Location Aided Energy Balancing Strategy in Green Cellular Networks

Most cellular network communication strategies are focused on data traffic scenarios rather than energy balance and efficient utilization. Thus mobile users in hot cells may suffer from low throughput due to energy loading imbalance problem. In state of art cellular network technologies, relay stations extend cell coverage and enhance signal strength for mobile users. However, busy traffic makes the relay stations in hot area run out of energy quickly. In this paper, we propose an energy balancing strategy in which the mobile nodes are able to dynamically select and hand over to the relay station with the highest potential energy capacity to resume communication. Key to the strategy is that each relay station merely maintains two parameters that contains the trend of its previous energy consumption and then predicts its future quantity of energy, which is defined as the relay station potential energy capacity. Then each mobile node can select the relay station with the highest potential energy capacity. Simulations demonstrate that our approach significantly increase the aggregate throughput and the average life time of relay stations in cellular network environment.Comment: 6 pages, 5 figures. arXiv admin note: text overlap with arXiv:1108.5493 by other author

Quantitative Performance Evaluation of Uncertainty-Aware Hybrid AADL Designs Using Statistical Model Checking

International audience— Architecture Analysis and Design Language (AADL) is widely used for the architecture design and analysis of safety-critical real-time systems. Based on the Hybrid annex which supports continuous behavior modeling, Hybrid AADL enables seamless interactions between embedded control systems and continuous physical environments. Although Hybrid AADL is promising in dependability prediction through analyzable architecture development, the worst-case performance analysis of Hybrid AADL designs can easily lead to an overly pessimistic estimation. So far, Hybrid AADL cannot be used to accurately quantify and reason the overall performance of complex systems which interact with external uncertain environments intensively. To address this problem, this paper proposes a statistical model checking based framework that can perform quantitative evaluation of uncertainty-aware Hybrid AADL designs against various performance queries. Our approach extends Hybrid AADL to support the modeling of environment uncertainties. Furthermore, we propose a set of transformation rules that can automatically translate AADL designs together with designers' requirements into Networks of Priced Timed Automata (NPTA) and performance queries, respectively. Comprehensive experimental results on the Movement Authority (MA) scenario of Chinese Train Control System Level 3 (CTCS-3) demonstrate the effectiveness of our approach

Reliability-driven energy-efficient task scheduling for multiprocessor real-time systems

This paper proposes a reliability-driven task scheduling scheme for multiprocessor real-time embedded systems that optimizes system energy consumption under stochastic fault occurrences. The task scheduling problem is formulated as an integer linear program where a novel fault adaptation variable is introduced to model the uncertainties of fault occurrences. The proposed scheme, which considers both the dynamic power and the leakage power, is able to handle the scheduling of independent tasks and tasks with precedence constraints, and is capable of scheduling tasks with varying deadlines. Experimental results have demonstrated that the proposed reliability-driven parallel scheduling scheme achieves energy savings of more than 15% when compared to the approach of designing for the corner case of fault occurrences. © 2006 IEEE

Design of a hard real-time multi-core testbed for energy measurement

This paper presents a systematic methodology for designing a hard real-time multi-core testbed to validate and benchmark various rate monotonic scheduling (RMS)-based task allocation and scheduling schemes in energy consumption. The hard real-time multi-core testbed comprises Intel Core Duo T2500 processor with dynamic voltage scaling (DVS) capability and runs the Linux Fedora 8 operating system supporting soft real-time scheduling. POSIX threads API and Linux FIFO scheduling policy are utilized to facilitate the design and Dhrystone-based tasks are generated to verify the design. A LabView-based DAQ system is designed to measure the energy consumption of CPU and system board of the testbed. A case study of task allocation and scheduling algorithms is also presented that aim to optimize the schedule feasibility and energy consumed by the processor and memory module in the multi-core platform. The experience from the implementation is summarized to serve as potential guidelines for other researchers and practitioners. © 2011 Elsevier Ltd. All rights reserved

Uncertainty-aware household appliance scheduling considering dynamic electricity pricing in smart home

High quality demand side management has become indispensable in the smart grid infrastructure for enhanced energy reduction and system control. In this paper, a new demand side management technique, namely, a new energy efficient scheduling algorithm, is proposed to arrange the household appliances for operation such that the monetary expense of a customer is minimized based on the time-varying pricing model. The proposed algorithm takes into account the uncertainties in household appliance operation time and intermittent renewable generation. Moreover, it considers the variable frequency drive and capacity-limited energy storage. Our technique first uses the linear programming to efficiently compute a deterministic scheduling solution without considering uncertainties. To handle the uncertainties in household appliance operation time and energy consumption, a stochastic scheduling technique, which involves an energy consumption adaptation variable β, is used to model the stochastic energy consumption patterns for various household appliances. To handle the intermittent behavior of the energy generated from the renewable resources, the offline static operation schedule is adapted to the runtime dynamic scheduling considering variations in renewable energy. The simulation results demonstrate the effectiveness of our approach. Compared to a traditional scheduling scheme which models typical household appliance operations in the traditional home scenario, the proposed deterministic linear programming based scheduling scheme achieves up to 45% monetary expense reduction, and the proposed stochastic design scheme achieves up to 41% monetary expense reduction. Compared to a worst case design where an appliance is assumed to consume the maximum amount of energy, the proposed stochastic design which considers the stochastic energy consumption patterns achieves up to 24% monetary expense reduction without violating the target trip rate of 0.5%. Furthermore, the proposed energy consumption scheduling algorithm can always generate the scheduling solution within 10 seconds, which is fast enough for household appliance applications. © 2010-2012 IEEE

Fixed-priority allocation and scheduling for energy-efficient fault tolerance in hard real-time multiprocessor systems

Energy-efficient task allocation and scheduling schemes with deterministic fault-tolerance capabilities are proposed for symmetric multiprocessor systems executing tasks with hard real-time constraints. The proposed heuristic is proven to achieve energy savings by optimally balancing application workload among processors in a system. Based on the observation that fault-free operation is expected to remain dominant in the near future and the probability of the worst case faults is low, an optimistic fault-tolerant heuristic is then proposed to achieve maximum energy savings in the absence of faults while degrading gradually to meet application timing requirements in the worst case of faults. Simulation results show that compared to state-of-art allocation and scheduling schemes proposed heuristic achieves average energy savings of up to 70%. It is also shown that optimistic approach is more resilient to variations in application utilizations and fault occurrences beyond system specifications. © 2008 IEEE