Using cloud Database as a Service (DBaaS) offerings instead of on-premise deployments is increasingly common. Key advantages include improved availability and scalability at a lower cost than on-premise alternatives. In this paper, we describe the design of Taurus, a new multi-tenant cloud database system. Taurus separates the compute and storage layers in a similar manner to Amazon Aurora and Microsoft Socrates and provides similar benefits, such as read replica support, low network utilization, hardware sharing and scalability. However, the Taurus architecture has several unique advantages. Taurus offers novel replication and recovery algorithms providing better availability than existing approaches using the same or fewer replicas. Also, Taurus is highly optimized for performance, using no more than one network hop on critical paths and exclusively using append-only storage, delivering faster writes, reduced device wear, and constant-time snapshots. This paper describes Taurus and provides a detailed description and analysis of the storage node architecture, which has not been previously available from the published literature.

Semi-structured data such as JSON has become the de facto standard for supporting data exchange on the Web. At the same time, relational support for JSON data poses new challenges due to the large number of attributes, sparse attributes and dynamic changes in both workload and data set, which are all typical in such data. In this paper, we address these challenges through a lightweight, in-memory relational database engine prototype and a flexible vertical partitioning algorithm that uses simple heuristics to adapt the data layout for the workload, on the fly. Our experimental evaluation using the Nobench dataset for JSON data, shows that we outperform Argo, a state-of-the-art data model that also maps the JSON data format into relational databases, by a factor of 3. We also outperform Hyrise, a state-of-the-art vertical partitioning algorithm designed for in-memory databases, by 24%. Furthermore, our algorithm is able to achieve around 40% better cache utilization and 35% better TLB utilization. Our experiments also show that our partitioning algorithm adapts to workload changes within a few seconds.

We introduce Ensemble, a runtime framework and associated tools for building application performance models on-the-fly. These dynamic performance models can be used to support complex, highly dimensional resource allocation, and/or what-if performance inquiry in modern heterogeneous environments, such as data centers and Clouds. Ensemble combines simple, partially specified, and lower-dimensionality models to provide good initial approximations for higher dimensionality application performance models. We evaluated Ensemble on industry-standard and scientific applications. The results show that Ensemble provides accurate, fast, and flexible performance models even in the presence of significant environment variability.

Due to the imperative need to reduce the management costs of large data centers, operators multiplex several concurrent database applications on a server farm connected to shared network attached storage. Determining and enforcing per-application resource quotas in the resulting cache hierarchy, on the fly, poses a complex resource allocation problem spanning the database server and the storage server tiers. This problem is further complicated by the need to provide strict Quality of Service (QoS) guarantees to hosted applications. In this paper, we design and implement a novel coordinated partitioning technique of the database buffer pool and storage cache between applications for any given cache replacement policy and per-application access pattern. We use statistical regression to dynamically determine the mapping between cache quota settings and the resulting per-application QoS. A resource controller embedded within the database engine actuates the partitioning of the two-level cache, converging towards the configuration with maximum application utility, expressed as the service provider revenue in that configuration, based on a set of latency sample points. Our experimental evaluation, using the MySQL database engine, a server farm with consolidated storage, and two e-commerce benchmarks, shows the effectiveness of our technique in enforcing application QoS, as well as maximizing the revenue of the service provider in shared server farms.

Most massively multiplayer game servers employ static partitioning of their game world into distinct mini-worlds that are hosted on separate servers. This limits cross-server interactions between players, and exposes the division of the world to players. We have designed and implemented an architecture in which the partitioning of game regions across servers is transparent to players and interactions are not limited to objects in a single region or server. This allows a finer grain partitioning, which combined with a dynamic load management algorithm enables us to better handle transient crowding by adaptively dispersing or aggregating regions from servers in response to quality of service violations.Our load balancing algorithm is aware of the spatial locality in the virtual game world. Based on localized information, the algorithm balances the load and reduces the cross server communication, while avoiding frequent reassignment of regions. Our results show that locality aware load balancing reduces the average user response time by up to a factor of 6 compared to a global algorithm that does not consider spatial locality and by up to a factor of 8 compared to static partitioning.