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We present the Cloud Operating System (COS), a middleware framework to support autonomous workload elasticity and scalability based on application-level migration as a reconfiguration strategy. While other scalable frameworks (e.g., MapReduce or Google App Engine) force application developers to write programs following specific APIs, COS provides(More)
Cloud computing's pay-per-use model greatly reduces upfront cost and also enables on-demand scalability as service demand grows or shrinks. Hybrid clouds are an attractive option in terms of cost benefit, however, without proper elastic resource management, computational resources could be over-provisioned or under-provisioned, resulting in wasting money or(More)
In this paper, we describe a programming model to enable reasoning about spatio-temporal data streams. A spatio-temporal data stream is one where each datum is related to a point in space and time. For example, sensors in a plane record airspeeds (v a) during a given flight. Similarly, GPS units record an airplane's flight path over the ground including(More)
Applications on smartphones are extremely popular as users can download and install them very easily from a service provider's application repository. Most of the applications are thoroughly tested and verified on a target smartphone platform; however, some applications could be very computationally intensive and overload the smartphone's resource(More)
Self-healing spatio-temporal data streaming systems enable error detection and data correction based on error signatures. Error signatures are mathematical function patterns with constraints and are used to identify and categorize errors in redundant spatio-temporal data streams. In this paper, we apply these methods to real data from a private Cessna(More)
Detecting and recovering from errors in data streams is paramount to developing successful autonomous real-time streaming applications. In this paper, we devise a multi-modal data error detection and recovery architecture to enable automated recovery from data errors in streaming applications based on available redundancy. We formally define error(More)
In this paper, we describe the design and implementation of PILOTS, a ProgrammIng Language for spatiO-Temporal data Streaming applications. Using PILOTS, application developers can easily program an application that handles spatio-temporal data streams by writing a high-level declarative program specification. Whereas spatio-temporal data is available with(More)
As we are facing ever increasing air traffic demand, it is critical to enhance air traffic capacity and alleviate humancontrollers' workload by viewing air traffic optimization as acontinuous/online streaming problem. Air traffic optimizationis commonly formulated as an integer linear programming(ILP) problem. Since ILP is NP-hard, it is(More)
Dynamic Data-Driven Avionics Systems (DDDAS) embody ideas from the Dynamic Data-Driven Application Systems paradigm by creating a data-driven feedback loop that analyzes spatio-temporal data streams coming from aircraft sensors and instruments, looks for errors in the data signaling potential failure modes, and corrects for erroneous data when possible. In(More)