12,917 research outputs found
Computing server power modeling in a data center: survey,taxonomy and performance evaluation
Data centers are large scale, energy-hungry infrastructure serving the
increasing computational demands as the world is becoming more connected in
smart cities. The emergence of advanced technologies such as cloud-based
services, internet of things (IoT) and big data analytics has augmented the
growth of global data centers, leading to high energy consumption. This upsurge
in energy consumption of the data centers not only incurs the issue of surging
high cost (operational and maintenance) but also has an adverse effect on the
environment. Dynamic power management in a data center environment requires the
cognizance of the correlation between the system and hardware level performance
counters and the power consumption. Power consumption modeling exhibits this
correlation and is crucial in designing energy-efficient optimization
strategies based on resource utilization. Several works in power modeling are
proposed and used in the literature. However, these power models have been
evaluated using different benchmarking applications, power measurement
techniques and error calculation formula on different machines. In this work,
we present a taxonomy and evaluation of 24 software-based power models using a
unified environment, benchmarking applications, power measurement technique and
error formula, with the aim of achieving an objective comparison. We use
different servers architectures to assess the impact of heterogeneity on the
models' comparison. The performance analysis of these models is elaborated in
the paper
Toward sustainable data centers: a comprehensive energy management strategy
Data centers are major contributors to the emission of carbon dioxide to the atmosphere, and this contribution is expected to increase in the following years. This has encouraged the development of techniques to reduce the energy consumption and the environmental footprint of data centers. Whereas some of these techniques have succeeded to reduce the energy consumption of the hardware equipment of data centers (including IT, cooling, and power supply systems), we claim that sustainable data centers will be only possible if the problem is faced by means of a holistic approach that includes not only the aforementioned techniques but also intelligent and unifying solutions that enable a synergistic and energy-aware management of data centers.
In this paper, we propose a comprehensive strategy to reduce the carbon footprint of data centers that uses the energy as a driver of their management procedures. In addition, we present a holistic management architecture for sustainable data centers that implements the aforementioned strategy, and we propose design guidelines to accomplish each step of the proposed strategy, referring to related achievements and enumerating the main challenges that must be still solved.Peer ReviewedPostprint (author's final draft
A methodology for full-system power modeling in heterogeneous data centers
The need for energy-awareness in current data centers has encouraged the use of power modeling to estimate their power consumption. However, existing models present noticeable limitations, which make them application-dependent, platform-dependent, inaccurate, or computationally complex. In this paper, we propose a platform-and application-agnostic methodology for full-system power modeling in heterogeneous data centers that overcomes those limitations. It derives a single model per platform, which works with high accuracy for heterogeneous applications with different patterns of resource usage and energy consumption, by systematically selecting a minimum set of resource usage indicators and extracting complex relations among them that capture the impact on energy consumption of all the resources in the system. We demonstrate our methodology by generating power models for heterogeneous platforms with very different power consumption profiles. Our validation experiments with real Cloud applications show that such models provide high accuracy (around 5% of average estimation error).This work is supported by the Spanish Ministry of Economy and Competitiveness under contract TIN2015-65316-P, by the Gener-
alitat de Catalunya under contract 2014-SGR-1051, and by the European Commission under FP7-SMARTCITIES-2013 contract 608679 (RenewIT) and FP7-ICT-2013-10 contracts 610874 (AS- CETiC) and 610456 (EuroServer).Peer ReviewedPostprint (author's final draft
Metascheduling of HPC Jobs in Day-Ahead Electricity Markets
High performance grid computing is a key enabler of large scale collaborative
computational science. With the promise of exascale computing, high performance
grid systems are expected to incur electricity bills that grow super-linearly
over time. In order to achieve cost effectiveness in these systems, it is
essential for the scheduling algorithms to exploit electricity price
variations, both in space and time, that are prevalent in the dynamic
electricity price markets. In this paper, we present a metascheduling algorithm
to optimize the placement of jobs in a compute grid which consumes electricity
from the day-ahead wholesale market. We formulate the scheduling problem as a
Minimum Cost Maximum Flow problem and leverage queue waiting time and
electricity price predictions to accurately estimate the cost of job execution
at a system. Using trace based simulation with real and synthetic workload
traces, and real electricity price data sets, we demonstrate our approach on
two currently operational grids, XSEDE and NorduGrid. Our experimental setup
collectively constitute more than 433K processors spread across 58 compute
systems in 17 geographically distributed locations. Experiments show that our
approach simultaneously optimizes the total electricity cost and the average
response time of the grid, without being unfair to users of the local batch
systems.Comment: Appears in IEEE Transactions on Parallel and Distributed System
On the feasibility of collaborative green data center ecosystems
The increasing awareness of the impact of the IT sector on the environment, together with economic factors, have fueled many research efforts to reduce the energy expenditure of data centers. Recent work proposes to achieve additional energy savings by exploiting, in concert with customers, service workloads and to reduce data centers’ carbon footprints by adopting demand-response mechanisms between data centers and their energy providers. In this paper, we debate about the incentives that customers and data centers can have to adopt such measures and propose a new service type and pricing scheme that is economically attractive and technically realizable. Simulation results based on real measurements confirm that our scheme can achieve additional energy savings while preserving service performance and the interests of data centers and customers.Peer ReviewedPostprint (author's final draft
SLO-aware Colocation of Data Center Tasks Based on Instantaneous Processor Requirements
In a cloud data center, a single physical machine simultaneously executes
dozens of highly heterogeneous tasks. Such colocation results in more efficient
utilization of machines, but, when tasks' requirements exceed available
resources, some of the tasks might be throttled down or preempted. We analyze
version 2.1 of the Google cluster trace that shows short-term (1 second) task
CPU usage. Contrary to the assumptions taken by many theoretical studies, we
demonstrate that the empirical distributions do not follow any single
distribution. However, high percentiles of the total processor usage (summed
over at least 10 tasks) can be reasonably estimated by the Gaussian
distribution. We use this result for a probabilistic fit test, called the
Gaussian Percentile Approximation (GPA), for standard bin-packing algorithms.
To check whether a new task will fit into a machine, GPA checks whether the
resulting distribution's percentile corresponding to the requested service
level objective, SLO is still below the machine's capacity. In our simulation
experiments, GPA resulted in colocations exceeding the machines' capacity with
a frequency similar to the requested SLO.Comment: Author's version of a paper published in ACM SoCC'1
CloudScope: diagnosing and managing performance interference in multi-tenant clouds
© 2015 IEEE.Virtual machine consolidation is attractive in cloud computing platforms for several reasons including reduced infrastructure costs, lower energy consumption and ease of management. However, the interference between co-resident workloads caused by virtualization can violate the service level objectives (SLOs) that the cloud platform guarantees. Existing solutions to minimize interference between virtual machines (VMs) are mostly based on comprehensive micro-benchmarks or online training which makes them computationally intensive. In this paper, we present CloudScope, a system for diagnosing interference for multi-tenant cloud systems in a lightweight way. CloudScope employs a discrete-time Markov Chain model for the online prediction of performance interference of co-resident VMs. It uses the results to optimally (re)assign VMs to physical machines and to optimize the hypervisor configuration, e.g. the CPU share it can use, for different workloads. We have implemented CloudScope on top of the Xen hypervisor and conducted experiments using a set of CPU, disk, and network intensive workloads and a real system (MapReduce). Our results show that CloudScope interference prediction achieves an average error of 9%. The interference-aware scheduler improves VM performance by up to 10% compared to the default scheduler. In addition, the hypervisor reconfiguration can improve network throughput by up to 30%
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