Courtney Powell

Towards Thought Control of Next-Generation Wearable Computing Devices

A new wearable computing era featuring devices such as Google Glass, smartwatches, and digital contact lenses is almost upon us, bringing with it usability issues that conventional human computer interaction (HCI) modalities cannot resolve. Brain computer interface (BCI) technology is also rapidly advancing and is now at a point where noninvasive BCIs are being used in games and in healthcare. Thought control of wearable devices is an intriguing vision and would facilitate more intuitive HCI; however, to achieve even a modicum of control BCI currently requires massive processing power that is not available on mobile devices. Cloud computing is a maturing paradigm in which elastic computing power is provided on demand over networks. In this paper, we review the three technologies and take a look at possible ways cloud computing can be harnessed to provide the computational power needed to facilitate practical thought control of next-generation wearable computing devices.

Evaluation of Three Steady-State NSGA-III Offspring Selection Schemes for Many-Objective Optimization

In this study, we implemented three steady-state versions of NSGA-III that differ by the manner in which the offspring combined with the parent population is selected. These three schemes were then evaluated on the standard problem sets DTLZ1–4 in terms of four popular criteria: inverse generational distance (IGD), hypervolume (HV), convergence, and diversity. The results obtained suggest that utilizing a selection scheme in which the offspring is selected from the first non-dominated rank results in better solutions than other steady-state offspring selection schemes.

Design of an SSO authentication infrastructure for heterogeneous inter-cloud environments

This paper outlines the design of an authentication infrastructure for linking distributed heterogeneous cloud systems managed by different cloud management middleware to enable them to interoperate as an integrated inter-cloud system. This authentication infrastructure achieves single sign-on (SSO), which allows users to log in once and access the various cloud systems without being asked to log in again at each system. Further, it does so without changing the design of the existing cloud systems by using a proxy certificate repository and Shibboleth authentication technology. We also report on a prototype implementation of the system that validates the design of the authentication infrastructure.

Structured Development of DHTML Programs from Abstract Ideas Based on the Equivalent Transformation Framework

Dynamic HTML (DHTML) is a term used for the combination of the technologies HyperText Markup Language (HTML), cascading style sheets (CSS), and JavaScript. This combination allows precise positioning, formatting and embellishment of document content to be realized; instead of arbitrary placement due to a browsers rendering choices. It also allows the creation of documents that: 1.contain inline animations and; 2.can change by themselves or in response to user interactions. However, as in low level languages, there is no structured means of developing a DHTML program from abstract ideas (system specifications). In this paper we propose the equivalent transformation (ET) framework as a model for aiding in the creation of optimized DHTML programs from abstract ideas. We also demonstrate the advantages inherent in using this framework by using the example of a ping-pong game.

Towards a Formal Behavioral Model for Rich Internet Applications

Rich Internet Applications (RIAs) seek to combine the best of traditional desktop applications with the best of the Web. However, due to the complexity of these applications, traditional Web Application methodologies and techniques are proving inadequate to properly model and implement them in a systematic way. In this paper we outline an equivalent transformation-based method for constructing a model for RIAs. This model provides some of the essential behavioral semantics of RIAs with a view towards facilitating simple and intuitive mapping of appropriate technologies to the model. Keywordsrich internet applications; web applications; equivalent transformation; formal methods; modelling;

Conceptual Modelling and Program Generation for Dynamic Interactive Systems

Correct and efficient Dynamic Interactive Systems (DISs) are very difficult to conceptualize, model, implement, and maintain due to their dynamism and multiple concurrent interactive processes. As a result construction of DISs is time-consuming and implemented DISs are error-prone and difficult to comprehend and analyze. In this paper we describe an intuitive and systematic process by which models for DISs can be incrementally conceptualized from ideas or specifications and then used to generate concrete DIS programs in a target language/platform. The models constructed and the generated DIS programs are mathematically comprehensible; are amenable to Formal Methods; and can be reasoned about and rigorously analyzed.

Towards a small diverse pareto-optimal solutions set generator for multiobjective optimization problems

Multiobjective evolutionary algorithms (MOEAs) try to produce enough and sufficiently diverse Pareto-optimal tradeoff solutions to cover the entire Pareto surface. However, in practical scenarios, presenting numerous solutions to stakeholders may result in confusion and indecision. This paper proposes a method for generating a small (user-specified) number of well-distributed Pareto-optimal feasible solutions for multiobjective problems. The proposed method can be applied to a set of aggregate solutions produced by (1) one MOEA over multiple runs, (2) several different MOEAs, or (3) a universal set of feasible solutions produced by one or more constraint solvers.

A Level-Wise Load Balanced Scientific Workflow Execution Optimization using NSGA-II

Over the past decade, cloud computing has grown in popularity for the processing of scientific applications as a result of the scalability of the cloud and the ready availability of on-demand computing and storage resources. It is also a cost-effective alternative for scientific workflow executions with a pay-per-use paradigm. However, providing services with optimal performance at the lowest financial resource deployment cost is still challenging. Several fine-grained tasks are included in scientific workflow applications, and efficient execution of these tasks according to their processing dependency to minimize the overall makespan during workflow execution is an important research area. In this paper, a system for level-wise workflow makespan optimization and virtual machine deployment cost minimization for overall workflow optimization in cloud infrastructure is proposed. Further, balanced task clustering, to ensure load balancing in different virtual machine instances at each workflow level during workflow execution, is also considered. The system retrieves the necessary workflow information from a directed acyclic graph and uses the non-dominated sorting genetic algorithm II (NSGA-II) to carry out multiobjective optimization. Pareto front solutions obtained for makespan time and instance resource deployment cost for several scientific workflow applications verify the efficacy of our system.

Multiobjective Level-Wise Scientific Workflow Optimization in IaaS Public Cloud Environment

Cloud computing in the field of scientific applications such as scientific big data processing and big data analytics has become popular because of its service oriented model that provides a pool of abstracted, virtualized, dynamically scalable computing resources and services on demand over the Internet. However, resource selection to make the right choice of instances for a certain application of interest is a challenging problem for researchers. In addition, providing services with optimal performance at the lowest financial resource deployment cost based on users’ resource selection is quite challenging for cloud service providers. Consequently, it is necessary to develop an optimization system that can provide benefits to both users and service providers. In this paper, we conduct scientific workflow optimization on three perspectives: makespan minimization, virtual machine deployment cost minimization, and virtual machine failure minimization in the cloud infrastructure in a level-wise manner. Further, balanced task assignment to the virtual machine instances at each level of the workflow is also considered. Finally, system efficiency verification is conducted through evaluation of the results with different multiobjective optimization algorithms such as SPEA2 and NSGA-II.

Multi-objective Hadoop Configuration Optimization Using Steady-State NSGA-II

Hadoop configuration optimization is very challenging because of the complexity of its framework. And optimized Hadoop parameter configuration settings depend significantly on the performance of MapReduce applications in the cluster. Although much research has been conducted on Hadoop parameters configuration optimization, configuring its resource setting parameters to minimize the execution time of MapReduce jobs in clusters still needs a lot of continuing researches. Further, determining the type of machine instances that should be used to minimize the resource usage cost for executing applications in clusters is also difficult. This paper addresses these problems by optimizing the instance resource usage and execution time of MapReduce tasks using a multi-objective steady-state Non-dominated Sorting Genetic Algorithm II (ssNSGA-II) approach. In this approach, the instance resource usage cost of MapReduce tasks is calculated based on the cost of machine instance types and the number of machine instances in the Hadoop cluster. The optimized configuration is identified by selecting an optimal setting that satisfies two objective functions associated with instance resource usage and execution time minimization, from Pareto optimal front solutions. Although dynamic machine instance type is considered within the search process in our system, dynamic cluster size is out of consideration and intended to be carried out in our future. Experiments conducting using workloads from the HiBench benchmark on a high specification 6-node Hadoop cluster verify the efficacy of our proposed approach.