Related papers: Modular Workflow Engine for Distributed Services u…
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…
Hybrid workflows combining traditional HPC and novel ML methodologies are transforming scientific computing. This paper presents the architecture and implementation of a scalable runtime system that extends RADICAL-Pilot with service-based…
We present a model for measuring the impact of offloading soft real-time jobs over multi-tier cloud infrastructures. The jobs originate in mobile devices and offloading strategies may choose to execute them locally, in neighbouring devices,…
Decentralised workflow management systems are a new research area, where most work to-date has focused on the system's overall architecture. As little attention has been given to the security aspects in such systems, we follow a security…
Serverless applications are typically composed of function workflows in which multiple short-lived functions are triggered to exchange data in response to events or state changes. Current serverless platforms coordinate and trigger…
Orchestrating centralised service-oriented workflows presents significant scalability challenges that include: the consumption of network bandwidth, degradation of performance, and single points of failure. This paper presents a high-level…
Programming a distributed system, such as a cluster, requires extended use of low-level communication libraries and can often become cumbersome and error prone for the average developer. In this work, we consider each node of a cluster as a…
Hardware heterogeneity is here to stay for high-performance computing. Large-scale systems are currently equipped with multiple GPU accelerators per compute node and are expected to incorporate more specialized hardware. This shift in the…
Dealing with a growing amount of data is a crucial challenge for the future of information and communication technologies. More and more devices are expected to transfer data through the Internet, therefore new solutions have to be designed…
Modern software engineering of electronic structure codes has seen a paradigm shift from monolithic workflows towards object-based modularity. Software objectivity allows for greater flexibility in the application of electronic structure…
The workflow of data scientists normally involves potentially inefficient processes such as data mining, feature engineering and model selection. Recent research has focused on automating this workflow, partly or in its entirety, to improve…
To process a large volume of data, modern data management systems use a collection of machines connected through a network. This paper looks into the feasibility of scaling up such a shared-nothing system while processing a compute- and…
This paper presents the overall design of a multi-agent framework for tuning the performance of an application executing in a distributed environment. The multi-agent framework provides services like resource brokering, analyzing…
This paper presents a platform to facilitate the deployment of applications in Internet of Things (IoT) devices. The platform allows to the programmers to use a Function-as-a-Service programming paradigm that are managed and configured in a…
OpenMOLE is a scientific workflow engine with a strong emphasis on workload distribution. Workflows are designed using a high level Domain Specific Language (DSL) built on top of Scala. It exposes natural parallelism constructs to easily…
A prototype of modular networked robot for autonomous monitoring works with full control over web through wireless connection has been developed. The robot is equipped with a particular set of built-in analyzing tools and appropriate…
Machine learning applications are increasingly deployed not only to serve predictions using static models, but also as tightly-integrated components of feedback loops involving dynamic, real-time decision making. These applications pose a…
Neuromorphic computing exhibits great potential to provide high-performance benefits in various applications beyond neural networks. However, a general-purpose program execution model that aligns with the features of neuromorphic computing…
In recent years, mobile devices have gained increasing development with stronger computation capability and larger storage space. Some of the computation-intensive machine learning tasks can now be run on mobile devices. To exploit the…
This paper studies a multi-user cooperative mobile-edge computing (MEC) system, in which a local mobile user can offload intensive computation tasks to multiple nearby edge devices serving as helpers for remote execution. We focus on the…