Section: New Results

Greening Clouds

Energy Models

Participants : Ehsan Ahvar, Loic Guegan, Anne-Cécile Orgerie, Martin Quinson.

Cloud computing allows users to outsource the computer resources required for their applications instead of using a local installation. It offers on-demand access to the resources through the Internet with a pay-as-you-go pricing model. However, this model hides the electricity cost of running these infrastructures.

The costs of current data centers are mostly driven by their energy consumption (specifically by the air conditioning, computing and networking infrastructure). Yet, current pricing models are usually static and rarely consider the facilities' energy consumption per user. The challenge is to provide a fair and predictable model to attribute the overall energy costs per virtual machine and to increase energy-awareness of users. We aim at proposing such energy cost models without heavily relying on physical wattmeters that may be costly to install and operate.

Another goal consists in better understanding the energy consumption of computing and networking resources of Clouds in order to provide energy cost models for the entire infrastructure including incentivizing cost models for both Cloud providers and energy suppliers. These models will be based on experimental measurement campaigns on heterogeneous devices. Inferring a cost model from energy measurements is an arduous task since simple models are not convincing, as shown in our previous work. We aim at proposing and validating energy cost models for the heterogeneous Cloud infrastructures in one hand, and the energy distribution grid on the other hand. These models will be integrated into simulation frameworks in order to validate our energy-efficient algorithms at larger scale.

Finally, a research result dating from 2015 was finally published after a long review and publication process [4]: to help the energy-aware co-design of IaaS and PaaS platforms, we conducted an extensive experimental evaluation of the effect of a range of Cloud infrastructure operations (start, stop, migrate VMs) on their computing throughput and energy consumption, and derived a model to help drive cloud reconfiguration operations according to performance/energy requirements.

End-to-end Energy Models for Internet of Things

Participant : Anne-Cécile Orgerie.

The development of IoT (Internet of Things) equipment, the popularization of mobile devices, and emerging wearable devices bring new opportunities for context-aware applications in cloud computing environments. The disruptive potential impact of IoT relies on its pervasiveness: it should constitute an integrated heterogeneous system connecting an unprecedented number of physical objects to the Internet. Among the many challenges raised by IoT, one is currently getting particular attention: making computing resources easily accessible from the connected objects to process the huge amount of data streaming out of them.

While computation offloading to edge cloud infrastructures can be beneficial from a Quality of Service (QoS) point of view, from an energy perspective, it is relying on less energy-efficient resources than centralized Cloud data centers. On the other hand, with the increasing number of applications moving on to the cloud, it may become untenable to meet the increasing energy demand which is already reaching worrying levels. Edge nodes could help to alleviate slightly this energy consumption as they could offload data centers from their overwhelming power load and reduce data movement and network traffic. In particular, as edge cloud infrastructures are smaller in size than centralized data center, they can make a better use of renewable energy.

We investigate the end-to-end energy consumption of IoT platforms. Our aim is to evaluate, on concrete use-cases, the benefits of edge computing platforms for IoT regarding energy consumption. We aim at proposing end-to-end energy models for estimating the consumption when offloading computation from the objects to the edge or to the core Cloud, depending on the number of devices and the desired application QoS, in particular trading-off between performance (response time) and reliability (service accuracy). This work has been published in [10].

Exploiting Renewable Energy in Distributed Clouds

Participants : Benjamin Camus, Anne-Cécile Orgerie.

The growing appetite of Internet services for Cloud resources leads to a consequent increase in data center (DC) facilities worldwide. This increase directly impacts the electricity bill of Cloud providers. Indeed, electricity is currently the largest part of the operation cost of a DC. Resource over-provisioning, energy non-proportional behavior of today's servers, and inefficient cooling systems have been identified as major contributors to the high energy consumption in DCs.

In a distributed Cloud environment, on-site renewable energy production and geographical energy-aware load balancing of virtual machines allocation can be associated to lower the brown (i.e. not renewable) energy consumption of DCs. Yet, combining these two approaches remains challenging in current distributed Clouds. Indeed, the variable and/or intermittent behavior of most renewable sources – like solar power for instance – is not correlated with the Cloud energy consumption, that depends on physical infrastructure characteristics and fluctuating unpredictable workloads.

We proposed NEMESIS: a Network-aware Energy-efficient Management framework for distributEd cloudS Infrastructures with on-Site photovoltaic production. The originality of NEMESIS lies in its combination of a greedy VM allocation algorithm, a network-aware live-migration algorithm, a dichotomous consolidation algorithm and a stochastic model of the renewable energy supply in order to optimize both green and brown energy consumption of a distributed cloud infrastructure with on-site renewable production. Our solution employs a centralized resource manager to schedule VM migrations in a network-aware and energy-efficient way, and consolidation techniques distributed in each data center to optimize the Cloud's overall energy consumption. This work has been published in [15] and [38].

Smart Grids

Participants : Anne Blavette, Benjamin Camus, Anne-Cécile Orgerie, Martin Quinson.

Smart grids allow to efficiently perform demand-side management in electrical grids in order to increase the integration of fluctuating and/or intermittent renewable energy sources in the energy mix. In this work, we consider a distributed computing cloud partially powered by photovoltaic panels as a self-consumer that can also benefit from geographical flexibility: the computing load can be moved from one data center to another one benefiting from better solar irradiance conditions. The various data centers composing the cloud can then cooperate to better synchronise their consumption with their photovoltaic production.

We aim at optimizing the self-power consumption of a distributed Cloud infrastructure with on-site photovoltaic electricity generation. We propose to rely on the flexibility brought by Smart Grids to exchange renewable energy between data centers and thus, to further increase the overall Cloud's self-consumption of the locally-produced renewable energy. Our solution is named SCORPIUS: Self-Consumption Optimization of Renewable energy Production In distribUted cloudS. It optimizes the Cloud's self-consumption by trading-off between VM migration and renewable energy exchange. This optimization is based on an original Smart Grid model to exchange renewable energy between distant sites. This work has been published in the distributed computing community [14] and in the electrical engineering community [37].

Involving Users in Energy Saving

Participants : David Guyon, Christine Morin, Anne-Cécile Orgerie.

In a Cloud moderately loaded, some servers may be turned off when not used for energy saving purpose. Cloud providers can apply resource management strategies to favor idle servers. Some of the existing solutions propose mechanisms to optimize VM scheduling in the Cloud. A common solution is to consolidate the mapping of the VMs in the Cloud by grouping them in a fewer number of servers. The unused servers can then be turned off in order to lower the global electricity consumption.

Indeed, current work focuses on possible levers at the virtual machine suppliers and/or services. However, users are not involved in the choice of using these levers while significant energy savings could be achieved with their help. For example, they might agree to delay slightly the calculation of the response to their applications on the Cloud or accept that it is supported by a remote data center, to save energy or wait for the availability of renewable energy. The VMs are black boxes from the Cloud provider point of view. So, the user is the only one to know the applications running on her VMs.

We explore possible collaborations between virtual machine suppliers, service providers and users of Clouds in order to provide users with ways of participating in the reduction of the Clouds energy consumption. This work will follow two directions: 1) to investigate compromises between power and performance/service quality that cloud providers can offer to their users and to propose them a variety of options adapted to their workload; and 2) to develop mechanisms for each layer of the Cloud software stack to provide users with a quantification of the energy consumed by each of their options as an incentive to become greener. This work was explored in the context of David Guyon's PhD thesis (defended on December 7, 2018). For 2018, it resulted in one publication in the International Journal of Grid and Utility Computing [8] and two publications in conferences: IC2E [23] and SBAC-PAD [24].