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Cloud, Fog, and Edge Computing

Cloud, Fog, and Edge Computing. CptS 464/564 April 24, 2019. Sources of Info. cloudcomputing.ieee.org . Includes some training classes.

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Cloud, Fog, and Edge Computing

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  1. Cloud, Fog, and Edge Computing CptS 464/564 April 24, 2019

  2. Sources of Info • cloudcomputing.ieee.org. Includes some training classes. • Kai Hwang, Geoffrey Fox, and Jack Dongerra. Distributed and Cloud Computing: From Parallel Processing to the Internet of Things, Morgan Kauffman, 2012, ISBN 978-0-12-358880-1 • T. Gamage et al. “Mission Critical Cloud Computing for Critical Infrastructures”, in Smart Grids: Clouds, Communications, Open Source, and Automation. D. Bakken and K. Inewski, eds, CRC Press, 2014, ISBN 9781482206111. • http://www.nist.gov/itl/cloud/. Stuff available there includes: • “The NIST Definition of Cloud Computing”, Special Publication 900-145. • “US Governmetn Cloud Computing Technology Roadmap Volume II: Useful Information for Cloud Adopters”, Special Publication 500-293. • Wikipedia.org articles on “Cloud computing” and “MapReduce”

  3. Sources of Info (2) • M. Ambrust et al, “Above the Clouds: A Berkeley View of Cloud Computing”, TR UCB/EECS-2009-28, February 10, 2009. • Cloud Computing Tutorial, www.tutorialspoint.com/cloud_computing/ • “What is Cloud Computing – A Tutorial”, http://leverhawk.com/what-is-cloud-computing-tutorial-2012120519. Short and sweet and to the point.

  4. Over-Hyped, Under-Defined Terms • Cloud • Service-Oriented Architecture (SOA) • …. many more on the early side of the Hype Cycle!

  5. Cloud Computing • The “next new thing” • Big data centers (probably hosted by power industry vendors or NERC or DHS/DoE, not Amazon or Google) • These permit “consolidation” • 10x or better reductions in cost of operation • Far better equipment utilization and management • New styles of elastic computing, potential to compute directly on massive data collections • Adds up to a new way of computing that forces us to undertake new kinds of thinking • But deliberately designed to trade off consistency for highest possible scalability

  6. Clouds From a Hardware Point of View • Illusion of infinite computing resources available on demand • Can start small with no need to provision far ahead • Elimination of up-front commitment by Cloud users • Saves precious capital • Can start small: what I call “just-in-time expansion” • Ability to pay for use of computing resources on a short-term basis as needed

  7. NIST’s Cloud Essential Characteristics • On-demand self-service • Broad network access • Resource pooling • Rapid elasticity • Measured service

  8. IBM’s and a Book’s Cloud Definition • “A cloud is a pool of virtualized computer resources. A cloud can host a variety of different workloads, including batch-style backend jobs and interactive and user-facing applications.” • “Cloud computing applies a virtualized platform with elastic resources on demand by provisioning hardware, software, and data sets dynamically.” [emphasis mine] • Placement problem is huge here (DS II from [CDKB5] chap. 2) • Computer scientists would love to pass in info to influence placement and be told what mapping decisions were made

  9. MapReduce • Programming model built for processing and generating large sets of data (e.g., Apache Hadoop) • Parallel & distributed algorithm on a cluster • Map() performs filtering and sorting • Divides problems into smaller sub-problems based on key & distributes them to worker nodes • Reduce(): Collects answers to sub-problems and combines in a given way for output • Much more scalable than sequential algorithms

  10. Cloud Deployment Models • Private Cloud: for use by a single organization • RTE France has its own data center • Public Cloud: open for use by public, many companies and users using simultaneously • Community Cloud: operated for a specific community of consumers with shared concerns • E.g. ISO New England is starting one, will involve others • This is ultimately what grids need, IMO, managed right • Hybrid Cloud: mix of more than one of the above 3. • Often private us used, but then public for overflow demand

  11. Service Models (Bottom Up) Client Apps • Infrastructure as a Service (IaaS) • Lets consumers provision processing, storage, network, data, Operating System, etc. Low-level. • Platform as a Service (Paas) • Provides much higher-level APIs: langauges, libraries, middleware, etc. (c.f. “platform” from DS II section, from [CDKB5] chap. 2). SaaS PaaS IaaS Virtual Machines • Software as a Service (SaaS) • Provider’s apps running in cloud. Often accessed by a thin client on a client machine (web browser, simple app on phone, …) • Popular examples include Google’s gmail and docs, Microsoft Sharepoint, … $10B in 2010, $21B by 2015 (Gartner)

  12. Service and Deployment Models (courtesy of Leverhawk.com)

  13. Risks of Cloud Computing • Security & Privacy • Vendor Lock-In • Platform Lock-in: APIs not portable • Data Lock-in: your data is stuck with a vendor who may legally own it • Tools Lock-In: Tools to manage and develop cloud apps may not work across multiple vendors • Isolation Failure • Management Interface Compromise • Insecure or Incomplete Data Deletion

  14. PANEL: Edge Computing for a More Adaptive and Manageable Grid Chair: David Bakken, Washington State University Paneilsts Prof. David Bakken, WSU Dr. Clifton Black, Southern Company Mr. Byron Flynn, GE Mr. Erik Felt, Real Time Innovations (formerly GE) IEEE Innovative Smart Grid Technologies (ISGT) 2018 Panel February 22, 2018

  15. Fog Connects the Cloud to the Edge Cloud • Fog-enabled Router • Fog router CPU Coprocessor Fog • Substation IED • Substation computer • IoT “thing” Edge with a Managed Infrastructure

  16. Fog’s Raison D'être Sources: Fog Computing and the Internet of Things: Extend the Cloud to Where the Things Are, Cisco Whitepaper, 2017; Nebbiolo TR

  17. Fog Definition (cont.) Fog Computing seamlessly extends cloud computing into edge for secure control and management of domain specific hardware, software, and standard compute, storage and network functions within the domain and enable secure rich data processing applications across the domain.

  18. From Cloud to Fog to Edge Source: Fog vs. Edge Computing, Nebbilio Technologies whitepaper, 2018.

  19. Key Features: Fog and Edge Source: Fog vs. Edge Computing, Nebbilio Technologies whitepaper, 2018.

  20. Differences: Fog vs. Traditional Edge Source: Fog vs. Edge Computing, Nebbilio Technologies whitepaper, 2018.

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