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- Genre: Doctoral Dissertation
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
A new algebraic system, Test Algebra (TA), is proposed for identifying faults in combinatorial testing for SaaS (Software-as-a-Service) applications. In the context of cloud computing, SaaS is a new software delivery model, in which mission-critical applications are composed, deployed, and executed on cloud platforms. Testing SaaS applications is challenging because new applications need to be tested once they are composed, and prior to their deployment. A composition of components providing services yields a configuration providing a SaaS application. While individual components
in the configuration may have been thoroughly tested, faults still arise due to interactions among the components composed, making the configuration faulty. When there are k components, combinatorial testing algorithms can be used to identify faulty interactions for t or fewer components, for some threshold 2 <= t <= k on the size of interactions considered. In general these methods do not identify specific faults, but rather indicate the presence or absence of some fault. To identify specific faults, an adaptive testing regime repeatedly constructs and tests configurations in order to determine, for each interaction of interest, whether it is faulty or not. In order to perform such testing in a loosely coupled distributed environment such as
the cloud, it is imperative that testing results can be combined from many different servers. The TA defines rules to permit results to be combined, and to identify the faulty interactions. Using the TA, configurations can be tested concurrently on different servers and in any order. The results, using the TA, remain the same.
in the configuration may have been thoroughly tested, faults still arise due to interactions among the components composed, making the configuration faulty. When there are k components, combinatorial testing algorithms can be used to identify faulty interactions for t or fewer components, for some threshold 2 <= t <= k on the size of interactions considered. In general these methods do not identify specific faults, but rather indicate the presence or absence of some fault. To identify specific faults, an adaptive testing regime repeatedly constructs and tests configurations in order to determine, for each interaction of interest, whether it is faulty or not. In order to perform such testing in a loosely coupled distributed environment such as
the cloud, it is imperative that testing results can be combined from many different servers. The TA defines rules to permit results to be combined, and to identify the faulty interactions. Using the TA, configurations can be tested concurrently on different servers and in any order. The results, using the TA, remain the same.
ContributorsQi, Guanqiu (Author) / Tsai, Wei-Tek (Thesis advisor) / Davulcu, Hasan (Committee member) / Sarjoughian, Hessam S. (Committee member) / Yu, Hongyu (Committee member) / Arizona State University (Publisher)
Created2014
Description
Most embedded applications are constructed with multiple threads to handle concurrent events. For optimization and debugging of the programs, dynamic program analysis is widely used to collect execution information while the program is running. Unfortunately, the non-deterministic behavior of multithreaded embedded software makes the dynamic analysis difficult. In addition, instrumentation overhead for gathering execution information may change the execution of a program, and lead to distorted analysis results, i.e., probe effect. This thesis presents a framework that tackles the non-determinism and probe effect incurred in dynamic analysis of embedded software. The thesis largely consists of three parts. First of all, we discusses a deterministic replay framework to provide reproducible execution. Once a program execution is recorded, software instrumentation can be safely applied during replay without probe effect. Second, a discussion of probe effect is presented and a simulation-based analysis is proposed to detect execution changes of a program caused by instrumentation overhead. The simulation-based analysis examines if the recording instrumentation changes the original program execution. Lastly, the thesis discusses data race detection algorithms that help to remove data races for correctness of the replay and the simulation-based analysis. The focus is to make the detection efficient for C/C++ programs, and to increase scalability of the detection on multi-core machines.
ContributorsSong, Young Wn (Author) / Lee, Yann-Hang (Thesis advisor) / Shrivastava, Aviral (Committee member) / Fainekos, Georgios (Committee member) / Lee, Joohyung (Committee member) / Arizona State University (Publisher)
Created2015
Description
The recent changes in the software markets gave users an unprecedented number
of alternatives for any given task. In such a competitive environment, it is imperative
to understand what drives user behavior. To that end, the research presented in
this dissertation, tries to uncover the impact of business strategies often used in the
software markets.
The dissertation is organized into three distinct studies into user choice and post
choice use of software. First using social judgment theory as foundation, zero price
strategies effects on user choice is investigated, with respect to product features,
consumer characteristics, and context effects. Second, role of social features in
moderating network effects on user choice is studied. And finally, the role of social
features on the effectiveness of add-on content strategy on continued user engagement
is investigated.
The findings of this dissertation highlight the alignments between popular business
strategies and broad software context. The dissertation contributes to the litera-
ture by uncovering hitherto overlooked complementarities between business strategy
and product features: (1) zero price strategy enhances utilitarian features but not
non-utilitarian features in software choice, (2) social features only enhance network
externalities but not social influence in user choice, (3) social features enhance the
effect of add-on content strategy in extending software engagement.
of alternatives for any given task. In such a competitive environment, it is imperative
to understand what drives user behavior. To that end, the research presented in
this dissertation, tries to uncover the impact of business strategies often used in the
software markets.
The dissertation is organized into three distinct studies into user choice and post
choice use of software. First using social judgment theory as foundation, zero price
strategies effects on user choice is investigated, with respect to product features,
consumer characteristics, and context effects. Second, role of social features in
moderating network effects on user choice is studied. And finally, the role of social
features on the effectiveness of add-on content strategy on continued user engagement
is investigated.
The findings of this dissertation highlight the alignments between popular business
strategies and broad software context. The dissertation contributes to the litera-
ture by uncovering hitherto overlooked complementarities between business strategy
and product features: (1) zero price strategy enhances utilitarian features but not
non-utilitarian features in software choice, (2) social features only enhance network
externalities but not social influence in user choice, (3) social features enhance the
effect of add-on content strategy in extending software engagement.
ContributorsKanat, Irfan (Author) / Santanam, Raghu (Thesis advisor) / Vinze, Ajay (Thesis advisor) / Gu, Bin (Committee member) / Arizona State University (Publisher)
Created2016
Description
Cloud computing has received significant attention recently as it is a new computing infrastructure to enable rapid delivery of computing resources as a utility in a dynamic, scalable, and visualized manner. SaaS (Software-as-a-Service) provide a now paradigm in cloud computing, which goal is to provide an effective and intelligent way to support end users' on-demand requirements to computing resources, including maturity levels of customizable, multi-tenancy and scalability. To meet requirements of on-demand, my thesis discusses several critical research problems and proposed solutions using real application scenarios. Service providers receive multiple requests from customers, how to prioritize those service requests to maximize the business values is one of the most important issues in cloud. An innovative prioritization model is proposed, which uses different types of information, including customer, service, environment and workflow information to optimize the performance of the system. To provide "on-demand" services, an accurate demand prediction and provision become critical for the successful of the cloud computing. An effective demand prediction model is proposed, and applied to a real mortgage application. To support SaaS customization and fulfill the various functional and quality requirements of individual tenants, a unified and innovative multi-layered customization framework is proposed to support and manage the variability of SaaS applications. To support scalable SaaS, a hybrid database design to support SaaS customization with two-layer database partitioning is proposed. To support secure SaaS, O-RBAC, an ontology based RBAC (Role based Access Control) model is used for Multi-Tenancy Architecture in clouds. To support a significant number of tenants, an easy to use SaaS construction framework is proposed. As a summary, this thesis discusses the most important research problems in cloud computing, towards effective and intelligent SaaS. The research in this thesis is critical to the development of cloud computing and provides fundamental solutions to those problems.
ContributorsShao, Qihong (Author) / Tsai, Wei-Tek (Thesis advisor) / Askin, Ronald (Committee member) / Ye, Jieping (Committee member) / Naphade, Milind (Committee member) / Arizona State University (Publisher)
Created2011
Description
Software-as-a-Service (SaaS) has received significant attention in recent years as major computer companies such as Google, Microsoft, Amazon, and Salesforce are adopting this new approach to develop software and systems. Cloud computing is a computing infrastructure to enable rapid delivery of computing resources as a utility in a dynamic, scalable, and virtualized manner. Computer Simulations are widely utilized to analyze the behaviors of software and test them before fully implementations. Simulation can further benefit SaaS application in a cost-effective way taking the advantages of cloud such as customizability, configurability and multi-tendency.
This research introduces Modeling, Simulation and Analysis for Software-as-Service in Cloud. The researches cover the following topics: service modeling, policy specification, code generation, dynamic simulation, timing, event and log analysis. Moreover, the framework integrates current advantages of cloud: configurability, Multi-Tenancy, scalability and recoverability.
The following chapters are provided in the architecture:
Multi-Tenancy Simulation Software-as-a-Service.
Policy Specification for MTA simulation environment.
Model Driven PaaS Based SaaS modeling.
Dynamic analysis and dynamic calibration for timing analysis.
Event-driven Service-Oriented Simulation Framework.
LTBD: A Triage Solution for SaaS.
This research introduces Modeling, Simulation and Analysis for Software-as-Service in Cloud. The researches cover the following topics: service modeling, policy specification, code generation, dynamic simulation, timing, event and log analysis. Moreover, the framework integrates current advantages of cloud: configurability, Multi-Tenancy, scalability and recoverability.
The following chapters are provided in the architecture:
Multi-Tenancy Simulation Software-as-a-Service.
Policy Specification for MTA simulation environment.
Model Driven PaaS Based SaaS modeling.
Dynamic analysis and dynamic calibration for timing analysis.
Event-driven Service-Oriented Simulation Framework.
LTBD: A Triage Solution for SaaS.
ContributorsLi, Wu (Author) / Tsai, Wei-Tek (Thesis advisor) / Sarjoughian, Hessam S. (Committee member) / Ye, Jieping (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2015
Description
Critical infrastructures in healthcare, power systems, and web services, incorporate cyber-physical systems (CPSes), where the software controlled computing systems interact with the physical environment through actuation and monitoring. Ensuring software safety in CPSes, to avoid hazards to property and human life as a result of un-controlled interactions, is essential and challenging. The principal hurdle in this regard is the characterization of the context driven interactions between software and the physical environment (cyber-physical interactions), which introduce multi-dimensional dynamics in space and time, complex non-linearities, and non-trivial aggregation of interaction in case of networked operations. Traditionally, CPS software is tested for safety either through experimental trials, which can be expensive, incomprehensive, and hazardous, or through static analysis of code, which ignore the cyber-physical interactions. This thesis considers model based engineering, a paradigm widely used in different disciplines of engineering, for safety verification of CPS software and contributes to three fundamental phases: a) modeling, building abstractions or models that characterize cyberphysical interactions in a mathematical framework, b) analysis, reasoning about safety based on properties of the model, and c) synthesis, implementing models on standard testbeds for performing preliminary experimental trials. In this regard, CPS modeling techniques are proposed that can accurately capture the context driven spatio-temporal aggregate cyber-physical interactions. Different levels of abstractions are considered, which result in high level architectural models, or more detailed formal behavioral models of CPSes. The outcomes include, a well defined architectural specification framework called CPS-DAS and a novel spatio-temporal formal model called Spatio-Temporal Hybrid Automata (STHA) for CPSes. Model analysis techniques are proposed for the CPS models, which can simulate the effects of dynamic context changes on non-linear spatio-temporal cyberphysical interactions, and characterize aggregate effects. The outcomes include tractable algorithms for simulation analysis and for theoretically proving safety properties of CPS software. Lastly a software synthesis technique is proposed that can automatically convert high level architectural models of CPSes in the healthcare domain into implementations in high level programming languages. The outcome is a tool called Health-Dev that can synthesize software implementations of CPS models in healthcare for experimental verification of safety properties.
ContributorsBanerjee, Ayan (Author) / Gupta, Sandeep K.S. (Thesis advisor) / Poovendran, Radha (Committee member) / Fainekos, Georgios (Committee member) / Maciejewski, Ross (Committee member) / Arizona State University (Publisher)
Created2012