% This data is distributed under the terms of the Open Data Commons Attribution License (ODC-By) v1.0 - See more at: http://opendatacommons.org/licenses/by/1-0/
% Volume 1, Issue 2, 2014
    @Article{OJCC-v1i2n01_Groppe,
        title     = {A Self-Optimizing Cloud Computing System for Distributed Storage and Processing of Semantic Web Data},
        author    = {Sven Groppe and
                     Johannes Blume and
                     Dennis Heinrich and
                     Stefan Werner},
        journal   = {Open Journal of Cloud Computing (OJCC)},
        issn      = {2199-1987},
        year      = {2014},
        volume    = {1},
        number    = {2},
        pages     = {1--14},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194478},
        urn       = {urn:nbn:de:101:1-201705194478},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Clouds are dynamic networks of common, off-the-shell computers to build computation farms. The rapid growth of databases in the context of the semantic web requires efficient ways to store and process this data. Using cloud technology for storing and processing Semantic Web data is an obvious way to overcome difficulties in storing and processing the enormously large present and future datasets of the Semantic Web. This paper presents a new approach for storing Semantic Web data, such that operations for the evaluation of Semantic Web queries are more likely to be processed only on local data, instead of using costly distributed operations. An experimental evaluation demonstrates the performance improvements in comparison to a naive distribution of Semantic Web data.}
    }
    @Article{OJCC-2014v1i2n02_Alwabel,
        title     = {Evaluation of Node Failures in Cloud Computing Using Empirical Data},
        author    = {Abdulelah Alwabel and
                     Robert John Walters and
                     Gary B. Wills},
        journal   = {Open Journal of Cloud Computing (OJCC)},
        issn      = {2199-1987},
        year      = {2014},
        volume    = {1},
        number    = {2},
        pages     = {15--24},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194435},
        urn       = {urn:nbn:de:101:1-201705194435},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Cloud has emerged as a new computing paradigm that promises to move into computing-as-utility era. Desktop Cloud is a new type of Cloud computing introduced to further achieve this ambition with an aim to reduce costs. It merges two computing models: Cloud computing and volunteer computing. The aim of Desktop Cloud is to provide Cloud services out of infrastructure that is not made for this purpose, like PCs and laptops. Such computing resources lead to a high level of volatility as a result of the fact that they can leave without prior knowledge. This paper studies the impact of node failures using evaluation metrics based on real data collected from public archive to simulate failure events in the infrastructure of a Desktop Cloud. The contribution of this paper is: (i) analysing the failure events, (ii) proposing metrics to evaluate Desktop Clouds, and (iii) evaluating several VM allocation mechanisms in the presence of node failures.}
    }