Robust Query Optimization Methods With Respect to Estimation Errors

Yin, Shi; Hameurlain, Abdelkader; Morvan, Franck

doi:10.1145/2854006.2854012

Cited by 18 publications

(10 citation statements)

References 65 publications

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“…In the future, we will include the aggregation operators into our cost model. After that, as previously said, a further research direction could be adapting robust query optimization methods [28] to the cloud context, in order to minimize the renegotiation risk.…”

Section: Discussionmentioning

confidence: 99%

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SLA Definition for Multi-tenant DBMS and its Impact on Query Optimization

Yin¹,

Hameurlain²,

Morvan³

2018

IEEE Trans. Knowl. Data Eng.

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In the cloud context, users are often called tenants. A cloud DBMS shared by many tenants is called a multi-tenant DBMS. The resource consolidation in such a DBMS allows the tenants to only pay for the resources that they consume, while providing the opportunity for the provider to increase its economic gain. For this, a Service Level Agreement (SLA) is usually established between the provider and a tenant. However, in the current systems, the SLA is often defined by the provider, while the tenant should agree with it before using the service. In addition, only the availability objective is described in the SLA, but not the performance objective. In this paper, an SLA negotiation framework is proposed, in which the provider and the tenant define the performance objective together in a fair way. To demonstrate the feasibility and the advantage of this framework, we evaluate its impact on query optimization. We formally define the problem by including the cost-efficiency aspect, we design a cost model and study the plan search space for this problem, we revise two search methods to adapt to the new context, and we propose a heuristic to solve the resource contention problem caused by concurrent queries of multiple tenants. We also conduct a performance evaluation to show that, our optimization approach (i.e., driven by the SLA) can be much more cost-effective than the traditional approach which always minimizes the query completion time.

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Section: Discussionmentioning

confidence: 99%

“…Evidently, the provider should minimize the renegotiation risk. A possible track is to adapt robust query optimization methods [28] to the cloud context.…”

Section: Steps For Generating the Sla Offermentioning

confidence: 99%

SLA Definition for Multi-tenant DBMS and its Impact on Query Optimization

Yin¹,

Hameurlain²,

Morvan³

2018

IEEE Trans. Knowl. Data Eng.

Self Cite

View full text Add to dashboard Cite

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“…Therefore, ''robust query optimization'' has aimed to minimize the plan sub-optimality risk. 32,33 Different robust query optimization methods have been discussed by the work of Yin et al 34 The robust query optimization methods for single-node, distributed, and parallel systems have been reviewed in two main approaches, ie, single-plan based and multi-plan based. Furthermore, estimation error sources, target query types, target optimization decisions, performance degradation risk, and engineering cost parameters have been investigated in this paper.…”

Section: Related Workmentioning

confidence: 99%

Deterministic and non‐deterministic query optimization techniques in the cloud computing

Azhir

Navimipour

Hosseinzadeh

et al. 2019

Concurrency and Computation

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Query optimization is considered as one of the main challenges of query processing phases in the cloud environments. The query optimizer attempts to provide the most optimal execution plan by considering the possible query plans. Therefore, the execution cost of a query can be affected by some factors, including communication costs, unavailability of resources, and access to large distributed data sets. In addition, it is known as NP-hard problem and many researchers are focused on this problem in recent years. Some techniques are proposed for solving this problem. Deterministic and non-deterministic methods are two main categories to study these techniques. The deterministic and non-deterministic query optimization methods can be further divided into three subcategories, cost-based query plan enumeration, multiple query optimization, and adaptive query optimization methods. Moreover, this paper presents the advantages and disadvantages of the algorithms for solving the query optimization problems in the cloud environments. Moreover, these techniques are compared in terms of optimization, time, cost, efficiency, and scalability. Finally, some key areas are offered to improve the cloud query optimization mechanisms in the future. KEYWORDScloud computing, database, query optimization, review INTRODUCTIONThe data transfer operation and resource sharing are facilitated by rapid progress of the distributed IT-based systems. 1,2 Cloud computing supports several computers through a network. 3 The cloud computing has a large-scale distributed architecture and virtualized services to deliver the requests to users. 4,5 Moreover, the cloud computing provides important financial advantages and long level cooperation possibilities for organizations and institutions. 6 The cloud computing is defined as a distributed IT-based technology based on service business model. 7This paradigm provides many benefits for users, such as the provision of computing capabilities, heterogeneous network access, scalability, and elasticity with measured services. 8,9 The cloud computing gives shared access to a large pool of resources, including data storage, memory, processing, and virtual machines. 10 A cloud client, such as a web browser and mobile app can be helpful in accessing these services. 11 Enormous amounts of data are retrieved from geo-distributed data sources and cross-layer data-handling requirements to make a change in business model. 12 The cloud storage as one of the main services is provided by cloud computing, 13 which allows the users to store their data in virtual pools instead of their servers. 14 In addition, subscribers can access the data from any area of cloud. 15 Therefore, the reliability and availability are necessary to recover the information and query processing.The query processing involves three main steps, as shown in Figure 1. First, the query is translated into an expression of the relational algebra.Second, an optimal evaluation plan for the query plan is generated. The query optimization is the main part o...

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“…Open research questions in robust query processing are regularly discussed in Dagstuhl seminars organized by Graefe and colleagues [10,11,12]. One approach to robust query processing is robust plan selection as classified in a recent survey [25]. The design space for robust plan selection strategies has similarities to the design space of a conventional query optimization.…”

Section: Related Workmentioning

confidence: 99%

Robustness metrics for relational query execution plans

et al. 2018

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The quality of query execution plans in database systems determines how fast a query can be executed. It has been shown that conventional query optimization still selects suboptimal or even bad execution plans, due to errors in the cardinality estimation. Although cardinality estimation errors are an evident problem, they are in general not considered in the selection of query execution plans. In this paper, we present three novel metrics for the robustness of relational query execution plans w.r.t. cardinality estimation errors. We also present a novel plan selection strategy that takes both, estimated cost and estimated robustness into account, when choosing a plan for execution. Finally, we share the results of our experimental comparison between robust and conventional plan selection on real world and synthetic benchmarks, showing a speedup of at most factor 3.49.

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Robust Query Optimization Methods With Respect to Estimation Errors

Cited by 18 publications

References 65 publications

SLA Definition for Multi-tenant DBMS and its Impact on Query Optimization

SLA Definition for Multi-tenant DBMS and its Impact on Query Optimization

Deterministic and non‐deterministic query optimization techniques in the cloud computing

Robustness metrics for relational query execution plans

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