IEEE SC2-2016
The 6th IEEE International Symposium on Cloud and Service Computing
Nadi, Fiji, December 7-10, 2016

Robust Allocation of Resources to Enhance System Performance

Prof. H. J. Siegel
Department of Electrical and Computer Engineering and Department of Computer Science Colorado State University
Fort Collins, Colorado, USA

    Throughout all fields of science and engineering, it is important that resources are allocated so that systems are robust against uncertainty. The robustness analysis approach presented here can be adapted to a variety of computing, communication, and information technology environments, such as high performance computing, clouds, grids, internet of vehicles, big data, security, embedded, multicore, content distribution, wireless, and sensor networks.
    What does it mean for a system to be “robust”? How can the performance of a system be robust against uncertainty? How can robustness be described? How does one determine if a claim of robustness is true? How can one decide which of two systems is more robust? We explore these general questions in the context of parallel and distributed computing systems. Such computing systems are often heterogeneous mixtures of machines, used to execute collections of tasks with diverse computational requirements. A critical research problem is how to allocate heterogeneous resources to tasks to optimize some performance objective. However, systems frequently have degraded performance due to uncertainties, such as inaccurate estimates of actual workload parameters. To avoid this degradation, we present a model for deriving the robustness of a resource allocation. The robustness of a resource allocation is quantified as the probability that a user-specified level of system performance can be met. We show how to use historical data to build a probabilistic model to evaluate the robustness of resource assignments and to design resource management techniques that produce robust allocations.

H. J. Siegel is the George T. Abell Endowed Chair Distinguished Professor of Electrical and Computer Engineering at Colorado State University (CSU), where he is also a Professor of Computer Science. Before joining CSU, he was a professor at Purdue University from 1976 to 2001. He received two B.S. degrees from the Massachusetts Institute of Technology (MIT), and the M.A., M.S.E., and Ph.D. degrees from Princeton University. He is a Fellow of the IEEE and a Fellow of the ACM. Prof. Siegel has co-authored over 440 published technical papers in the areas of parallel and distributed computing and communications, which have been cited over 15,000 times. He was a Coeditor-in-Chief of the Journal of Parallel and Distributed Computing, and was on the Editorial Boards of the IEEE Transactions on Parallel and Distributed Systems and the IEEE Transactions on Computers. For more information, please see


Urban Sensing: Making Smart Cities Friendly and Safe to Pedestrians

Kwei-Jay Lin
University of California, Irvine, USA,
NTU IoX Center, Taiwan,
and Nagoya Institute of Technology, Japan

    Most of the world’s population now live in big cities. As cities grow bigger, there are bound to be dark corners. Local people who are familiar with an area would avoid using certain side streets unless they have no other choice. However, for tourists from out of town and those who must work in the area, a smart pedestrian GPS with “urban sensors” would be very useful to guide people move around in the area. We study urban sensors that can identify specific types of people, events, and situations on city streets to build real-time pedestrian guiding systems. For example, homeless and drunk people may be detected and traced by street cameras that are now ubiquitous in all cities. Occasional accidents, fire or natural disasters may also be detected by urban sensors built from social or crowd sensing to mark certain areas too dangerous to use. Algorithms and techniques can be integrated for real time detection of urban events and situations. Combined with historical data analytics, urban sensing may make predictions on the perimeter of areas for people to avoid. In this talk, the issues, techniques and challenges for urban sensing are presented.

    Kwei-Jay Lin is a Professor at the University of California, Irvine. He is an Adjunct Professor at the National Taiwan University and National Tsinghua University, Taiwan; Zhejiang University, China; Nagoya Institute of Technology, Japan. He is a Chief Scientist at the NTU IoX Research Center at the National Taiwan University, Taipei. He was a Visiting Research Fellow at the Academia Sinica, Taiwan in Spring 2016.
    Prof. Lin is an IEEE Fellow, and Editor-In- Chief of the Springer Journal on Service-Oriented Computing and Applications (SOCA). He was the Co-Chair of the IEEE Technical Committee on Business Informatics and Systems (TCBIS) until 2012. He has served on many international conferences, recently as conference co-chairs of IEEE SOCA 2016. His research interest includes service-oriented systems, IoT systems, middleware, real-time computing, and distributed computing.