applications that are rep- resentative of those currently used, both in academic and commercial environments, such as e-science (ray tracing, imaging) and big data analytics (namely MapReduce as used by Google and others in production settings). Second, we obtained significant reductions in network traffic directed to servers, during execution, which improve server scalability and allow each server to handle larger computations with more participant nodes, i.e., scale to larger numbers of slaves to execute more tasks concurrently. Third, we improve the reliability of voluntary computing with a set of novel replica- tion and sampling techniques, that require colluders always to execute part of their tasks, and by imposing more coor- dination and overhead to successfully forge results, all this combined with efficient and low overhead checkpointing.

data in a distributed set of wireless networked sensors. The threat identification in prior research have primarily focused on identifying unidentified or authenticated nodes or devices on the network or profiling them based on characteristics that are either static or predictable. Once identified and authenticated in one of the numerous ways, nodes are entirely trusted and values from them are acted upon and propagated. However, it is entirely possible for any malicious player with physical access to or in physical proximity to the network to tamper with either the device or the conditions, making it essential to identify these data integrity issues among a set of authenticated sensor nodes. As outlined by the DHS [17], threats to Integrity are real, have a huge financial impact and remain unresolved at this point. We explore and evaluate this in the context of Precision agriculture that employs a distributed mesh sensor network due to the extensive real estate that needs to be monitored and maintained at a reasonable cost. The sensors on the network are often connected via cellular, Bluetooth, or Wi-Fi networks and rely on edge computing to make decisions at the source. The solution entails the use of spatial and temporal locality of sensors on the distributed network to detect data integrity threats as shown in Figure. 3.1.

Comparison of Cherangani Hills anthropogenic activities and environmental conditions with other previous records from East Africa over the past 5 Kyrs. The darker regions represent con[r]

own. In terms of the data plan, three of them had an unlimited data plan so they could easily access the Internet without any restrictions. Anna was the only participant who reported that she had a limited plan, but she stated that “I never thought that was not enough.” (Anna, 2 nd interview, 2017). High school participants also indicated that in high school, they had Wi-Fi on campus although the school Wi-Fi limited some content such as YouTube or social media including Facebook and Instagram. Out of school contexts, they had Wi-Fi at home, and most of the libraries or local coffee shops they visited to hang out with friends and study together offered unlimited Wi-Fi. In Kaye’s case, she did not have any data usage restrictions even on campus, as the university did not limit the access except to illegal websites. Although having unlimited plans or enough data access did not guarantee that they were on the Internet constantly, having this access suggested that participants’ mobile phone provided a means of continual communication and learning. With access to the Internet via Wi-Fi and unlimited data plans that participants could utilize the Internet at a cheaper or no cost, they had instant access to the rich resources such as various websites or applications they needed for both entertainment and academic work.

African retention in African American storytelling through performance of poetry, this research.. seeks to discover: 1) how African ancestral memories manifest in contemporary performanc[r]