The DreamCloud Project

The main objective addressed by DreamCloud is to enable dynamic resource allocation in many- core embedded and high performance systems while providing appropriate guarantees on performance and energy efficiency.

DreamCloud will address techniques to allocate computation and communication workloads onto computing platforms during run-time whilst respecting performance and energy budget requirements. To achieve this, DreamCloud brings together teams from embedded computing and high- performance computing (HPC) to address these common ambitious yet achievable goals:

  • Provide complex embedded systems with cloud-like capabilities such as those available in today’s high-performance computing, allowing them to dynamically tune resource usage but without sacrificing critical application-specific constraints in performance and energy.
  • Enable HPC and cloud computing systems to balance workload and manage resources so that they can offer more meaningful guarantees in terms of performance and energy, focussing not only on improving average behaviour but also on reducing variability and upper bounds of timing and energy metrics.

DreamCloud brings together industrial partners from deeply embedded systems (e.g. automotive), consumer embedded systems (e.g. household media), and high performance computing (e.g. HPC platforms); academic partners from embedded systems, real-time systems and HPC. This will enable DreamCloud to develop better approaches by  cross-fertilising  expertise  and  experience from  multiple  industrial  domains  and  academic  communities.

Click the video below for an introduction to the project and technologies being developed.

News

  • Textbook on DreamCloud dynamic resource allocation heuristics

    A textbook on the DreamCloud dynamic resource allocation heuristics will soon be published by River Publishers. Hardcopies will be available for purchase, and an ebook version will be made freely available here in the DreamCloud website. This arrangement was made possible thanks to the FP7 Post-Grant Open Access Pilot Scheme. The textbook will have the following chapters: Preface; 1. Introduction; 2. Load and Resource Models; 3. Feedback-based Admission Control Heuristics; 4. Feedback-based…

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    Started by Leandro Soares Indrusiak 0 Replies
  • Best Student Paper Award - SIGMAP 2016

    The paper entitled "Synthetic Workload Generation of Broadcast related HEVC Stream Decoding for Resource Constrained Systems" has won the SIGMAP 2016 Best Student Paper Award. The paper is authored by Hasham Roshanta Mendis and Leandro Soares Indrusiak, from the University of York, and supported by the collaborations with DreamCloud partner Rheon Media. It describes an approach to generate multi-stream video workload models, which in turn were used to evaluate and fine-tune some of the dynamic…

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    Started by Leandro Soares Indrusiak 0 Replies
  • Paper accepted at IEEE MCSoC-16

    A paper titled "Performance Prediction of Application Mapping in Manycore Systems with Artificial Neural Networks" will be presented by CNRS at the IEEE 10th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC-16), Lyon, France, 21 - 23 September, 2016

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    Started by Abdoulaye Gamatié 0 Replies
  • Panel at ReCoSoC 2016

    Leandro Indrusiak has organised a panel at ReCoSoC 2016, discussing the challenges and trends towards a common design approach to embedded, high-performance and cloud computing. The panel was motivated by the research work done in the DreamCloud project on dynamic resource allocation mechanisms that can be fine-tuned and used across all of those domains. Three panelists were invited to provide their views on this topic: Prof Jan Madsen, Technical University of Denmark, Denmark Prof Dirk…

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    Started by Leandro Soares Indrusiak 0 Replies

DreamCloud is a Specific Targeted Research Project (STREP) of the Seventh Framework Programme for research and technological development (FP7) - the European Union's chief instrument for funding research projects that commence over the period 2007 to 2013.


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