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Colorado State University
1.
Maxwell, Paul.
Robust resource allocation heuristics for military village search missions.
Degree: PhD, Electrical and Computer Engineering, 2012, Colorado State University
URL: http://hdl.handle.net/10217/67942 
► On the modern battlefield, cordon and search missions (a.k.a. village searches) are conducted daily. Creating resource allocations that assign different types of search teams (e.g.,…
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▼ On the modern battlefield, cordon and search missions (a.k.a. village searches) are conducted daily. Creating resource allocations that assign different types of search teams (e.g., soldiers, robots, unmanned aerial vehicles, military working dogs) to target buildings of various sizes is difficult and time consuming in the static planning environment. Efficiently and effectively creating resource allocations when needed during mission execution (a dynamic environment) is even more challenging. There are currently no automated means to create these static and dynamic resource allocations for military use. Military planners create village search plans using reference tables in Field Manuals and personal experience. These manual methods are time consuming and the quality of the plans produced are unpredictable and not quantifiable. This work creates a mathematical model of the village search environment, and proposes static and dynamic resource allocation heuristics using robustness concepts. The result is a mission plan that is resilient against uncertainty in the environment and that saves valuable time for military planning staff.
Advisors/Committee Members: Siegel, Howard Jay (advisor), Maciejewski, Anthony A. (advisor), Potter, Jerry (committee member), Smith, James (committee member), Hayne, Stephen (committee member).
Subjects/Keywords: robustness; resource allocation; village search; dynamic; petri net
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APA (6th Edition):
Maxwell, P. (2012). Robust resource allocation heuristics for military village search missions. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/67942
Chicago Manual of Style (16th Edition):
Maxwell, Paul. “Robust resource allocation heuristics for military village search missions.” 2012. Doctoral Dissertation, Colorado State University. Accessed January 28, 2021.
http://hdl.handle.net/10217/67942.
MLA Handbook (7th Edition):
Maxwell, Paul. “Robust resource allocation heuristics for military village search missions.” 2012. Web. 28 Jan 2021.
Vancouver:
Maxwell P. Robust resource allocation heuristics for military village search missions. [Internet] [Doctoral dissertation]. Colorado State University; 2012. [cited 2021 Jan 28].
Available from: http://hdl.handle.net/10217/67942.
Council of Science Editors:
Maxwell P. Robust resource allocation heuristics for military village search missions. [Doctoral Dissertation]. Colorado State University; 2012. Available from: http://hdl.handle.net/10217/67942
Colorado State University
2.
Eberhardt, Gerald M.
Design and implementation of a compact highly efficient 472kHz radio frequency generator for electrosurgery.
Degree: MS(M.S.), Electrical and Computer Engineering, 2011, Colorado State University
URL: http://hdl.handle.net/10217/51789
► This thesis explores the utilization of modern design practices and advance technologies to reduce the size of traditional 472kHz radio frequency generators used for electrosurgery.…
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▼ This thesis explores the utilization of modern design practices and advance technologies to reduce the size of traditional 472kHz radio frequency generators used for electrosurgery. Achieving the reduced size requires an innovative approach to increase the overall efficiency to lower the internal heat dissipation allowing the overall package size to shrink. This thesis covers the selection and design process to achieving the final topology of an innovative approach utilizing a variation of the Class-D amplifier to produce a resonance type power saturation amplifier. While using a high-efficiency power source to control the amplifier voltage rails, and to control the amplitude of the output signal will produce a sinusoidal power source capable of driving a radio frequency surgical scalpel.
Advisors/Committee Members: Collins, George J. (advisor), Siegel, Howard Jay (committee member), Chen, Thomas Wei (committee member), Sakurai, Hiroshi (committee member).
Subjects/Keywords: 472kHz; generator; electrosurgery
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APA (6th Edition):
Eberhardt, G. M. (2011). Design and implementation of a compact highly efficient 472kHz radio frequency generator for electrosurgery. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/51789
Chicago Manual of Style (16th Edition):
Eberhardt, Gerald M. “Design and implementation of a compact highly efficient 472kHz radio frequency generator for electrosurgery.” 2011. Masters Thesis, Colorado State University. Accessed January 28, 2021.
http://hdl.handle.net/10217/51789.
MLA Handbook (7th Edition):
Eberhardt, Gerald M. “Design and implementation of a compact highly efficient 472kHz radio frequency generator for electrosurgery.” 2011. Web. 28 Jan 2021.
Vancouver:
Eberhardt GM. Design and implementation of a compact highly efficient 472kHz radio frequency generator for electrosurgery. [Internet] [Masters thesis]. Colorado State University; 2011. [cited 2021 Jan 28].
Available from: http://hdl.handle.net/10217/51789.
Council of Science Editors:
Eberhardt GM. Design and implementation of a compact highly efficient 472kHz radio frequency generator for electrosurgery. [Masters Thesis]. Colorado State University; 2011. Available from: http://hdl.handle.net/10217/51789
Colorado State University
3.
Hansen, Timothy M.
Resource allocation optimization in the smart grid and high-performance computing.
Degree: PhD, Electrical and Computer Engineering, 2015, Colorado State University
URL: http://hdl.handle.net/10217/167096
► This dissertation examines resource allocation optimization in the areas of Smart Grid and high-performance computing (HPC). The primary focus of this work is resource allocation…
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▼ This dissertation examines resource allocation optimization in the areas of Smart Grid and high-performance computing (HPC). The primary focus of this work is resource allocation related to Smart Grid, particularly in the areas of aggregated demand response (DR) and demand side management (DSM). Towards that goal, a framework for heuristic optimization for DR in the Smart Grid is designed. The optimization problem, denoted Smart Grid resource allocation (SGRA), controls a large set of individual customer assets (e.g., smart appliances) to enact a beneficial change on the electric power system (e.g., peak load reduction). In one part of this dissertation, the SGRA heuristic framework uses a proposed aggregator-based approach. The aggregator is a for-profit entity that uses information about customers' smart appliances to create a schedule that maximizes its profit. To motivate the customers to participate with the aggregator, the aggregator offers a reduced rate of electricity called customer incentive pricing (CIP). A genetic algorithm is used to find a smart appliance schedule and CIP to maximize aggregator profit. By optimizing for aggregator profit, the peak load of the system is also reduced, resulting in a beneficial change for the entire system. Visualization techniques are adapted, and enhanced, to gain insight into the results of the aggregator-based optimization. A second approach to DR in the Smart Grid is taken in the form of a residential home energy management system (HEMS). The HEMS uses a non-myopic decision making technique, denoted partially-observable Markov decision process (POMDP), to make sequential decisions about energy usage within a residential household to minimize cost in a real-time pricing (RTP) environment. The POMDP HEMS significantly reduces the electricity cost for a residential customer with minimal impact on comfort. The secondary focus of the research is resource allocation for scientific applications in HPC using a dual-stage methodology. In the first stage, a batch scheduler assigns a number of homogeneous processors from a set of heterogeneous parallel machines to each application in a batch of parallel, scientific applications. The scheduler assigns machine resources to maximize the probability that all applications complete by a given time, denoted the makespan goal. This objective function is denoted robustness. The second stage uses runtime optimization in the form of dynamic loop scheduling to minimize the execution time of each application using the resources allocated in the first stage. It is shown that by combining the two optimization stages, better performance is achieved than by using either approach separately or by using neither. The specific contributions of this dissertation are: (a) heuristic frameworks and mathematical models for resource allocation in the Smart Grid and dual-stage HPC are designed, (b) CIP is introduced to allow an aggregator profit and encourage customer participation, and (c) heuristics and decision-making techniques are…
Advisors/Committee Members: Siegel, Howard Jay (advisor), Maciejewski, Anthony A. (advisor), Suryanarayanan, Siddharth (committee member), Bradley, Thomas H. (committee member).
Subjects/Keywords: high-performance computing; resource allocation; cyber-physical systems; smart grid; optimization
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APA (6th Edition):
Hansen, T. M. (2015). Resource allocation optimization in the smart grid and high-performance computing. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/167096
Chicago Manual of Style (16th Edition):
Hansen, Timothy M. “Resource allocation optimization in the smart grid and high-performance computing.” 2015. Doctoral Dissertation, Colorado State University. Accessed January 28, 2021.
http://hdl.handle.net/10217/167096.
MLA Handbook (7th Edition):
Hansen, Timothy M. “Resource allocation optimization in the smart grid and high-performance computing.” 2015. Web. 28 Jan 2021.
Vancouver:
Hansen TM. Resource allocation optimization in the smart grid and high-performance computing. [Internet] [Doctoral dissertation]. Colorado State University; 2015. [cited 2021 Jan 28].
Available from: http://hdl.handle.net/10217/167096.
Council of Science Editors:
Hansen TM. Resource allocation optimization in the smart grid and high-performance computing. [Doctoral Dissertation]. Colorado State University; 2015. Available from: http://hdl.handle.net/10217/167096
4.
Hogade, Ninad.
Minimizing energy costs for geographically distributed heterogeneous data centers.
Degree: MS(M.S.), Electrical and Computer Engineering, 2018, Colorado State University
URL: http://hdl.handle.net/10217/191286
► The recent proliferation and associated high electricity costs of distributed data centers have motivated researchers to study energy-cost minimization at the geo-distributed level. The development…
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▼ The recent proliferation and associated high electricity costs of distributed data centers have motivated researchers to study energy-cost minimization at the geo-distributed level. The development of time-of-use (TOU) electricity pricing models and renewable energy source models has provided the means for researchers to reduce these high energy costs through intelligent geographical workload distribution. However, neglecting important considerations such as data center cooling power, interference effects from task co-location in servers, net-metering, and peak demand pricing of electricity has led to sub-optimal results in prior work because these factors have a significant impact on energy costs and performance. In this thesis, we propose a set of workload management techniques that take a holistic approach to the energy minimization problem for geo-distributed data centers. Our approach considers detailed data center cooling power, co-location interference, TOU electricity pricing, renewable energy, net metering, and peak demand pricing distribution models. We demonstrate the value of utilizing such information by comparing against geo-distributed workload management techniques that possess varying amounts of system information. Our simulation results indicate that our best proposed technique is able to achieve a 61% (on average) cost reduction compared to
state-of-the-art prior work.
Advisors/Committee Members: Pasricha, Sudeep (advisor), Siegel, Howard Jay (advisor), Burns, Patrick J. (committee member).
Subjects/Keywords: memory interference; peak shaving; geo-distributed data centers; workload management; net metering
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APA ·
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APA (6th Edition):
Hogade, N. (2018). Minimizing energy costs for geographically distributed heterogeneous data centers. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/191286
Chicago Manual of Style (16th Edition):
Hogade, Ninad. “Minimizing energy costs for geographically distributed heterogeneous data centers.” 2018. Masters Thesis, Colorado State University. Accessed January 28, 2021.
http://hdl.handle.net/10217/191286.
MLA Handbook (7th Edition):
Hogade, Ninad. “Minimizing energy costs for geographically distributed heterogeneous data centers.” 2018. Web. 28 Jan 2021.
Vancouver:
Hogade N. Minimizing energy costs for geographically distributed heterogeneous data centers. [Internet] [Masters thesis]. Colorado State University; 2018. [cited 2021 Jan 28].
Available from: http://hdl.handle.net/10217/191286.
Council of Science Editors:
Hogade N. Minimizing energy costs for geographically distributed heterogeneous data centers. [Masters Thesis]. Colorado State University; 2018. Available from: http://hdl.handle.net/10217/191286
5.
Tarplee, Kyle M.
Highly scalable algorithms for scheduling tasks and provisioning machines on heterogeneous computing systems.
Degree: PhD, Electrical and Computer Engineering, 2015, Colorado State University
URL: http://hdl.handle.net/10217/167055
► As high performance computing systems increase in size, new and more efficient algorithms are needed to schedule work on the machines, understand the performance trade-offs…
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▼ As high performance computing systems increase in size, new and more efficient algorithms are needed to schedule work on the machines, understand the performance trade-offs inherent in the system, and determine which machines to provision. The extreme scale of these newer systems requires unique task scheduling algorithms that are capable of handling millions of tasks and thousands of machines. A highly scalable scheduling algorithm is developed that computes high quality schedules, especially for large problem sizes. Large-scale computing systems also consume vast amounts of electricity, leading to high operating costs. Through the use of novel resource allocation techniques, system administrators can examine this trade-off space to quantify how much a given performance level will cost in electricity, or see what kind of performance can be expected when given an energy budget. Trading-off energy and makespan is often difficult for companies because it is unclear how each affects the profit. A monetary-based model of high performance computing is presented and a highly scalable algorithm is developed to quickly find the schedule that maximizes the profit per unit time. As more high performance computing needs are being met with cloud computing, algorithms are needed to determine the types of machines that are best suited to a particular workload. An algorithm is designed to find the best set of computing resources to allocate to the workload that takes into account the uncertainty in the task arrival rates, task execution times, and power consumption. Reward rate, cost, failure rate, and power consumption can be optimized, as desired, to optimally trade-off these conflicting objectives.
Advisors/Committee Members: Maciejewski, Anthony A. (advisor), Siegel, Howard Jay (committee member), Chong, Edwin (committee member), Bates, Dan (committee member).
Subjects/Keywords: high performance computing; resource provisioning; stochastic programming; linear programming; heterogeneous computing; scheduling
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APA ·
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APA (6th Edition):
Tarplee, K. M. (2015). Highly scalable algorithms for scheduling tasks and provisioning machines on heterogeneous computing systems. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/167055
Chicago Manual of Style (16th Edition):
Tarplee, Kyle M. “Highly scalable algorithms for scheduling tasks and provisioning machines on heterogeneous computing systems.” 2015. Doctoral Dissertation, Colorado State University. Accessed January 28, 2021.
http://hdl.handle.net/10217/167055.
MLA Handbook (7th Edition):
Tarplee, Kyle M. “Highly scalable algorithms for scheduling tasks and provisioning machines on heterogeneous computing systems.” 2015. Web. 28 Jan 2021.
Vancouver:
Tarplee KM. Highly scalable algorithms for scheduling tasks and provisioning machines on heterogeneous computing systems. [Internet] [Doctoral dissertation]. Colorado State University; 2015. [cited 2021 Jan 28].
Available from: http://hdl.handle.net/10217/167055.
Council of Science Editors:
Tarplee KM. Highly scalable algorithms for scheduling tasks and provisioning machines on heterogeneous computing systems. [Doctoral Dissertation]. Colorado State University; 2015. Available from: http://hdl.handle.net/10217/167055
Colorado State University
6.
Dewri, Rinku.
Multi-criteria analysis in modern information management.
Degree: PhD, Computer Science, 2010, Colorado State University
URL: http://hdl.handle.net/10217/40284
► The past few years have witnessed an overwhelming amount of research in the field of information security and privacy. An encouraging outcome of this research…
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▼ The past few years have witnessed an overwhelming amount of research in the field of information security and privacy. An encouraging outcome of this research is the vast accumulation of theoretical models that help to capture the various threats that persistently hinder the best possible usage of today's powerful communication infrastructure. While theoretical models are essential to understanding the impact of any breakdown in the infrastructure, they are of limited application if the underlying business centric view is ignored. Information management in this context is the strategic management of the infrastructure, incorporating the knowledge about causes and consequences to arrive at the right balance between risk and profit. Modern information management systems are home to a vast repository of sensitive personal information. While these systems depend on quality data to boost the Quality of Service (QoS), they also run the risk of violating privacy regulations. The presence of network vulnerabilities also weaken these systems since security policies cannot always be enforced to prevent all forms of exploitation. This problem is more strongly grounded in the insufficient availability of resources, rather than the inability to predict zero-day attacks. System resources also impact the availability of access to information, which in itself is becoming more and more ubiquitous day by day. Information access times in such ubiquitous environments must be maintained within a specified QoS level. In short, modern information management must consider the mutual interactions between risks, resources and services to achieve wide scale acceptance. This dissertation explores these problems in the context of three important domains, namely disclosure control, security risk management and wireless data broadcasting. Research in these domains has been put together under the umbrella of multi-criteria decision making to signify that "business survival" is an equally important factor to consider while analyzing risks and providing solutions for their resolution. We emphasize that businesses are always bound by constraints in their effort to mitigate risks and therefore benefit the most from a framework that allows the exploration of solutions that abide by the constraints. Towards this end, we revisit the optimization problems being solved in these domains and argue that they oversee the underlying cost-benefit relationship. Our approach in this work is motivated by the inherent multi-objective nature of the problems. We propose formulations that help expose the cost-benefit relationship across the different objectives that must be met in these problems. Such an analysis provides a decision maker with the necessary information to make an informed decision on the impact of choosing a control measure over the business goals of an organization. The theories and tools necessary to perform this analysis are introduced to the community.
Advisors/Committee Members: Whitley, L. Darrell (advisor), Ray, Indrajit, 1966- (advisor), Ray, Indrakshi (committee member), Siegel, Howard Jay (committee member).
Subjects/Keywords: Information management; data broadcasting; multi-objective optimization; information security and privacy; Computer networks – Security measures; Computer security; Multiple criteria decision making
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APA (6th Edition):
Dewri, R. (2010). Multi-criteria analysis in modern information management. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/40284
Chicago Manual of Style (16th Edition):
Dewri, Rinku. “Multi-criteria analysis in modern information management.” 2010. Doctoral Dissertation, Colorado State University. Accessed January 28, 2021.
http://hdl.handle.net/10217/40284.
MLA Handbook (7th Edition):
Dewri, Rinku. “Multi-criteria analysis in modern information management.” 2010. Web. 28 Jan 2021.
Vancouver:
Dewri R. Multi-criteria analysis in modern information management. [Internet] [Doctoral dissertation]. Colorado State University; 2010. [cited 2021 Jan 28].
Available from: http://hdl.handle.net/10217/40284.
Council of Science Editors:
Dewri R. Multi-criteria analysis in modern information management. [Doctoral Dissertation]. Colorado State University; 2010. Available from: http://hdl.handle.net/10217/40284
Colorado State University
7.
Briceno Guerrero, Luis Diego.
Resource allocation for heterogeneous computing systems: performance criteria, robustness measures, optimization heuristics, and properties.
Degree: PhD, Electrical and Computer Engineering, 2010, Colorado State University
URL: http://hdl.handle.net/10217/40277
► Heterogeneous computing (HC) is the coordinated use of different types of machines, networks, and interfaces to maximize the combined performance and/or cost effectiveness of the…
(more)
▼ Heterogeneous computing (HC) is the coordinated use of different types of machines, networks, and interfaces to maximize the combined performance and/or cost effectiveness of the system. The application environments studied in this research are: a weather data processing system, a massive multi-player on-line gaming system, and a distributed satellite image processing system. Each one of these application environments was simulated on different computation platforms. Contributions for each environment: (1) mathematical model of environment, (2) defined a performance criterion, (3) defined robustness metric, (4) designed resource allocation heuristics based on performance and robustness measures, and (5) conducted simulation studies for evaluating and comparing heuristic techniques. We consider an iterative approach that decreases the finishing time of machines by repeatedly executing a resource allocation heuristic to minimize the make span of the considered machines and tasks. For each successive iteration, the make span machine of the previous iteration and the tasks assigned to it are removed from the set of considered machines and tasks. The contribution include identifying which characteristics heuristics need to generate improvement with the iterative approach, showing that the effectiveness of the iterative approach is heuristic dependent, and deriving a theorem to identify which heuristics cannot attain improvements.
Advisors/Committee Members: Siegel, Howard Jay (advisor), Maciejewski, Anthony A. (advisor), Böhm, Anton Pedro Willem, 1948- (committee member), Jayasumana, Anura P. (committee member), Smith, James T. (committee member).
Subjects/Keywords: Computing algorithms; heterogeneous computing; robustness; resource allocation; heuristics; Heterogeneous computing; Parallel processing (Electronic computers); Heuristic programming
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APA ·
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APA (6th Edition):
Briceno Guerrero, L. D. (2010). Resource allocation for heterogeneous computing systems: performance criteria, robustness measures, optimization heuristics, and properties. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/40277
Chicago Manual of Style (16th Edition):
Briceno Guerrero, Luis Diego. “Resource allocation for heterogeneous computing systems: performance criteria, robustness measures, optimization heuristics, and properties.” 2010. Doctoral Dissertation, Colorado State University. Accessed January 28, 2021.
http://hdl.handle.net/10217/40277.
MLA Handbook (7th Edition):
Briceno Guerrero, Luis Diego. “Resource allocation for heterogeneous computing systems: performance criteria, robustness measures, optimization heuristics, and properties.” 2010. Web. 28 Jan 2021.
Vancouver:
Briceno Guerrero LD. Resource allocation for heterogeneous computing systems: performance criteria, robustness measures, optimization heuristics, and properties. [Internet] [Doctoral dissertation]. Colorado State University; 2010. [cited 2021 Jan 28].
Available from: http://hdl.handle.net/10217/40277.
Council of Science Editors:
Briceno Guerrero LD. Resource allocation for heterogeneous computing systems: performance criteria, robustness measures, optimization heuristics, and properties. [Doctoral Dissertation]. Colorado State University; 2010. Available from: http://hdl.handle.net/10217/40277
. 
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