Genetic Algorithms for Job Scheduling in Cloud Computing

The cloud computing is the one that deals with the trading of the resources efficiently in accordance to the user’s need. A Job scheduling is the choice of an ideal resource for any job to be executed with regard to waiting time, cost or turnaround time. A cloud job scheduling will be an NP-hard problem that contains n jobs and m machines and every job is processed with each of these m machines to minimize the make span. The security here is one of the top most concerns in the cloud. In order to calculate the value of fitness the fuzzy inference system makes use of the membership function for determining the degree up to which the input parameters that belong to every fuzzy set is relevant. Here the fuzzy is used for the purpose of scheduling energy as well as security in the cloud computing.

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A Recommender for Resource Allocation in Compute Clouds Using Genetic Algorithms and SVR

10.5753/latinoware.2019.10336 ◽

2019 ◽

Author(s):

Thiago Nelson Faria Dos Reis ◽

Mário Antonio Meireles Teixeira ◽

João Dallyson Sousa De Almeida ◽

Anselmo Cardoso De Paiva

Keyword(s):

Resource Allocation ◽

Cloud Computing ◽

Genetic Algorithms ◽

Support Vector Regression ◽

Support Vector

Alocação de recursos em Cloud Computing tem sido feito de forma reativa, dificultando garantias de serviço e gerando carga desnecessária de recursos ociosos. Para mitigar esses problemas, este trabalho propõe e avalia uma abordagem de alocação de recursos preditiva, implementado como um recomendador de configuração, com base em Support Vector Regression (SVR) e Algoritmos Genéticos (AG). Esta combinação é utilizada para estimar tempo de execução do aplicativo e recomenda uma configuração viável e válida de recursos na nuvem, sobre o tempo de execução e custos monetários. Como estudo de caso, as aplicações de aprendizagem de máquina com base na ferramenta Weka são escolhidos. Os resultados mostram que os tempos previstos foram muito perto dos reais, conseguindo uma estimativa eficiente de tempo e custo e sua consequente redução.

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Current Trends in Cloud Computing for Data Science Experiments

International Journal of Cloud Applications and Computing ◽

10.4018/ijcac.2021100105 ◽

2021 ◽

Vol 11 (4) ◽

pp. 80-99

Author(s):

Syed Imran Jami ◽

Siraj Munir

Keyword(s):

Cloud Computing ◽

Resource Sharing ◽

Data Science ◽

Job Scheduling ◽

Resource Scheduling ◽

Data Intensive ◽

Organizational Issues ◽

Current Trends ◽

Recent Trends ◽

And Storage

Recent trends in data-intensive experiments require extensive computing and storage resources that are now handled using cloud resources. Industry experts and researchers use cloud-based services and resources to get analytics of their data to avoid inter-organizational issues including power overhead on local machines, cost associated with maintaining and running infrastructure, etc. This article provides detailed review of selected metrics for cloud computing according to the requirements of data science and big data that includes (1) load balancing, (2) resource scheduling, (3) resource allocation, (4) resource sharing, and (5) job scheduling. The major contribution of this review is the inclusion of these metrics collectively which is the first attempt towards evaluating the latest systems in the context of data science. The detailed analysis shows that cloud computing needs research in its association with data-intensive experiments with emphasis on the resource scheduling area.

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