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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The workload prediction and resource allocation significantly play aninevitable role in production of an efficient cloud environment. The proactiveestimation of future workload followed by decision of resource allocation havebecome a prior solution to handle other in-built challenges like theunder/over-loading of physical machines, resource wastage, Quality-of-Services(QoS) violations, load balancing,VM migration and many more. In this context,the paper presents a comprehensive survey of workload forecasting andpredictive resource management models in cloud environment. A conceptualframework for workload forecasting and resource management, categorization ofexisting machine learning based resources allocation techniques, and majorchallenges of inefficient distribution of physical resource distribution arediscussed pertaining to cloud computing. Thereafter, a thorough survey ofexisting state-of-the-art contributions empowering machine learning basedapproaches in the field of cloud workload prediction and resource managementare rendered. Finally, the paper explores and concludes various emergingchallenges and future research directions concerning elastic resourcemanagement in cloud environment.

workload forecasting and resource management models based on machine learning for cloud computing environments