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Evaluation in empirical computer science is essential to show progress and assess technologies developed. Several research domains such as information retrieval have long relied on systematic evaluation to measure progress: here, the…
Scientific computing often requires the availability of a massive number of computers for performing large scale experiments. Traditionally, these needs have been addressed by using high-performance computing solutions and installed…
Progress in science is deeply bound to the effective use of high-performance computing infrastructures and to the efficient extraction of knowledge from vast amounts of data. Such data comes from different sources that follow a cycle…
Cloud Computing is a paradigm of both parallel processing and distributed computing. It offers computing facilities as a utility service in pay as par use manner. Virtualization, self service provisioning, elasticity and pay per use are the…
Scientific workflows have become integral tools in broad scientific computing use cases. Science discovery is increasingly dependent on workflows to orchestrate large and complex scientific experiments that range from execution of a…
The Function-as-a-Service (FaaS) paradigm has a lot of potential as a computing model for fog environments comprising both cloud and edge nodes. When the request rate exceeds capacity limits at the edge, some functions need to be offloaded…
Cloud computing recently developed into a viable alternative to on-premises systems for executing high-performance computing (HPC) applications. With the emergence of new vendors and hardware options, there is now a growing need to…
The increased use of micro-services to build web applications has spurred the rapid growth of Function-as-a-Service (FaaS) or serverless computing platforms. While FaaS simplifies provisioning and scaling for application developers, it…
Infrastructure as a Service model of cloud computing is a desirable platform for the execution of cost and deadline constrained workflow applications as the elasticity of cloud computing allows large-scale complex scientific workflow…
Application energy efficiency can be improved by executing each application component on the compute element that consumes the least energy while also satisfying time constraints. In principle, the function as a service (FaaS) paradigm…
Serverless computing is a paradigm in which the underlying infrastructure is fully managed by the provider, enabling applications and services to be executed with elastic resource provisioning and minimal operational overhead. A core model…
In this paper we describe the architecture of a Platform as a Service (PaaS) oriented to computing and data analysis. In order to clarify the choices we made, we explain the features using practical examples, applied to several known usage…
With the advantages that cloud computing offers in terms of platform as a service, software as a service, and infrastructure as a service, data engineers and data scientists are able to leverage cloud computing for their ETL/ELT (extract,…
Running microbenchmark suites often and early in the development process enables developers to identify performance issues in their application. Microbenchmark suites of complex applications can comprise hundreds of individual benchmarks…
Although Cloud computing emerged for business applications in industry, public Cloud services have been widely accepted and encouraged for scientific computing in academia. The recently available Google Compute Engine (GCE) is claimed to…
Cloud-native is an approach to building and running scalable applications in modern cloud infrastructures, with the Kubernetes container orchestration platform being often considered as a fundamental cloud-native building block. In this…
Serverless computing has become a major trend among cloud providers. With serverless computing, developers fully delegate the task of managing the servers, dynamically allocating the required resources, as well as handling availability and…
Rapid adoption of the serverless (or Function-as-a-Service, FaaS) paradigm, pioneered by Amazon with AWS Lambda and followed by numerous commercial offerings and open source projects, introduces new challenges in designing the cloud…
Cloud computing, despite its inherent advantages (e.g., resource efficiency) still faces several challenges. the wide are network used to connect the cloud to end-users could cause high latency, which may not be tolerable for some…
Function-as-a-Service (FaaS) platforms and "serverless" cloud computing are becoming increasingly popular. Current FaaS offerings are targeted at stateless functions that do minimal I/O and communication. We argue that the benefits of…