Elastic online analytical processing on RAMCloud

Tinnefeld, Christian; Kossmann, Donald; Grund, Martin; Boese, Joos-Hendrik; Renkes, Frank; Sikka, Vishal; Plattner, Hasso

doi:10.1145/2452376.2452429

Cited by 13 publications

(14 citation statements)

References 18 publications

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“…RDMA-enabled network is changing the assumption in traditional distributed data management systems in which network I/O is the primary performance bottleneck. Some systems [32,33] have introduced RDMA inside, but they just carry out some add-on optimizations for RDMA later; the original architecture is obsolete. As a result, they cannot take full advantage of the opportunities presented by RDMA.…”

Section: System Architecture and Design Schemes In Platforms With Newmentioning

confidence: 99%

“…The RDMA-enabled network addresses the two most important difficulties encountered with traditional distributed transaction scaling: limited bandwidth and high CPU overhead in data transfers. Some RDMA-aware data management systems [32] have also emerged, but such systems are primarily concerned with direct RDMA support at the storage tier, and transaction processing is not the focus. Other RDMA-aware systems [125] focus on transactional processing, but they still adopt centralized managers that affect the scalability of distributed transactions.…”

Section: Transaction Processing In Platforms With New Hardwarementioning

confidence: 99%

See 1 more Smart Citation

The New Hardware Development Trend and the Challenges in Data Management and Analysis

Pan

Zhang

et al. 2018

Data Sci. Eng.

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Hardware techniques and environments underwent significant transformations in the field of information technology, represented by high-performance processors and hardware accelerators characterized by abundant heterogeneous parallelism, nonvolatile memory with hybrid storage hierarchies, and RDMA-enabled high-speed network. Recent hardware trends in these areas deeply affect data management and analysis applications. In this paper, we first introduce the development trend of the new hardware in computation, storage, and network dimensions. Then, the related research techniques which affect the upper data management system design are reviewed. Finally, challenges and opportunities are addressed for the key technologies of data management and analysis in new hardware environments.

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Section: System Architecture and Design Schemes In Platforms With Newmentioning

confidence: 99%

Section: Transaction Processing In Platforms With New Hardwarementioning

confidence: 99%

The New Hardware Development Trend and the Challenges in Data Management and Analysis

Pan

Zhang

et al. 2018

Data Sci. Eng.

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“…This technology shift is already yielding major changes in the application domain. New scalable in-memory applications and the services they are built on, such as key-value object stores [40,41], graph stores [48], data analytics [57] services, and transaction systems [34], are some of the examples that benefit from data-centric rack-scale architectures.…”

Section: Introductionmentioning

confidence: 99%

Memory-Side Protection With a Capability Enforcement Co-Processor

Azriel

Humbel

Achermann

et al. 2019

ACM Trans. Archit. Code Optim.

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Byte-addressable nonvolatile memory (NVM) blends the concepts of storage and memory and can radically improve data-centric applications, from in-memory databases to graph processing. By enabling large-capacity devices to be shared across multiple computing elements, fabric-attached NVM changes the nature of rackscale systems and enables short-latency direct memory access while retaining data persistence properties and simplifying the software stack. An adequate protection scheme is paramount when addressing shared and persistent memory, but mechanisms that rely on virtual memory paging suffer from the tension between performance (pushing toward large pages) and protection granularity (pushing toward small pages). To address this tension, capabilities are worth revisiting as a more powerful protection mechanism, but the long time needed to introduce new CPU features hampers the adoption of schemes that rely on instruction-set architecture support. This article proposes the Capability Enforcement Co-Processor (CEP), a programmable memory controller that implements fine-grain protection through the capability model without requiring instruction-set support in the application CPU. CEP decouples capabilities from the application CPU instruction-set architecture, shortens time to adoption, and can rapidly evolve to embrace new persistent memory technologies, from NVDIMMs to native NVM devices, either locally connected or fabric attached in rack-scale configurations. CEP exposes an application interface based on memory handles that get internally converted to extendedpointer capabilities. This article presents a proof of concept implementation of a distributed object store (Redis) with CEP. It also demonstrates a capability-enhanced file system (FUSE) implementation using CEP. Our proof of concept shows that CEP provides fine-grain protection while enabling direct memory access from application clients L. Azriel and L. Humbel made equal contributions.

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“…RAMCloud [4] is a system providing the aforementioned features. It is now used in various fields: analytics [11], or used as a low-latency storage for SDNs [12], [13], or for scientific workflows [14].…”

Section: Introductionmentioning

confidence: 99%

Characterizing Performance and Energy-Efficiency of the RAMCloud Storage System

Taleb

Ibrahim

Antoniu

et al. 2017

2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)

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Abstract-Most large popular web applications, like Facebook and Twitter, have been relying on large amounts of in-memory storage to cache data and offer a low response time. As the main memory capacity of clusters and clouds increases, it becomes possible to keep most of the data in the main memory. This motivates the introduction of in-memory storage systems. While prior work has focused on how to exploit the low-latency of in-memory access at scale, there is very little visibility into the energy-efficiency of in-memory storage systems. Even though it is known that main memory is a fundamental energy bottleneck in computing systems (i.e., DRAM consumes up to 40% of a server's power). In this paper, by the means of experimental evaluation, we have studied the performance and energy-efficiency of RAMCloud -a well-known in-memory storage system. We reveal that although RAMCloud is scalable for read-only applications, it exhibits non-proportional power consumption. We also find that the current replication scheme implemented in RAMCloud limits the performance and results in high energy consumption. Surprisingly, we show that replication can also play a negative role in crash-recovery.

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Elastic online analytical processing on RAMCloud

Cited by 13 publications

References 18 publications

The New Hardware Development Trend and the Challenges in Data Management and Analysis

The New Hardware Development Trend and the Challenges in Data Management and Analysis

Memory-Side Protection With a Capability Enforcement Co-Processor

Characterizing Performance and Energy-Efficiency of the RAMCloud Storage System

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