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Browsing by Author "Siegel, Howard Jay, advisor"

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    Minimizing energy costs for geographically distributed heterogeneous data centers
    (Colorado State University. Libraries, 2018) Hogade, Ninad, author; Pasricha, Sudeep, advisor; Siegel, Howard Jay, advisor; Burns, Patrick J., committee member
    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.
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    Resource allocation for heterogeneous computing systems: performance criteria, robustness measures, optimization heuristics, and properties
    (Colorado State University. Libraries, 2010) Briceno Guerrero, Luis Diego, author; 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
    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.
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    Resource allocation optimization in the smart grid and high-performance computing
    (Colorado State University. Libraries, 2015) Hansen, Timothy M., author; Siegel, Howard Jay, advisor; Maciejewski, Anthony A., advisor; Suryanarayanan, Siddharth, committee member; Bradley, Thomas H., committee member
    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 designed and analyzed within the two problem domains to evaluate their performance.
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    Robust resource allocation heuristics for military village search missions
    (Colorado State University. Libraries, 2012) Maxwell, Paul, author; Siegel, Howard Jay, advisor; Maciejewski, Anthony A., advisor; Potter, Jerry, committee member; Smith, James, committee member; Hayne, Stephen, committee member
    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.
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    Robust resource-allocation methods for QOS-constrained parallel and distributed computing systems
    (Colorado State University. Libraries, 2008) Shestak, Valdimir, author; Maciejewski, A. A., advisor; Siegel, Howard Jay, advisor
    This research investigates the problem of robust resource allocation for distributed computing systems operating under imposed Quality of Service (QoS) constraints. Often, such systems are expected to function in a physical environment replete with uncertainty, which causes the amount of processing required over time to fluctuate substantially. In the first two studies, we show how an effective resource allocation can be achieved in the heterogeneous shipboard distributed computing system and IBM cluster based imaging system. The general form for a stochastic robustness metric is then presented based on a mathematical model where the relationship between uncertainty in system parameters and its impact on system performance are described stochastically. The utility of the established metric is exploited in the design of optimization techniques based on greedy and iterative approaches that address the problem of resource allocation in a large class of distributed systems operating on periodically updated data sets. One of the major reasons for possible QoS violations in distributed systems is a loss of resources, frequently caused by abnormal operating conditions. One aspect that makes a resource allocation problem extremely challenging in such systems is a random nature of resource failures and recoveries. The last study presented in this work describes a solution method that was developed for this case based on the concepts of the Derman-Lieberman-Ross theorem. The experimental results indicate a significant potential of this approach to generate robust resource allocations in unstable distributed systems.