A pareto-based Artificial Bee Colony and product line for optimizing scheduling of VM on cloud computing

Benali, Asmae; Asri, Bouchra El; Kriouile, Houda

doi:10.1109/cloudtech.2015.7336980

Cited by 5 publications

(1 citation statement)

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“…Moreover, several observations and conclusion are still not exploited. Similarly, a new approach for a three-tier architecture cloud broker containing three scheduling levels was presented in [35], to obtain the resource required for tasks without wasting energy. The proposed algorithm inherits the basic idea from ABC algorithm to enhance the execution of cloud tasks and the performance of a non-preemptive scheduling.…”

Section: Natural Inspired Algorithms (Nia)mentioning

confidence: 99%

A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

et al. 2020

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Given the perpetual surging of cloud services' requests, energy consumption of cloud data centers with their related CO 2 emissions still represents major issues. Efficient use of cloud's resources becomes then the driven force ensuring both, the satisfaction of service-level agreements and the sobriety of cloud's energy consumption. This paper purports to survey for the first time, a comprehensive literature of some actual challenges facing various cloud's resources utilization scheduling approaches dealing with energy conservation. Indeed, cloud resources involve not only computing servers, but also a wide set of intra-and inter-cloud network's resources to be considered. These resources are mainly provisioned through two wellknown technologies: virtualization and/or containerization. As existing studies in the area of cloud resources provisioning are generally categorized into different groups, this research depicts a complete taxonomy of energy-efficient cloud resources scheduling. In this same perspective, we survey some recent efforts made in energy-efficient virtual and containerized resources, by emphasizing first the most used reactive then proactive techniques for managing the whole cloud resources energy efficiency scheduling.

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Section: Natural Inspired Algorithms (Nia)mentioning

confidence: 99%