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Dynamic Priority Job scheduling on a Hadoop YARN Platform

Dynamic Priority Job Scheduling on a Hadoop YARN Platform

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international Conference on parallel and distributed systems (ICPADS)

作者： Nana Du Yudong Ji Aiqin Hou Chase Wu Weike Nie School of Information Science and Technology Northwest University Xi’an China Department of Data Science New Jersey Institute of Technology Newark NJ USA

In Hadoop’s big data processing systems, YARN is responsible for resource management and job scheduling. The built-in job scheduling algorithms in YARN are simple to execute, but have some limitations such as job starvation, excessive server load, and load imbalance. In this paper, we propose a new Hybrid Dynamic Priority job scheduling algorithm (HDPS) to address these limitations. HDPS dynamically adjusts the priority of a job as its waiting time increases to prevent job starvation. It also features a task assignment strategy designed specifically to address data locality by considering the available resources of servers and the distribution of data blocks stored on servers to reduce data transfer time and improve job execution efficiency. We implement and integrate HDPS into YARN and conduct experiments in a real Hadoop system using built-in benchmark test cases of Hadoop. Experimental results show that HDPS exhibits comprehensive superior performance over existing algorithms in terms of execution efficiency and load balance.

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The improved greedy gang scheduling by minimizing context switch condition 26

The improved greedy gang scheduling by minimizing context sw...

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26th international Computer Conference of the Computer-Society-of-Iran

作者： Mastoori, Maryam Sadat Rahmanian, Ghazal Iran Univ Sci & Technol Sch Elect Engn Tehran Iran

ISBN: (纸本)9781665412414

An effective resource management, called scheduling, is essential for the performance of large-scale distributed systems. One scheduling technique is gang scheduling, performing scheduling for parallel jobs in gang type. In this paper, a new algorithm for gang scheduling is proposed. This method aims to reduce the average response time of gangs by increasing the serviceability of gangs in the shortest execution time possible. The performance of the proposed algorithm is examined and compared to the basic gang scheduling algorithm within the simulation. The results of the simulation indicated that the response time of the proposed modification compared to the basic method is reduced up to 40% with low values of multiprogramming and high pressure of workload (short inter-arrival time) in Adapted First Come First Served and Largest Gang First Served policies.

关键词： gang scheduling large-scale systems performance simulation parallel processing

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A Runtime Manager Integrated Emulation Environment for Heterogeneous SoC Design with RISC-V Cores

A Runtime Manager Integrated Emulation Environment for Heter...

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

作者： H. Umut Suluhan Serhan Gener Alexander Fusco Joshua Mack Ismet Dagli Mehmet Belviranli Cagatay Edemen Ali Akoglu Electrical and Computer Engineering Department The University of Arizona Tucson AZ USA Computer Science Department Colorado School of Mines Golden CO USA Electrical and Electronics Engineering Department Ozyegin University Istanbul Turkey

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

RISC-V based architectures have gained attention for deployment on platforms from SoC to HPC scale to meet the demands of a wide range of computational workloads that may require specialized hardware tailored to their specific performance goals. In previous works, we introduced CEDR, an open-source, unified compilation and runtime framework designed to facilitate productive hardware-agnostic application deployment on heterogeneous architectures, and lift the barriers to research on the design and evaluation of scheduling heuristics and accelerators for heterogeneous systems. In this study, we demonstrate the capability of CEDR in supporting heterogeneous systems composed of RISC-V cores and a pool of accelerators. We investigate the impact of employing customized lightweight RISC-V cores for managing accelerators individually. Such management approach, compared to centralized accelerator management model, scales down the stress on the runtime environment where compute resources are shared among multiple applications each with varying degrees of task-level parallelism. We perform evaluations on a heterogeneous SoC emulated on the Xilinx Virtex-7 FPGA based on execution of applications from radar, signal processing, and autonomous vehicles domains. We observe that releasing CPU-based compute cores from accelerator management through lightweight RISC-V cores results with up to 47% reduction in time spent on accelerator management. Since CPU-based compute cores exclusively become available for application task execution and contention on resource management is reduced, we observe speedup of up to 2.2x in application execution time based on experiments conducted with workloads consisting of dynamically arriving real-life application scenarios.

关键词： Runtime environment Processor scheduling Computer architecture parallel processing Radar signal processing Hardware resource management

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A Solution for Conference Room Cluster Audio System Based on distributed Architecture

A Solution for Conference Room Cluster Audio System Based on...

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ACIS international Conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed Computing (SNPD)

作者： Xiaoyi Xu Xin Zhang Zenglai Wei School of Music and Recording Arts Communication University of China Beijing China School of Computer and Cyber Sciences Communication University of China Beijing China

ISBN: (数字)9798350391954

ISBN: (纸本)9798350391961

With the rapid development of information technology, people have put forward higher requirements for the audio-visual experience and usage functions of conference spaces. The traditional, single-function conference room configuration can no longer adapt to the diverse needs of modern work and interactive activities. How to achieve efficient management and control, seamless interconnection, and resource sharing of audio systems across spaces, while ensuring the acoustic characteristics and flexibility of each independent space, has become a core issue that needs to be urgently resolved in building a multi-hall, multifunctional conference room cluster. Based on the audio system project of the Academic Center of the Communication University of China, this paper proposes a solution for a conference room cluster audio system based on a distributed architecture. This solution not only overcomes the limitations of traditional systems in terms of scalability, collaborative work, and resource scheduling but also promotes lossless transmission, real-time processing, and adaptive configuration of audio signals. Thus, it ensures the consistency and high quality of the audio experience within the entire cluster, providing practical guidance for the design and optimization of future conference room audio systems.

关键词： Technological innovation Federated learning Audio systems Scalability Computer architecture Aerospace electronics Propagation losses

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Improvement of Task scheduling with Energy Efficiency including Fault Tolerant with Resilient Computing System in parallel and distributed Communication Network

Improvement of Task Scheduling with Energy Efficiency includ...

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Augmented Intelligence and Sustainable systems (ICAISS), international Conference on

作者： Vijay Kumar P. Madhuri R. Yamini Palla Sravani Muthukumar K Department of Physics Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Hyderabad Institute of Technology and Management Gowdavelly(V) Medchal(M) Hyderabad Telangana India Department of Computing Technologies Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science and Engineering Raghu Engineering College Visakhapatnam Andhra Pradesh India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and Technology Coimbatore Tamilnadu India

In parallel and distributed communication networks, task scheduling is essential for attaining the best system performance. Innovative ways that may intelligently distribute computing resources while minimizing energy consumption and maintaining system resilience are required due to the rising need for energy efficiency and fault tolerance. This system, offers a unique approach to job scheduling in parallel and distributed communication networks that integrate genetic algorithms (GA) and machine learning (ML).Using ML approaches, proposed method analyses historical task execution data to discover patterns that may be applied to task execution time prediction in various computing systems. As a result, this can assign tasks to resources effectively, maximize resource usage, and cut down on execution time as a whole. In addition, using predefined objectives like energy efficiency and fault tolerance, GA is used to find the best task-resource assignments. This applies resilient computing approaches to the job scheduling procedure to overcome problems with fault tolerance. This prevents the execution of crucial operations from being hampered by system faults or resource shortages by utilizing fault detection and recovery technologies. This system can adapt dynamically to shifting network conditions through the combination of ML, GA, and resilient computing, which raises the entire system's reliability and availability.

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Adaptive Task-Oriented resource Allocation for Large Dynamic Workflows on Opportunistic resources

Adaptive Task-Oriented Resource Allocation for Large Dynamic...

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international Symposium on parallel and distributed Processing (IPDPS)

作者： Thanh Son Phung Douglas Thain University of Notre Dame

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Dynamic workflow management systems offer a solution to the problem of distributing a local application by packaging individual computations and their dependencies on-the-fly into tasks executable on remote workers. Such independent task execution allows workers to be launched in an opportunistic manner to maximize the current pool of resources at any given time, either through opportunistic systems (e.g., HTCondor, AWS Spot Instances), or conventional systems (e.g., SLURM, SGE) with backfilling enabled, as opposed to monolithic or message-passing applications requiring a fixed block of non-preemptible workers. However, the dynamic nature of task generation presents a significant challenge in terms of resource management as tasks must be allocated with some unknown amount of resources pre-execution but are only observable at runtime. This in turn results in potentially huge resource waste per task as (1) users lack direct knowledge about the relationship between tasks and resources, and thus cannot correctly specify the amount of resources a task needs in advance, and (2) workflows and tasks may exhibit stochastic behaviors at runtime, which complicates the process of resource *** this paper, we (1) argue for the need of an adaptive resource allocator capable of allocating tasks at runtime and adjusting to random fluctuations and abrupt changes in a dynamic workflow without requiring any prior knowledge, and (2) introduce Greedy Bucketing and Exhaustive Bucketing: two robust, online, general-purpose, and prior-free allocation algorithms capable of producing quality estimates of a task’s resource consumption as the workflow runs. Our results show that a resource allocator equipped with either algorithm consistently outperforms 5 alternative allocation algorithms on 7 diverse workflows and incurs at most 1.6 ms overhead per allocation in the steady state.

关键词： Runtime Fluctuations Heuristic algorithms Packaging Dynamic scheduling Remote working Steady-state

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Optimized Heuristic Technique for Task scheduling in Secure Cloud Storage Environment

Optimized Heuristic Technique for Task Scheduling in Secure ...

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international Conference on System Modeling & Advancement in Research Trends (SMART)

作者： S. Maheshwari J. Anitha Ruth Department of Computer Science SRMIST A.M. Jain College Vadapalani Chennai Department of Master of Computer Application Faculty of Science and Humanities Vadapalani Chennai

ISBN: (数字)9798350380583

ISBN: (纸本)9798350380590

Cloud computing is a highly scalable and frequently used expertise in today's rapidly evolving information and computer landscape. It runs on the payper-use, remote access, Internet-based, and on demand ideas, offering consumers a shared pool of programmable resources. However, as the number of user requests grows, efficient task scheduling and resource allocation have become critical criteria for effective workload balancing among cloud resources. It consequently improves overall cloud system performance and offers a new way to deliver services in distributed systems, paving the way for the execution of scientific applications. scheduling and processing millions of activities using Cloud resources in a lucrative manner with minimal computing time is an attractive study area. Current scheduling algorithms ignore dependent and independent jobs. To overcome this drawback, we anticipate a new dignified technique called the Optimized heuristic Min-Min algorithm, which is stimulated by the Min-Min algorithm for task scheduling and focuses on the concern of dependent and independent tasks, as well as the parallel processing of independent tasks, which directly results in profit by minimizing computation time. This study aims to produce optimal results by minimizing completion time and maximizing resource utilization, leading to higher throughput. Kerberos authentication service is implementing for cloud data storage security. Kerberos used in this work for creating tickets and granting them to every user in order to provide high security.

关键词： Cloud computing scheduling algorithms Heuristic algorithms System performance parallel processing Throughput scheduling Security resource management Secure storage

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Randomized Testing Framework for Dissecting NVIDIA GPGPU Thread Block-To-SM scheduling Mechanisms

Randomized Testing Framework for Dissecting NVIDIA GPGPU Thr...

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international Conference on parallel and distributed systems (ICPADS)

作者： Chongxi Wang Penghao Song Haoyu Zhao Fuxin Zhang Jian Wang Longbing Zhang State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

NVIDIA General Purpose Graphics Processing Units (GPGPUs) have been extensively utilized in scientific computing, artificial intelligence acceleration, and numerous other fields. The compute unified device architecture (CUDA) programming interface facilitates access to the enormous computing power of NVIDIA GPGPUs. However, the thread block-to-stream multiprocessor (SM) scheduler of NVIDIA GPGPUs has remained a black box, presenting challenges for performance modeling of CUDA programs, especially those featuring concurrent kernel execution, as well as hindering further exploration of the full potential of NVIDIA GPGPUs. To address these issues, we have devised a randomized testing framework targeting thread block-to-SM scheduling. In this framework, concurrent kernel sequences are randomly generated and assessed by a self-implemented scheduling predictor along with an actual NVIDIA GPGPU. Through iteratively scrutinizing the discrepancies between predictions and actual results, we dissect the key thread block-to-SM scheduling mechanisms intrinsic to NVIDIA GPGPUs, revealing their underlying logic and considerations. This process continuously refines the scheduling predictor until it accurately aligns with real-world NVIDIA GPGPU scheduling. Furthermore, we investigate the monitoring and allocation mechanisms for distinct resources in NVIDIA GPGPUs and SMs, elucidating the essential connection between resource management and thread block scheduling. Our comprehensive and precise analysis of Nvidia GPGPU thread block-to-SM scheduling contributes significantly to constructing more accurate simulators for GPGPU performance modeling. Moreover, it facilitates optimizing CUDA programs based on the scheduling mechanisms to exploit more parallelism among thread blocks of concurrent kernels.

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12th workshop on parallel Programming and Run-Time management Techniques for Many-Core Architectures and 10th workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms, PARMA-DITAM 2021

12th Workshop on Parallel Programming and Run-Time Managemen...

引用

12th workshop on parallel Programming and Run-Time management Techniques for Many-Core Architectures and 10th workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms, PARMA-DITAM 2021

ISBN: (纸本)9783959771818

The proceedings contain 5 papers. The topics discussed include: towards adaptive multi-alternative process network;BifurKTM: approximately consistent distributed transactional memory for GPUs;the impact of precision tuning on embedded systems performance: a case study on field-oriented control;resource aware GPU scheduling in kubernetes infrastructure;and HPC application cloudification: the streamflow toolkit.

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Dynamic VNF Deployment and resource Allocation in Mobile Edge Computing

Dynamic VNF Deployment and Resource Allocation in Mobile Edg...

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international Symposium on parallel and distributed Processing with Applications, ISPA

作者： Xiaolong Zhai Zhenli He Yuanfei Xiao Junjie Wu Xuejie Yu School of Software Yunnan University Kunming China Yunnan Key Laboratory of Software Engineering Yunnan University Kunming China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The explosive growth of terminal devices at the network edge, coupled with advancements in communication technology, poses significant challenges to traditional cloud computing models. Despite Mobile Edge Computing (MEC) mitigating some issues by enabling real-time data processing closer to the source, it faces challenges with linear growth in computational resources insufficient to meet the exponential growth in service demand. Existing research utilizing Software-Defined Networking (SDN), Network Functions Virtualization (NFV), and Service Function Chain (SFC) technologies has made progress but still faces critical issues, such as the inability to respond in real-time to dynamic demands and inefficiencies in resource management strategies. This paper addresses these challenges by optimizing Virtual Network Function (VNF) deployment strategies in MEC environments. We propose PPO-ERA, a novel algorithm leveraging deep reinforcement learning and the Karush-Kuhn-Tucker (KKT) method. This approach provides real-time, adaptive, and dynamic deployment policies for VNFs, significantly improving both the average response delay of tasks and resource utilization. Key contributions include rigorous SFC-based application modeling, dynamic VNF deployment algorithms, elastic resource allocation, uniform state representation, and extensive performance validation. These advancements enhance the adaptability, efficiency, and performance of VNF deployment strategies, addressing critical challenges in dynamic MEC environments.

关键词： Multi-access edge computing Service function chaining Heuristic algorithms Computational modeling Dynamic scheduling Real-time systems Explosives Network function virtualization resource management Faces

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