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.
Considering video conferencing applications, presented is the first utility accrual (or UA) real-time scheduling algorithm for multiple (m, k)-firm deadline-constrained streams running on multiprocessors, called the global multiprocessor utility accrual scheduling algorithm for (m, k)-firm deadline-constraint multimedia streams (or gMUA-MK). Analytical and experimental studies show that gMUA-MK achieves timeliness performance and relatively high quality of multimedia services compared to existing schemes including gMUA.Introduction: Video conferencing is one of highly dynamic real-time multimedia services, where multiple sources generate multiple streams of video frames, which are transmitted and played back at the intended destinations. Video conferencing allows multiple users to dynamically join and leave the conference, which sometimes imposes excessive computational workloads on the destinations. In its operation, one participant is speaking while all the others are listening for a period of time, which implies that the streams containing the gestures and voice of the speaker are more important than others containing those of listeners. A video stream includes a series of frames constrained by their own deadlines. Intrinsically, a few occasional deadline misses of frames are tolerable without significant degradation of multimedia service quality when it is played back at the destinations. Similar features of highly dynamic real-time multimedia services are also found in remote medical imaging, video surveillance, etc.These features lead us to designing real-time scheduling algorithms considering that 1. an overloaded computational workload should be addressed with graceful performance degradation, 2. the difference in the importance of each stream should be distinguishably considered, and 3. the tolerance to a few occasional deadline misses should be precisely expressed and handled.The urgency of a stream, represented as the deadline, is typically orthogonal to its importance, e.g. the most urgent activity can be the least important, and vice versa. Since meeting the deadlines of all streams is impossible in overloads, completing the most important streams irrespective of stream urgency is often desirable. Thus, a clear distinction has to be made between urgency and importance during overloads. For this reason, we consider the abstraction of time/utility functions (TUFs) that express the utility of completing an application activity as a function of that activity's completion time. Particularly, we consider step TUFs where the height and the length of a TUF represent the importance and deadline of a task, respectively. When the time constraints are specified by TUFs, the scheduling criteria are based on accrued utility, such as maximising the sum of the activities' attained utilities, which is called the utility accrual (UA) criteria. The latest UA scheduling for multiprocessors is the global multiprocessor utility accrual scheduling (gMUA) in [1].Weakly-hard real-time systems are defined as the ones that can...
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
We proposes a power-aware fixed-priority real-time scheduling algorithm for multimedia service, called static voltage scaling algorithm with multi-granularity resource reservation (STATIC-MULTIRSV). The multi-granularity resource reservation was introduced to deliver higher system utilization and better temporal isolation than the traditional approaches in [2]. Based on this, our STATIC-MULTIRSV is designed to reduce the power consumptions while guaranteeing that all I-frames of each video stream meet their deadlines. We implemented the proposed algorithm on top of ChronOS Real-time Linux [6]. We experimentally compared STATIC-MULTIRSV with other existing methods which showed that STATIC-MULTIRSV reduce power consumption by maximum 15% compared to its experimental counterparts.
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