41,481 research outputs found
Energy Saving In Data Centers
Globally CO2 emissions attributable to Information Technology are on par with those resulting from aviation. Recent growth in cloud service demand has elevated energy efficiency of data centers to a critical area within green computing. Cloud computing represents a backbone of IT services and recently there has been an increase in high-definition multimedia delivery, which has placed new burdens on energy resources. Hardware innovations together with energy-efficient techniques and algorithms are key to controlling power usage in an ever-expanding IT landscape. This special issue contains a number of contributions that show that data center energy efficiency should be addressed from diverse vantage points. © 2017 by the authors. Licensee MDPI, Basel, Switzerland
Towards Data-Driven Autonomics in Data Centers
Continued reliance on human operators for managing data centers is a major
impediment for them from ever reaching extreme dimensions. Large computer
systems in general, and data centers in particular, will ultimately be managed
using predictive computational and executable models obtained through
data-science tools, and at that point, the intervention of humans will be
limited to setting high-level goals and policies rather than performing
low-level operations. Data-driven autonomics, where management and control are
based on holistic predictive models that are built and updated using generated
data, opens one possible path towards limiting the role of operators in data
centers. In this paper, we present a data-science study of a public Google
dataset collected in a 12K-node cluster with the goal of building and
evaluating a predictive model for node failures. We use BigQuery, the big data
SQL platform from the Google Cloud suite, to process massive amounts of data
and generate a rich feature set characterizing machine state over time. We
describe how an ensemble classifier can be built out of many Random Forest
classifiers each trained on these features, to predict if machines will fail in
a future 24-hour window. Our evaluation reveals that if we limit false positive
rates to 5%, we can achieve true positive rates between 27% and 88% with
precision varying between 50% and 72%. We discuss the practicality of including
our predictive model as the central component of a data-driven autonomic
manager and operating it on-line with live data streams (rather than off-line
on data logs). All of the scripts used for BigQuery and classification analyses
are publicly available from the authors' website.Comment: 12 pages, 6 figure
Power Management Techniques for Data Centers: A Survey
With growing use of internet and exponential growth in amount of data to be
stored and processed (known as 'big data'), the size of data centers has
greatly increased. This, however, has resulted in significant increase in the
power consumption of the data centers. For this reason, managing power
consumption of data centers has become essential. In this paper, we highlight
the need of achieving energy efficiency in data centers and survey several
recent architectural techniques designed for power management of data centers.
We also present a classification of these techniques based on their
characteristics. This paper aims to provide insights into the techniques for
improving energy efficiency of data centers and encourage the designers to
invent novel solutions for managing the large power dissipation of data
centers.Comment: Keywords: Data Centers, Power Management, Low-power Design, Energy
Efficiency, Green Computing, DVFS, Server Consolidatio
Power Modelling for Heterogeneous Cloud-Edge Data Centers
Existing power modelling research focuses not on the method used for
developing models but rather on the model itself. This paper aims to develop a
method for deploying power models on emerging processors that will be used, for
example, in cloud-edge data centers. Our research first develops a hardware
counter selection method that appropriately selects counters most correlated to
power on ARM and Intel processors. Then, we propose a two stage power model
that works across multiple architectures. The key results are: (i) the
automated hardware performance counter selection method achieves comparable
selection to the manual selection methods reported in literature, and (ii) the
two stage power model can predict dynamic power more accurately on both ARM and
Intel processors when compared to classic power models.Comment: 10 pages,10 figures,conferenc
Energy-Aware Lease Scheduling in Virtualized Data Centers
Energy efficiency has become an important measurement of scheduling
algorithms in virtualized data centers. One of the challenges of
energy-efficient scheduling algorithms, however, is the trade-off between
minimizing energy consumption and satisfying quality of service (e.g.
performance, resource availability on time for reservation requests). We
consider resource needs in the context of virtualized data centers of a private
cloud system, which provides resource leases in terms of virtual machines (VMs)
for user applications. In this paper, we propose heuristics for scheduling VMs
that address the above challenge. On performance evaluation, simulated results
have shown a significant reduction on total energy consumption of our proposed
algorithms compared with an existing First-Come-First-Serve (FCFS) scheduling
algorithm with the same fulfillment of performance requirements. We also
discuss the improvement of energy saving when additionally using migration
policies to the above mentioned algorithms.Comment: 10 pages, 2 figures, Proceedings of the Fifth International
Conference on High Performance Scientific Computing, March 5-9, 2012, Hanoi,
Vietna
SDN-based virtual machine management for cloud data centers
Software-Defined Networking (SDN) is an emerging paradigm to logically centralize the network control plane and automate the configuration of individual network elements. At the same time, in Cloud Data Centers (DCs), even though network and server resources converge over the same infrastructure and typically over a single administrative entity, disjoint control mechanisms are used for their respective management. In this paper, we propose a unified server-network control mechanism for converged ICT environments. We present a SDN-based orchestration framework for live Virtual Machine (VM) management where server hypervisors exploit temporal network information to migrate VMs and minimize the network-wide communication cost of the resulting traffic dynamics. A prototype implementation is presented and Mininet is used to evaluate the impact of diverse orchestration algorithms
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