From the editors
Beyond the Cloud: Cyberphysical Systems George Hurlburt, Change Index Jeffrey Voas
C
louds offer a novel way to revitalize the seemingly archaic concept of time-sharing. Rather than relying on fixed mainframes, clouds thrive in the Internet. Clouds offer impressive efficiencies, unprecedented collaboration opportunities, and economies of scale for all manner of networked users. Yet cloud server farms have enormously costly power consumption footprints and require massive data pipelines to transport transactions and data to and from the servers. Such connectivity means that clouds rarely stand alone. They currently rely on other equally relevant leading-edge technologies— the Internet of Things (IOT), mobile apps, and big data. Relying on the Internet, clouds both influence how the Internet is used and are influenced by the Internet’s use. When connected to banks of remote autonomous sensors, clouds become cyberphysical clouds—that is, specific cloud-based instances of cyberphysical systems (CPSs). Thus, such clouds are citizens of the IOT.1 Clouds can also offer personal value through mobile apps—for example, reinforcing the idea of Bring Your Own Device. Such apps will continue growing in number and perceived use so long as
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they can rely on clouds to share data across devices and connect with users. For example, we prepared this article using Dropbox, calendar, and reminder apps working across three different devices. Interestingly, the majority of the cloud-borne transactions for transferring the data to multi ple devices were automatic after initial data entry. Furthermore, clouds harbor big data, be it in small quasirelated datasets or massively large data collections. Other clouds, often relying on Apache’s Hadoop open source framework for distributed data-intensive applications, broker big-data transactions and expedite novel views drawing from seemingly disparate datasets. Although few clouds freely interact with fellow or competitor clouds today, this is likely to change, 2 leading to larger networks of collaborative, autonomously operating clouds, each optimized for specific services. As clouds continue to expand and eventually aggregate, a framework must emerge for understanding the resulting dynamic, nonlinear space embodied in various cloudrelated networks.
Graph Theory Can Help Given that clouds are interdependent with other emergent technologies,
Published by the IEEE Computer Society
the question becomes one of a common denominator against which we can evaluate clouds, the IOT, mobile apps, and big data. The one common element underlying each of these phenomena is networks. Here, network refers to the interaction of nodes as related by directional or undirected edges. Both nodes and edges can also possess properties, thus adding to the potential of enacting robust discovery mechanisms. Using a Facebook analogy, the term graph extends far beyond the x-y algebraic plots we learn in high school. We can think of a Facebook profile as properties of a user (node) that’s related directly to other users via friendships (edges) and indirectly via properties. The specific links between friends, friends of friends, and so on represent subsets of the larger Facebook graph. For those familiar with graph theory, it’s not surprising that Facebook’s newest pillar—joining the timeline and newsfeed—is a search tool, appropriately named “Graph.” Likewise, graph theory constructs play significant roles in Google and Amazon strategies. Neither could rapidly respond to millions of simultaneous disparate users, each with instantaneously differing
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interests, without relying on graphrelated mathematics.3 Graph databases traverse edges to discover related nodes. They perform thousands of times faster than relational database systems (R DBSs). These NoSQL tools avoid the dreaded multitude of outside joins necessary to accomplish the same feat in traditional RDBMS environments. Moreover, the graph schema can be recreated on the fly when data is added or deleted. This dynamic frees the developer from the constraints of the rigid relational schemas that doggedly resist change once established. (Neo4j is an open source, multiplatform leader in the graph database arena. This data base interacts freely with Gephi, a French open source, multiplatform graph visualization tool.) Graph theory, with roots in 16th century Europe,4 is becoming increasingly important. It represents the new mathematics of networking and, as such, yields myriad useful metrics and constructs for rigorously describing networks and the nature of their internal relationships. Given this ability, graph theor y applies to almost any digitally networked phenomena. This implies a means of describing and defining all manners of graphs, including those surrounding clouds, IOT instantiations, mobile apps interacting across smart mobile devices and, of course, big data, particularly when unstructured. Graph theor y also ser ves to describe many other phenomena, including natural networks observed in the hard sciences, such as physics and biolog y. Significantly, graph theory applies equally well in the soft sciences, including sociology and economics. The significance of these relationships underpin social networks and gives rise to
New Editorial Board Member Robert R. Harmon is a professor of marketing and technology management and a Cameron Research Fellow in the School of Business at Portland State University, where he also serves as the director of the Strategic Marketing Area. His research interests are cloud-based sustainable IT services and service transition strategies for high-technology companies. His research has been funded by the National Science Foundation, Intel Corporation, and IBM. Harmon received his PhD in marketing and information systems from Arizona State University. He’s a member of the IEEE Computer Society. Contact him at
[email protected].
Graph theory represents the new mathematics of networking, yielding useful metrics and constructs for rigorously describing networks. the various direct adver tising schemes necessary to monetize these networks.5
The Next Step: Cyberphysical Systems If graph theory can be considered a first-order language of network science, perhaps CPSs are analogous to the higher-order constructs surrounding the growing field of network science. The field of CPS focuses on the link between a system’s computational and physical elements, including humans. CPS studies strive to integrate concepts involving applied controls, concurrency, communications, interoperability, scalability, complexity management, wireless sensing, and wireless actuation. It also addresses the related fields of reliability, performance, verification, validation, and cybersecurity.6 As such, the field of CPS brings the concept of “what is a system” full circle, including not only computational software and hardware but also their interact ion s w it h t hei r over a rch i ng
physical environment, well beyond the interaction of software and its computational hardware. These environments encompass numerous f ields of endeavor. CPSs clearly extend to robotics and autonomous vehicles with and without humans in the loop. In its call for proposals, the National Science Foundation states: “The December 2010 report of the President’s Council of Advisors on Science and Technology7 calls for continued investment in CPS research because of its scientific and technological importance as well as its potential impact on grand challenges in a number of sectors critical to US security and competitiveness.”8
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aken together, graph theory and CPS research appear to provide the basis for a robust unified framework for understanding both the microand macro-level integration of clouds, the IOT, mobile apps, and big data. Applied graph theory and CPS concepts transcend networks
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From the editors of all kinds and simultaneously relate well to larger fields of study touched by computational resources. The framework calls for both unparalleled multidisciplined collaboration and an understandi ng of a ppl i e d g r a ph m at h ematics across related networked phenomena. Furthermore, it creates a new view of systems integration and interoperability that can embrace existing standards while giving rise to new standards that address practical problem solving in the
dynamic, nonlinear space that represent s net work s of ma ny forms. CPSs, combined with graph theory, could be the “silver lining” for clouds going forward.
References 1. J.M. Rabaey, “The Swarm at the Edge of the Cloud—A New Perspective on Wireless, VLSI Circuits (VLSIC),” Proc. 2011 Symp. Signal Processing & Analysis, IEEE, 2011, pp. 6–8. 2. V. Cerf, keynote address, National Institute of Standards and Technology
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(NIST ) Cloud Computing and Big Data Workshop, 16 Jan. 2013; www.nist.gov/itl/cloud/nist-jointcloud-a nd-big-dat a-work shopwebcast.cfm. 3. H. Hama, T.T. Zin, and P. Tin, “Optimal Crawling Strategies for Multi media Search Engines,” Proc. 5th Int’l Conf. Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP 09), IEEE, 2009, pp. 182–185. 4. C.R. Spitznagel, Graph Theory and the Bridges of Königsberg, Mathematics Dept., John Caroll Univ., 2000. 5. M. Punceva et al., “Incentivising Resource Sharing in Social Clouds,” Proc. 2012 IEEE 21st Int’l Work shop Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE 12), IEEE, 2012, pp. 185–190. 6. D. Broman et al., “Cyberphysical Systems, A Concept Map,” University of California, Berkeley, 2012; www.cyberphysicalsystems.org. 7. “Designing a Digital Future: Federally Funded Research and Development in Networking and Information Technology,” President’s Council of Advisors on Science and Technology, Report to the President and Congress, Dec. 2010; www.whitehouse. gov/sites/default/files/microsites/ ostp/pcast-nitrd-report-2010.pdf. 8. “Cyber-Physical Systems (CPS),” Directorate for Computer & Information Science & Engineering, National Science Foundation, 2013; www.nsf.gov/funding/pgm_summ. jsp?pims_id=503286.
George F. Hurlburt, CEO of Change Index, has an abiding interest in applied complexity analysis. Contact him at
[email protected]. Jeffrey Voas is an associate EIC for IEEE IT Professional, is on the editorial advisory board of IEEE Spectrum, and is on the editorial board for Computer. Voas is an IEEE Fellow and a Fellow of the American Association for the Advancement of Science (AAAS). Contact him at
[email protected].
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