Online Transaction Processing (OLTP) deployments are migrating from on-premise to cloud settings in order to exploit the elasticity of cloud infrastructure which allows them to adapt to workload variations. However, cloud adaptation comes at the cost of redesigning the engine, which has led to the introduction of several, new, cloud-based transaction processing systems mainly focusing on: (i) the transaction coordination protocol, (ii) the data partitioning strategy, and, (iii) the resource isolation across multiple tenants. As a result, standalone OLTP engines cannot be easily deployed with an elastic setting in the cloud and they need to migrate to another, specialized deployment. In this paper, we focus on workload variations that can be addressed by modern multi-socket, multi-core servers and we present a system design for providing fine-grained elasticity to multi-tenant, scale-up OLTP deployments. We introduce novel components to the virtualization software stack that enable on-demand addition and removal of computing and memory resources. We provide a bi-directional, low-overhead communication stack between the virtual machine and the hypervisor, which allows the former to adapt to variations coming both from the workload and the resource availability. We show that our system achieves NUMA-aware, millisecond-level, stateful and fine-grained elasticity, while it is not intrusive to the design of state-of-the-art, in-memory OLTP engines. We evaluate our system through novel use cases demonstrating that scale-up elasticity increases resource utilization, while allowing tenants to pay for actual use of resources and not just their reservation.

GaussDB, and its open source version named openGauss, are Huawei's relational database management systems (RDBMS), featuring a primary disk-based storage engine. This paper presents a new storage engine for GaussDB that is optimized for main memory and many cores. We started from a research prototype which exploits the power of the hardware but is not useful for customers. This paper describes the details of turning this prototype to an industrial storage engine, including integration with GaussDB. Standard benchmarks show that the new engine provides more than 2.5x performance improvement to GaussDB for full TPC-C on Intel's x86 many-cores servers, as well as on Huawei TaiShan servers powered by ARM64-based Kunpeng CPUs.

The telco industry is gradually shifting from using monolithic software packages deployed on custom hardware to using modular virtualized software functions deployed on cloudified data centers using commodity hardware. This transformation is referred to as Network Function Virtualization (NFV). The scalability of the databases (DBs) underlying the virtual network functions is the cornerstone for reaping the benefits from the NFV transformation. This paper presents an industrial experience of applying shared-nothing techniques in order to achieve the scalability of a DB in an NFV setup. The special combination of requirements in NFV DBs are not easily met with conventional execution models. Therefore, we designed a special shared-nothing architecture that is based on cooperative multi-tasking using user-level threads (fibers). We further show that the fiber-based approach outperforms the approach built using conventional multi-threading and meets the variable deployment needs of the NFV transformation. Furthermore, fibers yield a simpler-to-maintain software and enable controlling a trade-off between long-duration computations and real-time requests.

Modern data-centric flows in the telecommunications industry require real time analytical processing over a rapidly changing and large dataset. The traditional approach of separating OLTP and OLAP workloads cannot satisfy this requirement. Instead, a new class of integrated solutions for handling hybrid workloads is needed. This paper presents an industrial use case and a novel architecture that integrates key-value-based event processing and SQL-based analytical processing on the same distributed store while minimizing the total cost of ownership. Our approach combines several well-known techniques such as shared scans, delta processing, a PAX-fashioned storage layout, and an interleaving of scanning and delta merging in a completely new way. Performance experiments show that our system scales out linearly with the number of servers. For instance, our system sustains event streams of 100,000 events per second while simultaneously processing 100 ad-hoc analytical queries per second, using a cluster of 12 commodity servers. In doing so, our system meets all response time goals of our telecommunication customers; that is, 10 milliseconds per event and 100 milliseconds for an ad-hoc analytical query. Moreover, our system beats commercial competitors by a factor of 2.5 in analytical and two orders of magnitude in update performance.

RESERVOIR project is developing an advanced system and service management approach that will serve as the infrastructure for cloud computing and communications and future Internet of services by creative coupling of service virtualization, grid computing, networking and service management techniques. This paper presents work in progress for the integration and management of such systems into a new generation of managed service infrastructure.

Distributed real-time transaction systems are useful for both real-time and high-performance database applications. Standard transaction management approaches that use the two-phase commit protocol suffer from its high costs and blocking behavior which is problematic in real-time computing environments. Our approach in this paper is to identify ways in which a commit protocol can be made adaptive in the sense that under situations that demand it, such as a transient local overload, the system can dynamically change to a different commitment strategy. The decision to do so can be taken autonomously at any site. The different commitment strategies exploit a trade-off between the cost of commitment and the obtained degree of atomicity. Our protocols are based on optimistic commitment strategies, and they rely on local compensatory actions to recover from non-atomic executions. We provide the necessary framework to study the logical and temporal correctness criteria, and we describe examples to illustrate the use of our strategies.

Recovery activities, like checkpointing and restart, in traditional database management systems are performed in a quiescent state where no transactions are active. This approach impairs the performance of online transaction processing systems, especially when a large volatile memory is used. An incremental scheme for performing recovery in main memory database systems (MMDBs), in parallel with transaction execution, is presented. A page-based incremental restart algorithm that enables the resumption of transaction processing as soon as the system is up is proposed. Pages are recovered individually and according to the demands of the post-crash transactions. A method for propagating updates from main memory to the backup database on disk is also provided. The emphasis is on decoupling the I/O activities related to the propagation to disk from the forward transaction execution in memory. The authors also construct a high-level recovery manager based on operation logging on top of the page-based algorithms. The proposed algorithms are motivated by the characteristics of large MMDBs, and exploit the technology of nonvolatile RAM.>

A page-based incremental restart algorithm is proposed that enables resuming transaction processing immediately after recovering from a crash. Data items are recovered individually and according to the demands of the post-crash transactions. The support that such an algorithm needs in terms of nonvolatile RAM and efficient log retrieval methods is outlined. It is discussed how to construct high-level recovery based on operation logging on top of the page-based algorithm proposed. It is argued that the unique ways stable memory is used and the structure that is imposed on the log are steps in the right direction in the anticipated evolution of recovery management.>

Supporting atomicity of multi-site transactions in a distributed transaction management system is equated with long-duration delays, blocking, and loss of autonomy of the individual sites.The two-phase Com-

article An optimistic commit protocol for distributed transaction management Share on Authors: Eliezer Levy Department of Computer Sciences, University of Texas at Austin, Austin, TX Department of Computer Sciences, University of Texas at Austin, Austin, TXView Profile , Henry F. Korth Department of Computer Sciences, University of Texas at Austin, Austin, TX Department of Computer Sciences, University of Texas at Austin, Austin, TXView Profile , Abraham Silberschatz Department of Computer Sciences, University of Texas at Austin, Austin, TX Department of Computer Sciences, University of Texas at Austin, Austin, TXView Profile Authors Info & Claims ACM SIGMOD RecordVolume 20Issue 2June 1991 pp 88–97https://doi.org/10.1145/119995.115800Online:01 April 1991Publication History 72citation911DownloadsMetricsTotal Citations72Total Downloads911Last 12 Months19Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

A fairness property, called U-fairness, is studied in the context of the design of distributed systems with multiparty interactions. This is done with an overlapping model of concurrency. A distributed algorithm implementing the fairness notion is presented. U-fairness is shown to be more appropriate to the design of distributed systems than other known fairness notions because it provides an abstraction for stable property detection whereas the other fairness notions do not.>