Massively multiplayer online games (MMOGs) are a new type of large-scale interactive applications providing a seamless virtual world for millions of gamers from all over the world. Traditionally, MMOGs are implemented using a client-server architecture. With the maturity of cloud computing, many MMOG providers such as Blizzard Entertainment and Jagex Limited have begun to utilize virtualized machines to serve their consumers due to the elasticity, adaptability, and cost advantages of clouds. It is a major challenge for MMOG providers how to dynamically allocate resources in an on-demand pattern in order to reduce the energy cost associated with operating a MMOG cloud while providing good-enough quality of experience (QoE) for MMOG gamers. In this paper, we propose a dynamic resource allocation scheme for MMOGs in heterogeneous cloud computing systems, which makes use of both dynamic virtual machine (VM) consolidation among physical machines (PMs) and long short-term memory based VM resizing at physical machine level to achieve the energy efficiency and desired QoE requirements. Our scheme considers multiple types of resources, characteristic of AFK (away from keyboard) gamers, heterogeneous PMs and VMs, strict QoE requirements and overheads incurred due to migrating VMs. Furthermore, a novel hybrid algorithm based on differential evolution and modified first-fit heuristic is presented for dynamic consolidation of VMs in heterogeneous cloud data centers. The experiment results show that, compared to the traditional over-provisioning policy, our resource allocation scheme can achieve up to 44.8% energy savings while ensuring the QoE requirements of MMOG gamers under rapidly changing workloads.

Prompted by the remarkable progress in both cloud computing and GPU virtualization, cloud gaming has been attracting more and more attention in the gaming industry. With the cloud gaming model, players do not need to download or install the game on local devices, and constantly upgrade their devices. Despite these advantages, cloud gaming faces several challenges for its success, including long response delay, poor game fairness, and high operational cost. To this end, this article proposes an edge computing-assisted multiplayer cloud gaming system named ECACG to improve multiplayer cloud gaming experiences and operating costs by offloading the game rendering task to the nearby edge server. Based on the ECACG, two decision processes are completed. One is player request scheduling and the other is rendering server allocation. The decision problem is formulated into a constrained multiobjective optimization model. A novel hybrid algorithm based on deep reinforcement learning and heuristic strategy is developed to solve the optimization problem. The effectiveness of the proposed ECACG is evaluated by simulation experiments based on the real-world parameters. The simulation results show that compared with the existing schemes, the proposed ECACG can achieve lower rental costs and better fairness, while providing the good-enough response delay for players.