Cloud resource management: towards efficient execution of large-scale scientific applications and workflows on complex infrastructures

Gonzalez, Nelson Mimura; Carvalho, Tereza Cristina de; Miers, Charles Christian

doi:10.1186/s13677-017-0081-4

Cited by 29 publications

(8 citation statements)

References 131 publications

(102 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The cloud is not only secure but also offers limitless access to information. That is, through cloud-based systems, users can access legal documents or data at any time and from anywhere through their user accounts with a significant reduction in operational and management costs (Gonzalez, de Brito Carvalho, and Miers, 2017;Shen et al, 2014). The user can engage the web-based IT artefact through a GUI that allows them to access different services like file sharing.…”

Section: System Architecturementioning

confidence: 99%

Further Developing the Judicial System Utilizing Cloud, VOIP, and Videoconferencing

Sakib¹

2021

Preprint

View full text Add to dashboard Cite

This venture targets giving a correspondence framework that can improve the legal framework execution and cooperation and furthermore make it simple to lead execution audits. This examination depicts restricted admittance to the right legitimate data just as admittance to courts and court administrations as the essential factors that limit admittance to equity. The basic target fundamental for accomplishing the point incorporates planning an online easy to understand data framework that would improve correspondence and association between legal professionals like legal advisors and disputants. The undertaking uses a subjective methodology that zeroed in on the substance investigation of essential information. Key members were court laborers like attorneys and agents, just as court clients like disputants. Pre-plan discoveries uncovered that current legitimate data frameworks – basically court sites – neglect to recognize their crowd by treating all court clients similarly comparative with the correspondence of lawful data. Likewise, the issue of admittance to equity includes the significant expense of equity also, restricted legal data that elevates admittance to equity. Post-plan discoveries from the online overview directed after the turn of events and execution of the IT curio uncovered a powerful and productive legitimate data framework. Exploration discoveries supported the advancement of an IT antiquity that records for huge shortcomings in current legal data frameworks, for example, their failure to pass on fitting legitimate data intelligently and productively. In outline, the investigation prescribes the reception of saw control to work fair and square of certainty of court clients and the differentiation between court clients to convey the right data to the right crowd.

show abstract

Section: System Architecturementioning

confidence: 99%

Further Developing the Judicial System Utilizing Cloud, VOIP, and Videoconferencing

Sakib¹

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Furthermore, resource management is a challenging issue, as the difficulty lies with its relatedness to several problems, such as resource heterogeneity, asymmetric communication, inconsistent workload and resources dependency [5,6]. Therefore, this study reviews related research and its contribution to SDN-based cloud in terms of network performance, and energy efficiency.…”

Section: Introductionmentioning

confidence: 99%

Resource Management in SDN-Based Cloud and SDN-Based Fog Computing: Taxonomy Study

et al. 2021

View full text Add to dashboard Cite

Software-defined networks (SDN) is an evolution in networking field where the data plane is separated from the control plane and all the controlling and management tasks are deployed in a centralized controller. Due to its features regarding ease management, it is emerged in other fields such as cloud and fog computing in order to manage asymmetric communication across nodes, thus improving the performance and reducing the power consumption. This study focused on research that were conducted in SDN-based clouds and SDN-based fogs. It overviewed the important contributions in SDN clouds in terms of improving network performances and energy optimization. Moreover, state-of-the-art studies in SDN fogs are presented. The features, methods, environment, dataset, simulation tool and main contributions are highlighted. Finally, the open issues related to both SDN clouds and SDN fogs are defined and discussed.

show abstract

“…Cloud computing evolved from the concept of utility computing, Workflows have emerged as a way to formalize and structure data analysis, thus becoming an increasingly popular paradigm for scientists to handle complex scientific processes [17]. The Kepler scientific workflow system [10] is an open-source, cross-project collaboration to serve scientists from different disciplines [18,19].…”

Section: Introductionmentioning

confidence: 99%

PEnBayes: A Multi-Layered Ensemble Approach for Learning Bayesian Network Structure from Big Data

Tang

Wang

Nguyen

et al. 2019

Sensors

View full text Add to dashboard Cite

Discovering the Bayesian network (BN) structure from big datasets containing rich causal relationships is becoming increasingly valuable for modeling and reasoning under uncertainties in many areas with big data gathered from sensors due to high volume and fast veracity. Most of the current BN structure learning algorithms have shortcomings facing big data. First, learning a BN structure from the entire big dataset is an expensive task which often ends in failure due to memory constraints. Second, it is quite difficult to select a learner from numerous BN structure learning algorithms to consistently achieve good learning accuracy. Lastly, there is a lack of an intelligent method that merges separately learned BN structures into a well structured BN network. To address these shortcomings, we introduce a novel parallel learning approach called PEnBayes (Parallel Ensemble-based Bayesian network learning). PEnBayes starts with an adaptive data preprocessing phase that calculates the Appropriate Learning Size and intelligently divides a big dataset for fast distributed local structure learning. Then, PEnBayes learns a collection of local BN Structures in parallel using a two-layered weighted adjacent matrix-based structure ensemble method. Lastly, PEnBayes merges the local BN Structures into a global network structure using the structure ensemble method at the global layer. For the experiment, we generate big data sets by simulating sensor data from patient monitoring, transportation, and disease diagnosis domains. The Experimental results show that PEnBayes achieves a significantly improved execution performance with more consistent and stable results compared with three baseline learning algorithms.

show abstract

Cloud resource management: towards efficient execution of large-scale scientific applications and workflows on complex infrastructures

Cited by 29 publications

References 131 publications

Further Developing the Judicial System Utilizing Cloud, VOIP, and Videoconferencing

Further Developing the Judicial System Utilizing Cloud, VOIP, and Videoconferencing

Resource Management in SDN-Based Cloud and SDN-Based Fog Computing: Taxonomy Study

PEnBayes: A Multi-Layered Ensemble Approach for Learning Bayesian Network Structure from Big Data

Contact Info

Product

Resources

About