565 research outputs found
Growth of relational model: Interdependence and complementary to big data
A database management system is a constant application of science that provides a platform for the creation, movement, and use of voluminous data. The area has witnessed a series of developments and technological advancements from its conventional structured database to the recent buzzword, bigdata. This paper aims to provide a complete model of a relational database that is still being widely used because of its well known ACID properties namely, atomicity, consistency, integrity and durability. Specifically, the objective of this paper is to highlight the adoption of relational model approaches by bigdata techniques. Towards addressing the reason for this in corporation, this paper qualitatively studied the advancements done over a while on the relational data model. First, the variations in the data storage layout are illustrated based on the needs of the application. Second, quick data retrieval techniques like indexing, query processing and concurrency control methods are revealed. The paper provides vital insights to appraise the efficiency of the structured database in the unstructured environment, particularly when both consistency and scalability become an issue in the working of the hybrid transactional and analytical database management system
Oze: Decentralized Graph-based Concurrency Control for Real-world Long Transactions on BoM Benchmark
In this paper, we propose Oze, a new concurrency control protocol that
handles heterogeneous workloads which include long-running update transactions.
Oze explores a large scheduling space using a fully precise multi-version
serialization graph to reduce false positives. Oze manages the graph in a
decentralized manner to exploit many cores in modern servers. We also propose a
new OLTP benchmark, BoMB (Bill of Materials Benchmark), based on a use case in
an actual manufacturing company. BoMB consists of one long-running update
transaction and five short transactions that conflict with each other.
Experiments using BoMB show that Oze keeps the abort rate of the long-running
update transaction at zero while reaching up to 1.7 Mtpm for short transactions
with near linear scalability, whereas state-of-the-art protocols cannot commit
the long transaction or experience performance degradation in short transaction
throughput
AIDA-DB: a data management architecture for the edge and cloud continuum
There is an increasing demand for stateful edge computing for both complex Virtual Network Functions (VNFs) and application services in emerging 5G networks. Managing a mutable persistent state in the edge does however bring new architectural, performance, and dependability challenges. Not only it has to be integrated with existing cloud-based systems, but also cope with both operational and analytical workloads and be compatible with a variety of SQL and NoSQL database management systems. We address these challenges with AIDA-DB, a polyglot data management architecture for the edge and cloud continuum. It leverages recent development in distributed transaction processing for a reliable mutable state in operational workloads, with a flexible synchronization mechanism for efficient data collection in cloud-based analytical workloads.Partially funded by project AIDA – Adaptive, Intelligent and Distributed Assurance Platform (POCI-01-0247-
FEDER-045907) co-financed by the European Regional Development Fund (ERDF) through the Operational Program for Competitiveness and Internationalisation (COMPETE 2020) and by the Portuguese Foundation for Science and Technology (FCT) under CMU Portugal
Improving the Performance and Endurance of Persistent Memory with Loose-Ordering Consistency
Persistent memory provides high-performance data persistence at main memory.
Memory writes need to be performed in strict order to satisfy storage
consistency requirements and enable correct recovery from system crashes.
Unfortunately, adhering to such a strict order significantly degrades system
performance and persistent memory endurance. This paper introduces a new
mechanism, Loose-Ordering Consistency (LOC), that satisfies the ordering
requirements at significantly lower performance and endurance loss. LOC
consists of two key techniques. First, Eager Commit eliminates the need to
perform a persistent commit record write within a transaction. We do so by
ensuring that we can determine the status of all committed transactions during
recovery by storing necessary metadata information statically with blocks of
data written to memory. Second, Speculative Persistence relaxes the write
ordering between transactions by allowing writes to be speculatively written to
persistent memory. A speculative write is made visible to software only after
its associated transaction commits. To enable this, our mechanism supports the
tracking of committed transaction ID and multi-versioning in the CPU cache. Our
evaluations show that LOC reduces the average performance overhead of memory
persistence from 66.9% to 34.9% and the memory write traffic overhead from
17.1% to 3.4% on a variety of workloads.Comment: This paper has been accepted by IEEE Transactions on Parallel and
Distributed System
Master/worker parallel discrete event simulation
The execution of parallel discrete event simulation across metacomputing infrastructures is examined. A master/worker architecture for parallel discrete event simulation is proposed providing robust executions under a dynamic set of services with system-level support for fault tolerance, semi-automated client-directed load balancing, portability across heterogeneous machines, and the ability to run codes on idle or time-sharing clients without significant interaction by users. Research questions and challenges associated with issues and limitations with the work distribution paradigm, targeted computational domain, performance metrics, and the intended class of applications to be used in this context are analyzed and discussed. A portable web services approach to master/worker parallel discrete event simulation is proposed and evaluated with subsequent optimizations to increase the efficiency of large-scale simulation execution through distributed master service design and intrinsic overhead reduction. New techniques for addressing challenges associated with optimistic parallel discrete event simulation across metacomputing such as rollbacks and message unsending with an inherently different computation paradigm utilizing master services and time windows are proposed and examined. Results indicate that a master/worker approach utilizing loosely coupled resources is a viable means for high throughput parallel discrete event simulation by enhancing existing computational capacity or providing alternate execution capability for less time-critical codes.Ph.D.Committee Chair: Fujimoto, Richard; Committee Member: Bader, David; Committee Member: Perumalla, Kalyan; Committee Member: Riley, George; Committee Member: Vuduc, Richar
AKARA: A flexible clustering protocol for demanding transactional workloads
Shared-nothing clusters are a well known and cost-effective approach to database server scalability, in particular, with highly intensive read-only workloads typical of many 3-tier web-based applications. The common reliance on a centralized component and a simplistic propagation strategy employed by mainstream solutions however conduct to poor scalability with traditional on-line transaction processing (OLTP), where the update ratio is high. Such approaches also pose an additional obstacle to high availability while introducing a single point of failure.
More recently, database replication protocols based on group communication have been shown to overcome such limitations, expanding the applicability of shared-nothing clusters to more demanding transactional workloads. These take simultaneous advantage of total order multicast and transactional semantics to improve on mainstream solutions. However, none has already been widely deployed in a general purpose database management system.
In this paper, we argue that a major hurdle for their acceptance is that these proposals have disappointing performance with specific subsets of real-world workloads. Such limitations are deep-rooted and working around them requires in-depth understanding of protocols and changes to applications. We address this issue with a novel protocol that combines multiple transaction execution mechanisms and replication techniques and then show how it avoids the identified pitfalls. Experimental results are obtained with a workload based on the industry standard TPC-C benchmark
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