Energy Aware Real Time Scheduling On Heterogeneous And Homogeneous Platforms In The Era Of Parallel Computing
DOWNLOAD
Download Energy Aware Real Time Scheduling On Heterogeneous And Homogeneous Platforms In The Era Of Parallel Computing PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Energy Aware Real Time Scheduling On Heterogeneous And Homogeneous Platforms In The Era Of Parallel Computing book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Energy Aware Real Time Scheduling On Heterogeneous And Homogeneous Platforms In The Era Of Parallel Computing
DOWNLOAD
Author : Ashik Ahmed Bhuiyan
language : en
Publisher:
Release Date : 2021
Energy Aware Real Time Scheduling On Heterogeneous And Homogeneous Platforms In The Era Of Parallel Computing written by Ashik Ahmed Bhuiyan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Multi-core processors increasingly appear as an enabling platform for embedded systems, e.g., mobile phones, tablets, computerized numerical controls, etc. The parallel task model, where a task can execute on multiple cores simultaneously, can efficiently exploit the multi-core platform’s computational ability. Many computation-intensive systems (e.g., self-driving cars) that demand stringent timing requirements often evolve in the form of parallel tasks. Several real-time embedded system applications demand predictable timing behavior and satisfy other system constraints, such as energy consumption.
Energy Aware Scheduling On Multiprocessor Platforms
DOWNLOAD
Author : Springer
language : en
Publisher:
Release Date : 2012-10-20
Energy Aware Scheduling On Multiprocessor Platforms written by Springer and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-10-20 with categories.
Energy Aware Scheduling On Heterogeneous Processors Using Machine Learning And Mobile Agents
DOWNLOAD
Author : Venkateswaran Shekar
language : en
Publisher:
Release Date : 2011
Energy Aware Scheduling On Heterogeneous Processors Using Machine Learning And Mobile Agents written by Venkateswaran Shekar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Algorithms categories.
This thesis focuses on scheduling tasks in a heterogeneous environment with DVS enabled processors to minimize both execution time and energy consumed. The proposed algorithm, called Energy Dynamic Level Scheduling (EDLS), favors low-energy consuming processors by introducing a cost factor that affects scheduling decisions.
Realenergy
DOWNLOAD
Author : Wei Song
language : en
Publisher:
Release Date : 2009
Realenergy written by Wei Song and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Computer algorithms categories.
Intelligent Decision Systems In Large Scale Distributed Environments
DOWNLOAD
Author : Pascal Bouvry
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-06-19
Intelligent Decision Systems In Large Scale Distributed Environments written by Pascal Bouvry and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-06-19 with Computers categories.
One of the most challenging issues for the intelligent decision systems is to effectively manage the large-scale complex distributed environments such as computational clouds, grids, ad hoc and P2P networks, under the different types of users, their relations, and real-world uncertainties. In this context the IT resources and services usually belong to different owners (institutions, enterprises, or individuals) and are managed by different administrators. These administrators conform to different sets of rules and configuration directives, and can impose different usage policies on the system users. Additionally, uncertainties are presented in various types of information that are incomplete, imprecise, fragmentary or overloading, which hinders the full and precise determination of the evaluation criteria, their subsequent and selection, the assignment scores, and eventually the final integrated decision result. This book presents new ideas, analysis, implementations and evaluation of the next generation intelligent techniques for solving complex decision problems in large-scale distributed systems. In 15 chapters several important formulations of the decision problems in heterogeneous environments are identified and a review of the recent approaches, from game theoretical models and computational intelligent techniques, such as genetic, memetic and evolutionary algorithms, to intelligent multi-agent systems and networking are presented. We believe that this volume will serve as a reference for the students, researchers and industry practitioners working in or are interested in joining interdisciplinary works in the areas of intelligent decision systems using emergent distributed computing paradigms. It will also allow newcomers to grasp key concerns and potential solutions on the selected topics.
Energy Aware Task Scheduling On Heterogeneous Systems
DOWNLOAD
Author : Rehab Farouk Abdel-Kader
language : en
Publisher:
Release Date : 2007
Energy Aware Task Scheduling On Heterogeneous Systems written by Rehab Farouk Abdel-Kader and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Computer scheduling categories.
Improving The Energy Efficiency Of Modern Computing Platforms Using High Resolution Real Time Energy Measurements
DOWNLOAD
Author : Digvijay Singh
language : en
Publisher:
Release Date : 2014
Improving The Energy Efficiency Of Modern Computing Platforms Using High Resolution Real Time Energy Measurements written by Digvijay Singh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
High-performance computing platforms have become critical in meeting the demands of modern computing applications. Rising performance requirements in a broad range of platforms from mobile devices to server systems combined with the proliferation of these high-performance computing platforms has increased the energy costs incurred and lead to an exigent need for improvement in platform energy efficiency. This requires infrastructure for monitoring of energy consumption and methods to reduce the platform energy costs. In this dissertation, we present a new measurement infrastructure to provide real-time event-synchronized platform energy measurements, demonstration of these energy measurement capabilities through application to network data transport and an operating system task scheduler that utilizes these energy measurements to greatly improve energy efficiency for multi-core computing platforms. The energy measurement infrastructure is integrated at the platform level and provides event-synchronized energy measurements for the complete platform along with important components such as the CPU, memory modules, secondary storage, peripherals and others. Furthermore, since modern secondary storage devices have buffering mechanisms that defer data write operations, the energy consumption of these operations is modeled and the model is integrated into the platform to characterize the impact of deferred operations. The energy measurement capabilities are demonstrated through application to network data transport where a data file is transported over a network link. The data compression scheme is dynamically selected using real-time energy measurements during transport of the data file to enable adaptation to the dynamic system and network conditions. The energy cost of transporting the data file is significantly reduced through the use of this energy aware compression algorithm. A novel task scheduler is presented and is designed to improve energy efficiency of multiprocessing platforms. It utilizes real-time energy measurements along with CPU performance monitoring units to identify inefficient tasks that suffer from co-run degradation due to resource contention. These inefficient tasks have their scheduling priority modified to avoid contention. Evaluation of the scheduler demonstrates large energy and execution time benefits on a quad-core platform.
Scheduling For Parallel Processing
DOWNLOAD
Author : Maciej Drozdowski
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-03-14
Scheduling For Parallel Processing written by Maciej Drozdowski and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-03-14 with Computers categories.
Overview and Goals This book is dedicated to scheduling for parallel processing. Presenting a research ?eld as broad as this one poses considerable dif?culties. Scheduling for parallel computing is an interdisciplinary subject joining many ?elds of science and te- nology. Thus, to understand the scheduling problems and the methods of solving them it is necessary to know the limitations in related areas. Another dif?culty is that the subject of scheduling parallel computations is immense. Even simple search in bibliographical databases reveals thousands of publications on this topic. The - versity in understanding scheduling problems is so great that it seems impossible to juxtapose them in one scheduling taxonomy. Therefore, most of the papers on scheduling for parallel processing refer to one scheduling problem resulting from one way of perceiving the reality. Only a few publications attempt to arrange this ?eld of knowledge systematically. In this book we will follow two guidelines. One guideline is a distinction - tween scheduling models which comprise a set of scheduling problems solved by dedicated algorithms. Thus, the aim of this book is to present scheduling models for parallel processing, problems de?ned on the grounds of certain scheduling models, and algorithms solving the scheduling problems. Most of the scheduling problems are combinatorial in nature. Therefore, the second guideline is the methodology of computational complexity theory. Inthisbookwepresentfourexamplesofschedulingmodels. Wewillgodeepinto the models, problems, and algorithms so that after acquiring some understanding of them we will attempt to draw conclusions on their mutual relationships.
Energy Aware Job Scheduling And Consolidation Approaches For Workflows In Cloud
DOWNLOAD
Author : Mustafa Khaleel
language : en
Publisher:
Release Date : 2016
Energy Aware Job Scheduling And Consolidation Approaches For Workflows In Cloud written by Mustafa Khaleel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Cloud computing categories.
Cloud computing offers several types of on-demand and scalable access to software, computing resources, and storage services through web browsers based on pay-as-you-go model. In order to meet the growing demand of active users and reduce the skyrocketing cost of electricity for powering the data centers, cloud service providers are highly motivated to implement performance guaranteed and cost-effective job schedulers. Many researchers have been focusing on scheduling jobs with high performance, and their primary concern has been execution time considerations. As a result of this thinking, little attention was paid to energy consumption and energy costs. However, nowadays energy cost has gained more and more attention from the service providers. This new reality has posed many new challenges for providers who are both concerned about meeting the execution time constraints and reducing energy costs. In recent years, there has been a growing body of research which focused on improving resource utilization by adopting new strategies and ideas that can be used to improve energy efficiency while maintaining high system throughput. One of these strategies used is known as task consolidation. This is one of the most effective techniques for increasing system-wide resource utilization. The research clearly shows that by switching off idle servers to sleep mode a vast amount of energy can be saved. In this research, a job scheduling approach called multi-procedure energy-aware heuristic scientific workflow scheduling method referred to as Time and Energy Aware Scheduling (TEAS) is proposed to tackle an energy optimization problem. This method is based on a rigorous cost and energy model that could be used to maximize resource utilization performance. The objectives focused on maximizing resource utilization and minimizing power consumption without compromising Quality of Service (QoS) such as workflow response time specified in the Service Level Agreements (SLA). The scientific applications are formulated as Directed Acycle Graph (DAG)-structured workflow to be processed as a group using virtualization techniques over cloud resources. Furthermore, the underlying cloud hardware/Virtual Machine (VM) resource availability is time-dependent because of the dual operation modes of on-demand and reservation. The resource provision and allocation algorithm can be separated into three steps with different objectives. The first step (Datacenter Selection) selects the most efficient data center to execute module applications. The second step (Time and Energy Aware Scheduling Forward Mapping) primarily focuses on estimating the execution time of scheduling a batch of workflows over VMs on underlying cloud servers and the objective is to achieve the minimum End-to-End Delay (EED). The last, and the most important step is related to the energy saving and resource utilization (Time and Energy Aware Scheduling Backward Mapping) which is concerned with minimizing energy consumption. This task is accomplished by restricting CPU usage between double thresholds and keeping the total utilization of the CPU by all the VMs allocated to a single server between these two thresholds. In addition, cloud module could migrate to other servers to either reduce the number of active servers or achieve better performance. In this case, the communication cost would be factored into the energy cost model. The performance of our algorithm is compared to algorithms such as the Pegasus Workflow Management system, Minimum Power Consumption Minimum Power Consumption (MPC-MPC) algorithm, and Greedy algorithm. The simulation results show that the Time and Energy Aware Scheduling heuristic can significantly decrease the power consumption of cloud servers with high resource utilization for the underlying clouds.
Real Time Scheduling For Energy Haversting Embedded Systems
DOWNLOAD
Author : Younès Chandarli
language : en
Publisher:
Release Date : 2014
Real Time Scheduling For Energy Haversting Embedded Systems written by Younès Chandarli and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
In this thesis, we are interested in the real-time fixed-priority scheduling problem of energy-harvesting systems. An energy-harvesting system is a system that can collect the energy from the environment in order to store it in a storage device and then to use it to supply an electronic device. This technology is used in small embedded systems that are required to run autonomously for a very long lifespan. Wireless sensor networks and medical implants are typical applications of this technology. Moreover, most of these devices have to execute many recurrent tasks within a limited time. Thus, these devices are subject to real-time constraints where the correctness of the system depends not only on the correctness of the results but also on the time in which they are delivered. This thesis focuses on the preemptive fixed-task-priority real-time scheduling for such systems in monoprocessor platforms. The problematic here is to find efficient scheduling algorithms and schedulability conditions that check the schedulability of a given task set in a given energy configuration. The first result of this thesis is the proposition of the PFPasap scheduling algorithm. It is an adaptation of the classical fixed-task-priority scheduling to the energy-harvesting context. It consists of executing tasks as soon as possible whenever the energy is sufficient to execute at least one time unit and replenishes otherwise. The replenishment periods are as long as needed to execute one time unit. We prove that PFPasap is optimal but only in the case of non-concrete systems where the first release time of tasks and the initial energy storage unit level are known only at run-time and where all the tasks consume more energy than the replenishment during execution times. A sufficient and necessary schedulability condition for such systems is also proposed. Unfortunately, when we relax the assumption of tasks energy consumption profile, by considering both tasks that consume more energy than the replenishment and the ones that consume less than the replenishment, PFPasap is no longer optimal and the worst-case scenario is no longer the synchronous release of all the tasks, which makes the precedent schedulability test only necessary. To cope with this limitation, we propose to upper bound tasks worst-case response time in order to build sufficient schedulability conditions instead of exact ones. Regarding algorithms optimality, we explore different ideas in order to build an optimal algorithm for the general model of fixed-task-priority tasks by considering all types of task sets and energy consumption profiles. We show through some counter examples the difficulty of finding such an algorithm and we show that most of intuitive scheduling algorithms are not optimal. After that, we discuss the possibility of finding such an algorithm. In order to better understand the scheduling problematic of fixed-priority scheduling for energy-harvesting systems, we also try to explore the solutions of similar scheduling problematics, especially the ones that delay executions in order to guarantee some requirements. The thermal-aware scheduling is one of these problematics. It consists of executing tasks such that a maximum temperature is never exceeded. This may lead to introduce additional idle times to cool down the system in order to prevent reaching the maximum temperature. As a first step, we propose in this thesis to adapt the solutions proposed for energy-harvesting systems to the thermal-aware model. Thus, we adapt the PFPasap algorithm to respect the thermal constraints and we propose a sufficient schedulability analysis based on worst-case response time upper bounds. Finally, we present YARTISS: the simulation tool used to evaluate the theoretical results presented in this dissertation.