Efficient optimal policy and resource allocation to provide qos services in multi-cloud

ABSTRACT: we propose a novel Service Level Agreement (SLA) framework  for cloud computing, in which a value control parameter is utilized to satisfy QoS needs for all classes in the market. The framework  utilizes reinforcement learning (RL) to infer a VM enlisting approach that can adjust to changes in the framework to ensure the QoS for all User classes. These progressions include: administration cost, framework limit, and the interest for administration. In displaying arrangements, when the CP rents more VMs to a class of Users, the QoS is debased for different classes because of a deficient number of VMs. In any case, our methodology coordinates processing assets adjustment with administration affirmation control dependent on the RL show. To the best of our insight, this investigation is the principal endeavor that encourages this mix to upgrade the CP's benefit and maintain a strategic distance from SLA infringement

Adaptive Resource Allocation and Provisioning in Multi-Service Cloud Environments

In the current cloud business environment, the cloud provider (CP) can provide a means for offering the required quality of service (QoS) for multiple classes of clients. We consider the cloud market where various resources such as CPUs, memory, and storage in the form of Virtual Machine (VM) instances can be provisioned and then leased to clients with QoS guarantees. Unlike existing works, we propose a novel Service Level Agreement (SLA) framework for cloud computing, in which a price control parameter is used to meet QoS demands for all classes in the market. The framework uses reinforcement learning (RL) to derive a VM hiring policy that can adapt to changes in the system to guarantee the QoS for all client classes. These changes include: service cost, system capacity, and the demand for service. In exhibiting solutions, when the CP leases more VMs to a class of clients, the QoS is degraded for other classes due to an inadequate number of VMs. However, our approach integrates computing resources adaptation with service admission control based on the RL model. To the best of our knowledge, this study is the first attempt that facilitates this integration to enhance the CP's profit and avoid SLA violation. Numerical analysis stresses the ability of our approach to avoid SLA violation while maximizing the CP’s profit under varying cloud environment conditions