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63
The Declarative Imperative Experiences and Conjectures in Distributed Logic
"... The rise of multicore processors and cloud computing is putting enormous pressure on the software community to find solutions to the difficulty of parallel and distributed programming. At the same time, there is more—and more varied—interest in data-centric programming languages than at any time in ..."
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Cited by 50 (5 self)
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The rise of multicore processors and cloud computing is putting enormous pressure on the software community to find solutions to the difficulty of parallel and distributed programming. At the same time, there is more—and more varied—interest in data-centric programming languages than at any time in computing history, in part because these languages parallelize naturally. This juxtaposition raises the possibility that the theory of declarative database query languages can provide a foundation for the next generation of parallel and distributed programming languages. In this paper I reflect on my group’s experience over seven years using Datalog extensions to build networking protocols and distributed systems. Based on that experience, I present a number of theoretical conjectures that may both interest the database community, and clarify important practical issues in distributed computing. Most importantly, I make a case for database researchers to take a leadership role in addressing the impending programming crisis. This is an extended version of an invited lecture at the ACM PODS 2010 conference [32]. 1.
Trusting the Cloud
"... More and more users store data in “clouds ” that are accessed remotely over the Internet. We survey well-known cryptographic tools for providing integrity and consistency for data stored in clouds and discuss recent research in cryptography and distributed computing addressing these problems. Storin ..."
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Cited by 49 (2 self)
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More and more users store data in “clouds ” that are accessed remotely over the Internet. We survey well-known cryptographic tools for providing integrity and consistency for data stored in clouds and discuss recent research in cryptography and distributed computing addressing these problems. Storing data in clouds Many providers now offer a wide variety of flexible online data storage services, ranging from passive ones, such as online archiving, to active ones, such as collaboration and social networking. They have become known as computing and storage “clouds. ” Such clouds allow users to abandon local storage and use online alternatives, such as Amazon S3, Nirvanix CloudNAS, or Microsoft SkyDrive. Some cloud providers utilize the fact that online storage can be accessed from any location connected to the Internet, and offer additional functionality; for example, Apple MobileMe allows users to synchronize common applications that run on multiples devices. Clouds also offer computation resources, such as Amazon EC2, which can significantly reduce the cost of maintaining such resources locally. Finally, online collaboration tools, such as Google Apps or versioning repositories for source code, make it easy to collaborate with colleagues across organizations and countries, as practiced by the authors of this paper. What can go wrong? Although the advantages of using clouds are unarguable, there are many risks involved with releasing control over your data. One concern that many users are aware of is loss of privacy. Nevertheless, the popularity of social networks and online data sharing repositories suggests that many users are willing to forfeit privacy,
Logic and Lattices for Distributed Programming
"... In recent years there has been interest in achieving application-level consistency criteria without the latency and availability costs of strongly consistent storage infrastructure. A standard technique is to adopt a vocabulary of commutative operations; this avoids the risk of inconsistency due to ..."
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Cited by 21 (2 self)
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In recent years there has been interest in achieving application-level consistency criteria without the latency and availability costs of strongly consistent storage infrastructure. A standard technique is to adopt a vocabulary of commutative operations; this avoids the risk of inconsistency due to message reordering. Another approach was recently captured by the CALM theorem, which proves that logically monotonic programs are guaranteed to be eventually consistent. In logic languages such as Bloom, CALM analysis can automatically verify that programs achieve consistency without coordination. In this paper we present Bloom L, an extension to Bloom that takes inspiration from both of these traditions. Bloom L generalizes Bloom to support lattices and extends the power of CALM analysis to whole programs containing arbitrary lattices. We show how the Bloom interpreter can be generalized to support efficient evaluation of lattice-based code using well-known strategies from logic programming. Finally, we use Bloom L to develop several practical distributed programs, including a key-value store similar to Amazon Dynamo, and show how Bloom L encourages the safe composition of small, easy-to-analyze lattices into larger programs.
Dr. Multicast: Rx for Data Center Communication Scalability ∗
"... Data centers avoid IP Multicast because of a series of problems with the technology. We propose Dr. Multicast (the MCMD), a system that maps traditional IPMC operations to either use a new point-to-point UDP multisend operation, or to a traditional IPMC address. The MCMD is designed to optimize reso ..."
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Cited by 18 (0 self)
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Data centers avoid IP Multicast because of a series of problems with the technology. We propose Dr. Multicast (the MCMD), a system that maps traditional IPMC operations to either use a new point-to-point UDP multisend operation, or to a traditional IPMC address. The MCMD is designed to optimize resource allocations, while simultaneously respecting an administrator-specified acceptable-use policy. We argue that with the resulting range of options, IPMC no longer represents a threat and could therefore be used much more widely. 1
State Machine Replication for the Masses with BFT-SMaRt
, 2013
"... Abstract—The last fifteen years have seen an impressive amount of work on protocols for Byzantine fault-tolerant (BFT) state machine replication (SMR). However, there is still a need for practical and reliable software libraries implementing this technique. BFT-SMART is an open-source Java-based lib ..."
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Cited by 17 (8 self)
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Abstract—The last fifteen years have seen an impressive amount of work on protocols for Byzantine fault-tolerant (BFT) state machine replication (SMR). However, there is still a need for practical and reliable software libraries implementing this technique. BFT-SMART is an open-source Java-based library implementing robust BFT state machine replication. Some of the key features of this library that distinguishes it from similar works (e.g., PBFT and UpRight) are improved reliabil-ity, modularity as a first-class property, multicore-awareness, reconfiguration support and a flexible programming interface. When compared to other SMR libraries, BFT-SMART achieves better performance and is able to withstand a number of real-world faults that previous implementations cannot. I.
Cloud Computing security: From single to multi-clouds
- In Proceed-ings of the HICSS
, 2011
"... Abstract General Terms Security ..."
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A Data Generator for Cloud-Scale Benchmarking
"... Abstract. In many fields of research and business data sizes are breaking the petabyte barrier. This imposes new problems and research possibilities for the database community. Usually, data of this size is stored in large clusters or clouds. Although clouds have become very popular in recent years, ..."
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Cited by 15 (9 self)
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Abstract. In many fields of research and business data sizes are breaking the petabyte barrier. This imposes new problems and research possibilities for the database community. Usually, data of this size is stored in large clusters or clouds. Although clouds have become very popular in recent years, there is only little work on benchmarking cloud applications. In this paper we present a data generator for cloud sized applications. Its architecture makes the data generator easy to extend and to configure. A key feature is the high degree of parallelism that allows linear scaling for arbitrary numbers of nodes. We show how distributions, relationships and dependencies in data can be computed in parallel with linear speed up. 1
Scalable atomic visibility with RAMP transactions
- In ACM SIGMOD Conference
, 2014
"... Databases can provide scalability by partitioning data across several servers. However, multi-partition, multi-operation transactional ac-cess is often expensive, employing coordination-intensive locking, validation, or scheduling mechanisms. Accordingly, many real-world systems avoid mechanisms tha ..."
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Cited by 13 (1 self)
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Databases can provide scalability by partitioning data across several servers. However, multi-partition, multi-operation transactional ac-cess is often expensive, employing coordination-intensive locking, validation, or scheduling mechanisms. Accordingly, many real-world systems avoid mechanisms that provide useful semantics for multi-partition operations. This leads to incorrect behavior for a large class of applications including secondary indexing, foreign key enforcement, and materialized view maintenance. In this work, we identify a new isolation model—Read Atomic (RA) isolation—that matches the requirements of these use cases by ensuring atomic vis-ibility: either all or none of each transaction’s updates are observed by other transactions. We present algorithms for Read Atomic Multi-Partition (RAMP) transactions that enforce atomic visibility while offering excellent scalability, guaranteed commit despite partial failures (via synchronization independence), and minimized com-munication between servers (via partition independence). These RAMP transactions correctly mediate atomic visibility of updates and provide readers with snapshot access to database state by using limited multi-versioning and by allowing clients to independently resolve non-atomic reads. We demonstrate that, in contrast with ex-isting algorithms, RAMP transactions incur limited overhead—even under high contention—and scale linearly to 100 servers. 1.
Blazes: Coordination analysis for distributed programs,”
- EECS Department, UC Berkeley, Tech. Rep.
, 2013
"... Abstract-Distributed consistency is perhaps the most discussed topic in distributed systems today. Coordination protocols can ensure consistency, but in practice they cause undesirable performance unless used judiciously. Scalable distributed architectures avoid coordination whenever possible, but ..."
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Cited by 12 (4 self)
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Abstract-Distributed consistency is perhaps the most discussed topic in distributed systems today. Coordination protocols can ensure consistency, but in practice they cause undesirable performance unless used judiciously. Scalable distributed architectures avoid coordination whenever possible, but undercoordinated systems can exhibit behavioral anomalies under fault, which are often extremely difficult to debug. This raises significant challenges for distributed system architects and developers. In this paper we present BLAZES, a cross-platform program analysis framework that (a) identifies program locations that require coordination to ensure consistent executions, and (b) automatically synthesizes application-specific coordination code that can significantly outperform general-purpose techniques. We present two case studies, one using annotated programs in the Twitter Storm system, and another using the Bloom declarative language.
