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HyperDex: A Distributed, Searchable Key-Value Store
"... Distributed key-value stores are now a standard component of high-performance web services and cloud computing applications. While key-value stores offer significant performance and scalability advantages compared to traditional databases, they achieve these properties through a restricted API that ..."
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Distributed key-value stores are now a standard component of high-performance web services and cloud computing applications. While key-value stores offer significant performance and scalability advantages compared to traditional databases, they achieve these properties through a restricted API that limits object retrieval—an object can only be retrieved by the (primary and only) key under which it was inserted. This paper presents HyperDex, a novel distributed key-value store that provides a unique search primitive that enables queries on secondary attributes. The key insight behind HyperDex is the concept of hyperspace hashing in which objects with multiple attributes are mapped into a multidimensional hyperspace. This mapping leads to efficient implementations not only for retrieval by primary key, but also for partially-specified secondary attribute searches and range queries. A novel chaining protocol enables the system to achieve strong consistency, maintain availability and guarantee fault tolerance. An evaluation of the full system shows that HyperDex is 12-13 × faster than Cassandra and MongoDB for finding partially specified objects. Additionally, HyperDex achieves 2-4 × higher throughput for get/put operations.
Filo: consolidated consensus as a cloud service
"... Abstract Consensus is at the core of many production-grade distributed systems. Given the prevalence of these systems, it is important to offer consensus as a cloud service. To match the multi-tenant requirements of the cloud, consensus as a service must provide performance guarantees, and prevent ..."
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Abstract Consensus is at the core of many production-grade distributed systems. Given the prevalence of these systems, it is important to offer consensus as a cloud service. To match the multi-tenant requirements of the cloud, consensus as a service must provide performance guarantees, and prevent aggressive tenants from disrupting the others. Fulfilling this goal is not trivial without overprovisioning and under-utilizing resources. We present Filo, the first system to provide consensus as a multi-tenant cloud service with throughput guarantees and efficient utilization of cloud resources. Tenants request an SLA by specifying their target throughput and degree of fault-tolerance. Filo then efficiently consolidates tenants on a shared set of servers using a novel placement algorithm that respects constraints imposed by the consensus problem. To respond to the load variations at runtime, Filo proposes a novel distributed controller that piggybacks on the consensus protocol to coordinate resource allocations across the servers and distribute the unused capacity fairly. Using a real testbed and simulations, we show that our placement algorithm is efficient at consolidating tenants, and while obtaining comparable efficiency and fairness, our distributed controller is ∼ 5x faster than the centralized baseline approach.
Practical Experience Report: The Performance of Paxos in the Cloud
"... Abstract—This experience report presents the results of an extensive performance evaluation conducted using four open-source implementations of Paxos deployed in Amazon’s EC2. Paxos is a fundamental algorithm for building fault-tolerant services, at the core of state-machine replication. Implementat ..."
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Abstract—This experience report presents the results of an extensive performance evaluation conducted using four open-source implementations of Paxos deployed in Amazon’s EC2. Paxos is a fundamental algorithm for building fault-tolerant services, at the core of state-machine replication. Implementations of Paxos are currently used in many prototypes and production systems in both academia and industry. Although all protocols surveyed in the paper implement Paxos, they are optimized in a number of different ways, resulting in very different behavior, as we show in the paper. We have considered a variety of configurations and failure-free and faulty executions. In addition to reporting our findings, we propose and assess additional optimizations to existing implementations. I.
