The energy management for embedded real-time systems is crucial due to their restricted power supplies. With the advancement of technologies, the static energy consumption of the embedded systems that is caused by their leakage power is growing. Thus, a number of research works have started focusing on reducing the static energy consumption by making the systems transit into low-power states, wherein some hardware components are temporarily shut down. Specifically, when a processor is idling, they attempt to set the processor into one of several low-power states. To make a processor remain in the low-power state as long as possible to minimize the energy consumption, the idle time should be maximally clustered. At the same time, in order to satisfy the real-time constraints of the tasks, the length of the clustered idle time should be estimated accurately. To achieve our objective, we propose energy-efficient real-time scheduling algorithms on symmetric homogeneous multiprocessors with a dynamic power management scheme for periodic real-time tasks. The proposed algorithms rely on a flow network model that effectively helps to cluster the idle time while respecting the real-time constraints. In our experimental evaluation, the proposed algorithms consume a comparable static energy to an existing off-line scheme that is the only suitable existing algorithm in the problem domain. Furthermore, we show that the proposed algorithms consume less static energy than the existing one in a case where the total workload of the given task set is low and the task completion is earlier than expected.INDEX TERMS Dynamic power management, energy-aware algorithm, flow network problem, multiprocessor unit, micro-controller unit, real-time scheduling.
Recent advances in mobile technologies have led to improved quality of multimedia services (QoMS) in a variety of mobile devices. Because multimedia has become a major form of content consumption for mobile users, satisfying user expectation on QoMS in energy-restricted mobile devices is critical. This need has motivated us to develop an aggressive and conservative low-power work demand analysis with multi-granularity (lpWDA-MG-AGG/CON) algorithm, designed to minimise power consumption in mobile devices by utilising a dynamic voltage scaling technique while simultaneously ensuring QoMS based on a resource reservation scheme. In addition, the authors analytically showed the schedulability of the proposed scheme under the rate monotonic scheduling policy. For performance evaluation, the authors implemented the two lpWDA-MG algorithms and several existing algorithms in a Linux operating system. Specifically, the authors measured power consumption with a power metre and determined that the proposed algorithms consume about 40% less dynamic power than the other existing algorithms. Moreover, the authors found that the proposed algorithms ensure acceptable QoMS.
To support ever-chainging user needs such as large storage volumes, web search, and high-performance computing, numerous companies have expanded their systems to cloud computing servers. Cloud environment systems generally consume large amounts of electrical power, leading to tremendously high operational costs. In addition, they require computing infrastructures to run various real-time applications such as financial analysis, cloud gaming, and web-based real-time services. To represent performance guarantees, the negotiated agreements in real-time computing, expressed as deadline (or latency), can be specified by service level agreements of cloud services between users and cloud server providers. Thus, a number of research works have started focusing on reducing the energy consumption and simultaneously satisfying the temporal constraint in a cloud environment. Although we previously proposed an optimal real-time scheduling algorithm for multiprocessors, it is difficult to use it for cloud environments handling a large number of cloud services because of the high computational complexity of Ω(N 3 logN ), where N is the number of tasks. Thus, we introduce a real-time task scheduling algorithm for cloud computing servers, which alleviates the computational complexity of O(N 2 ) from the complexity of the previous algorithm using a novel flow network-based optimization method. To the best of our knowledge, our scheduling algorithm in a cloud environment, which ensures optimality for real-time tasks and achieves energy savings using dynamic power management simultaneously, is the first in the problem domain. We show that the proposed scheduling algorithm guarantees an optimal schedule for real-time tasks and achieves energy savings simultaneously. Our experimental results show that the proposed algorithm outperforms the latest existing algorithms in terms of both time complexity and energy efficiency.INDEX TERMS Cloud computing, dynamic power management, energy-aware algorithm, flow network problem, optimal scheduling, real-time computing
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