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This article surveys the evolution of stock market trading over a 60-year period. It begins before 1960, when there was no database widely available to conduct a statistical analysis of stock price movements. This changed in the 1960s with the introduction of the Center for Research in Security Prices database. A major finding was the heavy-tailed nature of stock returns. The 1960s also brought major theoretical developments, including the martingale theory of stock price processes and the efficient market hypothesis. This hypothesis prevailed until the 1990s, when the discovery of market anomalies led to statistical arbitrage strategies. We describe the use of modern machine learning methods, such as AdaBoost and random forests, which can combine some of these strategies into an improved trading strategy. The twenty-first century was marked by the rapid evolution of electronic markets and the rise of computer-driven high-frequency trading based on computing technology, low latency access, and limit order book modeling.

Abstract: In dynamic real-time systems such as sensor networks, mobile ad hoc networking and autonomous systems, the mapping between level of service and resource requirements is often not fixed. Instead, the mapping depends on a combination of level of service and outside environmental factors over which the application has no direct control. An example of an application where environmental factors play a significant role is radar tracking. In radar systems, resources must be shared by a set of radar tasks including tracking, searching and target confirmation tasks. Environmental factors such as noise, heating constraints of the radar and the speed, distance and maneuverability of tracked targets dynamically affect the mapping between the level of service and resource requirements. The QoS manager in a radar system must be adaptive, responding to dynamic changes in the environment by efficiently reallocating resource to maintain an acceptable level of service. In this paper, we present an integrated QoS optimization and dwell scheduling scheme for a radar tracking application. QoS optimization is performed using the Q-RAM (Baugh, 1973, ghosh-et al.,2004a approach. Heuristics are used to achieve a two order magnitude of reduction in optimization time over the basic Q-RAM approach allowing QoS optimization and scheduling of a 100 task radar problem to be performed in as little as 700 ms with only a 0.1% QoS penality over Q-RAM alone.

In this 25th year anniversary paper for the IEEE Real Time Systems Symposium, we review the key results in real-time scheduling theory and the historical events that led to the establishment of the current real-time computing infrastructure. We conclude this paper by looking at the challenges ahead of us.

Most real-time computer-controlled systems are developed in two separate stages: controller design followed by its digital implementation. Computational tasks that implement the control algorithms are usually scheduled by treating their execution times and periods as unchangeable parameters. Task schedulability therefore depends only on the limited computing resources available. On the other hand, controller design is primarily based on the continuous-time dynamics of the physical system being controlled. The set of tasks resulting from this controller design may not be schedulable with the limited computing resources available. Even if the given set of tasks is schedulable, their overall performance may not be optimal in the sense that they do not make a full use of the computing resources. In this paper, we propose an integrated approach to controller design and task scheduling. Specifically, task frequencies (or periods) are allowed to vary within a certain range as long as such changes do not affect critical control functions such as the maintenance of system stability. We present an algorithm that determines the task frequencies such that a prescribed aspect of system performance is optimized subject to satisfaction of computing resource constraints. The tasks are then scheduled with the chosen frequencies. The proposed approach also addresses the issue of choosing controller processors.

The paper investigates two methods to calculate recoverable sets for continuous‐time, linear time‐invariant systems subjected to input and state constraints. A state is said to be recoverable if it can be driven to the equilibrium point while respecting the constraints. The recoverable set is the set of all the recoverable states. The first computational method discussed in the paper is based on the equivalence between recoverability and the existence of a time‐optimal control law. The second method uses short‐cuts that are applicable to two‐dimensional systems and reduce the number of computations by about an order of magnitude. Several examples with distinctive characteristics are presented. © 1998 John Wiley & Sons, Ltd.

This paper extends the asymptotic results for ordinary renewal processes to the superposition of independent renewal processes. In particular, the ordinary renewal functions, renewal equations, and the key renewal theorem are extended to the superposition of independent renewal processes. We fix the number of renewal processes, p,and study the asymptotic behavior of the superposition process when time, t,is large. The key superposition renewal theorem is applied to the study of queueing systems.

In many real-time applications, the set of tasks in the system, as well as the characteristics of the tasks, change during system execution. Specifically, the system moves from one mode of execution to another as its mission progresses. A mode change is characterized by the deletion of some tasks, addition of new tasks, or changes in the parameters of certain tasks, for example, increasing the sampling rate to obtain a more accurate result. This paper discusses how mode changes can be accommodated within a given framework of priority driven real-time scheduling.

Abstract: A real-time system consists of both aperiodic and periodic tasks. Periodic tasks have regular arrival times and hard deadlines. Aperiodic tasks have irregular arrival times and either soft or hard deadlines. In this article, we present a new algorithm, the Sporadic Server algorithm, which greatly improves response times for soft deadline aperiodic tasks and can guarantee hard deadlines for both periodic and aperiodic tasks. The operation of the Sporadic Server algorithm, its performance, and schedulability analysis are discussed and compared with previously published aperiodic service algorithms.

Messages arrive at a group of service channels in accordance with a time-dependent Poisson process. An arrival either (i) immediately begins k-stage Markovian service if an empty channel is reached, or (ii) balks and enters a retrial population if the channel sought is busy. Diffusion approximations to the number of messages in service (each stage) and in the retrial population are derived by writing stochastic differential equations (I+ 0). Steady-state distributions are found and compared with certain simulation results.

When periodic tasks with hard deadlines communicate over a bus, the problem of hard real-time bus scheduling arises. This paper addresses several problems of hard real-time bus scheduling, including the evaluation of scheduling algorithms and the issues of message packet pacing, preemption, priority granularity and buffering.