Section: New Results

Energy-aware computing

Participants : Jean-Marc Menaud, Shadi Ibrahim, Thomas Ledoux, Emile Cadorel, Yewan Wang, Jonathan Pastor.

Energy consumption is one of the major challenges of modern datacenters and supercomputers. Our works in Energy-aware computing can be categorized into two subdomains: Software level (SaaS, PaaS) and Infrastructure level (IaaS).

At Software level, we worked on the general Cloud applications architecture and HPC applications.

In particular, in his habilitation thesis [2], Thomas Ledoux shows that dynamic reconfiguration in Cloud computing can provide an answer to an important societal challenge, namely digital and energetic transitions. Unlike current work providing solutions in the lower layers of the Cloud to improve the energy efficiency of data centers, Thomas Ledoux advocates a software eco-elasticity approach on the high layers of the Cloud. Inspired by both the concept of frugal innovation (Jugaad) and the mechanism of energy brownout, he proposes a number of original artifacts – such as Cloud SLA, eco-elasticity in the SaaS layers, virtualization of energy or green energy-aware SaaS applications, etc. – to reduce the carbon footprint of Cloud architectures.

However, by applying Green Programming techniques, developers have to iteratively implement and test new versions of their software, thus evaluating the impact of each code version on their energy, power and performance objectives. This approach is manual and can be long, challenging and complicated, especially for High Performance Computing applications. In [21], we formally introduces the definition of the Code Version Variability (CVV) leverage and present a first approach to automate Green Programming (i.e., CVV usage) by studying the specific use-case of an HPC stencil-based numerical code, used in production. This approach is based on the automatic generation of code versions thanks to a Domain Specific Language (DSL), and on the automatic choice of code version through a set of actors. Moreover, a real case study is introduced and evaluated though a set of benchmarks to show that several trade-offs are introduced by CVV. Finally, different kinds of production scenarios are evaluated through simulation to illustrate possible benefits of applying various actors on top of the CVV automation. While this work takes HPC applications as a use-case the presented automated green programming technique could be applied to any kind of production application onto any kind of infrastructures.

In general, many Big Data processing applications nowadays run on large-scale multi-tenant clusters. Due to hardware heterogeneity and resource contentions, straggler problem has become the norm rather than the exception in such clusters. To handle the straggler problem, speculative execution has emerged as one of the most widely used straggler mitigation techniques. Although a number of speculative execution mechanisms have been proposed, as we have observed from real-world traces, the questions of “when” and “where” to launch speculative copies have not been fully discussed and hence cause inefficiencies on the performance and energy of Big Data applications. In [29], we propose a performance model and an energy consumption model to reveal the performance and energy variations with different speculative execution solutions. We further propose a window-based dynamic resource reservation and a heterogeneity-aware copy allocation technique to answer the “when” and “where” questions for speculative executions. Evaluations using real-world traces show that our proposed technique can improve the performance of Big Data applications by up to 30% and reduce the overall energy consumption by up to 34%.

At infrastructure level, we worked on power and thermal management from server to datacenter. In fact, with the advent of Cloud Computing, the size of datacenters is ever increasing and the management of servers and their power consumption and heat production have become challenges. The management of the heat produced by servers has been experimentally less explored than the management of their power consumption. It can be partly explained by the lack of a public testbed that provides reliable access to both thermal and power metrics of server rooms. In [34], [20], [19] we had describe SeDuCe, a testbed that targets research on power and thermal management of servers, by providing public access to precise data about the power consumption and the thermal dissipation of 48 servers integrated in Grid’5000 as the new ecotype cluster. We presented the chosen software and hardware architecture for the SeDuCe testbed. Future work will focus on two areas: adding renewable energy capabilities to the SeDuCe testbed, and improving the precision of temperature sensors.

If SeDuCe testbed is focused on the management of the power consumption and heat produced by servers at room level, we realized in [30], [24], [23] , studies on power consumption (and heat impact) of physical servers. First, we characterized some potential factors on the power variation of the servers, such as: original fabrication, position in the rack, voltage variation and temperature of components on motherboard. The results show that certain factors, such as original fabrication, ambient temperature and CPU temperature, have noticeable effects on the power consumption of servers. The experimental results emphasize the importance of adding these external factors into the metric, so as to build an energy predictive model adaptable in real situations.