No abstract

Cloud computing has paved the way to the flexible deployment of software applications. This flexibility offers service providers a number of options to tailor their deployments to the observed and foreseen customer workloads, without incurring in large capital costs. However, cloud deployments pose novel challenges regarding application reliability and performance. Examples include managing the reliability of deployments that make use of spot instances, or coping with the performance variability caused by multiple tenants in a virtualized environment. In this paper we introduce LINE, a tool for performance and reliability analysis of software applications. LINE solves Layered Queueing Network (LQN) models, a popular class of stochastic models in software performance engineering, by setting up and solving an associated system of ordinary differential equations. A key differentiator of LINE compared to existing solvers for LQNs is that LINE incorporates a model of the environment the application operates in. This enables the modeling of reliability and performance issues such as resource failures, server breakdowns and repairs, slow start-up times, resource interference due to multi-tenancy, among others. This paper describes the LINE tool, its support for performance and reliability modeling, and illustrates its potential by comparing LINE predictions against data obtained from a cloud deployment. We also illustrate the applicability of LINE with a case study on reliability-aware resource provisioning.

For years now, most researchers modeling physical and cognitive behavior have focused on one area or the other, dividing human performance into "neck up" and "neck down." But the current state of the art in both areas has advanced to the point that researchers should begin considering how the two areas interact to produce behaviors. In light of this, some common terms are defined so researchers working in different disciplines and application areas can understand each other better. Second, a crude "roadmap" is presented to suggest areas of interaction where researchers developing digital human form and other physical performance models might be able to collaborate with researchers developing cognitive models of human performance in order to advance the "state-of-the-art" in replicating and predicting human performance.

According to general aviation manufacturers, all aircraft rolling off the assembly line are or will be equipped with next-generation electronic flight instrument cockpits, called 'glass' cockpits. Because most pilots were trained with older analog displays, it becomes imperative to find out what human factors issues the pilots will encounter when they transition to glass displays. A comparative study was carried out in a general aviation aircraft simulator between instrumentation of the type used in conventional and glass cockpits for recovery from unusual attitudes. Glass displays showed longer recovery time than round-dial displays. Low-time pilots judged analog displays as more usable than glass displays. Suggestions are made to design a hybrid display of round dial and vertical tapes as well as examine unusual attitude training methods more closely.

No abstract