Open Stack and Cloud Stack: Open Source Solutions for Building Public and Private Clouds

Barkat, Amine; Santos, Alysson Diniz dos; Ho, Thi Thao Nguyen

doi:10.1109/synasc.2014.64

Cited by 9 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, since time series motifs are the repeated subsequences of the time series, rare motif discovery techniques cannot deal with temporal events, and thus, are insufficient for rare temporal pattern mining. A related approach concerns rare association rules [6], [7], [8], [9], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34] that find rare associations between items in the database. However, all the mentioned work can only discover rare association rules built among itemsets, and cannot deal with temporal events and the complex temporal relations between them.…”

Section: Related Workmentioning

confidence: 99%

Efficient temporal pattern mining in big time series using mutual information

Pedersen

2021

Proc. VLDB Endow.

View full text Add to dashboard Cite

Very large time series are increasingly available from an ever wider range of IoT-enabled sensors deployed in different environments. Significant insights can be gained by mining temporal patterns from these time series. Unlike traditional pattern mining, temporal pattern mining (TPM) adds event time intervals into extracted patterns, making them more expressive at the expense of increased time and space complexities. Existing TPM methods either cannot scale to large datasets, or work only on pre-processed temporal events rather than on time series. This paper presents our Frequent Temporal Pattern Mining from Time Series (FTPMfTS) approach providing: (1) The end-to-end FTPMfTS process taking time series as input and producing frequent temporal patterns as output. (2) The efficient Hierarchical Temporal Pattern Graph Mining (HTPGM) algorithm that uses efficient data structures for fast support and confidence computation, and employs effective pruning techniques for significantly faster mining. (3) An approximate version of HTPGM that uses mutual information, a measure of data correlation, to prune unpromising time series from the search space. (4) An extensive experimental evaluation showing that HTPGM outperforms the baselines in runtime and memory consumption, and can scale to big datasets. The approximate HTPGM is up to two orders of magnitude faster and less memory consuming than the baselines, while retaining high accuracy.

show abstract

Section: Related Workmentioning

confidence: 99%

Efficient temporal pattern mining in big time series using mutual information

Pedersen

2021

Proc. VLDB Endow.

View full text Add to dashboard Cite

show abstract

“…KVM, Xen) and networks (e.g. LinuxBridge, Open vSwitch, Cisco Nexus) (Barkat et al, 2014); (v) it is supported by an active and fast-growing community (Wen et al, 2012); and (vi) it adopts a flexible modular architecture that makes it able to delegate certain of its services, such as the authentication mechanism, to an OpenID (Khan et al, 2011) or LDAP server; this configuration has been chosen for Lab4CE to integrate the university information system. Our approach to operationalize remote practical activities distinguishes two distinct concepts: the experiment and the practical session.…”

Section: Lab4ce: the Big Picturementioning

confidence: 99%

Lab4CE: a Remote Laboratory for Computer Education

Broisin

Venant

Vidal

2015

Int J Artif Intell Educ

View full text Add to dashboard Cite

Remote practical activities have been demonstrated to be efficient when learners come to acquire inquiry skills. In computer science education, virtualization technologies are gaining popularity as this technological advance enables instructors to implement realistic practical learning activities, and learners to engage in authentic and problem-based learning. However, virtualization solutions have not been designed especially for education and do not address any pedagogical concern. Since several large-scale studies showed that instructional supports during practical activities are almost as important as technical features, this article investigates the following research question: how the scaffolding around the lab increases students' engagement in remote practical learning of computer science? To answer this question, we introduce the Lab4CE environment, a remote laboratory for computer education which adopts a distributed, modular and flexible architecture to integrate a set of scaffolding tools and services intended for instructors and learners. An exploratory study conducted with 139 undergraduate students enrolled in the first year of a computer science degree suggests a positive effect of the framework on learners' engagement when they come to practice system administration, and reveals a significant positive correlation between students' activity within the system and students' learning achievement.

show abstract

“…It presents in deep details the newest versions of OpenStack, CloudStack, OpenNebula, Eucalyptus and Nimbus and compare their general features and important properties, trying to provide useful information for users that need to choose an open source cloud software. This work is a continuation of the work done before [12], which compared only CloudStack and Openstack.…”

mentioning

confidence: 99%

Open Source Solutions for Building IaaS Clouds

Barkat¹,

Santos²,

Ikken³

2015

SCPE

Self Cite

View full text Add to dashboard Cite

Cloud Computing is not only a pool of resources and services offered through the internet, but also a technology solution that allows optimization of resources use, costs minimization and energy consumption reduction. Enterprises moving towards cloud technologies have to choose between public cloud services, such as: Amazon Web Services, Microsoft Cloud and Google Cloud services, or private self built clouds. While the firsts are offered with affordable fees, the others provide more privacy and control. In this context, many open source softwares approach the buiding of private, public or hybrid clouds depending on the users need and on the available capabilities. To choose among the different open source solutions, an analysis is necessary in order to select the most suitable according with the enterprise's goals and requirements. In this paper, we present a depth study and comparison of five open source frameworks that are gaining more attention recently and growing fast: CloudStack, OpenStack, Eucalyptus, OpenNebula and Nimbus. We present their architectures and discuss different properties, features, useful information and our own insights on these frameworks.

show abstract

Open Stack and Cloud Stack: Open Source Solutions for Building Public and Private Clouds

Cited by 9 publications

References 7 publications

Efficient temporal pattern mining in big time series using mutual information

Efficient temporal pattern mining in big time series using mutual information

Lab4CE: a Remote Laboratory for Computer Education

Open Source Solutions for Building IaaS Clouds

Contact Info

Product

Resources

About