Efficient resource utilization in shared-everything environments

Efficient resource usage is a key to achieve better performance in parallel database systems. Up to now, most research has focussed on balancing the load on several resources of the same type, i.e. balancing either CPU load or I/O load. In this paper, we present emph{floating probe, a strategy for parallel evaluation of pipelining segments in a shared-everything environment that provides dynamic load balancing between CPU- and I/O-resources. The key idea of floating probe is to overlap---as much as possible with respect to data dependencies---I/O-bound build phase and CPU-bound probe phase of pipelining segments to improve resource utilization. Simulation results show, that floating probe achieves shorter execution times while consuming less memory than conventional pipelining strategies

Middleware-based Database Replication: The Gaps between Theory and Practice

The need for high availability and performance in data management systems has been fueling a long running interest in database replication from both academia and industry. However, academic groups often attack replication problems in isolation, overlooking the need for completeness in their solutions, while commercial teams take a holistic approach that often misses opportunities for fundamental innovation. This has created over time a gap between academic research and industrial practice. This paper aims to characterize the gap along three axes: performance, availability, and administration. We build on our own experience developing and deploying replication systems in commercial and academic settings, as well as on a large body of prior related work. We sift through representative examples from the last decade of open-source, academic, and commercial database replication systems and combine this material with case studies from real systems deployed at Fortune 500 customers. We propose two agendas, one for academic research and one for industrial R&D, which we believe can bridge the gap within 5-10 years. This way, we hope to both motivate and help researchers in making the theory and practice of middleware-based database replication more relevant to each other.Comment: 14 pages. Appears in Proc. ACM SIGMOD International Conference on Management of Data, Vancouver, Canada, June 200

Data sharing in DHT based P2P systems

International audienceThe evolution of peer-to-peer (P2P) systems triggered the building of large scale distributed applications. The main application domain is data sharing across a very large number of highly autonomous participants. Building such data sharing systems is particularly challenging because of the "extreme" characteristics of P2P infrastructures: massive distribution, high churn rate, no global control, potentially untrusted participants... This article focuses on declarative querying support, query optimization and data privacy on a major class of P2P systems, that based on Distributed Hash Table (P2P DHT). The usual approaches and the algorithms used by classic distributed systems and databases forproviding data privacy and querying services are not well suited to P2P DHT systems. A considerable amount of work was required to adapt them for the new challenges such systems present. This paper describes the most important solutions found. It also identies important future research trends in data management in P2P DHT systems

Options in Scan Processing for Shared-Disk Parallel Database Systems

Shared-disk database systems offer a high degree of freedom in the allocation of workload compared to shared-nothing architectures. This creates a great potential for load balancing but also introduces additional complexity into the process of query scheduling. This report surveys the problems and opportunities faced in scan processing in a shared-disk environment. We list the parameters to tune and the decisions to make, as well as some known solutions and commonsense considerations, in order to identify the most promising areas of future research

Scalable Integration View Computation and Maintenance with Parallel, Adaptive and Grouping Techniques

Materialized integration views constructed by integrating data from multiple distributed data sources help to achieve better access, reliable performance, and high availability for a wide range of applications. In this dissertation, we propose parallel, adaptive, and grouping techniques to address scalability challenges in high-performance integration view computation and maintenance due to increasingly large data sources and high rates of source updates. State-of-the-art parallel integration view computation makes the common assumption that the maximal pipelined parallelism leads to superior performance. We instead propose segmented bushy parallel processing that combines pipelined parallelism with alternate forms of parallelism to achieve an overall more effective strategy. Experimental studies conducted over a cluster of high-performance PCs confirm that the proposed strategy has an on average of 50\% improvement in terms of total processing time in comparison to existing solutions. Run-time adaptation becomes critical for parallel integration view computation due to its long running and memory intensive nature. We investigate two types of state level adaptations, namely, state spill and state relocation, to address the run-time memory shortage. We propose lazy-disk and active-disk approaches that integrate both adaptations to maximize run-time query throughput in a memory constrained environment. We also propose global throughput-oriented state adaptation strategies for computation plans with multiple state intensive operators. Extensive experiments confirm the effectiveness of our proposed adaptation solutions. Once results have been computed and materialized, it\u27s typically more efficient to maintain them incrementally instead of full recomputation. However, state-of-the-art incremental view maintenance require O($n^2$) maintenance queries with n being the number of data sources that the view is defined upon. Moreover, they do not exploit view definitions and data source processing capabilities to further improve view maintenance performance. We propose novel grouping maintenance algorithms that dramatically reduce the number of maintenance queries to (O(n)). A cost-based view maintenance framework has been proposed to generate optimized maintenance plans tuned to particular environmental settings. Extensive experimental studies verify the effectiveness of our maintenance algorithms as well as the maintenance framework

Dynamic Query Scheduling in Parallel Data Warehouses

Data warehouse queries pose challenging performance problems that often necessitate the use of parallel database systems (PDBS). Although dynamic load balancing is of key importance in PDBS, to our knowledge it has not yet been investigated thoroughly for parallel data warehouses. In this study, we propose a scheduling strategy that simultaneously considers both processors and disks while utilizing the load balancing potential of a Shared Disk architecture. We compare the performance of this new method to several other approaches in a comprehensive simulation study, incorporating skew aspects and typical data warehouse features such as star schemas