English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Cloud Computing is becoming the leading paradigm for executing scientific andengineering workflows. The large-scale nature of the experiments they model andtheir variable workloads make clouds the ideal execution environment due toprompt and elastic access to huge amounts of computing resources. Autoscalersare middleware-level software components that allow scaling up and down thecomputing platform by acquiring or terminating virtual machines (VM) at thetime that workflow's tasks are being scheduled. In this work we propose a novelonline multi-objective autoscaler for workflows denominated CloudMulti-objective Intelligence (CMI), that aims at the minimization of makespan,monetary cost and the potential impact of errors derived from unreliable VMs.In addition, this problem is subject to monetary budget constraints. CMI isresponsible for periodically solving the autoscaling problems encountered alongthe execution of a workflow. Simulation experiments on four well-knownworkflows exhibit that CMI significantly outperforms a state-of-the-artautoscaler of similar characteristics called Spot Instances Aware Autoscaling(SIAA). These results convey a solid base for deepening in the study of othermeta-heuristic methods for autoscaling workflow applications using cheap butunreliable infrastructures.

CMI: An Online Multi-objective Genetic Autoscaler for Scientific and Engineering Workflows in Cloud Infrastructures with Unreliable Virtual Machines