We are very happy to announce that our partner Jheronimus Academy of Data Science (JADS) just released a video presenting the RADON Defect Prediction Tool.
Schulz, H., Okanovic, H., van Hoorn, and A., Tuma, P propose an integrated framework for generating load tests tailored to the context of interest, which a user can describe in a language they provide.
We are very happy to announce that our Project Coordinator, the Imperial College London just released a video presenting the RADON Decomposition Tool.
When modeling a deployment for a cloud application, there are many things to consider, like different cloud providers and infrastructure requirements, different architectural styles, and different deployment technologies and languages.
M. Adhikari, S. N. Srirama and T. Amgoth introduce an optimal application offloading strategy in the hierarchical fog-cloud environment using the accelerated particle swarm optimization (APSO) technique.
M. Adhikari, M. Mukherjee, and S. N. Srirama design a new delay-dependent priority-aware task offloading (DPTO) strategy for scheduling and processing the tasks, generated from the IoT devices to suitable computing devices.
The ability to fulfill the resource demand in runtime is encouraging businesses to migrate to the cloud. Recently, to provide real-time cloud services and to save network resources, fog computing is introduced.
C. Kumar Dehury and S. Narayana Srirama propose a novel deep reinforcement learning-based service dispersal approach for fog and cloud computing (DRLSD-FC) that is adopted for offering the service using both environments simultaneously.
The authors propose a novel data pipeline architecture for a serverless platform for providing an environment to develop applications that can be broken into independently deployable, schedulable, scalable, and reusable modules and efficiently manage the flow of data between different environments.
This paper introduces an efficient and novel data pipeline architecture, named CCoDaMiC (Coherent Coordination of Data Migration and Computation), which brings both the data migration operation and its computation together into one place.
We are very happy to announce that our partner Jheronimus Academy of Data Science (JADS) just released a video presenting the RADON Defect Prediction Tool.
Schulz, H., Okanovic, H., van Hoorn, and A., Tuma, P propose an integrated framework for generating load tests tailored to the context of interest, which a user can describe in a language they provide.
We are very happy to announce that our Project Coordinator, the Imperial College London just released a video presenting the RADON Decomposition Tool.
When modeling a deployment for a cloud application, there are many things to consider, like different cloud providers and infrastructure requirements, different architectural styles, and different deployment technologies and languages.
M. Adhikari, S. N. Srirama and T. Amgoth introduce an optimal application offloading strategy in the hierarchical fog-cloud environment using the accelerated particle swarm optimization (APSO) technique.
M. Adhikari, M. Mukherjee, and S. N. Srirama design a new delay-dependent priority-aware task offloading (DPTO) strategy for scheduling and processing the tasks, generated from the IoT devices to suitable computing devices.
The ability to fulfill the resource demand in runtime is encouraging businesses to migrate to the cloud. Recently, to provide real-time cloud services and to save network resources, fog computing is introduced.
C. Kumar Dehury and S. Narayana Srirama propose a novel deep reinforcement learning-based service dispersal approach for fog and cloud computing (DRLSD-FC) that is adopted for offering the service using both environments simultaneously.
The authors propose a novel data pipeline architecture for a serverless platform for providing an environment to develop applications that can be broken into independently deployable, schedulable, scalable, and reusable modules and efficiently manage the flow of data between different environments.
This paper introduces an efficient and novel data pipeline architecture, named CCoDaMiC (Coherent Coordination of Data Migration and Computation), which brings both the data migration operation and its computation together into one place.