Energy Efficient Scheduling Of Parallel Real Time Tasks On Heterogeneous Multicore Systems
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Energy Efficient Scheduling of Parallel Real-time Tasks on Heterogeneous Multicore Systems
Cyber physical systems (CPS) and Internet of Objects (IoT) are generating an unprecedented volume and variety of data that needs to be collected and stored on the cloud before being processed. By the time the data makes its way to the cloud for analysis, the opportunity to trigger a reply might be late. One approach to solve this problem is to analyze the most time-sensitive data at the network edge, close to where it is generated. Thus, only the pre-processed results are sent to the cloud. This computation model is know as *Fog Computing* or *Edge computing*. Critical CPS applications using the fog computing model may have real-time constraints because results must be delivered in a pre-determined time window. Furthermore, in many relevant applications of CPS, the processing can be parallelized by applying the same processing on different sub-sets of data at the same time by the mean parallel programming techniques. This allow to achieve a shorter response time, and then, a larger slack time, which can be used to reduce energy consumption. In this thesis we focus on the problem of scheduling a set of parallel tasks on multicore processors, with the goal of reducing the energy consumption while all deadlines are met. We propose several realistic task models on architectures with identical and heterogeneous cores, and we develop algorithms for allocating threads to processors, select the core frequencies, and perform schedulability analysis. The proposed task models can be realized by using OpenMP-like APIs.
Scheduling Parallel Applications on Heterogeneous Distributed Systems
This book focuses on scheduling algorithms for parallel applications on heterogeneous distributed systems, and addresses key scheduling requirements – high performance, low energy consumption, real time, and high reliability – from the perspectives of both theory and engineering practice. Further, it examines two typical application cases in automotive cyber-physical systems and cloud systems in detail, and discusses scheduling challenges in connection with resource costs, reliability and low energy. The book offers a comprehensive and systematic treatment of high-performance, low energy consumption, and high reliability issues on heterogeneous distributed systems, making it a particularly valuable resource for researchers, engineers and graduate students in the fields of computer science and engineering, information science and engineering, and automotive engineering, etc. The wealth of motivational examples with figures and tables make it easy to understand.
Energy-aware Scheduling on Multiprocessor Platforms
Author: Dawei Li
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-10-19
Multiprocessor platforms play important roles in modern computing systems, and appear in various applications, ranging from energy-limited hand-held devices to large data centers. As the performance requirements increase, energy-consumption in these systems also increases significantly. Dynamic Voltage and Frequency Scaling (DVFS), which allows processors to dynamically adjust the supply voltage and the clock frequency to operate on different power/energy levels, is considered an effective way to achieve the goal of energy-saving. This book surveys existing works that have been on energy-aware task scheduling on DVFS multiprocessor platforms. Energy-aware scheduling problems are intrinsically optimization problems, the formulations of which greatly depend on the platform and task models under consideration. Thus, Energy-aware Scheduling on Multiprocessor Platforms covers current research on this topic and classifies existing works according to two key standards, namely, homogeneity/heterogeneity of multiprocessor platforms and the task types considered. Under this classification, other sub-issues are also included, such as, slack reclamation, fixed/dynamic priority scheduling, partition-based/global scheduling, and application-specific power consumption, etc.