Abstract:

Traffic accidents are one of the leading causes of fatalities in the US. An important indicator of survival rates after an accident is the time between the accident and when emergency medical personnel are dispatched to the scene. Eliminating the time between when an accident occurs and when first responders are dispatched to the scene decreases mortality rates by 6%. One approach to eliminating the delay between accident occurrence and first responder dispatch is to use in-vehicle automatic accident detection and notification systems, which sense when traffic accidents occur and immediately notify emergency personnel. These in-vehicle systems, however, are not available in all cars and are expensive to retrofit for older vehicles.

This paper describes how smartphones, such as the iPhone and Google An- droid platforms, can automatically detect traffic accidents using accelerometers and accoustic data, immediately notify a central emergency dispatch server after an accident, and provide situational awareness through photographs, GPS coor- dinates, VOIP communication channels, and accident data recording. This paper provides the following contributions to the study of detecting traffic accidents via smartphones: (1) we present a formal model for accident detection that com- bines sensors and context data, (2) we show how smartphone sensors, network connections, and web services can be used to provide situational awarenss to first responders, and (3) we provide empirical results demonstrating the efficacy of dif- ferent approaches employed by smartphone accident detection systems to prevent false positives.

Abstract:

Cloud computing can reduce power consumption by using virtualized computational resources to provision an application’s computational resources on-demand. Auto- scaling is an important cloud computing technique that dynamically allocates compu- tational resources to applications to match their current loads precisely, thereby remov- ing resources that would otherwise remain idle and waste power. This paper presents a model-driven engineering approach to optimizing the configuration, energy consump- tion, and operating cost of cloud auto-scaling infrastructure to create greener comput- ing enviornments that reduce emissions resulting from superfluous idle resources. The paper provides four contributions to the study of model-driven configuration of cloud auto-scaling infrastructure by (1) explaining how virtual machine configurations can be captured in feature models, (2) describing how these models can be transformed into constraint satisfaction problems (CSPs) for configuration and energy consumption op- timization, (3) showing how optimal auto-scaling configurations can be derived from these CSPs with a constraint solver, and (4) presenting a case-study showing the energy consumption/cost reduction produced by this model-driven approach.

Bibliographic Info:

Brian Dougherty, Jules White, Douglas C. Schmidt, Model-driven Auto-scaling of Green Cloud Computing Infrastructure, Future Generation Computer Systems (to appear)