Shutdown Policies with Power Capping for Large Scale Computing Systems

International audienceLarge scale distributed systems are expected to consume huge amounts of energy. To solve this issue, shutdown policies constitute an appealing approach able to dynamically adapt the resource set to the actual workload. However, multiple constraints have to be taken into account for such policies to be applied on real infrastructures, in particular the time and energy cost of shutting down and waking up nodes, and power capping to avoid disruption of the system. In this paper, we propose models translating these various constraints into different shutdown policies that can be combined. Our models are validated through simulations on real workload traces and power measurements on real testbeds.

Evaluating the Tea Bag Index approach for different management practices in agroecosystems using long-term field experiments in Austria and Sweden

Litter decomposition is an important factor affecting local and global C cycles. It is known that decomposition through soil microbial activity in ecosystems is mainly influenced by soil type and climatic conditions. However, for agroecosystems, there remains a need for a better understanding of how management practices influence litter decomposition. This study examined the effect of different management practices on decomposition at 29 sites with long-term (mean duration of 38 years) field experiments (LTEs) using the Tea Bag Index (TBI) protocol with standard litter (rooibos and green tea) developed by Keuskamp et al. (2013). The objective was to determine if the TBI decomposition rate (k) and stabilization factor (S) are sensitive enough to detect differences in litter decomposition between management practices as well as how they interact with edaphic factors, crop type and local climatic conditions. Tea bags were buried and collected after ∼90 d at 16 Austrian and 13 Swedish sites. The treatments in the Austrian LTEs focused on mineral and organic fertilizer application, tillage systems and crop residue management, whereas those in Sweden addressed cropping systems, mineral fertilizer application and tillage systems. The results for Austria showed that the incorporation of crop residue and high-N fertilizer application increased k, compared with crop residue removal and low or no N application, respectively. Minimum tillage had significantly higher k compared with reduced and conventional tillage. In Sweden, fertilized plots showed higher S than non-fertilized plots and high-N fertilizer had the highest k. Growing spring cereal led to higher k than forage crops. Random forest regressions for Austria and Sweden jointly showed that k and S were mainly governed by climatic conditions, which explained more than 70 % of their variation. However, under similar climatic conditions, management practices strongly influenced decomposition dynamics. It would be appropriate to apply the TBI approach to a more large-scale network using LTEs for agroecosystems, in order to improve the index's usefulness as an indicator of the effect of management practices on litter decomposition dynamics, particularly linking it with the potential for C storage.</p

Burstiness-aware service level planning for enterprise application clouds

Abstract Enterprise applications are being increasingly deployed on cloud infrastructures. Often, a cloud service provider (SP) enters into a Service Level Agreement (SLA) with a cloud subscriber, which specifies performance requirements for the subscriber’s applications. An SP needs systematic Service Level Planning (SLP) tools that can help estimate the resources needed and hence the cost incurred to satisfy their customers’ SLAs. Enterprise applications typically experience bursty workloads and the impact of such bursts needs to be considered during SLP exercises. Unfortunately, most existing approaches do not consider workload burstiness. We propose a Resource Allocation Planning (RAP) technique, which allows an SP to identify a time varying allocation plan of resources to applications that satisfies bursts. Extensive simulation results show that the proposed RAP variants can identify resource allocation plans that satisfy SLAs without exhaustively generating all possible plans. Furthermore, the results show that RAP can permit SPs to more accurately determine the capacity required for meeting specified SLAs compared to other competing techniques especially for bursty workloads