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1. EC2000 Viewed through Amartya Sen’s Capability Framework

   R. A. Cheville (July 2023, ASEE Annual Conference proceedings)

   Most engineering programs in the United States are accredited by ABET under the guidelines known as EC-2000. The EC-2000 framework is broadly based on the continual quality management (CQM) movement in industry where programs are striving to constantly improve the quality of their output, in this case the skills of graduates. Broadly speaking, ABET evaluates engineering programs on eight different criteria; some are related to processes, some to resources, but those central to CQM are program educational objectives, that define hoped for long-term accomplishments of graduates, and outcomes which articulate what students can do when they graduate. Degree programs must convince ABET they have a documented and effective process to improve outcomes to gain accreditation. CQM of course is not the only framework by which educational development can be framed or measured. This paper explores ABET processes through the lens of the economist Amartya Sen’s capability approach, which is broadly applied in the developing world in areas of inequity, poverty, and human rights. The capability approach is often used when a focus on diverse individuals is desirable for understanding aspects of development. Central to Sen’s approach are capabilities and functionings. Capabilities are the resources and supports in an individual’s environment that provide opportunities to pursue a life they value. Functionings are what they actually become and do. Thus capabilities can be thought of as the potential for functionings; alternatively capabilities are opportunities and functionings are outcomes. This paper compares ABET’s accreditation criteria with a published set of capabilities in education. The comparison shows there are some areas where criteria overlap with capabilities, but also several areas where the overlap is low. The capabilities that aligned most with ABET criteria overlap with engineering epistemologies and a view of students as the ‘product’ of engineering education. 

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2. EC2000 Viewed through Amartya Sen’s Capability Framework

   R. A. Cheville (July 2023, ASEE Annual Conference proceedings)

   Most engineering programs in the United States are accredited by ABET under the guidelines known as EC-2000. The EC-2000 framework is broadly based on the continual quality management (CQM) movement in industry where programs are striving to constantly improve the quality of their output, in this case the skills of graduates. Broadly speaking, ABET evaluates engineering programs on eight different criteria; some are related to processes, some to resources, but those central to CQM are program educational objectives, that define hoped for long-term accomplishments of graduates, and outcomes which articulate what students can do when they graduate. Degree programs must convince ABET they have a documented and effective process to improve outcomes to gain accreditation. CQM of course is not the only framework by which educational development can be framed or measured. This paper explores ABET processes through the lens of the economist Amartya Sen’s capability approach, which is broadly applied in the developing world in areas of inequity, poverty, and human rights. The capability approach is often used when a focus on diverse individuals is desirable for understanding aspects of development. Central to Sen’s approach are capabilities and functionings. Capabilities are the resources and supports in an individual’s environment that provide opportunities to pursue a life they value. Functionings are what they actually become and do. Thus capabilities can be thought of as the potential for functionings; alternatively capabilities are opportunities and functionings are outcomes. This paper compares ABET’s accreditation criteria with a published set of capabilities in education. The comparison shows there are some areas where criteria overlap with capabilities, but also several areas where the overlap is low. The capabilities that aligned most with ABET criteria overlap with engineering epistemologies and a view of students as the ‘product’ of engineering education. 

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3. Holistic Resource Allocation Under Federated Scheduling for Parallel Real-time Tasks

   https://doi.org/10.1145/3489467  

   Nie, Lanshun; Fan, Chenghao; Lin, Shuang; Zhang, Li; Li, Yajuan; Li, Jing (January 2022, ACM Transactions on Embedded Computing Systems)

   With the technology trend of hardware and workload consolidation for embedded systems and the rapid development of edge computing, there has been increasing interest in supporting parallel real-time tasks to better utilize the multi-core platforms while meeting the stringent real-time constraints. For parallel real-time tasks, the federated scheduling paradigm, which assigns each parallel task a set of dedicated cores, achieves good theoretical bounds by ensuring exclusive use of processing resources to reduce interferences. However, because cores share the last-level cache and memory bandwidth resources, in practice tasks may still interfere with each other despite executing on dedicated cores. Such resource interferences due to concurrent accesses can be even more severe for embedded platforms or edge servers, where the computing power and cache/memory space are limited. To tackle this issue, in this work, we present a holistic resource allocation framework for parallel real-time tasks under federated scheduling. Under our proposed framework, in addition to dedicated cores, each parallel task is also assigned with dedicated cache and memory bandwidth resources. Further, we propose a holistic resource allocation algorithm that well balances the allocation between different resources to achieve good schedulability. Additionally, we provide a full implementation of our framework by extending the federated scheduling system with Intel’s Cache Allocation Technology and MemGuard. Finally, we demonstrate the practicality of our proposed framework via extensive numerical evaluations and empirical experiments using real benchmark programs. 

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4. A Water Evaluation and Planning-based framework for the long-term prediction of urban water demand and supply

   https://doi.org/10.1177/0037549720984250  

   Saleem, Arfa; Mahmood, Imran; Sarjoughian, Hessam; Nasir, Hasan Arshad; Malik, Asad Waqar (May 2021, SIMULATION)

   null (Ed.)

   Increased usage and non-efficient management of limited resources has created the risk of water resource scarcity. Due to climate change, urbanization, and lack of effective water resource management, countries like Pakistan are facing difficulties coping with the increasing water demand. Rapid urbanization and non-resilient infrastructures are the key barriers in sustainable urban water resource management. Therefore, there is an urgent need to address the challenges of urban water management through effective means. We propose a workflow for the modeling and simulation of sustainable urban water resource management and develop an integrated framework for the evaluation and planning of water resources in a typical urban setting. The proposed framework uses the Water Evaluation and Planning system to evaluate current and future water demand and the supply gap. Our simulation scenarios demonstrate that the demand–supply gap can effectively be dealt with by dynamic resource allocation, in the presence of assumptions, for example, those related to population and demand variation with the change of weather, and thus work as a tool for informed decisions for supply management. In the first scenario, 23% yearly water demand is reduced, while in the second scenario, no unmet demand is observed due to the 21% increase in supply delivered. Similarly, the overall demand is fulfilled through 23% decrease in water demand using water conservation. Demand-side management not only reduces the water usage in demand sites but also helps to save money, and preserve the environment. Our framework coupled with a visualization dashboard deployed in the water resource management department of a metropolitan area can assist in water planning and effective governance. 

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5. Application-specific, Dynamic Reservation of 5G Compute and Network Resources by Using Reinforcement Learning

   https://doi.org/10.1145/3538401.3546598  

   Gholami, Anousheh; Rao, Kunal; Hsiung, Wang-Pin; Po, Oliver; Sankaradas, Murugan; Baras, John S.; Chakradhar, Srimat (August 2022, Proceedings ACM SIGCOMM 2022 Workshop on Network-Application Integration (NAI'22))

   5G services and applications explicitly reserve compute and network resources in today’s complex and dynamic infrastructure of multi-tiered computing and cellular networking to ensure application-specific service quality metrics, and the infrastructure providers charge the 5G services for the resources reserved. A static, one-time reservation of resources at service deployment typically results in extended periods of under-utilization of reserved resources during the lifetime of the service operation. This is due to a plethora of reasons like changes in content from the IoT sensors (for example, change in number of people in the field of view of a camera) or a change in the environmental conditions around the IoT sensors (for example, time of the day, rain or fog can affect data acquisition by sensors). Under-utilization of a specific resource like compute can also be due to temporary inadequate availability of another resource like the network bandwidth in a dynamic 5G infrastructure. We propose a novel Reinforcement Learning-based online method to dynamically adjust an application’s compute and network resource reservations to minimize under-utilization of requested resources, while ensuring acceptable service quality metrics. We observe that a complex application-specific coupling exists between the compute and network usage of an application. Our proposed method learns this coupling during the operation of the service, and dynamically modulates the compute and network resource requests to minimize under-utilization of reserved resources. Through experimental evaluation using real-world video analytics application, we show that our technique is able to capture complex compute-network coupling relationship in an online manner i.e. while the application is running, and dynamically adapts and saves up to 65% compute and 93% network resources on average (over multiple runs), without significantly impacting application accuracy. 

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