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Register a new userCloud Computing - Architecture and Applications
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Added by
Jaydip Sen
Publication date
2017
fields
Informatics Engineering
and research's language is
English
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In the era of Internet of Things and with the explosive worldwide growth of electronic data volume, and associated need of processing, analysis, and storage of such humongous volume of data, it has now become mandatory to exploit the power of massively parallel architecture for fast computation. Cloud computing provides a cheap source of such computing framework for large volume of data for real-time applications. It is, therefore, not surprising to see that cloud computing has become a buzzword in the computing fraternity over the last decade. This book presents some critical applications in cloud frameworks along with some innovation design of algorithms and architecture for deployment in cloud environment. It is a valuable source of knowledge for researchers, engineers, practitioners, and graduate and doctoral students working in the field of cloud computing. It will also be useful for faculty members of graduate schools and universities.
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In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting ones health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.
The global economic recession and the shrinking budget of IT projects have led to the need of development of integrated information systems at a lower cost. Today, the emerging phenomenon of cloud computing aims at transforming the traditional way of computing by providing both software applications and hardware resources as a service. With the rapid evolution of Information Communication Technology (ICT) governments, organizations and businesses are looking for solutions to improve their services and integrate their IT infrastructures. In recent years advanced technologies such as SOA and Cloud computing have been evolved to address integration problems. The Clouds enormous capacity with comparable low cost makes it an ideal platform for SOA deployment. This paper deals with the combined approach of Cloud and Service Oriented Architecture along with a Case Study and a review.
Cloud Computing has become another buzzword after Web 2.0. However, there are dozens of different definitions for Cloud Computing and there seems to be no consensus on what a Cloud is. On the other hand, Cloud Computing is not a completely new concept; it has intricate connection to the relatively new but thirteen-year established Grid Computing paradigm, and other relevant technologies such as utility computing, cluster computing, and distributed systems in general. This paper strives to compare and contrast Cloud Computing with Grid Computing from various angles and give insights into the essential characteristics of both.
With many large science equipment constructing and putting into use, astronomy has stepped into the big data era. The new method and infrastructure of big data processing has become a new requirement of many astronomers. Cloud computing, Map/Reduce, Hadoop, Spark, etc. many new technology has sprung up in recent years. Comparing to the high performance computing(HPC), Data is the center of these new technology. So, a new computing architecture infrastructure is necessary, which can be shared by both HPC and big data processing. Based on Astronomy Cloud project of Chinese Virtual Observatory (China-VO), we have made much efforts to optimize the designation of the hybrid computing platform. which include the hardware architecture, cluster management, Job and Resource scheduling.
In hardware virtualization a hypervisor provides multiple Virtual Machines (VMs) on a single physical system, each executing a separate operating system instance. The hypervisor schedules execution of these VMs much as the scheduler in an operating system does, balancing factors such as fairness and I/O performance. As in an operating system, the scheduler may be vulnerable to malicious behavior on the part of users seeking to deny service to others or maximize their own resource usage. Recently, publically available cloud computing services such as Amazon EC2 have used virtualization to provide customers with virtual machines running on the providers hardware, typically charging by wall clock time rather than resources consumed. Under this business model, manipulation of the scheduler may allow theft of service at the expense of other customers, rather than merely reallocating resources within the same administrative domain. We describe a flaw in the Xen scheduler allowing virtual machines to consume almost all CPU time, in preference to other users, and demonstrate kernel-based and user-spa
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