http://rboutaba.cs.uwaterloo.ca/Papers/Journals/2010/Mosharaf10.pdf

A survey of network virtualization

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[서평]「Computer Organization and Design, The Hardware/Software Interface」

The Art of Multiprocessor Programming

We describe the design and implementation of Walter, a key-value store that supports transactions and replicates data across distant sites. A key feature behind Walter is a new property called Parallel Snapshot Isolation (PSI). PSI allows Walter to replicate data asynchronously, while providing strong guarantees within each site. PSI precludes write-write conflicts, so that developers need not worry about conflict-resolution logic. To prevent write-write conflicts and implement PSI, Walter uses two new and simple techniques: preferred sites and counting sets. We use Walter to build a social networking application and port a Twitter-like application.

/pdf/transactional-storage-for-geo-replicated-systems-2j5mhrj29h.pdf

Transactional storage for geo-replicated systems

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ACM Transactions on Database Systems

Internet applications increasingly rely on scalable data structures that must support high throughput and store huge amounts of data. These data structures can be hard to implement efficiently. Recent proposals have overcome this problem by giving up on generality and implementing specialized interfaces and functionality (e.g., Dynamo [4]). We present the design of a more general and flexible solution: a fault-tolerant and scalable distributed B-tree. In addition to the usual B-tree operations, our B-tree provides some important practical features: transactions for atomically executing several operations in one or more B-trees, online migration of B-tree nodes between servers for load-balancing, and dynamic addition and removal of servers for supporting incremental growth of the system.Our design is conceptually simple. Rather than using complex concurrency and locking protocols, we use distributed transactions to make changes to B-tree nodes. We show how to extend the B-tree and keep additional information so that these transactions execute quickly and efficiently. Our design relies on an underlying distributed data sharing service, Sinfonia [1], which provides fault tolerance and a light-weight distributed atomic primitive. We use this primitive to commit our transactions. We implemented our B-tree and show that it performs comparably to an existing open-source B-tree and that it scales to hundreds of machines. We believe that our approach is general and can be used to implement other distributed data structures easily.

/pdf/a-practical-scalable-distributed-b-tree-5ay4zq3uxj.pdf

A practical scalable distributed B-tree

Motivated by the increasing popularity of eventually consistent key-value stores as a commercial service, we address two important problems related to the consistency properties in a history of operations on a read/write register (i.e., the start time, finish time, argument, and response of every operation). First, we consider how to detect a consistency violation as soon as one happens. To this end, we formulate a specification for online verification algorithms, and we present such algorithms for several well-known consistency properties. Second, we consider how to quantify the severity of the violations, if a history is found to contain consistency violations. We investigate two quantities: one is the staleness of the reads, and the other is the commonality of violations. For staleness, we further consider time-based staleness and operation-count-based staleness. We present efficient algorithms that compute these quantities. We believe that addressing these problems helps both key-value store providers and users adopt data consistency as an important aspect of key-value store offerings.

http://www.hpl.hp.com/techreports/2011/HPL-2011-6.pdf

Analyzing consistency properties for fun and profit

Large-scale key-value storage systems sacrifice consistency in the interest of dependability (i.e., partition-tolerance and availability), as well as performance (i.e., latency). Such systems provide eventual consistency, which--to this point--has been difficult to quantify in real systems. Given the many implementations and deployments of eventually-consistent systems (e.g., NoSQL systems), attempts have been made to measure this consistency empirically, but they suffer from important drawbacks. For example, state-of-the art consistency benchmarks exercise the system only in restricted ways and disrupt the workload, which limits their accuracy.

In this paper, we take the position that a consistency benchmark should paint a comprehensive picture of the relationship between the storage system under consideration, the workload, the pattern of failures, and the consistency observed by clients. To illustrate our point, we first survey prior efforts to quantify eventual consistency. We then present a benchmarking technique that overcomes the shortcomings of existing techniques to measure the consistency observed by clients as they execute the workload under consideration. This method is versatile and minimally disruptive to the system under test. As a proof of concept, we demonstrate this tool on Cassandra.

/pdf/toward-a-principled-framework-for-benchmarking-consistency-420htypbwe.pdf

Toward a principled framework for benchmarking consistency

Linearizability is the gold standard among algorithm designers for deducing the correctness of a distributed algorithm using implemented shared objects from the correctness of the corresponding algorithm using atomic versions of the same objects. We show that linearizability does not suffice for this purpose when processes can exploit randomization, and we discuss the existence of alternative correctness conditions. This paper makes the following contributions: 1. Various examples demonstrate that using well-known linearizable implementations of objects (e.g., snapshots) in place of atomic objects can change the probability distribution of the outcomes that the adversary is able to generate. In some cases, an oblivious adversary can create a probability distribution of outcomes for an algorithm with implemented, linearizable objects, that not even a strong adversary can generate for the same algorithm with atomic objects. 2. A new correctness condition for shared object implementations, called strong inearizability, is defined. We prove that a strong adversary (i.e., one that sees the outcome of each coin flip immediately) gains no additional power when atomic objects are replaced by strongly linearizable implementations. In general, no strictly weaker correctness condition suffices to ensure this. We also show that strong linearizability is a local and composable property. 3. In contrast to the situation for the strong adversary, for a natural weaker adversary (one that cannot see a process' coin flip until its next operation on a shared object) we prove that there is no correspondingly general correctness condition. Specifically, any linearizable implementation of counters called terminating. from atomic registers and load-linked/store-conditional objects, that satisfies a natural locality property, necessarily gives the weak adversary more power than it has with atomic counters.

Linearizable implementations do not suffice for randomized distributed computation

Smart electricity meters have been replacing conventional meters worldwide, enabling automated collection of fine-grained (e.g., every 15 minutes or hourly) consumption data. A variety of smart meter analytics algorithms and applications have been proposed, mainly in the smart grid literature. However, the focus has been on what can be done with the data rather than how to do it efficiently. In this article, we examine smart meter analytics from a software performance perspective. First, we design a performance benchmark that includes common smart meter analytics tasks. These include offline feature extraction and model building as well as a framework for online anomaly detection that we propose. Second, since obtaining real smart meter data is difficult due to privacy issues, we present an algorithm for generating large realistic datasets from a small seed of real data. Third, we implement the proposed benchmark using five representative platforms: a traditional numeric computing platform (Matlab), a relational DBMS with a built-in machine learning toolkit (PostgreSQL/MADlib), a main-memory column store (“System C”), and two distributed data processing platforms (Hive and Spark/Spark Streaming). We compare the five platforms in terms of application development effort and performance on a multicore machine as well as a cluster of 16 commodity servers.

Wojciech Golab

Papers

A practical scalable distributed B-tree

Analyzing consistency properties for fun and profit

Toward a principled framework for benchmarking consistency

Linearizable implementations do not suffice for randomized distributed computation

Smart Meter Data Analytics: Systems, Algorithms, and Benchmarking