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A Dynamic distributed Scheduler for DNN Inference on the Edge

A Dynamic Distributed Scheduler for DNN Inference on the Edg...

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parallel Computing and distributed systems (PCDS), international Symposium on

作者： Fei Hu Shivakant Mishra Department of Computer Science University of Colorado Boulder CO USA

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

Edge computing plays a pivotal role in IoT applications that require rapid and secure data processing. How-ever, these applications are typically resource-demanding, and the resources available at the edge are often significantly less than those available in cloud environments. Thus, optimally utilizing these constrained resources is key to fulfilling various application requirements such as low latency, privacy, and cost-effectiveness. Given the dynamic and hybrid characteristics of IoT environments, static scheduling approaches frequently fail to adequately address these complex and varying demands. This paper presents the design, implementation, and evaluation of a dynamic distributed scheduler tailored for DNN inferences on edge computing platforms, based on sophisticated sub-model profiling techniques. This scheduler's core feature is its ability to continuously monitor the state of the IoT infrastructure, dynam-ically adapting the distribution of computing tasks according to real-time environmental and network conditions. Diverging from previous research that typically segments DNN models into two parts, this study proposes a more granular approach that splits models into multiple segments, thereby maximizing the utilization of diverse edge computing resources. The implementation of this scheduler, including integration with a GPU, demonstrates its effectiveness across various DNN models, highlighting its practical utility in leveraging edge computing capabilities.

关键词： Adaptation models Processor scheduling Computational modeling Graphics processing units Prototypes Dynamic scheduling Real-time systems Internet of Things resource management Edge computing

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NFVDC: A flexible and efficient NFV platform for dynamic service chaining

NFVDC: A flexible and efficient NFV platform for dynamic ser...

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international Symposium on parallel and distributed Processing with Applications, ISPA

作者： Qiuwen Lu Qingyun Liu Binxing Fang Institute of Information Engineering Chinese Academy of Sciences Beijing China National Engineering Research Center of Information Security Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Harbin Institute of Technology Shenzhen China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

This paper presents the design and implementation of a novel Network Function Virtualization (NFV) platform, NFVDC, specifically for dynamic service chaining. Unlike traditional service chaining methods, NFVDC supports real-time, reconfigurable service chains that accommodate service functions dynamically joining or leaving the chain. By integrating an advanced traffic steering method, optimized CPU scheduling, and a causal message-passing mechanism, NFVDC addresses challenges in deploying dynamic service chaining, such as traffic steering, resource scheduling, and session state management. Our evaluation demonstrates that NFVDC’s forwarding performance nearly doubles compared to existing solutions when extending service chain lengths to six.

关键词： distributed processing Scalability Refining Dynamic scheduling Robustness Real-time systems Network function virtualization

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Towards an Optimized Heterogeneous distributed Task Scheduler in OpenMP Cluster

Towards an Optimized Heterogeneous Distributed Task Schedule...

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High Performance Computing, Networking, Storage and Analysis, SC-W: workshops of the international Conference for

作者： Rémy Neveu Rodrigo Ceccato Gustavo Leite Guido Araujo Jose M. Monsalve Diaz Hervé Yviquel Instituto de Computação Universidade Estadual de Campinas (UNICAMP) Campinas Brazil

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

This paper addresses the challenges of optimizing task scheduling for a distributed, task-based execution model in OpenMP for cluster computing environments. Traditional OpenMP implementations are primarily designed for shared-memory parallelism and offer limited control over task scheduling. However, improved scheduling mechanisms are critical to achieving performance and portability in distributed and heterogeneous environments. OpenMP Cluster (OMPC) was introduced to overcome these limitations, extending OpenMP with the Heterogeneous Earliest Finish Time (HEFT) task scheduling algorithm tailored for large-scale systems. To improve scheduling and enable better system utilization, the runtime system must resolve challenges such as changes in the application balance, amount of parallelism, and varying communication *** work presents three key contributions: first, the refactoring of the OMPC runtime to unify task scheduling across devices and hosts; second, the optimization of the HEFT-based scheduling algorithm to ensure efficient task execution in distributed environments; and third, an extensive evaluation of Work Stealing and HEFT scheduling mechanisms in real-world clusters. While the HEFT implementation in OMPC is not fully optimized, this work provides a significant step toward improving distributed task scheduling in cluster computing, offering insights and incremental advancements that support the development of scalable and high-performance applications. Results show improvements of up to 24% in scheduling time while opening up to more extensions in the scheduling methods.

关键词： Runtime scheduling algorithms parallel programming Optimal scheduling Cluster computing parallel processing Dynamic scheduling Large-scale systems resource management Iterative methods

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Research on Distribution Job Shop scheduling and Order Allocation Based on Improved Genetic Algorithm

Research on Distribution Job Shop Scheduling and Order Alloc...

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Computer Science and Blockchain (CCSB), international Conference on

作者： Li Zhang School of systems Science Beijing Jiaotong University Beijing China

ISBN: (数字)9798350356502

ISBN: (纸本)9798350356519

distributed manufacturing, known for its cost-effectiveness and efficiency, is a growing trend in the industry. However, existing research on job shop scheduling often neglects machine quantity and workshop-specific requirements. To address this gap, this study proposes a two-stage model that addresses both aspects. The first phase develops a multi-workshop order allocation model, while the second phase constructs a spinning frame assignment model based on actual production needs. An enhanced genetic algorithm, combined with an exact algorithm, is utilized for problem-solving. The exact algorithm ensures precise machine allocation, while the genetic algorithm broadens search scope. A practical case study validates the model and algorithm's effectiveness, showing a capacity increase of approximately 10%, 94.61% higher than manual scheduling, while satisfying factory-specific constraints.

关键词： Job shop scheduling Processor scheduling Conferences Production Manuals Approximation algorithms resource management Spinning Genetic algorithms Textiles

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TS-EoH: An Edge Server Task scheduling Algorithm Based on Evolution of Heuristic

TS-EoH: An Edge Server Task Scheduling Algorithm Based on Ev...

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international Symposium on parallel and distributed Processing with Applications, ISPA

作者： Yatong Wang Yuchen Pei Yuqi Zhao School of Computer Science Central China Normal University Wuhan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

With the widespread adoption of 5G and Internet of Things (IoT) technologies, the low latency provided by edge computing has great importance for real-time processing. However, managing numerous simultaneous service requests poses a significant challenge to maintaining low latency. Current edge server task scheduling methods often fail to balance multiple optimization goals effectively. This paper introduces a novel task-scheduling approach based on Evolutionary Computing (EC) theory and heuristic algorithms. We model service requests as task sequences and evaluate various scheduling schemes during each evolutionary process using Large Language Models (LLMs) services. Experimental results show that our task-scheduling algorithm outperforms existing heuristic and traditional reinforcement learning methods. Additionally, we investigate the effects of different heuristic strategies and compare the evolutionary outcomes across various LLM services.

关键词： scheduling algorithms Heuristic algorithms Reinforcement learning scheduling Real-time systems Servers Internet of Things resource management Low latency communication Optimization

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A Study on Dynamic Task scheduling of a Multi-Tier distributed Query Engine for Power systems

A Study on Dynamic Task Scheduling of a Multi-Tier Distribut...

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Power System and New Energy Technology Innovation Forum (NPSIF), Boao New Power System international Forum -

作者： Zhengrong Wu Hua Li Xun Min Furong Yin China Southern Power Grid Co. Ltd Guangzhou Guangdong China Guizhou Grid Company Limited Guiyang Guizhou China Southern Power Grid Digital Grid Technology (Guangdong) Co. Ltd Guangzhou Guangdong China

ISBN: (数字)9798331504557

ISBN: (纸本)9798331504564

The conventional dynamic task scheduling method of multi-level distributed query engine mainly uses DAG (Directed Acyclic Graph) to generate task allocation scheduler, which is vulnerable to changes in task workflow, resulting in poor scheduling balance indicators. Therefore, an effective dynamic task scheduling method of multi-level distributed query engine is designed for power system. That is to divide multi-level distributed query engine dynamic scheduling tasks for power system, and design multi-level distributed query engine task parallel scheduling algorithm, so as to complete multi - level dynamic scheduling tasks without human query to the engine. The experimental results show that the dynamic task scheduling method of the multi-level distributed query engine has better scheduling effect, better scheduling balance index, reliability, and certain application value. It has made certain contributions to improving the quality of task comprehensive allocation and meeting the requirements of digital operation of power system.

关键词： Technological innovation scheduling algorithms Power system dynamics distributed databases Memory Dynamic scheduling Reliability engineering Power system reliability resource management Engines

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Optimizing MEC System Performance Through M/M/m Queueing Model Analysis

Optimizing MEC System Performance Through M/M/m Queueing Mod...

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international Symposium on parallel and distributed Processing with Applications, ISPA

作者： Jiale Liang Xiaowei Ma Houjian Ding Zhenli He School of Software Yunnan University Kunming China Hongta Tobacco (Group) Co. Ltd China Yunnan Key Laboratory of Software Engineering Yunnan University Kunming China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

This paper investigates the performance of Mobile Edge Computing (MEC) systems using the M/M/m queuing model, focusing on how server configurations and queuing rules affect task response times on edge servers. The study considers two types of tasks with different processing requirements: special tasks and general tasks. The study analyzes the response time under two queuing rules: first-come-first-served (FCFS) and head-of-line (HOL) priority. By evaluating the impact of server execution speed and the number of cores, this work provides insights for optimizing task scheduling and resource management in MEC environments, which ultimately improves system performance and reduces latency.

关键词： Analytical models distributed processing Multi-access edge computing System performance Computational modeling Focusing Servers Time factors resource management Queueing analysis

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Integrating Serverless and DRL for Infrastructure management in Streaming Data Processing across Edge-Cloud Continuum

Integrating Serverless and DRL for Infrastructure Management...

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international Conference on distributed Computing systems workshop

作者： Chinmaya Kumar Dehury Satish Narayana Srirama Institute of Computer Science University of Tartu Tartu Estonia School of Computer and Information Sciences University of Hyderabad Hyderabad India

ISBN: (数字)9798350354713

ISBN: (纸本)9798350354720

Advancements in stream data processing engines (SDPE) increasingly require the use of modern Deep Reinforcement Learning (DRL)-based management strategies and distributed computing paradigms like serverless computing to enhance efficiency and scalability across the edge-cloud continuum. This study explores the challenges associated with this integration, especially with Apache Spark. Despite their advanced capabilities, modern SDPEs still lack full maturity in terms of efficiently managing dynamic resource demands and seamlessly integrating with other technologies. To fill this gap, we propose the architecture of ISIM-SDP, acronym for Integrating Serverless and DRL for Infrastructure management in Streaming Data Processing across edge-cloud continuum. By implementing a DRL-based approach, the system dynamically adjusts resource allocation in real-time, enhancing the flexibility and scalability of computational resources. ISIM-SDP leverages serverless frameworks to reduce operational overhead and improve system responsiveness. Experimental results demonstrate the effectiveness of ISIM-SDP in optimizing resource usage and improving the throughput and latency of stream processing tasks.

关键词： Terminology Scalability Serverless computing Data processing Throughput Dynamic scheduling Deep reinforcement learning

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Simulation of distributed information networks load balancing and resource management 2

Simulation of distributed information networks load balancin...

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2nd international Conference on Conflict management in Global Information Networks, CMiGiN 2022

作者： Shestak, Yanina Myrutenko, Larysa Toliupa, Serhii Onyigwang, Ogbu James Solodovnyk, Valeriia Jinjikhadze, Melkisadeg Yaremko, Halyna Taras Shevchenko National University of Kyiv Bohdana Havrylyshyna 24 Kyiv02000 Ukraine Akaki Tsereteli State University Kutaisi Georgia Lviv Polytechnic National University S. Bandery Street 12 Lviv79013 Ukraine

Current approaches in parallel processing of requests used in modern distributed information systems are considered. The multilevel scheme of balancing the resources of the infrastructure of the distributed information system based on the graph of tasks is constructed. Methods for estimating the optimization of the graph of problems based on the indicators of the length of the normalized graph and the normalized energy distribution are determined. Mathematical modeling is performed for calculation methods by hierarchical structure, algorithms for graph partitioning, methods based on algebraic graph theory, and structuring of the type "Diamond Dags" with uniform, binomial, and geometric distributions. A comparison of the results of mathematical modeling with statistical data showed the level of adequacy of the developed mathematical model. © 2022 Copyright for this paper by its authors.

关键词： Energy utilization

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Improving Real-Time Service Quality Through parallel Monte Carlo Tree Search

Improving Real-Time Service Quality Through Parallel Monte C...

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international workshop on Quality of Service

作者： Jiajia Zhang Shuman Zhuang Yulin Wu School of Computer Science Harbin Institite of Technology(ShenZhen) Shenzhen China

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

In this paper, we present a new algorithm named distributed Multi-root Pipeline MCTS (DMP-MCTS), to improve real-time search efficiency in multi-machine scenarios. By utilizing root parallel technology with significance detection and pipeline pattern for parallel MCTS (3PMCTS), we not only reduce the repetitive computing tasks among subtrees but also allow for flexible operation time based resource allocation. The experiment shows this work achieves better computational performance under linear acceleration conditions, compared with other existing works.

关键词： Monte Carlo methods Pipelines Quality of service parallel processing Real-time systems resource management

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