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Grid Security Architecture: Requirements, Fundamentals, Standards and Models
Published in Yang Xiao, Security in Distributed, Grid, Mobile, and Pervasive Computing, 2007
Jose L. Vivas, Javier Lopez, Jose A. Montenegro
Security has been a central issue in grid computing from the outset, and has been regarded as the most significant challenge for grid computing [6]. This is particularly true for enterprise grids. Significant compromises in security might be the result of an inadequate understanding of the security implications of a grid. The security requirements and policies are determined largely by the architectures developed for these types of applications, which are distinguished from client-server architectures by the fact that grid environments assume a dynamic and simultaneous use of a large number of resources from a number of administrative domains. Although the intention has been from the outset to use available security mechanisms as much as possible, this requirement could not be met by mechanisms that were devised largely for insulating and protecting networks from their environment, as in intranets and virtual private networks. As a result, novel security technologies have been evolving all the time within the grid community, including solutions for the management of credentials and policies, new resource management protocols for coallocation of multiple resources and for secure remote access to data and computing resources, and new information query protocols and data management services [7].
Introduction
Published in John W. Rittinghouse, James F. Ransome, Cloud Computing, 2017
John W. Rittinghouse, James F. Ransome
Grid computing is often confused with cloud computing. Grid computing is a form of distributed computing that implements a virtual supercomputer made up of a cluster of networked or Internetworked computers acting in unison to perform very large tasks. Many cloud computing deployments today are powered by grid computing implementations and are billed like utilities, but cloud computing can and should be seen as an evolved next step away from the grid utility model. There is an ever-growing list of providers that have successfully used cloud architectures with little or no centralized infrastructure or billing systems, such as the peer-to-peer network BitTorrent and the volunteer computing initiative [email protected]
Green Cloud Computing
Published in Matthew N. O. Sadiku, Emerging Green Technologies, 2020
Technologies that are working behind the cloud computing include [4]: (1) virtualization, (2) service-oriented architecture (SOA), (3) grid computing, and (4) utility computing. The key feature of cloud computing is the idea of virtualization, which enables an operating system to run on several hardware deployments. Cloud computing is a superset of grid computing. Grid computing refers to a distributed architecture of a large number of computers connected to solve a complex problem. Like electricity supply, the cloud provides a new kind of “utility” that is delivered through wired or wireless networks.
An Intelligent Whole-Process Medical System Based on Cloud Platform
Published in Applied Artificial Intelligence, 2023
Cloud computing is a form of distributed computing that involves breaking down a large data computing program into numerous smaller programs via the “cloud” network. These small programs are then processed and analyzed by a system composed of multiple servers, with the result being returned to the user (Al-Ahmad and Kahtan 2018). Initially, cloud computing was simply distributed computing that addressed task distribution and the merging of calculation results, leading to its alternate name: grid computing. Thanks to this technology, thousands of data can be processed in mere seconds, resulting in powerful network services. In today’s era of cloud computing, the “cloud” performs both storage and computing for us. This group of computers includes hundreds of thousands, if not millions, of machines, each of which can be updated at any time to ensure the “cloud” remains immortal. Google, Microsoft, Yahoo, Amazon, and other IT giants all have their own clouds or are currently building them.
Improved Implicit Stochastic Optimization technique under drought conditions: the case study of Agri–Sinni water system
Published in International Journal of River Basin Management, 2018
Grid computing provides the ability to solve large-scale computation problems using a robust computer network that connects heterogeneous resources and services. The goal is to access those large sets of diverse, geographically distributed resources/services that are collected into a virtual computer for High Performance Computation (HPC). Grid computing also allows to scale the problem so that even small home computers can make a useful contribution.
Distributed behavior model orchestration in cognitive internet of things solution
Published in Enterprise Information Systems, 2018
Chung-Sheng Li, Frederica Darema, Victor Chang
Fog computing seeks to provide superior user experience and overall system redundancy in case of failure. It emphasizes the notions of information processing to generate the knowledge nearer the entity that needs it, regardless whether the entity is a human or an engineered device (or collections thereof). A CIoT solution, when either latency or throughput is critical (as indicated in the upper left, upper right, and lower right quadrants of Figure 7), could substantially benefit from exploiting fog computing concepts. It should be noted however that while the term fog computing concept was introduced in 2012, the concepts of distributed computing and distributed resource management – across a range of platforms, including computing at the edges (e.g. mobile devices, personal portable devices, sensors, etc) were ideas articulated many years before that (Darema 1998, 2005a, 2005b), and will be discussed more in the next section. Also, to note that in the mid- to late-90s, Grid computing concepts (Foster and Kesselma 1999) provided the impetus for moving from client-server computing to more general distributed computing (or metacomputing – a term coined in 1987 by Larry Smarr [Smarr 1987]). Grid computing emphasized the coordination of multiple distributed computation, storage and communication resources. It has successfully demonstrated interoperability across multiple computational platforms used to support the runtime of a given application, as well as multiple applications executing concurrently. It should also be noted that the notion of virtualization, emphasized in cloud computing, was never precluded in the Grid concept. And, cloud computing has evolved over the years from a homogeneous centralized services concept to a more (geographically and otherwise) distributed and heterogeneous collections of platforms, with concomitant challenges of interoperability as were present in Grid computing. It is likely that Fog computing (or edge computing) may also evolve towards a hybrid and heterogeneous environment similar to cloud computing.