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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)
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)
—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(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)
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)
—Spatio-temporal data streams generated from sensors can be erroneous and could lead to serious problems. For example, pitot tubes icing which occurred to Air France flight 447 (AF447) in June 2009 led to faulty airspeed readings and eventually caused a fatal accident killing all 228 people on board. As an effort to develop self-healing spatio-temporal data(More)
—As we are facing ever increasing air traffic demand, it is critical to enhance air traffic capacity and alleviate human controllers' workload by viewing air traffic optimization as a continuous/online streaming problem. Air traffic optimization is 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)
—Cloud computing adds great on-demand scala-bility to stream processing systems with its pay-per-use cost model. However, to promise service level agreements to users while keeping resource allocation cost low is a challenging task due to uncertainties coming from various sources, such as the target application's scalability, future computational demand,(More)