Energy - Efficient IaaS-PaaS Co-Design for Flexible Cloud Deployment of Scientific Applications

Abstract

Reducing the massive amount of energy consumed by cloud datacenters becomes of major importance. In the usual approach where resources are consolidated into fewer servers in order to power down the others, it still remains periods of time when servers are not fully utilized. Consequently, it exists unused resources that are not exploited although they could be used to execute applications compatible with the variable availability of these resources. In this work, we propose a cloud system where the Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS) layers interact to find execution trade-offs that exploit the unused resources at IaaS level. PaaS users are involved in the energy optimization by proposing to delay their executions and adapt resource sizes in order to fit with the available unused resources. Our evaluation by simulation is based on real data and expresses a realistic large scale cloud scenario. Results show that according to the proportion of energy-aware users, this system is able to reduce the amount of servers by using resources that would have been wasted otherwise. Therefore, our solution allows datacenters to consume less energy than with usual resource managers where all applications start their execution at submission time with their initial resource size.

Characterization of different user behaviors for demand response in data centers

“…The scheduler is a bin-packing scheduler with shutdown (same as Guyon et al [8,9]). It is a greedy algorithm trying to schedule ("pack") all the jobs in the least possible machines and shut down idle machines.…”

“…Orgerie et al [15] save energy through thermal-aware scheduling and smart resource switch off by letting the users choose between different submission times on the basis of energy consumption estimations for each of the alternatives. A combination of both spatial and thermal consolidation is proposed in an other work by Guyon et al [8] or in the All4Green project [2], where user involvement is leveraged through contracts between the energy supplier, the data center and the user. The latter work, also in a context of demand response, is the closest to our approach.…”

“…The other is to consider the users as a lever for flexibility in the scheduling, ie., they accept to compromise occasionally on their quality of service to allow some optimizations. The degradation can be spatial [9] (reducing the amount of resources allocated for the jobs), temporal [15] (delaying their execution) or both [8].…”