Existing Cloud resource management approaches favor Cloud service providers because of their data-center origin. The onus of efficiently managing the resources is on the end-user. Present provisioning/deprovisioning methods and the pricing model could be modified to ensure that the end-user reaps the benefits. The current study proposes a context-aware Cloud resource management approach based on a workload-type provisioning and a just-in-time deprovisioning design. Furthermore, it also recommends an alternative pricing model. The design intends to provision/deprovision individual resources like CPU, memory, and so on, according to the workload-type rather than instance-type as done in existing Cloud resource management approaches. The proposed approach was commissioned on a private lab infrastructure and several experiments were conducted. It was observed that the proposed approach was able to detect sample workload types with an average sensitivity of 82% and specificity of 76%. The average cost of the proposed approach was found to be 20% less than the present approach. K E Y W O R D S alternative cloud pricing, cloud resource management, cloud scaling, just-in-time deprovisioning 1 INTRODUCTION Cloud computing is an evolving paradigm and end-users could benefit more from this model if the supporting technologies like resource management and pricing provide more options. Existing resource management approaches are either rule-based and reactive (subsequently referred to as the traditional approach) or predictive. 1-4 The authors, in their earlier study, observed that provisioning was better using the predictive approach than the traditional approach. 5,6 Enterprise workloads are complex and nonstationary due to large, concurrent, and dependent applications that get executed in parallel or sequence. Simultaneous requests from such applications in a short time-period result in burst/spike, leading to resource management challenges. 7-9 An efficient Cloud resource management framework needs to classify the user request into workload type (WT) to offset this uncertainty. The WT of a user-request prescribes the resources required for fulfilling the demand. For example, a compute-intensive on-off WT would rather require additional CPU than storage, while a read-write-intensive exponential WT would require more disk IOPS than CPU. Earlier studies report a measure of self-similarity as a proficient approach for identifying anomalies that could be leveraged for WT classification. 10-12 It must be noted that existing Cloud resource management approaches do not include WT while provisioning resources. Currently, Cloud resources such as CPU, memory, disk, and network are grouped into logical units called instance-types (virtual machines) for provisioning/deprovisioning. Although the demand for individual resources like CPU, memory, and so on, is a frequent occurrence in enterprise Cloud applications, the only choice for the end-user is to make use of instance-types. An instance-type-based approach would make it is easier f...