  The workload prediction and resource allocation significantly play an
inevitable role in production of an efficient cloud environment. The proactive
estimation of future workload followed by decision of resource allocation have
become a prior solution to handle other in-built challenges like the
under/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 and
predictive resource management models in cloud environment. A conceptual
framework for workload forecasting and resource management, categorization of
existing machine learning based resources allocation techniques, and major
challenges of inefficient distribution of physical resource distribution are
discussed pertaining to cloud computing. Thereafter, a thorough survey of
existing state-of-the-art contributions empowering machine learning based
approaches in the field of cloud workload prediction and resource management
are rendered. Finally, the paper explores and concludes various emerging
challenges and future research directions concerning elastic resource
management in cloud environment.
