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This paper presents an innovative approach, applicable to all research-based fields, that identifies and broadly engages future computer science researchers. The Computing Alliance of Hispanic Serving Institutions (CAHSI) piloted a national virtual Research Experience for Undergraduates (vREU) during the summer of 2020. Funded by an NSF grant, the goal of the program was to ensure that students, in particular those with financial need, had opportunities to engage in research and gain critical skills while advancing their knowledge and financial resources to complete their undergraduate degrees and possibly move to advanced studies. The vREU pilot provided undergraduate research experiences for 51 students and 21 faculty drawn from 14 colleges and universities. The Affinity Research Group (ARG) model, based on a cooperative learning model, was used to guide faculty mentors throughout the eight-week vREU. ARG is a CAHSI signature practice with a focus on deliberate, structured faculty and student research, technical, communication, and professional skills development. At weekly meetings, faculty were provided resources and discussed a specific skill to support students’ research experience and development, which faculty put into immediate practice with their students. Evaluation findings include no statistical difference in student development between the face-to-face and virtual models with faculty andmore »Free, publicly-accessible full text available July 1, 2023
Abstract—Virtual Network Functions (VNFs) are software implementation of middleboxes (MBs) (e.g., firewalls and proxy servers) that provide performance and security guarantees for virtual machine (VM) cloud applications. In this paper, we study a new VM flow migration problem for dynamic VNF-enabled cloud data centers (VDCs). The goal is to migrate the VM flows in the dynamic VDCs to minimize the total network traffic while load-balancing VNFs with limited processing capabilities. We refer to the problem as FMDV: flow migration in dynamic VDCs. We propose an optimal and efficient minimum cost flow-based flow migration algorithm and two benefit-based efficient heuristic algorithms to solve the FMDV. Via extensive simulations, we show that our algorithms are effective in mitigating dynamic cloud traffic while achieving load balance among VNFs. In particular, all our algorithms reduce dynamic network traffic in all cases and our optimal algorithm always achieves the best traffic-mitigation effect, reducing the network traffic by up to 28% compared to the case without flow migration.Free, publicly-accessible full text available May 1, 2023