129,693 research outputs found
Performance engineering for microservices and serverless applications: the RADON approach
Microservices and serverless are becoming integral parts of mod-ern cloud-based applications. Tailored performance engineering isneeded for assuring that the applications meet their requirementsfor quality attributes such as timeliness, resource efficiency, andelasticity. A novel DevOps-based framework for developing mi-croservices and serverless applications is being developed in theRADON project. RADON contributes to performance engineeringby including novel approaches for modeling, deployment optimiza-tion, testing, and runtime management. This paper summarizes thecontents of our tutorial presented at the 11th ACM/SPEC Interna-tional Conference on Performance Engineering (ICPE)
Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World
This report documents the program and the outcomes of GI-Dagstuhl Seminar
16394 "Software Performance Engineering in the DevOps World".
The seminar addressed the problem of performance-aware DevOps. Both, DevOps
and performance engineering have been growing trends over the past one to two
years, in no small part due to the rise in importance of identifying
performance anomalies in the operations (Ops) of cloud and big data systems and
feeding these back to the development (Dev). However, so far, the research
community has treated software engineering, performance engineering, and cloud
computing mostly as individual research areas. We aimed to identify
cross-community collaboration, and to set the path for long-lasting
collaborations towards performance-aware DevOps.
The main goal of the seminar was to bring together young researchers (PhD
students in a later stage of their PhD, as well as PostDocs or Junior
Professors) in the areas of (i) software engineering, (ii) performance
engineering, and (iii) cloud computing and big data to present their current
research projects, to exchange experience and expertise, to discuss research
challenges, and to develop ideas for future collaborations
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
CoCalc as a Learning Tool for Neural Network Simulation in the Special Course "Foundations of Mathematic Informatics"
The role of neural network modeling in the learning content of the special
course "Foundations of Mathematical Informatics" was discussed. The course was
developed for the students of technical universities - future IT-specialists
and directed to breaking the gap between theoretic computer science and it's
applied applications: software, system and computing engineering. CoCalc was
justified as a learning tool of mathematical informatics in general and neural
network modeling in particular. The elements of technique of using CoCalc at
studying topic "Neural network and pattern recognition" of the special course
"Foundations of Mathematic Informatics" are shown. The program code was
presented in a CoffeeScript language, which implements the basic components of
artificial neural network: neurons, synaptic connections, functions of
activations (tangential, sigmoid, stepped) and their derivatives, methods of
calculating the network's weights, etc. The features of the Kolmogorov-Arnold
representation theorem application were discussed for determination the
architecture of multilayer neural networks. The implementation of the
disjunctive logical element and approximation of an arbitrary function using a
three-layer neural network were given as an examples. According to the
simulation results, a conclusion was made as for the limits of the use of
constructed networks, in which they retain their adequacy. The framework topics
of individual research of the artificial neural networks is proposed.Comment: 16 pages, 3 figures, Proceedings of the 13th International Conference
on ICT in Education, Research and Industrial Applications. Integration,
Harmonization and Knowledge Transfer (ICTERI, 2018
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
Profiling SLAs for cloud system infrastructures and user interactions
Cloud computing has emerged as a cutting-edge technology which is widely used by both private and public institutions, since it eliminates the capital expense of buying, maintaining, and setting up both hardware and software. Clients pay for the services they use, under the so-called Service Level Agreements (SLAs), which are the contracts that establish the terms and costs of the services. In this paper, we propose the CloudCost UML profile, which allows the modeling of cloud architectures and the users’ behavior when they interact with the cloud to request resources. We then investigate how to increase the profits of cloud infrastructures by using price schemes. For this purpose, we distinguish between two types of users in the SLAs: regular and high-priority users. Regular users do not require a continuous service, so they can wait to be attended to. In contrast, high-priority users require a constant and immediate service, so they pay a greater price for their services. In addition, a computer-aided design tool, called MSCC (Modeling SLAs Cost Cloud), has been implemented to support the CloudCost profile, which enables the creation of specific cloud scenarios, as well as their edition and validation. Finally, we present a complete case study to illustrate the applicability of the CloudCost profile, thus making it possible to draw conclusions about how to increase the profits of the cloud infrastructures studied by adjusting the different cloud parameters and the resource configuration.This work was supported by the Spanish Ministry of Science and Innovation (co-financed
by European Union FEDER funds) project “FAME (Formal modeling and advanced
testing methods. Applications to medicine and computing systems) and MASSIVE
(Engineering adaptive software by and for the people in a highly connected world)”,
references RTI2018-093608-B-C32 and RTI2018-095255-B-I00. There was also support
from the Junta de Comunidades de Castilla-La Mancha project SBPLY/17/180501/000276/
01 (cofunded with FEDER funds, EU), the Region of Madrid (grant number FORTE-CM,
S2018/TCS-4314), and the Madrid Government (Comunidad de Madrid-Spain) under the
Multiannual Agreement with the Complutense University as part of the Program to
Stimulate Research for Young Doctors in the context of the V PRICIT (Regional
Programme of Research and Technological Innovation) under grant PR65/19-2245
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