Big Data and Large-scale Data Analytics: Efficiency of Sustainable Scalability and Security of Centralized Clouds and Edge Deployment Architectures

One of the significant shifts of the next-generation computing technologies will certainly be in the development of Big Data (BD) deployment architectures. Apache Hadoop, the BD landmark, evolved as a widely deployed BD operating system. Its new features include federation structure and many associated frameworks, which provide Hadoop 3.x with the maturity to serve different markets. This dissertation addresses two leading issues involved in exploiting BD and large-scale data analytics realm using the Hadoop platform. Namely, (i)Scalability that directly affects the system performance and overall throughput using portable Docker containers. (ii) Security that spread the adoption of data protection practices among practitioners using access controls. An Enhanced Mapreduce Environment (EME), OPportunistic and Elastic Resource Allocation (OPERA) scheduler, BD Federation Access Broker (BDFAB), and a Secure Intelligent Transportation System (SITS) of multi-tiers architecture for data streaming to the cloud computing are the main contribution of this thesis study

A Review on Modern Distributed Computing Paradigms: Cloud Computing, Jungle Computing and Fog Computing

The distributed computing attempts to improve performance in large-scale computing problems by resource sharing. Moreover, rising low-cost computing power coupled with advances in communications/networking and the advent of big data, now enables new distributed computing paradigms such as Cloud, Jungle and Fog computing.Cloud computing brings a number of advantages to consumers in terms of accessibility and elasticity. It is based on centralization of resources that possess huge processing power and storage capacities. Fog computing, in contrast, is pushing the frontier of computing away from centralized nodes to the edge of a network, to enable computing at the source of the data. On the other hand, Jungle computing includes a simultaneous combination of clusters, grids, clouds, and so on, in order to gain maximum potential computing power.To understand these new buzzwords, reviewing these paradigms together can be useful. Therefore, this paper describes the advent of new forms of distributed computing. It provides a definition for Cloud, Jungle and Fog computing, and the key characteristics of them are determined. In addition, their architectures are illustrated and, finally, several main use cases are introduced

Building Scientific Clouds: The Distributed, Peer-to-Peer Approach

The Scientific community is constantly growing in size. The increase in personnel number and projects have resulted in the requirement of large amounts of storage, CPU power and other computing resources. It has also become necessary to acquire these resources in an affordable manner that is sensitive to work loads. In this thesis, the author presents a novel approach that provides the communication platform that will support such large scale scientific projects. These resources could be difficult to acquire due to NATs, firewalls and other site-based restrictions and policies. Methods used to overcome these hurdles have been discussed in detail along with other advantages of using such a system, which include: increased availability of necessary computing infrastructure; increased grid resource utilization; reduced user dependability; reduced job execution time. Experiments conducted included local infrastructure on the Clemson University Campus as well as resources provided by other federated grid sites

Modern computing: Vision and challenges

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress

Minimally Invasive Solutions to Challenges Posed by Mobility Changes

Today, things have changed radically. As network technologies have proliferated and evolved, the components of, and participants in, computerized systems have become increasingly decoupled. Users travel and commute while connecting to their office computer or home media server. Hardware devices may be carried by users, move on their own, or reside in data centers, never to be seen or touched by end-users. Even operating systems (OSes) and applications may now migrate across the network while executing, thanks to advances in virtualization that are only just beginning to remake the computing landscape. The decoupling of users, devices, and software has invalidated properties that enabled desired functionality: resulting in compromised function. Power interfaces utilize physi- cal user interactions to determine when transitions between high and lower power states should occur; what happens when users are no longer physically present? Operating system execution often relies on components such as CPU and local disk responding with tightly bounded delays; what should be done when the OS itself is in the process of migrating between two separate physical machines? The fundamental question explored by this dissertation is: Can we find highly adoptable solutions to restore desired functionality that has been lost because of changed mobility characteristics? Our emphasis on adoptability stems from pragmatic concerns: if a solution is difficult to adopt, it is highly unlikely to be used. Consequently, while many potential approaches may involve changes to the network itself, our work focuses on modifying end-point behavior. We show that practical solutions implemented solely in software and deployed only on network endpoints can be developed for a wide problem range. We consider concrete challenges arising from user, device, and software mobility changes, affecting sub-disciplines spanning cloud computing, green computing, and wireless networks. Cloud Computing: Users increasingly utilize virtual machine (VM) technology to migrate and replicate OS and software amongst networked hosts. Traditional execution required one VM image copy on each host's local storage. By transitioning to networked execution, dozens, if not hundreds, of VM replicas may now be distributed from a single networked storage location to a commensurately large set of physical machines. As these systems expand, they have come to be plagued by boot storms (and similar problems) caused when networked access to storage becomes a major bottleneck, drastically delaying VM distribution and execution. Can we develop techniques that resolve this network bottleneck without the need for expensive hardware over-provisioning? Green Computing: Remote access technologies have enabled users to travel while still interacting with computational machinery left in the office or home. Yet, energy savings mechanisms have traditionally relied on the activity of attached peripherals to determine power usage. The shift to remote interaction, which bypasses physically attached peripherals, has effectively broken these energy savings mechanisms. Can we build an economic and practical system that accommodates energy efficiency without compromising the fluid remote interactions users have now come to expect? Wireless Computing: Increasingly advanced mobile devices have provoked a shift towards heavy usage of 3G and 4G bandwidth use. Accordingly, the capacity of infrastructure wireless networks becomes increasingly strained. Can we find a way of supplementing this relatively low-latency infrastructure with high-latency, high-bandwidth opportunistic content exchange? In each scenario, we design a solution that aims to strike the proper balance between adoptability and technical efficiency - producing what we believe are rigorous, practical and adoptable solutions