As the energy consumption of embedded multiprocessor systems becomes increasingly prominent, the real-time energy-efficient scheduling in multiprocessor systems becomes an urgent problem to reduce the system energy co...
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As the energy consumption of embedded multiprocessor systems becomes increasingly prominent, the real-time energy-efficient scheduling in multiprocessor systems becomes an urgent problem to reduce the system energy consumption while meeting real-time constraints. For a multiprocessor with independent DVFS and DPM at each processor, this paper proposes an energy-efficient real-time scheduling algorithm named LRE-DVFS-EACH, based on LRE-TL which is an optimal real-time scheduling algorithm for sporadic tasks. LRE-DVFS-EACH utilizes the concept of TL plane and the idea of fluid scheduling to dynamically scale the voltage and frequency of processors at the initial time of each TL plane as well as the release time of a sporadic task in each TL plane. Consequently, LRE-DVFS-EACH can obtain a reasonable tradeoff between the real-time constraints and the energy saving. LRE-DVFS-EACH is also adaptive to the change of workload caused by the dynamic release of sporadic tasks, which can obtain more energy savings. The experimental results show that compared with existing algorithms, LRE-DVFS-EACH can not only guarantee the optimal feasibility of sporadic tasks, but also achieve more energy savings in all cases, especially in the case of high workloads.
To reduce the energy consumption and build a sustainable computer infrastructure now becomes a major goal of the high performance community. A number of research projects have been carried out in the field of energy-a...
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To reduce the energy consumption and build a sustainable computer infrastructure now becomes a major goal of the high performance community. A number of research projects have been carried out in the field of energy-aware high performance computing. This paper is devoted to categorize energy-aware computing methods for the high-end computing infrastructures, such as servers, clusters, data centers, and Grids/Clouds. Based on a taxonomy of methods and system scales, this paper reviews the current status of energy-aware HPC research and summarizes open questions and research directions of software architecture for future energy-aware HPC studies.
Analyzing and contracting of E-Business service processes is a crucial step between discovery and execution of Semantic Web Services. This paper proposes a service modelling framework consists of constraint-based serv...
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Analyzing and contracting of E-Business service processes is a crucial step between discovery and execution of Semantic Web Services. This paper proposes a service modelling framework consists of constraint-based service contract, agent style communication messages and service process specification. Service contract is used as shared knowledge between participants and instrument for advertising and requesting of services. The Constraint Process Specification proposed is capable of describing complex data and control-flow restrictions as well as data content restrictions. A state transition based operational semantic for this framework is given. A virtual machine is built for analyzing and contracting of services. We illustrate the expressiveness of this framework within an online shopping scenario.
Extensive data generated by peers of nodes in wireless sensor networks (WSNs) needs to be analysed and processed in order to extract information that is meaningful to the user. Data processing techniques that achieve ...
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Energy-efficient computing has now become a key challenge not only for data-center operations, but also for many other energy-driven systems, with the focus on reducing of all energy-related costs, and operational exp...
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Energy-efficient computing has now become a key challenge not only for data-center operations, but also for many other energy-driven systems, with the focus on reducing of all energy-related costs, and operational expenses, as well as its corresponding and environmental impacts. Intelligent machine-learning systems are typically performance driven. For instance, most non-parametric model-free approaches are often known to require high computational cost in order to find the global optima. Designing more accurate machine-learning systems to satisfy the market needs will hence lead to a higher likelihood of energy waste due to the increased computational cost. This paper thus introduces an energy-efficient framework for large-scale data modeling and classification. It can achieve a test error comparable to or better than the state-of-the-art machine-learning models, while at the same time, maintaining a low computational cost when dealing with large-scale data. The effectiveness of the proposed approaches has been demonstrated by our experiments with two large-scale KDD datasets: Mtv-1 and Mtv-2.
Collection, processing, storage and maintenance of samples to facilitate long-term cohort studies in biobanks, requires a system to manage samples in an effective way to prevent sample mix up and loss. Sample identifi...
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Collection, processing, storage and maintenance of samples to facilitate long-term cohort studies in biobanks, requires a system to manage samples in an effective way to prevent sample mix up and loss. Sample identification and tracking system aims to reserve data on the samples at all the times, hence RFID technology is employed. This technology allows information to be stored on the tags attached to tubes containing samples. A system is proposed, designed and prototype in the Prostate Cancer Research Consortium biobank.
Agent-based crowd simulation has been widely applied in the analysis of evacuation safety under disastrous and terrorist circumstances. In crowd simulation, the virtual environment plays an important role in influenci...
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This paper presents a tool for the visualization and simulation of automated stowage plan generation for large containership. The stowage plan is generated automatically based on a heuristic algorithm. The allocation ...
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ISBN:
(纸本)9789881701282
This paper presents a tool for the visualization and simulation of automated stowage plan generation for large containership. The stowage plan is generated automatically based on a heuristic algorithm. The allocation algorithm generates different stowage plan in three main stages which includes basic allocation, special container allocation and various stages of stability adjustments. The purpose of this study is to describe how visualization of stowage plan and simulation of allocation sequences can help in the formulation of new and better stowage allocation algorithm.
Stowage planning for container vessels concerns the core competence of the shipping lines. As such, automated stowage planning has attracted much research in the past two decades, but with few documented successes. In...
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ISBN:
(纸本)9789881701282
Stowage planning for container vessels concerns the core competence of the shipping lines. As such, automated stowage planning has attracted much research in the past two decades, but with few documented successes. In an ongoing project, we are developing a prototype stowage planning system aiming for large containerships. The system consists of three modules: the stowage plan generator, the stability adjustment module, and the optimization engine. This paper mainly focuses on the stability adjustment module. The objective of the stability adjustment module is to check the global ship stability of the stowage plan produced by the stowage plan generator and resolve the stability issues by applying a heuristic algorithm to search for alternative feasible locations for containers that violate some of the stability criteria. We demonstrate that the procedure proposed is capable of solving the stability problems for a large containership with more than 5000 TEUs.
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