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IOGP: An Incremental Online Graph Partitioning Algorithm for distributed Graph databases 17

IOGP: An Incremental Online Graph Partitioning Algorithm for...

引用

26th ACM international symposium on High-Performance parallel and distributed Computing (HPDC)

作者： Dai, Dong Zhang, Wei Chen, Yong Texas Tech Univ Lubbock TX 79409 USA

ISBN: (纸本)9781450346993

Graphs have become increasingly important in many applications and domains such as querying relationships in social networks or managing rich metadata generated in scientific computing. Many of these use cases require high-performance distributed graph databases for serving continuous updates from clients and, at the same time, answering complex queries regarding the current graph. These operations in graph databases, also referred to as online transaction processing (OLTP) operations, have specific design and implementation requirements for graph partitioning algorithms. In this research, we argue it is necessary to consider the connectivity and the vertex degree changes during graph partitioning. Based on this idea, we designed an Incremental Online Graph Partitioning (IOGP) algorithm that responds accordingly to the incremental changes of vertex degree. IOGP helps achieve better locality, generate balanced partitions, and increase the parallelism for accessing high-degree vertices of the graph. Over both real-world and synthetic graphs, IOGP demonstrates as much as 2x better query performance with a less than 10% overhead when compared against state-of-the-art graph partitioning algorithms.

关键词： Graph Database OLTP Graph Partitioning

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Dynamic Load Balancing and Channel Strategy for Apache Flume Collecting Real-time Data Stream 15

Dynamic Load Balancing and Channel Strategy for Apache Flume...

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15th IEEE international symposium on parallel and distributed Processing with Applications (ISPA) / 16th IEEE international Conference on Ubiquitous Computing and Communications (IUCC)

作者： Shu, Buqing Chen, Haopeng Sun, Meng Shanghai Jiao Tong Univ Sch Software Shanghai Peoples R China

ISBN: (纸本)9781538637906

With the development of information technology, real-time data stream processing(RTDSP) has become a popular research topic. The first step of RTDSP is collecting data, requiring a data collector to receive data from the source and send them to the sink. Apache Flume, a distributed and reliable framework, used for this purpose, has some limitations and drawbacks on load balancing and storage. In this paper, we aim to improve performance and availability for collecting unstable real-time big data stream. So we propose a new load balancing strategy based on the free memory size and a storage strategy of integration memory channel with the multi-file channel to reduce the overhead of disk and network. Finally, the experimental results show that the availability and performance are improved under the condition of a poor network, high availability requirements, intense competition in memory resources and large data size. Specifically, the availability is higher than 99.999%, and the performance can be improved by 10%-50% under different conditions.

关键词： Load management Real-time systems distributed databases Throughput Monitoring Dynamic scheduling Memory management

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CEBDA 2018 Keynote

CEBDA 2018 Keynote

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Franck Cappello Argonne National Laboratory USA

ISBN: (纸本)9781538655566;9781538655559

Extreme-scale scientific simulations are already generating more data that can be communicated stored and analyzed. The data flood will get even worse with future exascale systems. This is true for output data and also for checkpoint/restart data. Scientific data reduction is a necessity to drastically accelerate I/O, reduce data footprint on storage and also to speed-up significantly computation, as demonstrated by the 2017 Gordon Bell award winner. But reduction should be performed wisely, for execution correctness (checkpoint/restart) and to keep the information that matters for the scientists. We can try to develop application-specific lossy data reduction techniques or to compress the dataset with advanced generic lossless compression algorithms. Unfortunately, these two approaches are either unpractical for most applications or do not provide enough data reduction for scientific datasets. Other domains already familiar with Big Data massively employ lossy compression to reduce the data size. However, lossy compression has very rarely been applied to scientific simulation data and as a result it is not well understood. In this talk, we will present challenges and opportunities in terms of compression algorithms and application of lossy compression to scientific data. We will detail not only the best-in-class compression algorithms but also the tools to assess comprehensively the error introduced by lossy compression. We will give examples of lossy compression of scientific datasets with application to visualization and checkpoint/restart. Lossy compression of scientific data reveal itself as a fascinating young research domain with many opportunities to explore and discover new techniques.

关键词： Data models Compression algorithms Data visualization distributed processing Conferences distributed databases Analytical models

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E-Storm: Replication-based State Management in distributed Stream Processing systems 46

E-Storm: Replication-based State Management in Distributed S...

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46th international Conference on parallel Processing Workshops (ICPPW)

作者： Liu, Xunyun Harwood, Aaron Karunasekera, Shanika Rubinstein, Benjamin Buyya, Rajkumar Univ Melbourne Sch Comp & Informat Syst Cloud Comp & Distributed Syst CLOUDS Lab Melbourne Vic Australia

ISBN: (纸本)9781538610428

Apache Storm is a fault-tolerant, distributed in-memory computation system for processing large volumes of high-velocity data in real-time. As an integral part of the fault-tolerance mechanism, Storm's state management is achieved by a checkpointing framework, which commits states regularly and recovers lost states from the latest checkpoint. However, this method involves a remote data store for state preservation and access, resulting in significant overheads to the performance of error-free execution. In this paper, we propose E-Storm, a replication-based state management system that actively maintains multiple state backups on different worker nodes. We build a prototype on top of Storm by extending it with monitoring and recovery modules to support inter-task state transfer whenever needed. The experiments carried out on synthetic and real-world streaming applications confirm that E-Storm outperforms the existing checkpointing method in terms of the resulting application performance, obtaining as much as 9.44 times throughput improvement while reducing the application latency down to 9.8%.

关键词： Storms Monitoring Topology distributed databases Fault tolerance Fault tolerant systems Checkpointing

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A Horizontally Scalable and Reliable Architecture for Location-based Publish-Subscribe 36

A Horizontally Scalable and Reliable Architecture for Locati...

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36th IEEE symposium on Reliable distributed systems (SRDS)

作者： Chapuis, Bertil Garbinato, Benoit Mourot, Lucas Univ Lausanne Lausanne Switzerland Ecole Polytech Fed Lausanne Lausanne Switzerland

ISBN: (纸本)9781538616796

With billions of connected users and objects, location-based services face a massive scalability challenge. We propose a horizontally-scalable and reliable location-based publish/subscribe architecture that can be deployed on a cluster made of commodity hardware. As many modern location-based publish/subscribe systems, our architecture supports moving publishers, as well as moving subscribers. When a publication moves in the range of a subscription, the owner of this subscription is instantly notified via a server-initiated event, usually in the form of a push notification. To achieve this, most existing solutions rely on classic indexing data structures, such as R-trees, and they struggle at scaling beyond the scope of a single computing unit. Our architecture introduces a multi-step routing mechanism that, to achieve horizontal scalability, efficiently combines range partitioning, consistent hashing and a min-wise hashing agreement. In case of node failure, an active replication strategy ensures a reliable delivery of publication throughout the multistep routing mechanism. From an algorithmic perspective, we show that the number of messages required to compute a match is optimal in the execution model we consider and that the number of routing steps is constant. Using experimental results, we show that our method achieves high throughput, low latency and scales horizontally. For example, with a cluster made of 200 nodes, our architecture can process up to 190'000 location updates per second for a fleet of nearly 1'900'000 moving entities, producing more than 130'000 matches per second.

关键词： Internet of Things Publish subscribe systems distributed databases Spatial databases Scalability Fault tolerance

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DottedDB: Anti-Entropy without Merkle Trees, Deletes without Tombstones 36

DottedDB: Anti-Entropy without Merkle Trees, Deletes without...

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36th IEEE symposium on Reliable distributed systems (SRDS)

作者： Goncalves, Ricardo Almeida, Paulo Sergio Baquero, Carlos Fonte, Vitor Univ Minho HASLab INESC TEC Braga Portugal

ISBN: (纸本)9781538616796

To achieve high availability in the face of network partitions, many distributed databases adopt eventual consistency, allow temporary conflicts due to concurrent writes, and use some form of per-key logical clock to detect and resolve such conflicts. Furthermore, nodes synchronize periodically to ensure replica convergence in a process called anti-entropy, normally using Merkle Trees. We present the design of Dotted-DB, a Dynamo-like key-value store, which uses a novel node-wide logical clock framework, overcoming three fundamental limitations of the state of the art: (1) minimize the metadata per key necessary to track causality, avoiding its growth even in the face of node churn;(2) correctly and durably delete keys, with no need for tombstones;(3) offer a lightweight antientropy mechanism to converge replicated data, avoiding the need for Merkle Trees. We evaluate DottedDB against MerkleDB, an otherwise identical database, but using per-key logical clocks and Merkle Trees for anti-entropy, to precisely measure the impact of the novel approach. Results show that: causality metadata per object always converges rapidly to only one id-counter pair;distributed deletes are correctly achieved without global coordination and with constant metadata;divergent nodes are synchronized faster, with less memory-footprint and with less communication overhead than using Merkle Trees.

关键词： distributed databases Causality Logical Clocks Anti-Entropy Merkle Trees Partial Replication

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Incremental elasticity for NoSQL data stores 36

Incremental elasticity for NoSQL data stores

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36th IEEE symposium on Reliable distributed systems (SRDS)

作者： Papaioannou, Antonis Magoutis, Kostas Fdn Res & Technol Hellas Inst Comp Sci Iraklion 70013 Greece Univ Crete Comp Sci Dept Iraklion 70013 Greece Univ Ioannina Dept Comp Sci & Engn GR-45110 Ioannina Greece

ISBN: (纸本)9781538616796

Service elasticity, the ability to rapidly expand or shrink service processing capacity on demand, has become a first-class property in the domain of infrastructure services. Scalable NoSQL data stores are the de-facto choice of applications aiming for scalable, highly available data persistence. The elasticity of such data stores is still challenging, due to the complexity and performance impact of moving large amounts of data over the network to take advantage of new resources (servers). In this paper we propose incremental elasticity, a new mechanism that progressively increases processing capacity in a fine-grain manner during an elasticity action by making sub-sections of the transferred data available for access on the new server, prior to completing the full transfer. In addition, by scheduling data transfers during an elasticity action in sequence (rather than as simultaneous transfers) between each pre-existing server involved and the new server, incremental elasticity leads to smoother elasticity actions, reducing their overall impact on performance.

关键词： Elasticity Throughput Servers Data transfer distributed databases Schedules Receivers

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proceedings - 15th IEEE international symposium on parallel and distributed Processing with Applications and 16th IEEE international Conference on Ubiquitous Computing and Communications, ISPA/IUCC 2017

Proceedings - 15th IEEE International Symposium on Parallel ...

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15th IEEE international symposium on parallel and distributed Processing with Applications and 16th IEEE international Conference on Ubiquitous Computing and Communications, ISPA/IUCC 2017

ISBN: (纸本)9781538637906

The proceedings contain 210 papers. The topics discussed include: toward complex search for encrypted cloud data via blind index storage;cooperative game approach for energy-aware load balancing in clouds;a lightweight privacy aware friend locator in mobile social networks;a routing scheme for software-defined satellite network;an efficient scheduling algorithm for energy consumption constrained parallel applications on heterogeneous distributed systems;popularity and cost aware energy-balanced strategy for named data wireless ad-hoc networks;an efficient hardware prefetcher exploiting the prefetch potential of long-stride access pattern on virtual address;and an immune-based optimization algorithm of multi-tenant resource allocation for geo-distributed data centers.

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Exploiting RDMA for distributed Low-Latency Key/Value Store on Non-Volatile Main Memory 23

Exploiting RDMA for Distributed Low-Latency Key/Value Store ...

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23rd IEEE international Conference on parallel and distributed systems (ICPADS)

作者： Dong, Kangping Huang, Linpeng Zhu, Yanmin Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9781538621295

In-memory key/value stores have been widely deployed in many large systems for high throughput and low latency data access. To offer durability and fault tolerance, many of them (e.g., Memcached, Redis) flush data to RAID or SSD in local and remote with primary backup replication (PBR), in which the RAID and network become the bottleneck of throughput and latency. Recently, two promising technologies are emerging, i.e., non-volatile memory (NVM) and remote direct memory access (RDMA). NVMs are integrating into the main memory for their high density, high access speed, low cost and byte addressability. And RDMA allows direct access to the memory of a remote machine without invoking the peer CPU, thus reducing latency. To overcome the limitations of traditional key/value stores, this paper explores the design of a distributed low-latency key/value store on non-volatile main memory by leveraging RDMA. NVM not only eliminates disk IO operations, but also makes fast replication possible. For a PUT operation, we collect modifications (data and metadata) at the local NVM of the primary server and replicate them to the same positions of backup servers by RDMA writes. A prototype distributed key/value store, called NVDS, is designed and implemented. Extensive experiments on the micro benchmark show that NVDS achieves significant latency reduction, compared to existing key/value stores. It replicates a 32bytes key/value PUT operation to two backup servers in less than 2 mu s and the whole request latency is less than 10 mu s.

关键词： Servers Nonvolatile memory Random access memory Semantics Throughput Payloads distributed databases

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On the Stability of distributed On-Chip Low Dropout Regulators 60

On the Stability of Distributed On-Chip Low Dropout Regulato...

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60th IEEE international Midwest symposium on Circuits and systems (MWSCAS)

作者： Ciprut, Albert Friedman, Eby G. Univ Rochester Dept Elect & Comp Engn Rochester NY 14627 USA

ISBN: (纸本)9781509063895

distributed on-chip low dropout (LDO) voltage regulators have become common due to the increasing number of on-chip voltage domains, dynamic voltage scaling, and the need for high quality power. Due to the current limitations of on-chip LDOs, hundreds of these regulators are required to deliver high quality power within modern high performance microprocessors. As the number of parallel connected LDOs increases, however, the stability of the power grid degrades due to the off-chip and in-package parasitic inductance. In this paper, the stability of the on-chip power grid composed of multiple distributed LDO regulators is evaluated from the perspective of the parasitic impedance. The relation between the grid stability and the number of LDOs that share the grid is described, and a guideline for evaluating grid stability is proposed. Moreover, several design solutions are offered to compensate for the off-chip inductance to enhance the stability of on-chip power grids, supporting the deployment of a large number of LDO regulators.

关键词： Inductance

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