The Tapis framework and APIs enable a wide range of research and scientific discovery by automating job execution on remote resources, including HPC clusters and servers running in the cloud. While Tapis simplifies interactions with cyberinfrastructure (CI), users still need to specify all details of a job they want to run, including the system, queue, node count, and maximum run time, among other attributes. Moreover, system and application resources must be defined in Tapis before a job is submitted. It can be a challenge for scientists to provide exact configurations and resource requirements for their jobs, which if incorrectly specified can delay their work.In this paper, we investigate developing an intelligent job scheduling capability in Tapis, where job configuration can be partially automated and computational resources can be dynamically provisioned by Tapis. We outline an architecture for such a feature and identify a set of core challenges to be solved. Then, we focus on one specific challenge: predicting queue times for a job on different HPC systems and queues, and we present two sets of results based on machine learning methods. Our first set casts the problem as a regression, which can be used to select the best system from a list of existing options. Our second set frames the problem as a classification, which allows us to compare the use of an existing system with a dynamically provisioned resource.

Summary The explosion of IoT devices and sensors in recent years has led to a demand for efficiently storing, processing and analyzing time‐series data. Geoscience researchers use time‐series data stores such as Hydroserver, Virtual Observatory and Ecological Informatics System (VOEIS), and Cloud‐Hosted Real‐time Data Service (CHORDS). Many of these tools require a great deal of infrastructure to deploy and expertise to manage and scale. The Tapis framework, an NSF funded project, provides science as a service APIs to allow researchers to achieve faster scientific results, by eliminating the need to set up a complex infrastructure stack. The University of Hawai'i (UH) and Texas Advanced Computing Center (TACC) have collaborated to develop an open source Tapis Streams API that builds on the concepts of the CHORDS time series data service to support research. This new hosted service allows storing, processing, annotating, archiving, and querying time‐series data in the Tapis multi‐user and multi‐tenant collaborative platform. The Streams API provides a hosted production level middleware service that enables new data‐driven event workflows capabilities that may be leveraged by researchers and Tapis powered science gateways for handling spatially indexed time‐series datasets.

The difficulty of developing and deploying commercial web applications increases as the number of technologies they use increases and as the interactions between these technologies become more complex. This paper describes a way to avoid this increasing complexity by re-examining the basic requirements of web applications. Our approach is to first separate client concerns from server concerns, and then to reduce the interaction between client and server to its most elemental: parameter passing. We define a simplified programming model for form-based web applications and we use XForms and a subset of J2EE as enabling technologies. We describe our implementation of an MVC-based application builder for this model, which automatically generates the code needed to marshal input and output data between clients and servers. This marshalling uses type checking and other forms of validation on both clients and servers. We also show how our programming model and application builder support the customization of web applications for different execution targets, including, for example, different client devices.

Object-oriented frameworks are a popular mechanism for building and evolving large applications and software product lines. We describe an alternative approach to software construction, Java Layers (JL), and evaluate JL and frameworks in terms of flexibility, ease of use, and support for evolution. Our experiment compares Schmidt's (1998) ACE framework against a set of ACE design patterns that have been implemented in JL. We show how problems of framework evolution and overfeaturing can be avoided using JL's component model, and we demonstrate that JL scales better than frameworks as the number of possible application features increases. Finally, we describe how constrained parametric polymorphism and a small number of language features can support JL's model of loosely coupled components and stepwise program refinement.