Bid based scheduler with backfilling for a multiprocessor system

We consider a virtual computing environment that provides computational resources on demand to users with multi attribute task descriptions that include a valuation, resource (CPU) needs and a completion deadline. Achieving a high quality of service in this environment depends on finding a balance between processing high priority tasks before their deadlines expire, while maximizing resource utilization. The problem becomes more challenging in an economic setting, where the task valuation is private. We propose a bid-based server that publishes a history of the success rate table (SRT) for processed tasks. Clients use the history to optimize their bid for resources on a (single) multiprocessor server. The server schedules tasks in descending order of their bid- Highest Bid First (HBF) and backfills the schedule with smaller tasks when resources are still available. The scheduler follows a hard deadline model where tasks cannot be processed after their deadline. We propose three variations of the SRT where biding history is publicized at different granularity. Using a simulation based study, we analyze the behavior of clients' bids in respond to the SRT. We compare the best HBF variant with an efficient Earliest Deadline First (EDF) mechanism that charges a fixed price. Our results show that the HBF mechanism is able to exploit price discrimination and therefore complete the execution of more high value jobs under a heavy workload, leading to better weighted throughput. HBF can also maximize server profit and client surplus (the difference between value and the client bid) in different settings. Thus, HBF may yield solutions that benefit both the client and the server.