Energy-efficient cloud computing using hardware diversity and elastic scalability

The GreenClouds project studies how to reduce the energy footprint of modern High Performance Computing systems (like Clouds) that are distributed, elastically scalable, and contain a variety of hardware (accelerators and hybrid networks). The project takes a system-level approach and studies the problem of how to map high-performance applications onto such distributed systems, taking both performance and energy consumption into account. We will explore three ideas to reduce energy: " Exploit the diversity of computing architectures (e.g. GPUs, multicores) to run computations on those architectures that perform them in the most energy-efficient way; " Dynamically adapt the number of resources to the application needs accounting for computational and energy efficiency; " Use optical and photonic networks to transport data and computations in a more energy-efficient way. The project will create the GreenClouds Knowledge Base System (GKBS) based on semantic web technology, which will provide detailed information on the energy characteristics of various applications (e.g., obtained from previous execution runs) and the different parts of the distributed system, including the network. Also, the project will study a broad range of applications and determine which classes of applications can reduce their energy consumption using accelerators. Finally, it will study energy reductions through dynamic adaptation of computing and networking resources. The project will make extensive use of the DAS-4 infrastructure, which is a wide-area testbed for computer scientists, to be equipped with many types of accelerators, a photonic network, and energy sensors. The results of the project will be utilized by the SARA national HPC center that operates a supercomputer, clusters, accelerator systems, and an HPC cloud. Today, the costs of energy over the lifetime of these systems are already larger than their acquisition costs, so reducing energy is vitally important for centers like SARA. Moreover, the results will be utilized in DAS-4 itself.