Optimal Negotiation of Service Level Agreements for Cloud-Based Services through Autonomous Agents

Abstract

Cloud-based services have become a cornerstone of today's IT. The self-service feature inherent in Cloud systems allows customers to play a greater role in service procurement. However, this restricts the value propositions and Service Level Agreements (SLAs) that Cloud providers offer because Quality of Service (QoS) and Non Functional Property (NFP) requirements vary from customer to customer. In feature-rich SLA templates, the contract space gets large, objectives are confidential and preferences over QoS and NFP often conflict between providers and customers. Hence, an SLA-gap exists between the two and contemporary providers bind their offerings to the inflexible take-it-or-leave-it SLAs. In this work, we address this problem by presenting a robust and computationally inexpensive negotiation strategy, using which agents can efficiently create near-optimal SLAs under time constraints. Experimental evaluations validate that our strategy performs at par with state of the art learning and non-learning strategies against a variety of metrics including utility, social welfare, social utility and the Pareto-optimal bids. This enables a dynamic SLA negotiation mechanism on top of our OpenShift (PaaS) based Cloud system designed using Service Oriented Cloud Computing Infrastructure (SOCCI) architecture. Negotiated procurement of services is shown to improve satisfaction of participants and reducing the SLA-gap.