Popular massively multiplayer online role-playing games (MMORPG) typically have millions of players and are played throughout the world. Worldwide revenues for MMORPGs have increased to billions of dollars each year. Unfortunately, the complex architecture of MMORPGs make them hard to maintain, resulting in considerable costs and development risks. For normal operation, MMORPGs have to access huge amounts of diverse data. With increasing numbers of players, managing growing volumes of data in a relational database becomes a big challenge, which cannot be overcome by simply adding new servers. Cloud storage systems are emerging solutions, focusing on providing scalability and high performance for Cloud applications, social media, etc. However, Cloud storage systems are in general not designed for processing transactions or providing high levels of consistency. In this paper, we analyze the existing architecture of MMORPGs, classify data, highlight the design requirements, identify the major research challenges, propose a Cloud-based model for MMORPGs, and present a series of data management solutions.
As outlined for instance by the CAP theorem, achieving consistency guarantees within a 100% available and faulttolerant distributed system is impossible. Nevertheless, in real-life applications actual properties are neither black nor white and the degree of fulfilment of requirements depends on the likelihood of failures and communication parameters of distributed systems. While typical Cloud-based applications weaken consistency in accordance with less strict applications requirements, strong consistency can also be achieved, for instance by tunable consistency. This, however, often comes with a strong degradation of scalability (performance of growing clusters) and availability. Based on a project investigating the usefulness of Cloud DBMS for Massively Multi-player Online Role-Playing Games (MMORPGs) we describe how strong consistency can be provided for such a scenario, by still proving a high-level of availability and performance suitable for this specific application. For this purpose we implement a lightweight mechanism to detect failures based on timestamps and only react accordingly if required.
Popularity and complexity of cloud data management systems are increasing rapidly. Thus providing sophisticated features becomes more important. The focus of this paper is on (self-)tuning where we contribute the following: (1) we illustrate why (self-)tuning for cloud data management is necessary but yet a much more complex task than for traditional data management, and (2) propose an model to solve some of the outlined problems by clustering nodes in zones across data management layers for applications with similar requirements.
The well-known problems of tuning and self-tuning of data management systems are amplified in the context of Cloud environments that promise self management along with properties like elasticity and scalability. The intricate criteria of Cloud storage systems such as their modular, distributed, and multi-layered architecture add to the complexity of the tuning and self-tuning process. In this paper, we provide an architecture for a self-tuning framework for Cloud data storage clusters. The framework consists of components to observe and model certain performance criteria and a decision model to adjust tuning parameters according to specified requirements. As part of its implementation, we provide an overview on benchmarking and performance modeling components along with experimental results.
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