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26th IEEE international Conference on parallel and distributed systems (IEEE ICPADS)

作者： Du, He Jiang, Xu Yang, Tao Lv, Ming Song Yi, Wang Northeastern Univ Sch Comp Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9781728190747

Locking protocol is an essential component in resource management of real-time systems, which coordinates mutually exclusive accesses to shared resources from different tasks. OpenMP is a promising framework for multi-core real-time embedded systems as well as provides spin locks to protect shared resources. In this paper, we propose a resource model for analyzing OpenMP programs with spin locks. Based on our resource model, we also develop a technique for analyzing the blocking time which impacts the total workload. Notably, the resource model provides detailed resource access behavior of the programs, making our blocking analysis more accurate. Further, we derive the schedulability analysis for real-time OpenMP tasks with spin locks protecting shared resources. Experiments with realistic OpenMP programs are conducted to evaluate the performance of our method.

关键词： OpenMP parallel Tasks Real-Time scheduling Spin Locks

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11th Mining Humanistic Data workshop, MHDW 2022, 7th 5G-Putting Intelligence to the Network Edge workshop, 5G-PINE 2022, 1st workshop on AI in Energy, Building and Micro-Grids, AIBMG 2022, 1st workshop/Special Session on Machine Learning and Big Data in Health Care, ML@HC 2022 and 2nd workshop on Artificial Intelligence in Biomedical Engineering and Informatics, AIBEI 2022 held as parallel events of the 18th IFIP WG 12.5 international Conference on Artificial Intelligence Applications and Innovations, AIAI 2022

11th Mining Humanistic Data Workshop, MHDW 2022, 7th 5G-Putt...

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11th Mining Humanistic Data workshop, MHDW 2022, 7th 5G-Putting Intelligence to the Network Edge workshop, 5G-PINE 2022, 1st workshop on AI in Energy, Building and Micro-Grids, AIBMG 2022, 1st workshop/Special Session on Machine Learning and Big Data in Health Care, ML@HC 2022 and 2nd workshop on Artificial Intelligence in Biomedical Engineering and Informatics, AIBEI 2022 held as parallel events of the 18th IFIP WG 12.5 international Conference on Artificial Intelligence Applications and Innovations, AIAI 2022

ISBN: (纸本)9783031083402

The proceedings contain 35 papers. The special focus in this conference is on Mining Humanistic Data. The topics include: Digitally Assisted Planning and Monitoring of Supportive Recommendations in Cancer Patients;CAIPI in Practice: Towards Explainable Interactive Medical Image Classification;a Deep Q Network-Based Multi-connectivity Algorithm for Heterogeneous 4G/5G Cellular systems;simulating Blockchain Consensus Protocols in Julia: Proof of Work vs Proof of Stake;Maximum Likelihood Estimators on MCMC Sampling Algorithms for Decision Making;employing Natural Language Processing Techniques for Online Job Vacancies Classification;Probabilistic Quantile Multi-step Forecasting of Energy Market Prices: A UK Case Study;proactive Buildings: A Prescriptive Maintenance Approach;performance Meta-analysis for Big-Data Univariate Auto-Imputation in the Building Sector;non-intrusive Diagnostics for Legacy Heat-Pump Performance Degradation;a 5G-Based Architecture for Localization Accuracy;anomaly Detection in Small-Scale Industrial and Household Appliances;an Innovative Software Platform for Efficient Energy, Environmental and Cost Planning in Buildings Retrofitting;deep Learning-Based Segmentation of the Atherosclerotic Carotid Plaque in Ultrasonic Images;An Intelligent Grammar-Based Platform for RNA H-type Pseudoknot Prediction;An Automated 2D U-Net Segmentation Method for the Identification of Cancer Brain Metastases Using MRI Images;The Use of Robotics in Critical Use Cases: The 5G-ERA Project Solution;fundamental Features of the Smart5Grid Platform Towards Realizing 5G Implementation;experimentation Scenarios for Machine Learning-Based resource management;efficient Data management and Interoperability Middleware in Business-Oriented Smart Port Use Cases;5G for the Support of Smart Power Grids: Millisecond Level Precise distributed Generation Monitoring and Real-Time Wide Area Monitoring;monitoring Neurological Disorder Patients via Deep Learning Based Facial Expressions An

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SABO: Dynamic MPI+OpenMP resource Balancer

SABO: Dynamic MPI+OpenMP Resource Balancer

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IEEE/ACM Annual workshop on Emerging parallel and distributed Runtime systems and Middleware (IPDRM)

作者： Cassandra Rocha Barbosa Pierre Lemarinier Guillaume Papauré Marc Pérache Michaël Krajecki Atos Echirolles France LICIIS LRC DIGIT University of Reims Champagne Ardennes Reims France CEA DAM DIF and CEA DAM DIF LRC DIGIT Arpajon France CEA Laboratoire en Informatique Haute Performance pour le Calcul et la simulation Université Paris-Saclay Bruyéres le Châtel France

ISBN: (纸本)9781665475099

Scientific parallel applications often use MPI for inter-node communications and OpenMP for intra-node orches-tration. parallel applications such as particle transport, seismic wave propagation simulator, or Finite-Element applications often exhibit workload imbalance due to their nature of ongoing data movement. These applications usually develop software balancing strategies triggered when some imbalance thresholds are detected to reduce this imbalance. These developments are complex to implement and impact the entire distributed applications' performance by synchronizing and exchanging the load over the network. This paper proposes a method to dynamically detect load imbalance and balance the computation by redistributing OpenMP threads between MPI processes local to the node. With minimal impact on the applications' codes, we demonstrate how this technique can improve the overall applications' performance up to 28% on MiniFE, 17 % on Quicksilver, and 3 % on Ondes3D. We also present its impact when executing multiple nodes and our proposed approach's limitations.

关键词： Runtime Instruction sets Conferences Load management Dynamic scheduling Libraries Finite element analysis

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An appointment scheduling policy for healthcare systems with parallel servers and pre-determined quality of service

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OMEGA-international JOURNAL OF management SCIENCE 2020年 97卷 102095-102095页

作者： Shnits, Boris Bendavid, Illana Marmor, Yariv N. ORT Braude Coll Engn Dept Ind Engn & Management Snunit 51 StPOB 78 IL-2161002 Karmiel Israel

The appointment scheduling problem is well-known in the literature. The use of appointment systems has been adopted widely in many different fields, including service industries and especially healthcare. This research focuses on healthcare systems where patients arrive according to pre-assigned appointments. We consider healthcare systems with several parallel servers, where a given sequence of patients, with randomly distributed service durations and a possibility of no-shows, is to be scheduled. The aim is to minimize the end of day and increase resource utilization while a minimal probability of each appointment starting on time (quality of service) is required. We formulated the problem using mathematical programing and developed a multi-server numerical-based (MSN) algorithm to solve it. We conducted some experimental runs and checked the impact of the problem parameters on the end of day, customers' average waiting time and the percentage of customers that waited for service. We also show how server pooling improves the above system measures. Finally, once the appointments are set, we develop a methodology to determine the shift length so as to balance overtime costs (costs of overtime hours) against undertime costs (costs of regular, unused hours). (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Appointment scheduling Healthcare systems Health service Optimization Simulation

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DFMan: A Graph-based Optimization of Dataflow scheduling on High-Performance Computing systems

DFMan: A Graph-based Optimization of Dataflow Scheduling on ...

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international Symposium on parallel and distributed Processing (IPDPS)

作者： Fahim Chowdhury Francesco Di Natale Adam Moody Kathryn Mohror Weikuan Yu Florida State University Lawrence Livermore National Laboratory

Scientific research and development campaigns are materialized by workflows of applications executing on high-performance computing (HPC) systems. These applications con-sist of tasks that can have inter- or intra-application flows of data to achieve the research goals successfully. These dataflows create dependencies among the tasks and cause resource con-tention on shared storage systems, thus limiting the aggregated I/O bandwidth achieved by the workflow. However, these I/O performance issues are often solved by tedious and manual efforts that demand holistic knowledge about the data dependencies in the workflow and the information about the infrastructure being utilized. Taking this into consideration, we design DFMan, a graph-based dataflow management and optimization framework for maximizing I/O bandwidth by leveraging the powerful storage stack on HPC systems to manage data sharing optimally among the tasks in the workflows. In particular, we devise a graph-based optimization algorithm that can leverage an intuitive graph representation of dataflow- and system-related information, and automatically carry out co-scheduling of task and data placement. According to our experiments, DFMan optimizes a wide variety of scientific workflows such as Hurricane 3D on Cloud Model 1 (CM1), Montage Carina Nebula (NGC3372), and an emulated dataflow kernel of the Multiscale Machine-learned Modeling Infrastructure (MuMMI I/O) on the Lassen supercomputer, and improves their aggregated I/O bandwidth by up to 5.42 x, 2.12 x and 1.29 x, respectively, compared to the baseline bandwidth.

关键词： Solid modeling Three-dimensional displays Processor scheduling High performance computing Bandwidth Manuals Supercomputers

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A Method for IoT Device management and Traffic scheduling in Distribution Station Area Based on distributed SDN Architecture

A Method for IoT Device Management and Traffic Scheduling in...

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2022 Asian Conference on Frontiers of Power and Energy, ACFPE 2022

作者： Guanqiang, Lin Jinyong, Lei Quan, Xu Xiaoguang, Wang Lei, Yu Fukui, Li Jingnan, Lin Huizhou Power Supply Bureau of Guangdong Power Grid Corporation Gunagdong China Electric Power Research Institute Csg Gunagdong China

ISBN: (数字)9781665470841

ISBN: (纸本)9781665470841

Aiming at the increasing data transmission and computation-intensive analysis and decision-making in the construction of the distribution Internet of Things, an Internet of Things system in the distribution station area based on the distributed SDN architecture is constructed. Including distributed control module, data exchange module and adaptive access of IoT devices, different types of IoT devices can be associated with heterogeneous access points, and exchange information with switches by coordinating multiple controllers. In this paper, the controller transforms the mobile device management and access point assignment problem into a joint optimization problem and solves it using adaptive time windows as a tool. Finally, a deep reinforcement learning traffic scheduling algorithm is used to optimize the capacity of the access point and reduce the delay of data transmission. The simulation results show that the method can find the best access point within the tolerable delay time, realize the interconnection technology of the IoT gateway in the distribution station area, and ensure the stable operation of the business in the distribution station area. © 2022 IEEE.

关键词： Internet of things

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NEPTUNE: Network- and GPU-aware management of Serverless Functions at the Edge

NEPTUNE: Network- and GPU-aware Management of Serverless Fun...

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SEAMS international workshop on Software Engineering for Adaptive and Self-Managing systems, ICSE

作者： Luciano Baresi Davide Yi Xian Hu Giovanni Quattrocchi Luca Terracciano Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy

ISBN: (纸本)9781665452120

Nowadays a wide range of applications is constrained by low-latency requirements that cloud infrastructures cannot meet. Multi-access Edge Computing (MEC) has been proposed as the reference architecture for executing applications closer to users and reducing latency, but new challenges arise: edge nodes are resource-constrained, the workload can vary significantly since users are nomadic, and task complexity is increasing (e.g., machine learning inference). To overcome these problems, the paper presents NEPTUNE, a serverless-based framework for managing complex MEC solutions. NEPTUNE i) places functions on edge nodes according to user locations, ii) avoids the saturation of single nodes, iii) exploits GPUs when available, and iv) allocates resources (CPU cores) dynamically to meet foreseen execution times. A prototype, built on top of K3S, was used to evaluate NEPTUNE on a set of experiments that demonstrate a significant reduction in terms of response time, network overhead, and resource consumption compared to three well-known approaches. CCS CONCEPTS • Theory of computation → scheduling algorithms; • Computing methodologies →distributed computing methodologies; • Computer systems organization →distributed architectures.

关键词： Multi-access edge computing scheduling algorithms Prototypes Computer architecture Organizations Machine learning Time factors

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APDCM 2022 Keynote Talk: Solving QUBOs on Digital and Quantum Computers

APDCM 2022 Keynote Talk: Solving QUBOs on Digital and Quantu...

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IEEE international Symposium on parallel and distributed Processing workshops and Phd Forum (IPDPSW)

作者： Thorsten Koch Daniel Rehfeldt Yuji Shinano ZIB

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Combinatorial Optimization searches for an optimum object in a finite but usually vast collection of objects. This can be used for many practical purposes, like efficient allocation of limited resources, network planning, and hundreds of other applications in almost all fields, e.g., finance, production, scheduling, and inventory control. However, many combinatorial optimization problems are known to be NP-hard, which is often translated simplistically as “intractable.” It is regularly claimed that quantum computers will bring breakthrough progress in solving such challenging combinatorial optimization problems relevant in practice. In particular, Quadratic Unconstraint Binary Optimization (QUBO) problems are said to be the model of choice for use in (adiabatic) quantum systems. We explain some of the meaning and implications, review the state of affairs, and give some computational results to underpin our conclusions.

关键词： Optimization Quantum computing distributed processing Conferences Computers Search problems resource management

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Towards Scalable resource management for Supercomputers

Towards Scalable Resource Management for Supercomputers

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Supercomputing Conference

作者： Yiqin Dai Yong Dong Kai Lu Ruibo Wang Wei Zhang Juan Chen Mingtian Shao Zheng Wang National University of Defense Technology Changsha China University of Leeds Leeds United Kingdom

ISBN: (纸本)9781665454452

Today's supercomputers offer massive computation resources to execute a large number of user jobs. Effectively managing such large-scale hardware parallelism and workloads is essential for supercomputers. However, existing HPC resource management (RM) systems fail to capitalize on the hardware parallelism by following a centralized design used decades ago. They give poor scalability and inefficient performance on today's supercomputers, which will worsen in exascale computing. We present ESlurm, a better RM for supercomputers. As a departure from existing HPC RMs, ESlurm implements a distributed communication structure. It employs a new communication tree strategy and uses job runtime estimation to improve communications and job scheduling efficiency. ESlurm is deployed into production in a real supercomputer. We evaluate ESlurm on up to 20K nodes. Compared to state-of-the-art RM solutions, ESlurm exhibits better scalability, significantly reducing the resource usage of master nodes and improving data transfer and job scheduling efficiency by a large margin.

关键词： Runtime Processor scheduling Scalability Estimation Production parallel processing Supercomputers

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resource management method of distributed parallel database based on directed graph 6

Resource management method of distributed parallel database ...

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6th Annual international Conference on Network and Information systems for Computers, ICNISC 2020

作者： Shi, Yingjie Zhang, Huayun Wang, Xiang Zhou, Yuchao Jiang, Shusong China Realtime Database Co.Ltd. Building 6 Big Data Industrial Park No.180 software Avenue Yuhuatai District Nanjing China

A distributed parallel database resource management method based on directed graph is proposed. By using content distributor based on distributed unstructured P2P network association, the high performance and stability of distributed parallel database system in dynamic changing environment are guaranteed. The problem of network congestion caused by too many redundant messages and the "barrel effect" caused by resource imbalance due to differential configuration is solved, through resource search algorithm based on directed graph lookahead, and the query node cached the resource information of two-level neighbor nodes. By adopting the Linux cgroups resource management mechanism, fully considering the multi tenant and multi factor based resource scheduling strategy, reduce resource fragments and better meet the problem of distributed parallel database storage or hot spot processing. © 2020 Journal of Physics: Conference Series.

关键词： Directed graphs

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