Adaptive Quality of Service Engine with Dynamic Queue Control

While the current routing and congestion control algorithms in use today are often sufficient for networks with relatively static topology, these algorithms may not be sufficient for military networks where a certain level of quality of service (QoS) needs to be achieved to complete a mission. Current networking technology limits a network\u27s ability to adapt to changes and interactions in the network, often resulting in sub-optimal performance. This research investigates the use of queue size predictions to create a network controller to optimize computer networks. These queue size predictions are made possible through the use of Kalman filters to detect network congestion. The premise is that intelligent agents can use such predictions to form context-aware, cognitive processes for managing communication in mobile networks. The network controller designed and implement in this thesis will take in the current and predicted network conditions and make intelligent choices to optimize the network

Energy-optimal schedules of real-time jobs with hard deadlines

In this thesis, we develop algorithms that make optimal use of frequency scaling to schedule jobs with real??time requirements. Dynamic voltage scaling is a technique used to reduce energy consumption in wide variety of systems. Reducing supply voltage results in a lower processor clock speed since the supply voltage has a proportional dependency on the clock speed of the processing system. In hard real??time systems, unduly reducing the speed of processor could result in jobs missing their deadlines. The voltage scaling in such systems should therefore take into consideration the deadline of jobs. This thesis will address two questions: First, given a set of discrete frequency levels, we determine an energy-optimal sched- ule of a given set of real-time jobs. We model the problem as a network flow graph and use linear programming to solve the problem. The schedule can be used on processors with discrete frequencies (like Transmeta Efficeon Processor and AMD Turion 64 Processor). Second, given a set of real??time jobs, we determine a set of optimal frequency levels which minimizes the energy consumption while meeting all the timing con- straints. This can be used to model variable-capacity facilities in operations re- search, where the capacity of the facility can be controlled at a cost

Network Flow Techniques for Dynamic Voltage Scaling in Hard Real-Time Systems

Energy consumption is an important performance parameter for portable and wireless embedded systems. However, energy consumption must be carefully balanced with real-time responsiveness in hard realtime systems. In this paper, we present two offline dynamic voltage scaling (DVS) schemes for dynamic power management in such systems. In the first method, we develop a generalized network flow (GNF) model for the uniprocessor DVS problem and solve it optimally using an efficient network flow algorithm. The proposed method outperforms existing DVS schemes for several popular embedded processors where the number of processor speeds is limited to a few values. The solutions for the GNF model provide theoretical lower bounds on energy consumption using DVS in hard real-time systems. We also describe a minimum-cost network flow model whose solutions are near-optimal. The minimum-cost models perform at par with competing methods for processor models with a large range of operating voltages, and better than them for processor models with a limited set of operating voltages

Network flow techniques for dynamic voltage scaling in hard real-time systems

Abstract—Energy consumption is an important performance parameter for portable and wireless embedded systems. However, energy consumption must be carefully balanced with real-time responsiveness in hard real-time systems. In this paper, we present two offline dynamic voltage scaling (DVS) schemes for dynamic power management in such systems. In the first method, we develop a generalized network flow (GNF) model for the uniprocessor DVS problem and solve it optimally using an efficient network flow algorithm. The proposed method outperforms existing DVS schemes for several popular embedded processors where the number of processor speeds is limited to a few values. The solutions for the GNF model provide theoretical lower bounds on energy consumption using DVS in hard real-time systems. We also describe a minimum-cost network flow model whose solutions are near-optimal. The minimum-cost models perform at par with competing methods for processor models with a large range of operating voltages, and better than them for processor models with a limited set of operating voltages. Index Terms—Deadlines, dynamic power management, embedded systems, low-energy, low-power, network flow models, real-time operating systems. I