The comfort automatic tuning project

Weikum, Gerhard; Hasse, Christof; Mönkeberg, Axel; Zabback, Peter

doi:10.1016/0306-4379(94)90004-3

Cited by 79 publications

(40 citation statements)

References 35 publications

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“…In the database community, there is a long tradition of auto-tuning approaches, with AutoAdmin [14] and COM-FORT [63] as some of its seminal projects. Recently, more attention has focused on auto-tuning in the cloud setting.…”

Section: Auto-tuning In the Cloudmentioning

confidence: 99%

ClouDiA: a deployment advisor for public clouds

et al. 2015

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An increasing number of distributed data-driven applications are moving into shared public clouds. By sharing resources and operating at scale, public clouds promise higher utilization and lower costs than private clusters. To achieve high utilization, however, cloud providers inevitably allocate virtual machine instances non-contiguously; i.e., instances of a given application may end-up in physically distant machines in the cloud. This allocation strategy can lead to large differences in average latency between instances. For a large class of applications, this difference can result in significant performance degradation, unless care is taken in how application components are mapped to instances. In this paper, we propose ClouDiA, a general deployment advisor that selects application node deployments minimizing either (i) the largest latency between application nodes, or (ii) the longest critical path among all application nodes. ClouDiA employs a number of algorithmic techniques, including mixed-integer programming and constraint programming techniques, to efficiently search the space of possible mappings of application nodes to instances. Through experiments with synthetic and real applications in Amazon EC2, we show that mean latency is a robust metric to model

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Section: Auto-tuning In the Cloudmentioning

confidence: 99%

ClouDiA: a deployment advisor for public clouds

et al. 2015

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“…Dynamic methods continuously monitor the database and adapt to the workload as it is at the moment and are thus viewing a sequence of operations. Dynamic methods are part of the trend towards fully automatic tuning [31], which has become a popular research direction. Recently, work has appeared aiming at integrating vertical and physical partitioning while also taking other physical design features like indices and materialized views into consideration [1].…”

Section: Related Workmentioning

confidence: 99%

DYFRAM: dynamic fragmentation and replica management in distributed database systems

Hauglid

Ryeng

Nørvåg

2010

Distrib Parallel Databases

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In distributed database systems, tables are frequently fragmented and replicated over a number of sites in order to reduce network communication costs. How to fragment, when to replicate and how to allocate the fragments to the sites are challenging problems that has previously been solved either by static fragmentation, replication and allocation, or based on a priori query analysis. Many emerging applications of distributed database systems generate very dynamic workloads with frequent changes in access patterns from different sites. In such contexts, continuous refragmentation and reallocation can significantly improve performance. In this paper we present DYFRAM, a decentralized approach for dynamic table fragmentation and allocation in distributed database systems based on observation of the access patterns of sites to tables. The approach performs fragmentation, replication, and reallocation based on recent access history, aiming at maximizing the number of local accesses compared to accesses from remote sites. We show through simulations and experiments on the DASCOSA distributed database system that the approach significantly reduces communication costs for typical access patterns, thus demonstrating the feasibility of our approach.

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“…Admission control is known to be effective to prevent database thrashing due to severe data/ resource contentions [21]. It can also improve the miss ratio of real-time transactions significantly, especially when overloaded [8], [9].…”

Section: Qmf Architecture and Interactions Among Key System Componentsmentioning

confidence: 99%

Managing deadline miss ratio and sensor data freshness in real-time databases

Kang

Son

Stankovic

2004

IEEE Trans. Knowl. Data Eng.

112

121

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Abstract-The demand for real-time data services is increasing in many applications including e-commerce, agile manufacturing, and telecommunications network management. In these applications, it is desirable to execute transactions within their deadlines, i.e., before the real-world status changes, using fresh (temporally consistent) data. However, meeting these fundamental requirements is challenging due to dynamic workloads and data access patterns in these applications. Further, transaction timeliness and data freshness requirements may conflict. In this paper, we define average/transient deadline miss ratio and new data freshness metrics to let a database administrator specify the desired quality of real-time data services for a specific application. We also present a novel QoS management architecture for real-time databases to support the desired QoS even in the presence of unpredictable workloads and access patterns. To prevent overload and support the desired QoS, the presented architecture applies feedback control, admission control, and flexible freshness management schemes. A simulation study shows that our QoS-aware approach can achieve a near zero miss ratio and perfect freshness, meeting basic requirements for real-time transaction processing. In contrast, baseline approaches fail to support the desired miss ratio and/or freshness in the presence of unpredictable workloads and data access patterns.

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The comfort automatic tuning project

Cited by 79 publications

References 35 publications

ClouDiA: a deployment advisor for public clouds

ClouDiA: a deployment advisor for public clouds

DYFRAM: dynamic fragmentation and replica management in distributed database systems

Managing deadline miss ratio and sensor data freshness in real-time databases

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