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Experiment-in-the-Loop Computing (EILC) requires support for numerous types of processing and the management of heterogeneous infrastructure over a dynamic range of scales: from the edge to the cloud and HPC, and intermediate resources.…
Serverless computing has emerged as an attractive paradigm due to the efficiency of development and the ease of deployment without managing any underlying infrastructure. Nevertheless, serverless computing approaches face numerous…
Streaming systems are present throughout modern applications, processing continuous data in real-time. Existing streaming languages have a variety of semantic models and guarantees that are often incompatible. Yet all these languages are…
Efficient data streaming is essential for real-time data analytics, visualization, and machine learning model training, particularly when dealing with high-volume datasets. Various streaming technologies and serialization protocols have…
Serverless computing is an approach to cloud computing that allows programmers to run serverless functions in response to external events. Serverless functions are priced at sub-second granularity, support transparent elasticity, and…
While cloud environments and auto-scaling solutions have been widely applied to traditional monolithic applications, they face significant limitations when it comes to microservices-based architectures. Microservices introduce additional…
Serverless computing has become very popular today since it largely simplifies cloud programming. Developers do not need to longer worry about provisioning or operating servers, and they pay only for the compute resources used when their…
This proposal presents a graph computing framework intending to support both online and offline computing on large dynamic graphs efficiently. The framework proposes a new data model to support rich evolving vertex and edge data types. It…
Continuous reasoning has proven effective in incrementally analysing changes in application codebases within Continuous Integration/Continuous Deployment (CI/CD) software release pipelines. In this article, we present a novel declarative…
Finding patterns in large highly connected datasets is critical for value discovery in business development and scientific research. This work focuses on the problem of subgraph matching on streaming graphs, which provides utility in a…
We consider two classes of stream-based computations which admit taking linear combinations of execution runs: probabilistic sampling and generalized animation. The dataflow architecture is a natural platform for programming with streams.…
Using multiple streams can improve the overall system performance by mitigating the data transfer overhead on heterogeneous systems. Currently, very few cases have been streamed to demonstrate the streaming performance impact and a…
Fog computing extends the cloud computing paradigm by allocating substantial portions of computations and services towards the edge of a network, and is, therefore, particularly suitable for large-scale, geo-distributed, and data-intensive…
Spatial computing devices have been shown to significantly accelerate stencil computations, but have so far relied on unrolling the iterative dimension of a single stencil operation to increase temporal locality. This work considers the…
Serverless computing has attracted a broad range of applications due to its ease of use and resource elasticity. However, developing serverless applications often poses a dilemma -- relying on general-purpose serverless platforms can fall…
Software Defined Networking (SDN) has emerged as a revolutionary paradigm to manage cloud infrastructure. SDN lacks scalable trust setup and verification mechanism between Data Plane-Control Plane elements, Control Plane elements, and…
A cloud-based data stream management system (DSMS) handles fast data by utilizing the massively parallel processing capabilities of the underlying platform. An important property of such a DSMS is elasticity, meaning that nodes can be…
Recent advances in computing architectures and networking are bringing parallel computing systems to the masses so increasing the number of potential users of these kinds of systems. In particular, two important technological evolutions are…
Processing sensitive data, such as those produced by body sensors, on third-party untrusted clouds is particularly challenging without compromising the privacy of the users generating it. Typically, these sensors generate large quantities…
With the establishment of cloud computing as the environment of choice for most modern applications, auto-scaling is an economic matter of great importance. For applications like stream computing that process ever changing amounts of data,…