ElasTraS: An Elastic Transactional Data Store in the Cloud

Over the last couple of years, "Cloud Computing" or "Elastic Computing" has emerged as a compelling and successful paradigm for internet scale computing. One of the major contributing factors to this success is the elasticity of resources. In spite of the elasticity provided by the infrastructure and the scalable design of the applications, the elephant (or the underlying database), which drives most of these web-based applications, is not very elastic and scalable, and hence limits scalability. In this paper, we propose ElasTraS which addresses this issue of scalability and elasticity of the data store in a cloud computing environment to leverage from the elastic nature of the underlying infrastructure, while providing scalable transactional data access. This paper aims at providing the design of a system in progress, highlighting the major design choices, analyzing the different guarantees provided by the system, and identifying several important challenges for the research community striving for computing in the cloud.Comment: 5 Pages, In Proc. of USENIX HotCloud 200

On a Catalogue of Metrics for Evaluating Commercial Cloud Services

Given the continually increasing amount of commercial Cloud services in the market, evaluation of different services plays a significant role in cost-benefit analysis or decision making for choosing Cloud Computing. In particular, employing suitable metrics is essential in evaluation implementations. However, to the best of our knowledge, there is not any systematic discussion about metrics for evaluating Cloud services. By using the method of Systematic Literature Review (SLR), we have collected the de facto metrics adopted in the existing Cloud services evaluation work. The collected metrics were arranged following different Cloud service features to be evaluated, which essentially constructed an evaluation metrics catalogue, as shown in this paper. This metrics catalogue can be used to facilitate the future practice and research in the area of Cloud services evaluation. Moreover, considering metrics selection is a prerequisite of benchmark selection in evaluation implementations, this work also supplements the existing research in benchmarking the commercial Cloud services.Comment: 10 pages, Proceedings of the 13th ACM/IEEE International Conference on Grid Computing (Grid 2012), pp. 164-173, Beijing, China, September 20-23, 201

MDCC: Multi-Data Center Consistency

Replicating data across multiple data centers not only allows moving the data closer to the user and, thus, reduces latency for applications, but also increases the availability in the event of a data center failure. Therefore, it is not surprising that companies like Google, Yahoo, and Netflix already replicate user data across geographically different regions. However, replication across data centers is expensive. Inter-data center network delays are in the hundreds of milliseconds and vary significantly. Synchronous wide-area replication is therefore considered to be unfeasible with strong consistency and current solutions either settle for asynchronous replication which implies the risk of losing data in the event of failures, restrict consistency to small partitions, or give up consistency entirely. With MDCC (Multi-Data Center Consistency), we describe the first optimistic commit protocol, that does not require a master or partitioning, and is strongly consistent at a cost similar to eventually consistent protocols. MDCC can commit transactions in a single round-trip across data centers in the normal operational case. We further propose a new programming model which empowers the application developer to handle longer and unpredictable latencies caused by inter-data center communication. Our evaluation using the TPC-W benchmark with MDCC deployed across 5 geographically diverse data centers shows that MDCC is able to achieve throughput and latency similar to eventually consistent quorum protocols and that MDCC is able to sustain a data center outage without a significant impact on response times while guaranteeing strong consistency

The End of a Myth: Distributed Transactions Can Scale

The common wisdom is that distributed transactions do not scale. But what if distributed transactions could be made scalable using the next generation of networks and a redesign of distributed databases? There would be no need for developers anymore to worry about co-partitioning schemes to achieve decent performance. Application development would become easier as data placement would no longer determine how scalable an application is. Hardware provisioning would be simplified as the system administrator can expect a linear scale-out when adding more machines rather than some complex sub-linear function, which is highly application specific. In this paper, we present the design of our novel scalable database system NAM-DB and show that distributed transactions with the very common Snapshot Isolation guarantee can indeed scale using the next generation of RDMA-enabled network technology without any inherent bottlenecks. Our experiments with the TPC-C benchmark show that our system scales linearly to over 6.5 million new-order (14.5 million total) distributed transactions per second on 56 machines.Comment: 12 page

The End of Slow Networks: It's Time for a Redesign

Next generation high-performance RDMA-capable networks will require a fundamental rethinking of the design and architecture of modern distributed DBMSs. These systems are commonly designed and optimized under the assumption that the network is the bottleneck: the network is slow and "thin", and thus needs to be avoided as much as possible. Yet this assumption no longer holds true. With InfiniBand FDR 4x, the bandwidth available to transfer data across network is in the same ballpark as the bandwidth of one memory channel, and it increases even further with the most recent EDR standard. Moreover, with the increasing advances of RDMA, the latency improves similarly fast. In this paper, we first argue that the "old" distributed database design is not capable of taking full advantage of the network. Second, we propose architectural redesigns for OLTP, OLAP and advanced analytical frameworks to take better advantage of the improved bandwidth, latency and RDMA capabilities. Finally, for each of the workload categories, we show that remarkable performance improvements can be achieved

A simple approach to shared storage database servers

This paper introduces a generic technique to obtain a shared-storage database cluster from an off-the-shelf database management system, without needing to heavily refactor server software to deal with distributed locking, buffer invalidation, and recovery from partial cluster failure. Instead, the core of the proposal is the combination of a replication protocol and a surprisingly simple modification to the common copy-on-write logical volume management technique: One of the servers is allowed to skip copy-on-write and directly update the original backing store. This makes it possible to use any shared-nothing database server software in a shared or partially shared storage configuration, thus allowing large cluster configurations with a small number of copies of data.(undefined

The Case for Learned Index Structures

Indexes are models: a B-Tree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a record within an unsorted array, and a BitMap-Index as a model to indicate if a data record exists or not. In this exploratory research paper, we start from this premise and posit that all existing index structures can be replaced with other types of models, including deep-learning models, which we term learned indexes. The key idea is that a model can learn the sort order or structure of lookup keys and use this signal to effectively predict the position or existence of records. We theoretically analyze under which conditions learned indexes outperform traditional index structures and describe the main challenges in designing learned index structures. Our initial results show, that by using neural nets we are able to outperform cache-optimized B-Trees by up to 70% in speed while saving an order-of-magnitude in memory over several real-world data sets. More importantly though, we believe that the idea of replacing core components of a data management system through learned models has far reaching implications for future systems designs and that this work just provides a glimpse of what might be possible