Robust resource allocation in a massive multiplayer online gaming environment

Abstract

The environment considered in this research is a massive multiplayer online gaming (MMOG) environment. Each user controls an avatar (an image that represents and is manipulated by a user) in a virtual world and interacts with other users. An important aspect of MMOG is maintaining a fair environment among users (i.e., not give an unfair advantage to users with faster connections or more powerful computers). The experience (either positive or negative) the user has with the MMOG environment is dependent on how quickly the game world responds to the user's actions. This study focuses on scaling the system based on demand, while maintaining an environment that guarantees fairness. Consider an environment where there is a main server (MS) that controls the state of the virtual world. If the performance falls below acceptable standards, the MS can off-load calculations to secondary servers (SSs). An SS is a user's computer that is converted into a server. Four heuristics are proposed for determining the number of SSs, which users are converted to SSs, and how users are assigned to the SSs and the MS. The goal of the heuristics is to provide a "fair" environment for all the users, and to be "robust" against the uncertainty of the number of new players that may join a given system configuration. The heuristics are evaluated and compared by simulation.

Autonomous massively multiplayer online game operation on unreliable resources

“…Much recent work focuses on (soft) QoS guarantees for MMOG operation [10,3,22]. Wong [22] proposes a resource provisioning algorithm with QoS guarantees, but considers only networking aspects, whereas we focus on maintaining QoS even during total resource failures.…”

SLA-based operation of massively multiplayer online games in competition-based environments

“…Recent work focuses on (soft) QoS guarantees for MMOG operation [3,10,21]. Wong [21] proposes a resource provisioning algorithm with QoS guarantees, but considers only networking aspects, whereas we consider here more resource types and a more realistic cloud model [15].…”

“…Wong [21] proposes a resource provisioning algorithm with QoS guarantees, but considers only networking aspects, whereas we consider here more resource types and a more realistic cloud model [15]. Briceno et al [3] study resource allocation for MMOGs but, unlike our work, consider only computational requirements and use a simplified workload model (not traces from a real MMOG). Lee and Chen [10] investigate MMOG server consolidation techniques, focusing on the energy consumption; our study complements theirs.…”

A proposed framework for heuristic approaches to resource allocation in the emerging smart grid

“…In the stochastic model, the resulting optimization would be a probability based on the PMFs of each task. Like in the unit commitment problem, both genetic algorithms [18], [23] and particle swarm optimizations [23] have been used. In addition, many other heuristics have been used such as Tabu [18], [23], simulated annealing [18], and k-percent best [24], [25].…”