Structured vs. Unstructured Large Neighborhood Search: A Case Study on Job-Shop Scheduling Problems with Earliness and Tardiness Costs

Danna, Emilie; Perron, Laurent

doi:10.1007/978-3-540-45193-8_59

Cited by 32 publications

(27 citation statements)

References 3 publications

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“…Horticulture employs one such approach, called large-neighborhood search (LNS), to explore potential designs off-line in a guided manner [21,18]. LNS compares potential solutions with a cost model that estimates how well the DBMS will perform using a particular design for the sample workload trace without needing to actually deploy the database.…”

Section: Database Design Challengesmentioning

confidence: 99%

“…designs using local search in Step 4, Horticulture verifies whether a design is feasible for the target cluster (i.e., the total size of the data stored on each node is less than its storage limit) [18]. Non-feasible designs are immediately discarded.…”

Section: Large-neighborhood Searchmentioning

confidence: 99%

“…As put forth in the original LNS papers [18,21], the number of relaxed variables (i.e., tables) is based on how much search time remains as defined by the administrator. Initially, this size is 25% of the total number of tables in the database; as time elapses, the limit increases up to 50%…”

Section: Relaxationmentioning

confidence: 99%

“…Increasing the number of tables relaxed over time in this manner is predicated on the idea that a tighter upper bound will be found more quickly if the initial search rounds use a smaller number of tables, thereby allowing larger portions of the solution space to be discarded in later rounds [18,21].…”

Section: Relaxationmentioning

confidence: 99%

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Skew-aware automatic database partitioning in shared-nothing, parallel OLTP systems

Pavlo

Curino

Zdonik

2012

Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data

224

172

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The advent of affordable, shared-nothing computing systems portends a new class of parallel database management systems (DBMS) for on-line transaction processing (OLTP) applications that scale without sacrificing ACID guarantees [7,9]. The performance of these DBMSs is predicated on the existence of an optimal database design that is tailored for the unique characteristics of OLTP workloads [43]. Deriving such designs for modern DBMSs is difficult, especially for enterprise-class OLTP systems, since they impose extra challenges: the use of stored procedures, the need for load balancing in the presence of time-varying skew, complex schemas, and deployments with larger number of partitions.To this purpose, we present a novel approach to automatically partitioning databases for enterprise-class OLTP systems that significantly extends the state of the art by: (1) minimizing the number distributed transactions, while concurrently mitigating the effects of temporal skew in both the data distribution and accesses, (2) extending the design space to include replicated secondary indexes, (4) organically handling stored procedure routing, and (3) scaling of schema complexity, data size, and number of partitions. This effort builds on two key technical contributions: an analytical cost model that can be used to quickly estimate the relative coordination cost and skew for a given workload and a candidate database design, and an informed exploration of the huge solution space based on large neighborhood search. To evaluate our methods, we integrated our database design tool with a high-performance parallel, main memory DBMS and compared our methods against both popular heuristics and a state-of-the-art research prototype [17]. Using a diverse set of benchmarks, we show that our approach improves throughput by up to a factor of 16× over these other approaches.

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Section: Database Design Challengesmentioning

confidence: 99%

Section: Large-neighborhood Searchmentioning

confidence: 99%

Section: Relaxationmentioning

confidence: 99%

Section: Relaxationmentioning

confidence: 99%

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Skew-aware automatic database partitioning in shared-nothing, parallel OLTP systems

Pavlo

Curino

Zdonik

2012

Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data

224

172

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“…• ET-JSP : Danna and Perron (2003) proposed complete ("unstructured large neighborhood search" uLN S) and incomplete ("structured large neighborhood search method" sLN S) methods, incorporating MIP-based large neighborhood search (Shaw (1998)). …”

Section: State-of-the-art Systematic and Metaheuristic Comparison Metmentioning

confidence: 99%

Solving Variants of the Job Shop Scheduling Problem Through Conflict-Directed Search

Grimes

Hébrard

2015

INFORMS Journal on Computing

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We introduce a simple technique for disjunctive machine scheduling problems and show that this method can match or even outperform state of the art algorithms on a number of problem types. Our approach combines a number of generic search techniques such as restarts, adaptive heuristics and solution guided branching on a simple model based on a decomposition of disjunctive constraints and on the reification of these disjuncts. This paper describes the method and its application to variants of the job shop scheduling problem (JSP ).We show that our method can easily be adapted to handle additional side constraints and different objective functions, often outperforming the state of the art and closing a number of open problems.* Moreover, we perform in-depth analysis of the various factors that makes this approach efficient. We show that, while most of the factors give moderate benefits, the variable and value ordering components are key.

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Adaptive Memory Projection Methods for Integer Programming

Glover

Operations Research/Computer Science Interfaces Series

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Structured vs. Unstructured Large Neighborhood Search: A Case Study on Job-Shop Scheduling Problems with Earliness and Tardiness Costs

Cited by 32 publications

References 3 publications

Skew-aware automatic database partitioning in shared-nothing, parallel OLTP systems

Skew-aware automatic database partitioning in shared-nothing, parallel OLTP systems

Solving Variants of the Job Shop Scheduling Problem Through Conflict-Directed Search

Adaptive Memory Projection Methods for Integer Programming

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