用于在波动数据流上自动扩展资源的细粒度任务调度策略

IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Yinuo Fan , Dawei Sun , Minghui Wu , Shang Gao , Rajkumar Buyya
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引用次数: 0

摘要

资源伸缩对于波动数据流场景下的流计算系统至关重要。计算资源利用率随着数据流速率的变化而显著波动,常常导致这些系统中出现明显的资源过剩和稀缺问题。现有的研究主要集中在解决运行时的资源不足;然而,处理可变数据流的有效解决方案仍然有限。此外,在资源调整过程中,在任务放置过程中忽略任务通信依赖关系可能导致通信成本增加,从而影响系统性能。为了解决这些挑战,我们提出了Ra-Stream,一种细粒度任务调度策略,用于在波动数据流上自动扩展资源。Ra-Stream不仅可以动态调整资源以适应不同的数据流,还可以采用细粒度调度来进一步优化系统性能。本文从以下几个方面对Ra-Stream进行了说明:(1)形式化:通过构建和分析流应用模型、通信模型和资源模型,形式化了应用子图划分问题、资源伸缩问题和任务调度问题。(2)资源缩放和启发式划分:我们提出了一种资源缩放算法来缩放计算资源以适应波动的数据流。引入了一种启发式子图划分算法,使通信开销均匀地最小化。(3)细粒度任务调度:提出了一种细粒度任务调度算法,通过线程级任务部署最小化计算资源利用率,同时降低通信成本。(4)综合评估:我们在真实的分布式流计算环境中评估多个指标,包括延迟、吞吐量和资源利用率。实验结果表明,与最先进的方法相比,Ra-Stream将系统延迟降低了36.37%至47.45%,将系统最大吞吐量提高了26.2%至60.55%,并节省了40%至46.25%的资源利用率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A fine-grained task scheduling strategy for resource auto-scaling over fluctuating data streams
Resource scaling is crucial for stream computing systems in fluctuating data stream scenarios. Computational resource utilization fluctuates significantly with changes in data stream rates, often leading to pronounced issues of resource surplus and scarcity within these systems. Existing research has primarily focused on addressing resource insufficiency at runtime; however, effective solutions for handling variable data streams remain limited. Furthermore, overlooking task communication dependencies during task placement in resource adjustment may lead to increased communication cost, consequently impairing system performance. To address these challenges, we propose Ra-Stream, a fine-grained task scheduling strategy for resource auto-scaling over fluctuating data streams. Ra-Stream not only dynamically adjusts resources to accommodate varying data streams, but also employs fine-grained scheduling to optimize system performance further. This paper explains Ra-Stream through the following aspects: (1) Formalization: We formalize the application subgraph partitioning problem, the resource scaling problem and the task scheduling problem by constructing and analyzing a stream application model, a communication model, and a resource model. (2) Resource scaling and heuristic partitioning: We propose a resource scaling algorithm to scale computational resource for adapting to fluctuating data streams. A heuristic subgraph partitioning algorithm is also introduced to minimize communication cost evenly. (3) Fine-grained task scheduling: We present a fine-grained task scheduling algorithm to minimize computational resource utilization while reducing communication cost through thread-level task deployment. (4) Comprehensive evaluation: We evaluate multiple metrics, including latency, throughput and resource utilization in a real-world distributed stream computing environment. Experimental results demonstrate that, compared to state-of-the-art approaches, Ra-Stream reduces system latency by 36.37 % to 47.45 %, enhances system maximum throughput by 26.2 % to 60.55 %, and saves 40 % to 46.25 % in resource utilization.
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来源期刊
CiteScore
19.90
自引率
2.70%
发文量
376
审稿时长
10.6 months
期刊介绍: Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.
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