Robert J. T. Morris

Smarter Cities and Their Innovation Challenges

The transformation to smarter cities will require innovation in planning, management, and operations. Several ongoing projects around the world illustrate the opportunities and challenges of this transformation. Cities must get smarter to address an array of emerging urbanization challenges, and as the projects highlighted in this article show, several distinct paths are available. The number of cities worldwide pursuing smarter transformation is growing rapidly. However, these efforts face many political, socioeconomic, and technical hurdles. Changing the status quo is always difficult for city administrators, and smarter city initiatives often require extensive coordination, sponsorship, and support across multiple functional silos. The need to visibly demonstrate a continuous return on investment also presents a challenge. The technical obstacles will center on achieving system interoperability, ensuring security and privacy, accommodating a proliferation of sensors and devices, and adopting a new closed-loop human-computer interaction paradigm.

Optimal constrained selection of a measurable subset

Abstract Necessary and sufficient conditions are given for a class of optimization problems involving optimal selection of a measurable subset from a given measure space subject to set function inequality constraints. Results are developed firstly for the case where the set functions involved possess a differentiability property and secondly where a type of convexity is present. These results are then used to develop numerical methods. It is shown that in a special case the optimal set can be obtained via solution of a fixed point problem in Euclidean space.

Heuristic methods for flexible flow line scheduling

Abstract Heuristic methods are presented for scheduling a flexible flow line. Two problems are considered: entry point sequencing, that is, deciding the order in which to present the jobs to the system, and dispatching, that is, deciding at each machine which job to start next. The results of these methods on test and realistic problems are described. The approach takes into account various line phenomena, such as setups, finite buffers, blocking and starvation, machine downtimes, and the current and subsequent states (at rescheduling intervals) of the line. Various optimization techniques are investigated including myopic and local search methods. The most successful technique developed uses local perturbation to obtain successively improved schedules. In test cases where the optimal schedule is obtainable, we are able to produce solutions within a few percent of the optimal. Our experience with these methods is sufficiently encouraging to lead us to conjecture that they are capable of producing entry point sequences that are, for all practical purposes, optimal. Various methods of dispatching are considered which try to minimize the effects of setup times and blocking. While dispatching is found to be useful in setup-intensive lines, one of our conclusions is that care must be taken, as plausibly helpful rules can lead to unexpected deterioration of performance.

The evolution of storage systems

Storage systems are built by taking the basic capability of a storage device, such as the hard disk drive, and adding layers of hardware and software to obtain a highly reliable, high-performance, and easily managed system. We explain in this paper how storage systems have evolved over five decades to meet changing customer needs. First, we briefly trace the development of the control unit, RAID (redundant array of independent disks) technologies, copy services, and basic storage management technologies. Then, we describe how the emergence of low-cost local area data networking has allowed the development of network-attached storage (NAS) and storage area network (SAN) technologies, and we explain how block virtualization and SAN file systems are necessary to fully reap the benefits of these technologies. We also discuss how the recent trend in storage systems toward managing complexity, ease-of-use, and lowering the total cost of ownership has led to the development of autonomic storage. We conclude with our assessment of the current state-of-the-art by presenting a set of challenges driving research and development efforts in storage systems.

Performance analysis of locking and optimistic concurrency control algorithms

Abstract New analytic models are presented which predict the maximum throughput of locking and optimistic concurrency control algorithms for a centralized database system. By making several simplifying assumptions, these models can be easily solved. The analytic results are tested against simulation and are shown to have an accuracy considerably better than some previously reported methods. The models are used to carry out a comparison between locking and optimistic control under stated assumptions. It is found that locking schemes consistently have higher maximum throughput than optimistic schemes.

Computer detection of typographical errors

Describes a computer program written for the UNIX time-sharing system which reduces by several orders of magnitude the task of finding words in a document which contain typographical errors. The program is adaptive in the sense that it uses statistics from the document itself for its analysis. In a first pass through the document, a table of diagram and trigram frequencies is prepared. The second pass through the document breaks out individual words and compares the diagrams and trigrams in each word with the frequencies from the table. An index is given to each word which reflects the hypothesis that the trigrams in the given word were produced from the same source that produced the trigram table. The words are sorted in decreasing order of their indices and printed. Printing is suppressed for words appearing in a table of 2726 common technical English words.

Neural network control of communications systems

Neural networks appear well suited to applications in the control of communications systems for two reasons: adaptivity and high speed. This paper describes application of neural networks to two problems, admission control and switch control, which exploit the adaptivity and speed property, respectively. The admission control problem is the selective admission of a set of calls from a number of inhomogeneous call classes, which may have widely differing characteristics as to their rate and variability of traffic, onto a network. It is usually unknown in advance which combinations of calls can be simultaneously accepted so as to ensure satisfactory performance. The approach adopted is that key network performance parameters are observed while carrying various combinations of calls, and their relationship is learned by a neural network structure. The network model chosen has the ability to interpolate or extrapolate from the past results and the ability to adapt to new and changing conditions. The switch control problem is the service policy used by a switch controller in transmitting packets. In a crossbar switch with input queueing, significant loss of throughput can occur when head-of-line service order is employed. A solution can be based on an algorithm which maximizes throughput. However since this solution is typically required in less than one microsecond, software implementation policy is infeasible. We will carry out an analysis of the benefits of such a policy, describe some existing proposed schemes for its implementation, and propose a further scheme that provides this submicrosecond optimization.

Interactive visual modeling for performance

An overview is given of Q+, an interactive tool for performance modeling that uses graphical input and visual output. Two major enhancements are a subnetwork capability for structuring models hierarchically and an integrated expression capability. New capabilities are custom icons and temporal browsing. With a Q+ icon palette, users can draw their own icons and manipulate existing ones. The browser allows browsing, editing and updating Q+ information, which can be textual or graphical. Automatic model building, operations management, and experimental design with Q+ are discussed.<<ETX>>

Benchmark synthesis using the LRU cache hit function

The LRU cache hit function is used as a general characterization of locality of reference to address the synthesis question of whether benchmarks can be created that have a required locality of reference. Several results are given that show circumstances under which this synthesis can or cannot be achieved. An additional characterization called the warm-start cache hit function is introduced and shown to be efficiently computable. The operations of repetition and replication are used to form new programs, and their characteristics are derived. Using these operations, a general benchmark synthesis technique is obtained and demonstrated with an example. >

Neural network techniques for object orientation detection. Solution by optimal feedforward network and learning vector quantization approaches

The computer-vision problem of determining object orientation from the consensus of orientations of individual symbols or marks is examined. The problem arises in automatic inspection where orientation can be detected from printed text but there is no knowledge of the content of the text. This is a high-dimensional classification problem, and there is a requirement for highly accurate detection and rapid processing. The typical multilayer threshold networks are seen as unsuitable, and the optimal Bayesian detector is derived and found to have the highly parallel structure of a feedforward network. The learning vector quantization neural network method of T. Kohonen (1988) is also applied. Experimental results, comparisons, and a complete implementation are described. >