This paper presents Tebaldi, a distributed key-value store that explores new ways to harness the performance opportunity of combining different specialized concurrency control mechanisms (CCs) within the same database. Tebaldi partitions conflicts at a fine granularity and matches them to specialized CCs within a hierarchical framework that is modular, extensible, and able to support a wide variety of concurrency control techniques, from single-version to multiversion and from lock-based to timestamp-based. When running the TPC-C benchmark, Tebaldi yields more than 20× the throughput of the basic two-phase locking protocol, and over 3.7× the throughput of Callas, a recent system that, like Tebaldi, aims to combine different CCs.

In order to minimize user perceived latency while ensuring high data availability, cloud applications desire to select servers from one of the multiple data centers (i.e., server clusters) in different geographical locations, which are able to provide desired services with low latency and low cost. This paper presents CloudGPS, a new server selection scheme of the cloud computing environment that achieves high scalability and ISP-friendliness. CloudGPS proposes a configurable global performance function that allows Internet service providers (ISPs) and cloud service providers (CSPs) to leverage the cost in terms of inter-domain transit traffic and the quality of service in terms of network latency. CloudGPS bounds the overall burden to be linear with the number of end users. Moreover, compared with traditional approaches, CloudGPS significantly reduces network distance measurement cost (i.e., from O(N) to O(1) for each end user in an application using N data centers). Furthermore, CloudGPS achieves ISP-friendliness by significantly decreasing inter-domain transit traffic.

Network Coordinate (NC) systems provide an efficient and scalable mechanism to estimate latencies among hosts. However, many popular algorithms like Vivaldi suffer greatly from the existence of Triangle Inequality Violations (TIVs). Two-layer systems like Pharos and hierarchical Vivaldi have been proposed to remedy the impact of TIVs. They divide the whole space into several location-based clusters and run NC systems on both global layer and local layer. However, the two-layer model is only able to optimize the intra-cluster links relating to a limited portion of TIV triangles. In this paper, we propose a new NC system, Tarantula, which divides the space in a novel way. By categorizing the TIVs into three classes, we show that Tarantula handles a much larger portion of existing TIVs than two-layer systems. Moreover, we present two techniques to further strengthen the Tarantula system: 1) relate the updating step size in the Vivaldi algorithm used in Tarantula to ground-truth latency so as to improve the prediction for short links; 2) propose Dynamic Cluster Optimization to dynamically adjust clustering of hosts. Our experimental results show that Tarantula outperforms Pharos and Vivaldi significantly in terms of estimation accuracy. When implementing different NC systems in the application of server selection and detour finding, Tarantula again performs the best.

Computing the shortest-path distances between nodes is a key problem in analyzing social graphs. Traditional methods like breadth-first search (BFS) do not scale well with graph size. Recently, a Graph Coordinate System, called Orion, has been proposed to estimate shortest-path distances in a scalable way. Orion uses a landmark-based approach, which does not take account of the shortest-path distances between non-landmark nodes in coordinate calculation. Such biased input for the coordinate system cannot characterize the graph structure well. In this paper, we propose Pomelo, which calculates the graph coordinates in a decentralized manner. Every node in Pomelo computes its shortest-path distances to both nearby neighbors and some random distant neighbors. By introducing the novel partial BFS, the computational overhead of Pomelo is tunable. Our experimental results from different representative social graphs show that Pomelo greatly outperforms Orion in estimation accuracy while maintaining the same computational overhead.