Automated Bidding for Media Services at the Edge of a Content Delivery Network
We investigate the problem of providing media services to multiple autonomous wireless users at the edge of a content delivery network (CDN) in a setting where wireless resources are priced based on real-time market demands. Our focus is on the multimedia service resource negotiation process, which is performed prior to the actual media transmission. We adopt the progressive second price (PSP) auction mechanism, which is used to determine the network resource allocation to the users and a corresponding tax for the consumed resources. Our interest in this negotiation mechanism lies in understanding a single user's (or agent's) ability to learn to improve its bids over time in order to increase its own utility in the face of time-varying resource valuations and contention for resources with other users. We pay particular attention to the implementation complexity and the information requirements of the agent's deployed learning rule, and we quantify the impact of these factors on the rule's ultimate performance (i.e., the cumulative utility achieved over time) and efficiency (i.e., the utility gained per unit of computation). These factors are especially important in the mobile video streaming context, where limited resources must be efficiently utilized, and where communication and computation overheads can significantly impact the quality of service experienced by the user.
content delivery networks resource negotiation multiagent learning multimedia service middleware session negotiation