Email this article Efficient optimal policy and resource allocation to provide qos services in multi-cloud
Abstract
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.
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