The United States military is currently in an educational quandary. With the substantial force and resource reductions following both the Cold and Gulf Wars, skilled manpower is at a premium. Concurrent with downsizing, the Services have been increasingly deployed on short notice to execute diverse operational missions. These comparatively short tours and long deployments, in locations where traditional forms of education and training are limited, are compounding the educational issue. This combination of events and circumstances has put a spotlight on the need to adjust the military’s current training systems to meet changing mission requirements. [01]
The military’s current training systems are, by and large, classroom oriented. All students are required to be at the facility in which training occurs and are, for all practical purposes, removed from operational status for the duration of the training. So, how can commands, which are already undermanned, release personnel for training and higher education opportunities? On top of this, the resource issues faced by most commands are making more education possibilities less and less cost effective. This not only has the potential to limit our military member’s technical development but can have a detrimental effect on morale and retention. So, how will it be possible to maintain a sailors or soldiers technical competence in this
Distributed Learning utilizing multicast can bring the classroom to nearly any Department of Defense (DoD) computer terminal, providing improved training and increased learning opportunities for just about every military member. [02]
With the proliferation of computer technology and Internet access throughout the DoD, Distributed Learning can put education and training at the finger tips of most military members. It can even bring education to the field.
It is only limited by the networks, data delivery methods, and bandwidth provided military units. Providing multicast and Distributed Learning sources on DoD networks is the next logical step forward regarding information dissemination and training for all DoD employees. [02]
Of further consideration, DoD and other government personnel lose productive time walking to and from a meeting hall or conference room to view briefs or attend seminars or project meetings. In large organizations, this may mean traveling to another building where parking may be limited. For seminars or project meetings, the participants may be traveling from many geographical locations consuming both travel funds and time. With multicast and the current information technology (IT) infrastructure, personnel should be able to participate in these same events on their desktop workstations or at local distributed locations, potentially increasing worker productivity and reducing time away from primary tasks. Can current Government, and DoD networks in particular, support these applications while continuing to support their current quality of
question, a hard look must be taken at current multicast routing protocols and the network in which they are used.
Furthermore, a set of metrics that can illustrate the current efficiency and QoS of a given network, without multicast and distributed learning applications, will need to be defined. Then tests to provide data for these metrics will need to be designed and performed. Once the current or baseline state of a network is determined, then multicast and distributed learning traffic should be introduced into the network and the tests performed again. The contrast between these two data points will provide a good view of the impact of multicast and distributed learning traffic on the network.
This thesis provides insight into the capabilities that a network requires in order to provide a sufficient QoS to sailors and solders in support of Distributed Learning via multicast. Multicast being a very efficient method of delivering data to multiple recipients and is the underlying technology that can allow interactive Distributed Learning. Thus, multicast is the primary focus of this thesis.
Curiosity is and always has been the driving force behind humanity’s ingenuity and its need to know. So, the questions that an entity is willing to ask, define its reality and perception of the world. The harder the question, the greater the reward once the answer is found.
Thus, it follows that if an organization is unwilling or
multicast is viable or needed on the NPS network. It is not asking why multicast does not work, can it work, or how it can be made to work on its network. This thesis was developed in order to answer these hard questions and is the driving force behind it. But to answer them, the following questions have to be answered first:
1. Exactly, what is multicast and how is it used in distributed learning applications?
2. What network architectures and topologies best support multicasts, and does it matter?
3. What are the most used multicast routing algorithms on commercial and educational networks today?
4. What requirements for multicast applications does the NPS network documentation include?
5. What multicast network services are currently available on the NPS network? Were any implemented with the new Foundry Network?
6. Will the current NPS network support multicast?
Questions, the pursuit of knowledge, and discovery of truths are what make a thesis. So finding the answers to these questions is the value of this thesis. The experiments in chapter four were thus conducted, using the networks laboratory equipment and the current NPS network, in order to answer these questions. The data collected during these experiments was analyzed to assess the impact of multicast traffic on the NPS network, determine the current state of the NPS network as it relates to multicast
capability. This information was then used to develop the suggested guidelines for implementing multicast on DoD networks in Chapter VI.
The rest of this thesis is broken down into chapters and appendices. Chapter II contains background information on distributed learning and multicast. It also answers question 1 above. Chapter III is a description of the multicast routing protocols utilized at NPS and answers questions 2 and 3 above. Chapter IV describes the experiments conducted in support of this thesis. Chapter 5 contains the results of the experiments and an analysis of the data collected during them. The sixth chapter holds the recommendations and suggestions developed from this thesis and chapter 7 is the conclusion. Finally, Appendix A is the initial test plan used during the research for this thesis.
Now, in order to better understand the concepts presented later in this document, a firm understanding of the background of both distributed learning and multicast is needed.
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