5 research outputs found
A Transformative Process Control Solution
Get PDFKnowing that a technology invented almost hundred years ago (PID controller) is still dominating industrial process control, a historical review was done to understand how the control field evolved. Model dependency and high level of mathematics appear as the main reasons that prevent other technologies from penetrating the engineering practice. A relatively novel methodology introduced by J. Han in 1998 called Active Disturbance Rejection Control (ADRC) came with characteristics that matches process control needs and restrictions on model dependency. This study will present a transformative solution for process control based on that. The control algorithm is designed and discretized for digital implementation in PLC or DSC. The tuning process is explained in a logical and intuitive way based on time and frequency domain characteristics. The idea was to use the language familiar to industry practitioners. To show its applicability, a case study was done for server’s temperature control; and the results show energy savings of 30% when compared to PID controllers. This solution is not yet optimal, since it is generally applicable for a wide range of processes, but it aims to be a step further in process control
A Transformative Process Control Solution
Get PDFKnowing that a technology invented almost hundred years ago (PID controller) is still dominating industrial process control, a historical review was done to understand how the control field evolved. Model dependency and high level of mathematics appear as the main reasons that prevent other technologies from penetrating the engineering practice. A relatively novel methodology introduced by J. Han in 1998 called Active Disturbance Rejection Control (ADRC) came with characteristics that matches process control needs and restrictions on model dependency. This study will present a transformative solution for process control based on that. The control algorithm is designed and discretized for digital implementation in PLC or DSC. The tuning process is explained in a logical and intuitive way based on time and frequency domain characteristics. The idea was to use the language familiar to industry practitioners. To show its applicability, a case study was done for server’s temperature control; and the results show energy savings of 30% when compared to PID controllers. This solution is not yet optimal, since it is generally applicable for a wide range of processes, but it aims to be a step further in process control
Improving efficiency and resilience in large-scale computing systems through analytics and data-driven management
Full text linkApplications running in large-scale computing systems such as high performance computing (HPC) or cloud data centers are essential to many aspects of modern society, from weather forecasting to financial services. As the number and size of data centers increase with the growing computing demand, scalable and efficient management becomes crucial. However, data center management is a challenging task due to the complex interactions between applications, middleware, and hardware layers such as processors, network, and cooling units.
This thesis claims that to improve robustness and efficiency of large-scale computing systems, significantly higher levels of automated support than what is available in today's systems are needed, and this automation should leverage the data continuously collected from various system layers. Towards this claim, we propose novel methodologies to automatically diagnose the root causes of performance and configuration problems and to improve efficiency through data-driven system management.
We first propose a framework to diagnose software and hardware anomalies that cause undesired performance variations in large-scale computing systems. We show that by training machine learning models on resource usage and performance data collected from servers, our approach successfully diagnoses 98% of the injected anomalies at runtime in real-world HPC clusters with negligible computational overhead.
We then introduce an analytics framework to address another major source of performance anomalies in cloud data centers: software misconfigurations. Our framework discovers and extracts configuration information from cloud instances such as containers or virtual machines. This is the first framework to provide comprehensive visibility into software configurations in multi-tenant cloud platforms, enabling systematic analysis for validating the correctness of software configurations.
This thesis also contributes to the design of robust and efficient system management methods that leverage continuously monitored resource usage data. To improve performance under power constraints, we propose a workload- and cooling-aware power budgeting algorithm that distributes the available power among servers and cooling units in a data center, achieving up to 21% improvement in throughput per Watt compared to the state-of-the-art. Additionally, we design a network- and communication-aware HPC workload placement policy that reduces communication overhead by up to 30% in terms of hop-bytes compared to existing policies.2019-07-02T00:00:00
