Skip to Main Content
Today's Massively Multiplayer Online Games (MMOGs) can include millions of concurrent players spread across the world and interacting with each other within a single session. Faced with high resource demand variability and with misfit resource renting policies, the current industry practice is to overprovision for each game tens of self-owned data centers, making the market entry affordable only for big companies. Focusing on the reduction of entry and operational costs, we investigate a new dynamic resource provisioning method for MMOG operation using external data centers as low-cost resource providers. First, we identify in the various types of player interaction a source of short-term load variability, which complements the long-term load variability due to the size of the player population. Then, we introduce a combined MMOG processor, network, and memory load model that takes into account both the player interaction type and the population size. Our model is best used for estimating the MMOG resource demand dynamically, and thus, for dynamic resource provisioning based on the game world entity distribution. We evaluate several classes of online predictors for MMOG entity distribution and propose and tune a neural network-based predictor to deliver good accuracy consistently under real-time performance constraints. We assess using trace-based simulation the impact of the data center policies on the quality of resource provisioning. We find that the dynamic resource provisioning can be much more efficient than its static alternative even when the external data centers are busy, and that data centers with policies unsuitable for MMOGs are penalized by our dynamic resource provisioning method. Finally, we present experimental results showing the real-time parallelization and load balancing of a real game prototype using data center resources provisioned using our method and show its advantage against a rudimentary client threshold approach.