Router and Firewall Redundancy with OpenBSD and CARP

As more reliance is placed on computing and networking systems, the need for redundancy increases. The Common Address Redundancy Protocol (CARP) protocol and OpenBSD’s pfsync utility provide a means by which to implement redundant routers and firewalls. This paper details how CARP and pfsync work together to provide this redundancy and explores the performance one can expect from the open source solutions. Two experiments were run: one showing the relationship between firewall state creation and state synchronization traffic and the other showing how TCP sessions are transparently maintained in the event of a router failure. Discussion of these simulations along with background information gives an overview of how OpenBSD, CARP, and pfsync can provide redundant routers and firewalls for today’s Internet.

Joint Computing and Network Resource Scheduling in a Lambda Grid Network

Data-intensive Grid applications require huge data transfers between grid computing nodes. These computing nodes, where computing jobs are executed, are usually geographically separated. A grid network that employs optical wavelength division multiplexing (WDM) technology and optical switches to interconnect computing resources with dynamically provisioned multi-gigabit rate bandwidth lightpath is called a Lambda Grid network. A computing task may be executed on any one of several computing nodes which possesses the necessary resources. In order to reflect the reality in job scheduling, allocation of network resources for data transfer should be taken into consideration. However, few scheduling methods consider the communication contention on Lambda Grids. In this paper, we investigate the joint scheduling problem while considering both optical network and computing resources in a Lambda Grid network. The objective of our work is to maximize the total number of jobs that can be scheduled in a Lambda Grid network. An adaptive routing algorithm is proposed and implemented for accomplishing the communication tasks for every job submitted in the network. Four heuristics (FIFO, ESTF, LJF, RS) are implemented for job scheduling of the computational tasks. Simulation results prove the feasibility and efficiency of the proposed solution.

Joint Computing and Network Resource Scheduling in a Lambda Grid Network

Data-intensive Grid applications require huge data transfers between grid computing nodes. These computing nodes, where computing jobs are executed, are usually geographically separated. A grid network that employs optical wavelength division multiplexing (WDM) technology and optical switches to interconnect computing resources with dynamically provisioned multi-gigabit rate bandwidth lightpath is called a Lambda Grid network. A computing task may be executed on any one of several computing nodes which possesses the necessary resources. In order to reflect the reality in job scheduling, allocation of network resources for data transfer should be taken into consideration. However, few scheduling methods consider the communication contention on Lambda Grids. In this paper, we investigate the joint scheduling problem while considering both optical network and computing resources in a Lambda Grid network. The objective of our work is to maximize the total number of jobs that can be scheduled in a Lambda Grid network. An adaptive routing algorithm is proposed and implemented for accomplishing the communication tasks for every job submitted in the network. Four heuristics (FIFO, ESTF, LJF, RS) are implemented for job scheduling of the computational tasks. Simulation results prove the feasibility and efficiency of the proposed solution.

Student and Faculty Perceptions of Technology’s Usefulness in Community College General Education Courses

Educational institutions of all levels invest large amounts of time and resources into instructional technology, with the goal of enhancing the educational effectiveness of the learning environment. The decisions made by instructors and institutions regarding the implementation of technology are guided by perceptions of usefulness held by those who are in control. The primary objective of this mixed methods study was to examine the student and faculty perceptions of technology being used in general education courses at a community college. This study builds upon and challenges the assertions of writers such as Prensky (2001a, 2001b) and Tapscott (1998) who claim that a vast difference in technology perception exists between generational groups, resulting in a diminished usefulness of technology in instruction. In this study, data were gathered through student surveys and interviews, and through faculty surveys and interviews. Analysis of the data used Kendall’s Tau test for correlation between various student and faculty variables in various groupings, and also typological analysis of the transcribed interview data. The analysis of the quantitative data revealed no relationship between age and perception of technology’s usefulness. A positive relationship was found to exist between the perception of the frequency of technology use and the perception of technology’s effectiveness, suggesting that both faculty members and students believed that the more technology is used, the more useful it is in instruction. The analysis of the qualitative data revealed that both faculty and students perceive technology to be useful, and that the most significant barriers to technology’s usefulness include faulty hardware and software systems,lack of user support, and lack of training for faculty. The results of the study suggest that the differences in perception of technology between generations that are proposed by Prensky may not exist when comparing adults from the younger generation with adults from the older generation. Further, the study suggests that institutions continue to invest in instructional technology, with a focus on high levels of support and training for faculty, and more universal availability of specific technologies, including web access, in class video, and presentation software. Adviser: Ronald Joekel