Convex optimization framework for intermediate deadline assignment in soft and hard real-time distributed systems

It is generally challenging to determine end-to-end delays of applications for maximizing the aggregate system utility subject to timing constraints. Many practical approaches suggest the use of intermediate deadline of tasks in order to control and upper-bound their end-to-end delays. This paper proposes a unified framework for different time-sensitive, global optimization problems, and solves them in a distributed manner using Lagrangian duality. The framework uses global viewpoints to assign intermediate deadlines, taking resource contention among tasks into consideration. For soft real-time tasks, the proposed framework effectively addresses the deadline assignment problem while maximizing the aggregate quality of service. For hard real-time tasks, we show that existing heuristic solutions to the deadline assignment problem can be incorporated into the proposed framework, enriching their mathematical interpretation

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Adaptive Quality of Service Control in Distributed Real-Time Embedded Systems

An increasing number of distributed real-time embedded systems face the critical challenge of providing Quality of Service (QoS) guarantees in open and unpredictable environments. For example, such systems often need to enforce CPU utilization bounds on multiple processors in order to avoid overload and meet end-to-end dead-lines, even when task execution times deviate significantly from their estimated values or change dynamically at run-time. This dissertation presents an adaptive QoS control framework which includes a set of control design methodologies to provide robust QoS assurance for systems at different scales. To demonstrate its effectiveness, we have applied the framework to the end-to-end CPU utilization control problem for a common class of distributed real-time embedded systems with end-to-end tasks. We formulate the utilization control problem as a constrained multi-input-multi-output control model. We then present a centralized control algorithm for small or medium size systems, and a decentralized control algorithm for large-scale systems. Both algorithms are designed systematically based on model predictive control theory to dynamically enforce desired utilizations. We also introduce novel task allocation algorithms to ensure that the system is controllable and feasible for utilization control. Furthermore, we integrate our control algorithms with fault-tolerance mechanisms as an effective way to develop robust middleware systems, which maintain both system reliability and real-time performance even when the system is in face of malicious external resource contentions and permanent processor failures. Both control analysis and extensive experiments demonstrate that our control algorithms and middleware systems can achieve robust utilization guarantees. The control framework has also been successfully applied to other distributed real-time applications such as end-to-end delay control in real-time image transmission. Our results show that adaptive QoS control middleware is a step towards self-managing, self-healing and self-tuning distributed computing platform