Matching Items (4)
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- All Subjects: Cloud Computing
- All Subjects: Phones
- Creators: Collofello, James
Description
The pay-as-you-go economic model of cloud computing increases the visibility, traceability, and verifiability of software costs. Application developers must understand how their software uses resources when running in the cloud in order to stay within budgeted costs and/or produce expected profits. Cloud computing's unique economic model also leads naturally to an earn-as-you-go profit model for many cloud based applications. These applications can benefit from low level analyses for cost optimization and verification. Testing cloud applications to ensure they meet monetary cost objectives has not been well explored in the current literature. When considering revenues and costs for cloud applications, the resource economic model can be scaled down to the transaction level in order to associate source code with costs incurred while running in the cloud. Both static and dynamic analysis techniques can be developed and applied to understand how and where cloud applications incur costs. Such analyses can help optimize (i.e. minimize) costs and verify that they stay within expected tolerances. An adaptation of Worst Case Execution Time (WCET) analysis is presented here to statically determine worst case monetary costs of cloud applications. This analysis is used to produce an algorithm for determining control flow paths within an application that can exceed a given cost threshold. The corresponding results are used to identify path sections that contribute most to cost excess. A hybrid approach for determining cost excesses is also presented that is comprised mostly of dynamic measurements but that also incorporates calculations that are based on the static analysis approach. This approach uses operational profiles to increase the precision and usefulness of the calculations.
ContributorsBuell, Kevin, Ph.D (Author) / Collofello, James (Thesis advisor) / Davulcu, Hasan (Committee member) / Lindquist, Timothy (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2012
Description
Machine learning tutorials often employ an application and runtime specific solution for a given problem in which users are expected to have a broad understanding of data analysis and software programming. This thesis focuses on designing and implementing a new, hands-on approach to teaching machine learning by streamlining the process of generating Inertial Movement Unit (IMU) data from multirotor flight sessions, training a linear classifier, and applying said classifier to solve Multi-rotor Activity Recognition (MAR) problems in an online lab setting. MAR labs leverage cloud computing and data storage technologies to host a versatile environment capable of logging, orchestrating, and visualizing the solution for an MAR problem through a user interface. MAR labs extends Arizona State University’s Visual IoT/Robotics Programming Language Environment (VIPLE) as a control platform for multi-rotors used in data collection. VIPLE is a platform developed for teaching computational thinking, visual programming, Internet of Things (IoT) and robotics application development. As a part of this education platform, this work also develops a 3D simulator capable of simulating the programmable behaviors of a robot within a maze environment and builds a physical quadrotor for use in MAR lab experiments.
ContributorsDe La Rosa, Matthew Lee (Author) / Chen, Yinong (Thesis advisor) / Collofello, James (Committee member) / Huang, Dijiang (Committee member) / Arizona State University (Publisher)
Created2018
Description
Smartphones have become increasingly common over the past few years, and mobile games continue to be the most common type of application (Apple, Inc., 2013). For many people, the social aspect of gaming is very important, and thus most mobile games include support for playing with multiple players. However, there is a lack of common knowledge about which implementation of this functionality is most favorable from a development standpoint. In this study, we evaluate three different types of multiplayer gameplay (pass-and-play, Bluetooth, and GameCenter) via development cost and user interviews. We find that pass-and-play, the most easily-implemented mode, is not favored by players due to its inconvenience. We also find that GameCenter is not as well favored as expected due to latency of GameCenter's servers, and that Bluetooth multiplayer is the most well favored for social play due to its similarity to real-life play. Despite there being a large overhead in developing and testing Bluetooth and GameCenter multiplayer due to Apple's development process, this is irrelevant since professional developers must enroll in this process anyway. Therefore, the most effective multiplayer mode to develop is mostly determined by whether Internet play is desirable: Bluetooth if not, GameCenter if so. Future studies involving more complete development work and more types of multiplayer modes could yield more promising results.
ContributorsBradley, Michael Robert (Author) / Collofello, James (Thesis director) / Wilkerson, Kelly (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor)
Created2013-12
Description
Smartphone privacy is a growing concern around the world; smartphone applications routinely take personal information from our phones and monetize it for their own profit. Worse, they're doing it legally. The Terms of Service allow companies to use this information to market, promote, and sell personal data. Most users seem to be either unaware of it, or unconcerned by it. This has negative implications for the future of privacy, particularly as the idea of smart home technology becomes a reality. If this is what privacy looks like now, with only one major type of smart device on the market, what will the future hold, when the smart home systems come into play. In order to examine this question, I investigated how much awareness/knowledge smartphone users of a specific demographic (millennials aged 18-25) knew about their smartphone's data and where it goes. I wanted three questions answered: - For what purposes do millennials use their smartphones? - What do they know about smartphone privacy and security? - How will this affect the future of privacy? To accomplish this, I gathered information using a distributed survey to millennials attending Arizona State University. Using statistical analysis, I exposed trends for this demographic, discovering that there isn't a lack of knowledge among millennials; most are aware that smartphone apps can collect and share data and many of the participants are not comfortable with the current state of smartphone privacy. However, more than half of the study participants indicated that they never read an app's Terms of Service. Due to the nature of the privacy vs. convenience argument, users will willingly agree to let apps take their personal in- formation, since they don't want to give up the convenience.
ContributorsJones, Scott Spenser (Author) / Atkinson, Robert (Thesis director) / Chavez-Echeagaray, Maria Elena (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12