Download or read book Efficient Resource Scheduling in Multiprocessors written by Soumen Chakrabarti and published by . This book was released on 1996 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multiprocessor Scheduling for Real Time Systems written by Sanjoy Baruah and published by Springer. This book was released on 2015-01-02 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive overview of both theoretical and pragmatic aspects of resource-allocation and scheduling in multiprocessor and multicore hard-real-time systems. The authors derive new, abstract models of real-time tasks that capture accurately the salient features of real application systems that are to be implemented on multiprocessor platforms, and identify rules for mapping application systems onto the most appropriate models. New run-time multiprocessor scheduling algorithms are presented, which are demonstrably better than those currently used, both in terms of run-time efficiency and tractability of off-line analysis. Readers will benefit from a new design and analysis framework for multiprocessor real-time systems, which will translate into a significantly enhanced ability to provide formally verified, safety-critical real-time systems at a significantly lower cost.

Download or read book Resource efficient Scheduling of Multiprocessor Mixed criticality Real time Systems written by Jaewoo Lee and published by . This book was released on 2017 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: Timing guarantee is critical to ensure the correctness of embedded software systems that interact with the physical environment. As modern embedded real-time systems evolves, they face three challenges: resource constraints, mixed-criticality, and multiprocessors. This dissertation focuses on resource-efficient scheduling techniques for mixed-criticality systems on multiprocessor platforms.While Mixed-Criticality (MC) scheduling has been extensively studied on uniprocessor platforms, the problem on multiprocessor platforms has been largely open. Multiprocessor algorithms are broadly classified into two categories: global and partitioned. Global scheduling approaches use a global run-queue and migrate tasks among processors for improved schedulability. Partitioned scheduling approaches use per processor run-queues and can reduce preemption/migration overheads in real implementation. Existing global scheduling schemes for MC systems have suffered from low schedulability. Our goal in the first work is to improve the schedulability of MC scheduling algorithms. Inspired by the fluid scheduling model in a regular (non-MC) domain, we have developed the MC-Fluid scheduling algorithm that executes a task with criticality-dependent rates. We have evaluated MC-Fluid in terms of the processor speedup factor: MC-Fluid is a multiprocessor MC scheduling algorithm with a speed factor of 4/3, which is known to be optimal. In other words, MC-Fluid can schedule any feasible mixed-criticality task system if each processor is sped up by a factor of 4/3. Although MC-Fluid is speedup-optimal, it is not directly implementable on multiprocessor platforms of real processors due to the fractional processor assumption where multiple task can be executed on one processor at the same time. In the second work, we have considered the characteristic of a real processor (executing only one task at a time) and have developed the MC-Discrete scheduling algorithm for regular (non-fluid) scheduling platforms. We have shown that MC-Discrete is also speedup-optimal. While our previous two works consider global scheduling approaches, our last work considers partitioned scheduling approaches, which are widely used in practice because of low implementation overheads. In addition to partitioned scheduling, the work considers the limitation of conventional MC scheduling algorithms that drops all low-criticality tasks when violating a certain threshold of actual execution times. In practice, the system designer wants to execute the tasks as much as possible. To address the issue, we have developed the MC-ADAPT scheduling framework under uniprocessor platforms to drop as few low-criticality tasks as possible. Extending the framework with partitioned multiprocessor platforms, we further reduce the dropping of low-criticality tasks by allowing migration of low-criticality tasks at the moment of a criticality switch. We have evaluated the quality of task dropping solution in terms of speedup factor. In existing work, the speedup factor has been used to evaluate MC scheduling algorithms in terms of schedulability under the worst-case scheduling scenario. In this work, we apply the speedup factor to evaluate MC scheduling algorithms in terms of the quality of their task dropping solution under various MC scheduling scenarios. We have derived that MC-ADAPT has a speedup factor of 1.618 for task dropping solution.

Download or read book Energy aware Scheduling on Multiprocessor Platforms written by Dawei Li and published by Springer Science & Business Media. This book was released on 2012-10-19 with total page 67 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Computational Methods for Energy Efficient and Reliable Stochastic Scheduling and Resource Allocation in Heterogeneous Multi processor Systems written by Kres̆imir Mihié and published by . This book was released on 2011 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Efficient Near optimal Scheduling of Multiprocessor Systems written by Marc Thomas Kaufman and published by . This book was released on 1973 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Efficient Resource Management in Heterogeneous Multiprocessor Systems written by Julio M. Merino Vidal and published by . This book was released on 2007 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Partitioning and Scheduling Parallel Programs for Multiprocessors written by Vivek Sarkar and published by Pitman Publishing. This book was released on 1989 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is one of the first to address the problem of forming useful parallelism from potential parallelism and to provide a general solution. The book presents two approaches to automatic partitioning and scheduling so that the same parallel program can be made to execute efficiently on widely different multiprocessors. The first approach is based on a macro dataflow model in which the program is partitioned into tasks at compile time and the tasks are scheduled on processors at run time. The second approach is based on a compile time scheduling model, where both the partitioning and scheduling are performed at compile time. Both approaches have been implemented in partition programs written in the single assignment language SISAL. The inputs to the partitioning and scheduling algorithms are a graphical representation of the parallel program and a list of parameters describing the target multiprocessor. Execution profile information is used to derive compile-time estimates of execution times and data sizes in the program. Both the macro dataflow and compile-time scheduling problems are expressed as optimization problems and are shown to be NP complete in the strong sense. Efficient approximation algorithms for these problems are presented. Finally, the effectiveness of the partitioning and scheduling algorithms is studied by multiprocessor simulations of various SISAL benchmark programs for different target multiprocessor parameters. Vivek Sarkar is a Member of Research Staff at the IBM T. J. Watson Research Center. Partitioning and Scheduling Parallel Programs for Multiprocessing is included in the series Research Monographs in Parallel and Distributed Computing. Copublished with Pitman Publishing.

Download or read book Reliable and Energy Efficient Streaming Multiprocessor Systems written by Anup Kumar Das and published by Springer. This book was released on 2018-01-03 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses analysis, design and optimization techniques for streaming multiprocessor systems, while satisfying a given area, performance, and energy budget. The authors describe design flows for both application-specific and general purpose streaming systems. Coverage also includes the use of machine learning for thermal optimization at run-time, when an application is being executed. The design flow described in this book extends to thermal and energy optimization with multiple applications running sequentially and concurrently.

Download or read book Embedded Multiprocessors written by Sundararajan Sriram and published by CRC Press. This book was released on 2018-10-03 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Techniques for Optimizing Multiprocessor Implementations of Signal Processing Applications An indispensable component of the information age, signal processing is embedded in a variety of consumer devices, including cell phones and digital television, as well as in communication infrastructure, such as media servers and cellular base stations. Multiple programmable processors, along with custom hardware running in parallel, are needed to achieve the computation throughput required of such applications. Reviews important research in key areas related to the multiprocessor implementation of multimedia systems Embedded Multiprocessors: Scheduling and Synchronization, Second Edition presents architectures and design methodologies for parallel systems in embedded digital signal processing (DSP) applications. It discusses application modeling techniques for multimedia systems, the incorporation of interprocessor communication costs into multiprocessor scheduling decisions, and a modeling methodology (the synchronization graph) for multiprocessor system performance analysis. The book also applies the synchronization graph model to develop hardware and software optimizations that can significantly reduce the interprocessor communication overhead of a given schedule. Chronicles recent activity dealing with single-chip multiprocessors and dataflow models This edition updates the background material on existing embedded multiprocessors, including single-chip multiprocessors. It also summarizes the new research on dataflow models for signal processing that has been carried out since the publication of the first edition. Harness the power of multiprocessors This book explores the optimization of interprocessor communication and synchronization in embedded multiprocessor systems. It shows you how to design multiprocessor computer systems that are streamlined for multimedia applications.

Download or read book Efficient scheduling of tasks without full use of processor resources written by Jeffrey M. Jaffe and published by . This book was released on 1979 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Machine Learning Approach for Cloud Data Analytics in IoT written by Sachi Nandan Mohanty and published by John Wiley & Sons. This book was released on 2021-07-14 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning Approach for Cloud Data Analytics in IoT The book covers the multidimensional perspective of machine learning through the perspective of cloud computing and Internet of Things ranging from fundamentals to advanced applications Sustainable computing paradigms like cloud and fog are capable of handling issues related to performance, storage and processing, maintenance, security, efficiency, integration, cost, energy and latency in an expeditious manner. In order to expedite decision-making involved in the complex computation and processing of collected data, IoT devices are connected to the cloud or fog environment. Since machine learning as a service provides the best support in business intelligence, organizations have been making significant investments in this technology. Machine Learning Approach for Cloud Data Analytics in IoT elucidates some of the best practices and their respective outcomes in cloud and fog computing environments. It focuses on all the various research issues related to big data storage and analysis, large-scale data processing, knowledge discovery and knowledge management, computational intelligence, data security and privacy, data representation and visualization, and data analytics. The featured technologies presented in the book optimizes various industry processes using business intelligence in engineering and technology. Light is also shed on cloud-based embedded software development practices to integrate complex machines so as to increase productivity and reduce operational costs. The various practices of data science and analytics which are used in all sectors to understand big data and analyze massive data patterns are also detailed in the book.

Download or read book Task Scheduling for Multiprocessor Systems written by Savina Bansal and published by LAP Lambert Academic Publishing. This book was released on 2010-10 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: Parallel processing has seen incredible growth with increasingly complex scientific & engg applications coming to their realm.Task scheduling, a key issue for its success,& design of efficient algorithms play a crucial role in it.Due to NP-completeness of the problem research efforts are mainly focussed at heuristic-based approaches to generate near-optimal schedules within reasonable time & resource constraints.Most of the heuristics, however, ignore practical aspect and/or compromise too much on complexity or performance.This monograph provides an in-depth insight into these issues.An overview of state-of-the-art scheduling, reflecting changing paradigms, is also provided.In addition, for scheduling DAG structured applications,3 efficient algorithms are presented & analyzed for homogeneous, heterogeneous & mixed-parallel computing environments.Selective-duplication heuristic is introduced and its usefulness in dealing with IPC overhead is shown in comparison to list, clustering and duplication-based heuristics.An A-cube performance model is suggested to comprehensively study behavior of algorithm, application & architecture in heterogeneous environment.

Download or read book Memory Efficient Scheduling for Multicore Real time Systems written by Ahmed Alhammad and published by . This book was released on 2016 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern real-time systems are becoming increasingly complex and requiring significant computational power to meet their demands. Since the increase in uniprocessor speed has slowed down in the last decade, multicore processors are now the preferred way to supply the increased performance demand of real-time systems. A significant amount of work in the real-time community has focused on scheduling solutions for multicore processors for both sequential and parallel real-time tasks. Even though such solutions are able to provide strict timing guarantees on the overall response time of real-time tasks, they rely on the assumption that the worst-case execution time (WCET) of each individual task is known. However, physical shared resources such as main memory and I/O are heavily employed in multicore processors. These resources are limited and therefore subject to contention. In fact, the execution time of one task when run in parallel with other tasks is significantly larger than the execution time of the same task when run in isolation. In addition, the presence of shared resources increases the timing unpredictability due to the conflicts generated by multiple cores. As a result, the adoption of multicore processors for real-time systems is dependent upon solving such sources of unpredictability. In this dissertation, we investigate memory bus contention. In particular, two main problems are associated with memory contention: (1) unpredictable behavior and (2) hindrance of performance. We show how to mitigate these two problems through scheduling. Scheduling is an attractive tool that can be easily integrated into the system without the need for hardware modifications. We adopt an execution model that exposes memory as a resource to the scheduling algorithm. Thus, the theory of real-time multiprocessor scheduling, that has seen significant advances in recent years, can be utilized to schedule both processor cores and memory. Since the real-time workload on multicore processors can be modeled as sequential or parallel tasks, we also study parallel task scheduling by taking memory time into account.

Download or read book Adaptive Resource Management and Scheduling for Cloud Computing written by Florin Pop and published by Springer. This book was released on 2014-11-25 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the First International Workshop on Adaptive Resource Management and Scheduling for Cloud Computing, ARMS-CC 2014, held in Conjunction with ACM Symposium on Principles of Distributed Computing, PODC 2014, in Paris, France, in July 2014. The 14 revised full papers (including 2 invited talks) were carefully reviewed and selected from 29 submissions and cover topics such as scheduling methods and algorithms, services and applications, fundamental models for resource management in the cloud.

Download or read book Job Scheduling Strategies for Parallel Processing written by Dalibor Klusáček and published by Springer. This book was released on 2019-01-12 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the 22nd International Workshop on Job Scheduling Strategies for Parallel Processing, JSSPP 2018, held in Vancouver, Canada, in May 2018. The 7 revised full papers presented were carefully reviewed and selected from12 submissions. The papers cover topics in the fields of design and evaluation of new scheduling approaches. They focus on several interesting problems in resource management and scheduling.

Download or read book Toward Efficient Scheduling for Parallel Real time Tasks on Multiprocessors written by Son Ngoc Dinh and published by . This book was released on 2020 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern real-time applications are becoming more demanding computationally while their temporal requirements, dictated by the physical world, often remain unchanged. This coupled with the increasing prevalence of multiprocessors in real-time systems necessitates that highly computation-demanding real-time tasks need to be parallelized to exploit the parallelism offered by the underlying hardware, in order to satisfy their temporal constraints. Scheduling parallel real-time tasks, however, introduces a new layer of complexity due to the allowance for intra-task parallelism. This dissertation addresses the problem of scheduling parallel real-time tasks in which tasks may (or may not) access shared non-processor resources, such as in-memory buffers or data structures. Specifically, for independent tasks, we propose new scheduling algorithms and schedulability analyses for parallel tasks with these characteristics, under federated and global scheduling.Experimental results show that the proposed algorithms and analyses improve the previously introduced methods. For parallel tasks that may access shared non-processor resources, we present a blocking analysis for two different types of spinlocks; through evaluations, we make a recommendation for a preferable ordering of locks. We also study practical runtime parallel scheduler designs for soft real-time applications and present a design that is more suitable for soft real-time systems.