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This thesis surveyed the capabilities, similarities, and differences regarding the Army's Military Occupational Specialty (MOS) 17C Cyber Operations and the associated difference and similarities between the career paths in the private sector. The question that guided the research was "What are the recruitment, retention, and sustainment issues the U.S. Army is experiencing within the 17C MOS?" Methodology included a comparative analysis of the learning objectives required by the U.S. Army for MOS 17C and private institutions' ability to train personnel to an equivalent knowledge and skill level of a 17C MOS soldier. It was determined that the U.S. Army's attrition rate is highest between the enlistment periods of thirty-six months through seventy-two months. Reenlistment for the 17C MOS is between thirty and forty percent after a seventy-two-month window. The U.S. Army invests a significant amount of time and funding to train 17C soldiers, and valuable experience is gained over a seventy-two-month enlistment obligation. By offering additional compensation bonuses, similar work-life balance packages, and varying rank enlistment flexibilities, the U.S. Army may be able to sustain its 17C soldiers for the long term. Civilian, Department of the Army Major, United States Marine Corps Civilian, Department of the Navy Approved for public release. Distribution is unlimited.

Current plans for the MUOS satellite communication system require military units and other users of MUOS communication resources to submit one or more satellite access requests (SARs) to a “chop-chain” approval process. The approval process can take considerable time, and the submitter cannot know whether the SAR will be successful until after the process completes. The problem compounded by the fact that reassignment of satellite beam carrier (SBC) frequencies to better suit new SARs is requires massive computation via the MUOS Performance Model (MPM) algorithms that can take over a day to run. To address these problems, this work demonstrates a dual-component software tool or planner. The first component is the SAR evaluator that evaluates a SAR's likelihood of success according to the same criteria that are considered when submitting a SAR over the SIPRNET for approval. It also shows a graphical assessment over time of the SAR's projected impact to the availability of various MUOS resources. The second component is the optimizer that rapidly determines SBC frequency assignments that minimize multi-access interference (MAI) on one or more MOUS satellites, given the newly specified SARs and currently committed traffic. The optimizer can aid the SAR submission process and can calculate “what-if scenarios” for SAR evaluators who must determine regional or global bandwidth allocations. The investigation includes benchmarks on proof-of-concept algorithms to demonstrate the capability of the optimizer to determine the carrier-frequency assignments in a few seconds when operating on portable devices such as an ordinary laptop as well as a PDA having limited computational bandwidth.