4,062 research outputs found
Autonomic Cloud Computing: Open Challenges and Architectural Elements
As Clouds are complex, large-scale, and heterogeneous distributed systems,
management of their resources is a challenging task. They need automated and
integrated intelligent strategies for provisioning of resources to offer
services that are secure, reliable, and cost-efficient. Hence, effective
management of services becomes fundamental in software platforms that
constitute the fabric of computing Clouds. In this direction, this paper
identifies open issues in autonomic resource provisioning and presents
innovative management techniques for supporting SaaS applications hosted on
Clouds. We present a conceptual architecture and early results evidencing the
benefits of autonomic management of Clouds.Comment: 8 pages, 6 figures, conference keynote pape
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
Energy-aware scheduling in distributed computing systems
Distributed computing systems, such as data centers, are key for supporting modern computing demands. However, the energy consumption of data centers has become a major concern over the last decade. Worldwide energy consumption in 2012 was estimated to be around 270 TWh, and grim forecasts predict it will quadruple by 2030. Maximizing energy efficiency while also maximizing computing efficiency is a major challenge for modern data centers. This work addresses this challenge by scheduling the operation of modern data centers, considering a multi-objective approach for simultaneously optimizing both efficiency objectives. Multiple data center scenarios are studied, such as scheduling a single data center and scheduling a federation of several geographically-distributed data centers. Mathematical models are formulated for each scenario, considering the modeling of their most relevant components such as computing resources, computing workload, cooling system, networking, and green energy generators, among others. A set of accurate heuristic and metaheuristic algorithms are designed for addressing the scheduling problem. These scheduling algorithms are comprehensively studied, and compared with each other, using statistical tools to evaluate their efficacy when addressing realistic workloads and scenarios. Experimental results show the designed scheduling algorithms are able to significantly increase the energy efficiency of data centers when compared to traditional scheduling methods, while providing a diverse set of trade-off solutions regarding the computing efficiency of the data center. These results confirm the effectiveness of the proposed algorithmic approaches for data center infrastructures.Los sistemas informáticos distribuidos, como los centros de datos, son clave para satisfacer la demanda informática moderna. Sin embargo, su consumo de energético se ha convertido en una gran preocupación. Se estima que mundialmente su consumo energético rondó los 270 TWh en el año 2012, y algunos prevén que este consumo se cuadruplicará para el año 2030. Maximizar simultáneamente la eficiencia energética y computacional de los centros de datos es un desafío crítico. Esta tesis aborda dicho desafío mediante la planificación de la operativa del centro de datos considerando un enfoque multiobjetivo para optimizar simultáneamente ambos objetivos de eficiencia. En esta tesis se estudian múltiples variantes del problema, desde la planificación de un único centro de datos hasta la de una federación de múltiples centros de datos geográficmentea distribuidos. Para esto, se formulan modelos matemáticos para cada variante del problema, modelado sus componentes más relevantes, como: recursos computacionales, carga de trabajo, refrigeración, redes, energía verde, etc. Para resolver el problema de planificación planteado, se diseñan un conjunto de algoritmos heurísticos y metaheurísticos. Estos son estudiados exhaustivamente y su eficiencia es evaluada utilizando una batería de herramientas estadísticas. Los resultados experimentales muestran que los algoritmos de planificación diseñados son capaces de aumentar significativamente la eficiencia energética de un centros de datos en comparación con métodos tradicionales planificación. A su vez, los métodos propuestos proporcionan un conjunto diverso de soluciones con diferente nivel de compromiso respecto a la eficiencia computacional del centro de datos. Estos resultados confirman la eficacia del enfoque algorítmico propuesto
Dynamic energy-aware scheduling for parallel task-based application in cloud computing
Green Computing is a recent trend in computer science, which tries to reduce the energy consumption and carbon footprint produced by computers on distributed platforms such as clusters, grids, and clouds. Traditional scheduling solutions attempt to minimize processing times without taking into account the energetic cost. One of the methods for reducing energy consumption is providing scheduling policies in order to allocate tasks on specific resources that impact over the processing times and energy consumption. In this paper, we propose a real-time dynamic scheduling system to execute efficiently task-based applications on distributed computing platforms in order to minimize the energy consumption. Scheduling tasks on multiprocessors is a well known NP-hard problem and optimal solution of these problems is not feasible, we present a polynomial-time algorithm that combines a set of heuristic rules and a resource allocation technique in order to get good solutions on an affordable time scale. The proposed algorithm minimizes a multi-objective function which combines the energy-consumption and execution time according to the energy-performance importance factor provided by the resource provider or user, also taking into account sequence-dependent setup times between tasks, setup times and down times for virtual machines (VM) and energy profiles for different architectures. A prototype implementation of the scheduler has been tested with different kinds of DAG generated at random as well as on real task-based COMPSs applications. We have tested the system with different size instances and importance factors, and we have evaluated which combination provides a better solution and energy savings. Moreover, we have also evaluated the introduced overhead by measuring the time for getting the scheduling solutions for a different number of tasks, kinds of DAG, and resources, concluding that our method is suitable for run-time scheduling.This work has been supported by the Spanish Government (contracts TIN2015-65316-P, TIN2012-34557, CSD2007-00050, CAC2007-00052 and SEV-2011-00067), by Generalitat de Catalunya (contract 2014-SGR-1051), by the European Commission
(Euroserver project, contract 610456) and by Consejo Nacional de Ciencia y Tecnología of Mexico (special program for postdoctoral
position BSC-CNS-CONACYT contract 290790, grant number 265937).Peer ReviewedAward-winningPostprint (published version
Sustainable Edge Computing: Challenges and Future Directions
An increasing amount of data is being injected into the network from IoT
(Internet of Things) applications. Many of these applications, developed to
improve society's quality of life, are latency-critical and inject large
amounts of data into the network. These requirements of IoT applications
trigger the emergence of Edge computing paradigm. Currently, data centers are
responsible for a global energy use between 2% and 3%. However, this trend is
difficult to maintain, as bringing computing infrastructures closer to the edge
of the network comes with its own set of challenges for energy efficiency. In
this paper, we propose our approach for the sustainability of future computing
infrastructures to provide (i) an energy-efficient and economically viable
deployment, (ii) a fault-tolerant automated operation, and (iii) a
collaborative resource management to improve resource efficiency. We identify
the main limitations of applying Cloud-based approaches close to the data
sources and present the research challenges to Edge sustainability arising from
these constraints. We propose two-phase immersion cooling, formal modeling,
machine learning, and energy-centric federated management as Edge-enabling
technologies. We present our early results towards the sustainability of an
Edge infrastructure to demonstrate the benefits of our approach for future
computing environments and deployments.Comment: 26 pages, 16 figure
Security supportive energy-aware scheduling and energy policies for cloud environments
Cloud computing (CC) systems are the most popular computational environments for providing elastic
and scalable services on a massive scale. The nature of such systems often results in energy-related
problems that have to be solved for sustainability, cost reduction, and environment protection.
In this paper we defined and developed a set of performance and energy-aware strategies for resource
allocation, task scheduling, and for the hibernation of virtual machines. The idea behind this model is to
combine energy and performance-aware scheduling policies in order to hibernate those virtual machines
that operate in idle state. The efficiency achieved by applying the proposed models has been tested using
a realistic large-scale CC system simulator. Obtained results show that a balance between low energy
consumption and short makespan can be achieved.
Several security constraints may be considered in this model. Each security constraint is characterized
by: (a) Security Demands (SD) of tasks; and (b) Trust Levels (TL) provided by virtual machines. SD and TL
are computed during the scheduling process in order to provide proper security services.
Experimental results show that the proposed solution reduces up to 45% of the energy consumption
of the CC system. Such significant improvement was achieved by the combination of an energy-aware
scheduler with energy-efficiency policies focused on the hibernation of VMs.COST Action IC140
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