Intelligent Agents for Automated Cloud Computing Negotiation

Presently, cloud providers offer “off-the-shelf” Service Level Agreements (SLA), on a “take it or leave it” basis. This paper, alternatively, proposes customized SLAs. An automated negotiation is needed to establish customized SLAs between service providers and consumers with no previous knowledge of each other. Traditional negotiations between humans are often fraught with difficulty. Thus, in this work, the use of intelligent agents to represent cloud providers and consumers is advocated. Rubinstein’s Alternating Offers Protocol offers a suitable technical solution for this challenging problem. The purpose of this paper is to apply the state-of-the-art in negotiation automated algorithms/agents within a described Cloud Computing SLA framework, and to evaluate the most appropriate negotiation approach based on many criteria

Contribution to Agents-Based Negotiation and Monitoring of Cloud Computing Service Level Agreement

Cloud Computing environments are dynamic and open systems, where cloud providers and consumers frequently join and leave the cloud marketplaces. Due to the increasing number of cloud consumers and providers, it is becoming almost impossible to facilitate face to face meetings to negotiate and establish a Service Level Agreement (SLA); thus automated negotiation is needed to establish SLAs between service providers and consumers with no previous knowledge of each other. In this thesis, I propose, an Automated Cloud Service Level Agreement framework (ACSLA). ACSLA is made up of five stages, and the corresponding software agent components: Gathering, Filtering, Negotiation, SLA Agreement and Monitoring. In the Gathering stage all the information about the providers and what they can offer is gathered. In the Filtering stage the customer’s agent will send the request to ACSLA, which will filter all the providers in order to recommend the best matched candidates. In Negotiation stage the customer’s agent will negotiate separately with each candidate provider using different negotiation algorithms, which will be evaluated and for which recommendations and guidelines will be provided. The output of this stage is that the best outcome from the customer’s perspective will be picked up, which will be the agreed value for each parameter in the SLA. In SLA Agreement stage the provider’s agent and the customer‘s agent will be informed about the Agreement, which will be specified in measurable terms. The output of the SLA Agreement stage will be a list of metrics that can be monitored in the Monitoring stage. Customer’s agent and provider’s agent will also negotiate and agree about the penalties and actions will be taken in case the SLA has been violated and unfulfilled. There is a variety of actions that can be taken, like informing both sides, recommending solutions, self-healing and hot-swapping. ACSLA is evaluated using case studies which show its flexibility and effectiveness. ACSAL offers a novel approach to tackle many challenging issues in the current and likely future, cloud computing market. It is the first complete automated framework for cloud SLA. There are many automated negotiation algorithms and protocols, which have been developed over the years in other research areas; establishing functional solutions applicable to the cloud-computing environment is not an easy task. Rubinstein’s Alternating Offers Protocol, also known as the Rubinstein bargaining model, has been investigated for application in automated cloud SLA, and it offers a satisfactory technical solution for this challenging problem. The purpose of this research was also to apply the state of the art in negotiation automated algorithms/agents within a described Cloud Computing SLA framework, to develop new algorithms, and to evaluate and recommend the most appropriate negotiation approach based on many criteria

An intelligent spatial land use planning support system using socially rational agents

\u3cp\u3eThis research presents an intelligent planning support system based on multi-agent systems for spatial urban land use planning. The proposed system consists of two main phases: a pre-negotiation phase and an automated negotiation phase. The pre-negotiation phase involves interaction between human actors and intelligent software agents in order to elicit the actors’ social preferences. The agents employ social value orientation theory, which is rooted in social psychology, in order to model actors’ social preferences. The automated negotiation phase involves negotiation among autonomous software agents, the aim being to achieve consensus about the spatial problem on behalf of the relevant actors and using the information obtained. This study employs a computationally effective Bayesian learning technique, along with social value orientation theory, to design socially rational intelligent agents who work on behalf of real actors. The proposed system is applied to a real world urban land use planning case study. Human actors participate in a pre-negotiation phase, and their social preferences are elicited by intelligent software agents through a number of interactions. Then, software agents come together to engage in an automated negotiation phase and eventually reach an agreement on the spatial configuration of urban land uses on behalf of the actors. The results of the study show that the proposed system is effective at performing an automated negotiation, plus that the final plan–which is the output of the automated negotiation–produces higher social utility and better spatial land use configurations for the agents.\u3c/p\u3