Arindam Das

A cognitive simulation model for novice text entry on cell phone keypads

Motivation -- To create a cognitive simulation model that predicts text entry performance and learning on cell phone keypads by novice users. Research approach -- A programmable cognitive architecture, ACT-R, is used to execute the simulation model. Part of the simulation result is compared with the result of a previous user study. Findings/Design -- The proposed model is an a priori model (not tuned to any real user data) that predicts the amount of time spent in finding a key on the keypad and pressing it repeatedly. The predicted amount of time in finding a key differs by 6% and the time between two repeated key-presses of the same key by 27% compared to the results of a previous user study. The model also captures the learning of keypad layout by novice users. Memorization of keypad layout is simulated using task repetition. Research limitations/Implications -- This research has several limitations described towards the end of this paper. An important one among them is that the work does not model the impact of visual distracters in the field of view (frontal surface of the handset) on user performance. Originality/Value -- This is the first cognitive simulation model of novice users text entry performance and learning on cell phone keypads. Take away message -- This work introduces an a priori congnitive model of text entry by novice users. This forms a basis for systematic exploration of keypad designs for cell phones in shorter time and lower cost.

Modeling learning effects in mobile texting

No work on mobile text messaging so far has taken into account the effect of learning on the change in visual exploration behavior as users progress from non-expert to expert level. We discuss within the domain of multi-tap texting on mobile phone and address the process of searching versus selecting a letter on the keypad interface. We develop a simulation model that forecasts the probability of letter location recall by non-expert users and thereby models learning, as the user acquires expertise in recalling, with practice, session after session. We then plugin this probability within a model of visual strategy that combines the effect of different ways visual exploration: non-expert users search for a letter while expert users select a letter. The observed non-expert non-motor time preceding a key press (for a letter) correlates extremely well with the simulation results.

Performability evaluation of mobile client-server systems

Recent advances in mobile computing have made it possible for customers carrying handheld devices to have access to data and information services regardless of their physical location. Customers expect the same level of service in terms of availability and performance from the mobile applications as with their non-mobile counterparts. Different types of client-server computing architectures are used today that facilitates such mobile access of data. In order to achieve high performance and availability, replicas of data servers are usually added to tolerate failures and balance workloads. This paper introduces a modeling technique to evaluate combined performance and availability that not only considers the failures of the mobile (i.e. wireless) links, mobile devices and the data servers but also takes into account the rate of mobility of the clients. It demonstrates the applicability of the model by building and analyzing models for two client-server architectures.

Comparing cognitive effort in spatial learning of text entry keyboards and ShapeWriters

Stable structured layouts of buttons are a primary means of control for input in current graphical user interfaces. Such layouts are ubiquitous---from tiny iPhone screens to large kiosk screens in the malls---they are found everywhere. Yet, there has been relatively little theoretical account that compares the impact of cognitive effort on learning such stable layouts. In this paper, we demonstrate that prior empirical results on cognitive effort in learning stable layouts are theoretically predictable through the memory activation model of a cognitive architecture, ACT-R. We go beyond previous work by quantitatively comparing the level of cognitive effort in terms of a newly introduced parameter in the declarative memory model of ACT-R. We theoretically compare the cognitive effort of two different layouts of graphical buttons with respect to their label representativeness in the domains of traditional keyboard and ShapeWriter.

Effect of Human Learning on Performance of Cloud Applications

End users of cloud applications range from novice to expert depending on how experienced they are in using them. With repeated usage of an application, a users think time gradually decreases, known as human learning phenomenon. This decrease impacts the system workload thereby affecting the applications transient performance. However, such impact of human learning on system performance has never been accounted for. In this work we propose a closed queueing network model that accounts for human learning in analyzing the transient performance of a tiered cloud application. We solve the model using discrete event simulation. The model results show that the lack of accountability of human learning may lead to incorrectly selecting a virtual machine configuration that fails to meet the system response time requirement.

Estimating Response Time Percentiles of Cloud-Based Tiered Web Applications in Presence of VM Failures

Increasingly, software applications are being deployed in clouds because cloud computing offers several advantages -- for example, it relieves the application service providers from buying and maintaining data centers thereby reducing the operational costs, it allows dynamic scaling of virtual machines as required on a pay-per-use basis, and, it promotes easy deployment in multiple geographic locations at minimal cost. A key challenge in deploying a multi-tier web application in cloud is to achieve low variability in its response time. In this paper, we analyze the behaviour of a 3-tier cloud-based web application. We propose a hierarchical model to compute the response-time distribution that considers performance degradation of the application due to VM failures. Our model applies order statistics to describe the applications availability behavior and open queueing network to describe its performance behavior. We solve the open queueing network using discrete event simulation. The results show that in a 3-tier system, a configuration with large number of virtual machines (VMs) does not necessarily perform better than a configuration with smaller number of VMs. Moreover, for a given set of performance and availability parameters, the results further show that different system configurations containing the same number of VMs yield different performance depending on the replication level of the VMs in different tiers. We demonstrate that our model can be exploited to support the selection of appropriate number of replicas for different tiers that would meet the service-level agreement specified in terms of response-time percentiles.

Fault-tree based evaluation of tiered autonomic systems

The success of self configuration in autonomic distributed systems depends on the availability of the management components and their interconnections. This paper describes a fault tree model to evaluate the availability of a tiered autonomic system that considers the failures of management components. The model predictions will guide the selection and placement of the management components to meet the service-level availability requirements at substantially lower cost and time.