دانلود رایگان مقاله استفاده از کنترل کننده های الهام گرفته از PID ساده برای مدیریت منابع انعطاف پذیر آنلاین – سال 2019
مشخصات مقاله:
عنوان فارسی مقاله:
استفاده از کنترل کننده های الهام گرفته از PID ساده برای مدیریت منابع انعطاف پذیر آنلاین از جریان های علمی توزیع شده
عنوان انگلیسی مقاله:
Using simple PID-inspired controllers for online resilient resource management of distributed scientific workflows
کلمات کلیدی مقاله:
جریان های علمی، تشخیص و رسیدگی به خطا، جریان داده های انعطاف پذیر بزرگ، محاسبات ارادی
مناسب برای رشته های دانشگاهی زیر:
مهندسی کامپیوتر – مهندسی فناوری اطلاعات
مناسب برای گرایش های دانشگاهی زیر:
معماری سیستم های کامپیوتری – هوش مصنوعی – اینترنت و شبکه های گسترده
وضعیت مقاله انگلیسی و ترجمه:
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فهرست مطالب:
Outline
Highlights
Abstract
Keywords
۱٫ Introduction
۲٫ Related work
۳٫ General resilient resource management process
۴٫ Modeling simple controller-inspired Processes
۵٫ Experimental evaluation
۶٫ Tuning PID controllers
۷٫ Conclusion
Acknowledgments
References
قسمتی از مقاله انگلیسی:
Abstract
Scientific workflows have become mainstream for conducting large-scale scientific research. As a result, many workflow applications and Workflow Management Systems (WMSs) have been developed as part of the cyberinfrastructure to allow scientists to execute their applications seamlessly on a range of distributed platforms. Although the scientific community has addressed this challenge from both theoretical and practical approaches, failure prediction, detection, and recovery still raise many research questions. In this paper, we propose an approach inspired by the control theory developed as part of autonomic computing to predict failures before they happen, and mitigated them when possible. The proposed approach is inspired on the proportional–integral–derivative controller (PID controller) control loop mechanism, which is widely used in industrial control systems, where the controller will react to adjust its output to mitigate faults. PID controllers aim to detect the possibility of a non-steady state far enough in advance so that an action can be performed to prevent it from happening. To demonstrate the feasibility of the approach, we tackle two common execution faults of large scale data-intensive workflows—data storage overload and memory overflow. We developed a simulator, which implements and evaluates simple standalone PID-inspired controllers to autonomously manage data and memory usage of a data-intensive bioinformatics workflow that consumes/produces over 4.4 TB of data, and requires over 24 TB of memory to run all tasks concurrently. Experimental results obtained via simulation indicate that workflow executions may significantly benefit from the controller-inspired approach, in particular under online and unknown conditions. Simulation results show that nearly-optimal executions (slowdown of 1.01) can be attained when using our proposed method, and faults are detected and mitigated far in advance of their occurrence.
1. Introduction
Scientists want to extract the maximum information out of their data—which are often obtained from scientific instruments and processed in large-scale distributed systems. Today’s computational and data science applications may comprise thousands of computational tasks and process large data sets (from remote sensors, instruments, etc.), which are often distributed and stored on heterogeneous resources. Scientific workflows are a mainstream solution to process large-scale scientific computations in distributed systems, and have supported traditional and breakthrough research across several domains [1]. As a result, many workflow applications and Workflow Management Systems (WMSs) have been developed as part of the cyberinfrastructure to allow scientists to execute their applications seamlessly on a range of distributed platforms [2,3].
