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To deliver the required QoS, the cloud provider is asked to efficiently execute the tenants’ tasks and manages a huge amount of distributed and shared data. Hence, task scheduling and data replication are interdependent techniques that can improve the overall system performance and guarantee efficient data accessing. These operations must also preserve the economic profit of the cloud provider, which is very challenging. In this paper, we present a novel combination between a scheduling algorithm called Bottleneck Value Scheduling (BVS) algorithm with a dynamic data replication strategy called Correlation and Economic Model-based Replication (CEMR). Our aim is to improve data access effectiveness in order to meet service level objectives in terms of response time S LO_RT and minimum availability S LO_MA, while preserving the provider profit. Simulation results demonstrate that the proposed scheduling …