We present an approach to support massively multi-player games on peer-to-peer overlays. Our approach exploits the fact that players in MMGs display locality of interest, and therefore can form self-organizing groups based on their locations in the virtual world. To this end, we have designed scalable mechanisms to distribute the game state to the participating players and to maintain consistency in the face of node failures. The resulting system dynamically scales with the number of online players. It is more flexible and has a lower deployment cost than centralized games servers. We have implemented a simple game we call SimMud, and experimented with up to 4000 players to demonstrate the applicability of this approach.

We present a replication-based approach to fault-tolerant distributed stream processing in the face of node failures, network failures, and network partitions. Our approach aims to reduce the degree of inconsistency in the system while guaranteeing that available inputs capable of being processed are processed within a specified time threshold. This threshold allows a user to trade availability for consistency: a larger time threshold decreases availability but limits inconsistency, while a smaller threshold increases availability but produces more inconsistent results based on partial data. In addition, when failures heal, our scheme corrects previously produced results, ensuring eventual consistency. Our scheme uses a data-serializing operator to ensure that all replicas process data in the same order, and thus remain consistent in the absence of failures. To regain consistency after a failure heals, we experimentally compare approaches based on checkpoint/redo and undo/redo techniques and illustrate the performance trade-offs between these schemes. 1.

While the use of MapReduce systems (such as Hadoop) for large scale data analysis has been widely recognized and studied, we have recently seen an explosion in the number of systems developed for cloud data serving. These newer systems address “cloud OLTP ” applications, though they typically do not support ACID transactions. Examples of systems proposed for cloud serving use include BigTable, PNUTS, Cassandra, HBase, Azure, CouchDB, SimpleDB, Voldemort, and many others. Further, they are being applied to a diverse range of applications that differ considerably from traditional (e.g., TPC-C like) serving workloads. The number of emerging cloud serving systems and the wide range of proposed applications, coupled with a lack of applesto-apples performance comparisons, makes it difficult to understand the tradeoffs between systems and the workloads for which they are suited. We present the Yahoo! Cloud Serving Benchmark (YCSB) framework, with the goal of facilitating performance comparisons of the new generation of cloud data serving systems. We define a core set of benchmarks and report results for four widely used systems: Cassandra, HBase, Yahoo!’s PNUTS, and a simple sharded MySQL implementation. We also hope to foster the development of additional cloud benchmark suites that represent other classes of applications by making our benchmark tool available via open source. In this regard, a key feature of the YCSB framework/tool is that it is extensible—it supports easy definition of new workloads, in addition to making it easy to benchmark new systems.

We present PRACTI, a new approach for large-scale replication. PRACTI systems can replicate or cache any subset of data on any node (Partial Replication), provide a broad range of consistency guarantees (Arbitrary Consistency), and permit any node to send information to any other node (Topology Independence). A PRACTI architecture yields two significant advantages. First, by providing all three PRACTI properties, it enables better trade-offs than existing mechanisms that support at most two of the three desirable properties. The PRACTI approach thus exposes new points in the design space for replication systems. Second, the flexibility of PRACTI protocols simplifies the design of replication systems by allowing a single architecture to subsume a broad range of existing systems and to reduce development costs for new ones. To illustrate both advantages, we use our PRACTI prototype to emulate existing server replication, client-server, and object replication systems and to implement novel policies that improve performance for mobile users, web edge servers, and grid computing by as much as an order of magnitude.

This paper presents Rosebud, a new Byzantine faulttolerant storage architecture designed to be highly scalable and deployable in the wide-area. To support massive amounts of data, we need to partition the data among the nodes. To support long-lived operation, we need to allow the set of nodes in the system to change. To our knowledge, we are the first to present a complete design and a running implementation of Byzantine-fault-tolerant storage algorithms for a large scale, dynamic membership. We deployed Rosebud in a wide area testbed and ran experiments to evaluate its performance, and our experiments show that it performs well. We show that our storage algorithms perform equivalently to highly optimized replication algorithms in the wide-area. We also show that performance degradation is minor when the system reconfigures.

There has been a great deal of hype about Amazon’s simple storage service (S3). S3 provides infinite scalability and high availability at low cost. Currently, S3 is used mostly to store multi-media documents (videos, photos, audio) which are shared by a community of people and rarely updated. The purpose of this paper is to demonstrate the opportunities and limitations of using S3 as a storage system for general-purpose database applications which involve small objects and frequent updates. Read, write, and commit protocols are presented. Furthermore, the cost ($), performance, and consistency properties of such a storage system are studied.

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Recently the cloud computing paradigm has been receiving significant excitement and attention in the media and blogosphere. To some, cloud computing seems to be little more than a marketing umbrella, encompassing topics such as distributed computing, grid computing, utility computing, and softwareas-a-service, that have already received significant research focus and commercial implementation. Nonetheless, there exist an increasing number of large companies that are offering cloud computing infrastructure products and services that do not entirely resemble the visions of these individual component topics. In this article we discuss the limitations and opportunities of deploying data management issues on these emerging cloud computing platforms (e.g., Amazon Web Services). We speculate that large scale data analysis tasks, decision support systems, and application specific data marts are more likely to take advantage of cloud computing platforms than operational, transactional database systems (at least initially). We present a list of features that a DBMS designed for large scale data analysis tasks running on an Amazon-style offering should contain. We then discuss some currently available open source and commercial database options that can be used to perform such analysis tasks, and conclude that none of these options, as presently architected, match the requisite features. We thus express the need for a new DBMS, designed specifically for cloud computing environments. 1

While many public cloud providers offer pay-as-you-go computing, their varying approaches to infrastructure, virtualization, and software services lead to a problem of plenty. To help customers pick a cloud that fits their needs, we develop CloudCmp, a systematic comparator of the performance and cost of cloud providers. CloudCmp measures the elastic computing, persistent storage, and networking services offered by a cloud along metrics that directly reflect their impact on the performance of customer applications. CloudCmp strives to ensure fairness, representativeness, and compliance of these measurements while limiting measurement cost. Applying CloudCmp to four cloud providers that together account for most of the cloud customers today, we find that their offered services vary widely in performance and costs, underscoring the need for thoughtful provider selection. From case studies on three representative cloud applications, we show that CloudCmp can guide customers in selecting the best-performing provider for their applications. Categories and Subject Descriptors C.4 [Performance of Systems]: General—measurement techniques,

Testing large-scale distributed systems is a challenge, because some errors manifest themselves only after a distributed sequence of events that involves machine and network failures. D³S is a checker that allows developers to specify predicates on distributed properties of a deployed system, and that checks these predicates while the system is running. When D³S finds a problem it produces the sequence of state changes that led to the problem, allowing developers to quickly find the root cause. Developers write predicates in a simple and sequential programming style, while D³S checks these predicates in a distributed and parallel manner to allow checking to be scalable to large systems and fault tolerant. By using binary instrumentation, D³S works transparently with legacy systems and can change predicates to be checked at runtime. An evaluation with 5 deployed systems shows that D 3 S can detect non-trivial correctness and performance bugs at runtime and with low performance overhead (less than 8%).