Theodoros Rekatsinas, A. Deshpande, X. Dong, L. Getoor, D. Srivastava
Recently there has been a rapid increase in the number of data sources and data services, such as cloud-based data markets and data portals, that facilitate the collection, publishing and trading of data. Data sources typically exhibit large heterogeneity in the type and quality of data they provide. Unfortunately, when the number of data sources is large, it is difficult for users to reason about the actual usefulness of sources for their applications and the trade-offs between the benefits and costs of acquiring and integrating sources. In this demonstration we present textsc{SourceSight}, a system that allows users to interactively explore a large number of heterogeneous data sources, and discover valuable sets of sources for diverse integration tasks. textsc{SourceSight}~uses a novel multi-level source quality index that enables effective source selection at different granularity levels, and introduces a collection of new techniques to discover and evaluate relevant sources for integration.
{"title":"SourceSight: Enabling Effective Source Selection","authors":"Theodoros Rekatsinas, A. Deshpande, X. Dong, L. Getoor, D. Srivastava","doi":"10.1145/2882903.2899403","DOIUrl":"https://doi.org/10.1145/2882903.2899403","url":null,"abstract":"Recently there has been a rapid increase in the number of data sources and data services, such as cloud-based data markets and data portals, that facilitate the collection, publishing and trading of data. Data sources typically exhibit large heterogeneity in the type and quality of data they provide. Unfortunately, when the number of data sources is large, it is difficult for users to reason about the actual usefulness of sources for their applications and the trade-offs between the benefits and costs of acquiring and integrating sources. In this demonstration we present textsc{SourceSight}, a system that allows users to interactively explore a large number of heterogeneous data sources, and discover valuable sets of sources for diverse integration tasks. textsc{SourceSight}~uses a novel multi-level source quality index that enables effective source selection at different granularity levels, and introduces a collection of new techniques to discover and evaluate relevant sources for integration.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81102324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yaguang Li, Han Su, Ugur Demiryurek, Bolong Zheng, Kai Zeng, C. Shahabi
In this paper, we study a route summarization framework for Personalized Navigation dubbed PerNav - with which the goal is to generate more intuitive and customized turn-by-turn directions based on user generated content. The turn-by-turn directions provided in the existing navigation applications are exclusively derived from underlying road network topology information i.e., the connectivity of nodes to each other. Therefore, the turn-by-turn directions are simplified as metric translation of physical world (e.g. distance/time to turn) to spoken language. Such translation- that ignores human cognition about the geographic space- is often verbose and redundant for the drivers who have knowledge about the geographical areas. PerNav utilizes wealth of user generated historical trajectory data to extract namely "landmarks" (e.g., point of interests or intersections) and frequently visited routes between them from the road network. Then this extracted information is used to obtain cognitive turn-by-turn directions customized for each user.
{"title":"PerNav: A Route Summarization Framework for Personalized Navigation","authors":"Yaguang Li, Han Su, Ugur Demiryurek, Bolong Zheng, Kai Zeng, C. Shahabi","doi":"10.1145/2882903.2899384","DOIUrl":"https://doi.org/10.1145/2882903.2899384","url":null,"abstract":"In this paper, we study a route summarization framework for Personalized Navigation dubbed PerNav - with which the goal is to generate more intuitive and customized turn-by-turn directions based on user generated content. The turn-by-turn directions provided in the existing navigation applications are exclusively derived from underlying road network topology information i.e., the connectivity of nodes to each other. Therefore, the turn-by-turn directions are simplified as metric translation of physical world (e.g. distance/time to turn) to spoken language. Such translation- that ignores human cognition about the geographic space- is often verbose and redundant for the drivers who have knowledge about the geographical areas. PerNav utilizes wealth of user generated historical trajectory data to extract namely \"landmarks\" (e.g., point of interests or intersections) and frequently visited routes between them from the road network. Then this extracted information is used to obtain cognitive turn-by-turn directions customized for each user.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89388677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Query optimization is hard and the current proliferation of "modern" hardware does nothing to make it any easier. In addition, the tools that are commonly used by performance engineers, such as compiler intrinsics, static analyzers or hardware performance counters are neither integrated with data management systems nor easy to learn. This fact makes it (unnecessarily) hard to educate engineers, to prototype and to optimize database query plans for modern hardware. To address this problem, we developed a system called Candomblé that lets database performance engineers interactively examine, optimize and evaluate query plans using a touch-based interface. Candomblé puts attendants in the place of a physical query optimizer that has to rewrite a physical query plan into a better equivalent plan. Attendants experience the challenges when ad-hoc optimizing a physical plan for processing devices such as GPUs and CPUs and capture their gained knowledge in rules to be used by a rule-based optimizer.
{"title":"What Makes a Good Physical plan?: Experiencing Hardware-Conscious Query Optimization with Candomblé","authors":"H. Pirk, Oscar Moll, S. Madden","doi":"10.1145/2882903.2899410","DOIUrl":"https://doi.org/10.1145/2882903.2899410","url":null,"abstract":"Query optimization is hard and the current proliferation of \"modern\" hardware does nothing to make it any easier. In addition, the tools that are commonly used by performance engineers, such as compiler intrinsics, static analyzers or hardware performance counters are neither integrated with data management systems nor easy to learn. This fact makes it (unnecessarily) hard to educate engineers, to prototype and to optimize database query plans for modern hardware. To address this problem, we developed a system called Candomblé that lets database performance engineers interactively examine, optimize and evaluate query plans using a touch-based interface. Candomblé puts attendants in the place of a physical query optimizer that has to rewrite a physical query plan into a better equivalent plan. Attendants experience the challenges when ad-hoc optimizing a physical plan for processing devices such as GPUs and CPUs and capture their gained knowledge in rules to be used by a rule-based optimizer.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84601491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Scientific applications are generating an ever-increasing volume of multi-dimensional data that are largely processed inside distributed array databases and frameworks. Similarity join is a fundamental operation across scientific workloads that requires complex processing over an unbounded number of pairs of multi-dimensional points. In this paper, we introduce a novel distributed similarity join operator for multi-dimensional arrays. Unlike immediate extensions to array join and relational similarity join, the proposed operator minimizes the overall data transfer and network congestion while providing load-balancing, without completely repartitioning and replicating the input arrays. We define formally array similarity join and present the design, optimization strategies, and evaluation of the first array similarity join operator.
{"title":"Similarity Join over Array Data","authors":"Weijie Zhao, Florin Rusu, Bin Dong, Kesheng Wu","doi":"10.1145/2882903.2915247","DOIUrl":"https://doi.org/10.1145/2882903.2915247","url":null,"abstract":"Scientific applications are generating an ever-increasing volume of multi-dimensional data that are largely processed inside distributed array databases and frameworks. Similarity join is a fundamental operation across scientific workloads that requires complex processing over an unbounded number of pairs of multi-dimensional points. In this paper, we introduce a novel distributed similarity join operator for multi-dimensional arrays. Unlike immediate extensions to array join and relational similarity join, the proposed operator minimizes the overall data transfer and network congestion while providing load-balancing, without completely repartitioning and replicating the input arrays. We define formally array similarity join and present the design, optimization strategies, and evaluation of the first array similarity join operator.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77286377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Technology limitations are making the use of heterogeneous computing devices much more than an academic curiosity. In fact, the use of such devices is widely acknowledged to be the only promising way to achieve application-speedups that users urgently need and expect. However, building a robust and efficient query engine for heterogeneous co-processor environments is still a significant challenge. In this paper, we identify two effects that limit performance in case co-processor resources become scarce. Cache thrashing occurs when the working set of queries does not fit into the co-processor's data cache, resulting in performance degradations up to a factor of 24. Heap contention occurs when multiple operators run in parallel on a co-processor and when their accumulated memory footprint exceeds the main memory capacity of the co-processor, slowing down query execution by up to a factor of six. We propose solutions for both effects. Data-driven operator placement avoids data movements when they might be harmful; query chopping limits co-processor memory usage and thus avoids contention. The combined approach-data-driven query chopping-achieves robust and scalable performance on co-processors. We validate our proposal with our open-source GPU-accelerated database engine CoGaDB and the popular star schema and TPC-H benchmarks.
{"title":"Robust Query Processing in Co-Processor-accelerated Databases","authors":"S. Breß, Henning Funke, J. Teubner","doi":"10.1145/2882903.2882936","DOIUrl":"https://doi.org/10.1145/2882903.2882936","url":null,"abstract":"Technology limitations are making the use of heterogeneous computing devices much more than an academic curiosity. In fact, the use of such devices is widely acknowledged to be the only promising way to achieve application-speedups that users urgently need and expect. However, building a robust and efficient query engine for heterogeneous co-processor environments is still a significant challenge. In this paper, we identify two effects that limit performance in case co-processor resources become scarce. Cache thrashing occurs when the working set of queries does not fit into the co-processor's data cache, resulting in performance degradations up to a factor of 24. Heap contention occurs when multiple operators run in parallel on a co-processor and when their accumulated memory footprint exceeds the main memory capacity of the co-processor, slowing down query execution by up to a factor of six. We propose solutions for both effects. Data-driven operator placement avoids data movements when they might be harmful; query chopping limits co-processor memory usage and thus avoids contention. The combined approach-data-driven query chopping-achieves robust and scalable performance on co-processors. We validate our proposal with our open-source GPU-accelerated database engine CoGaDB and the popular star schema and TPC-H benchmarks.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81810565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Varun Pandey, Andreas Kipf, Dimitri Vorona, Tobias Mühlbauer, Thomas Neumann, A. Kemper
In the past few years, massive amounts of location-based data has been captured. Numerous datasets containing user location information are readily available to the public. Analyzing such datasets can lead to fascinating insights into the mobility patterns and behaviors of users. Moreover, in recent times a number of geospatial data-driven companies like Uber, Lyft, and Foursquare have emerged. Real-time analysis of geospatial data is essential and enables an emerging class of applications. Database support for geospatial operations is turning into a necessity instead of a distinct feature provided by only a few databases. Even though a lot of database systems provide geospatial support nowadays, queries often do not consider the most current database state. Geospatial queries are inherently slow given the fact that some of these queries require a couple of geometric computations. Disk-based database systems that do support geospatial datatypes and queries, provide rich features and functions, but they fall behind when performance is considered: specifically if real-time analysis of the latest transactional state is a requirement. In this demonstration, we present HyPerSpace, an extension to the high-performance main-memory database system HyPer developed at the Technical University of Munich, capable of processing geospatial queries with sub-second latencies.
{"title":"High-Performance Geospatial Analytics in HyPerSpace","authors":"Varun Pandey, Andreas Kipf, Dimitri Vorona, Tobias Mühlbauer, Thomas Neumann, A. Kemper","doi":"10.1145/2882903.2899412","DOIUrl":"https://doi.org/10.1145/2882903.2899412","url":null,"abstract":"In the past few years, massive amounts of location-based data has been captured. Numerous datasets containing user location information are readily available to the public. Analyzing such datasets can lead to fascinating insights into the mobility patterns and behaviors of users. Moreover, in recent times a number of geospatial data-driven companies like Uber, Lyft, and Foursquare have emerged. Real-time analysis of geospatial data is essential and enables an emerging class of applications. Database support for geospatial operations is turning into a necessity instead of a distinct feature provided by only a few databases. Even though a lot of database systems provide geospatial support nowadays, queries often do not consider the most current database state. Geospatial queries are inherently slow given the fact that some of these queries require a couple of geometric computations. Disk-based database systems that do support geospatial datatypes and queries, provide rich features and functions, but they fall behind when performance is considered: specifically if real-time analysis of the latest transactional state is a requirement. In this demonstration, we present HyPerSpace, an extension to the high-performance main-memory database system HyPer developed at the Technical University of Munich, capable of processing geospatial queries with sub-second latencies.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88662807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A key promise of SQL is that the optimizer will find the most efficient execution plan, regardless of how the query is formulated. In general, query optimizers of modern database systems are able to keep this promise, with the notable exception of nested queries. While several optimization techniques for nested queries have been proposed, their adoption in practice has been limited. In this paper, we argue that the NF2 (non-first normal form) algebra, which was originally designed to process nested tables, is a better approach to nested query optimization as it fulfills two key requirements. First, the NF2 algebra can represent all types of nested queries as well as both existing and novel optimization techniques based on its equivalences. Second, performance benefits can be achieved with little changes to existing transformation-based query optimizers as the NF2 algebra is an extension of the relational algebra.
{"title":"Optimization of Nested Queries using the NF2 Algebra","authors":"Jürgen Hölsch, Michael Grossniklaus, M. Scholl","doi":"10.1145/2882903.2915241","DOIUrl":"https://doi.org/10.1145/2882903.2915241","url":null,"abstract":"A key promise of SQL is that the optimizer will find the most efficient execution plan, regardless of how the query is formulated. In general, query optimizers of modern database systems are able to keep this promise, with the notable exception of nested queries. While several optimization techniques for nested queries have been proposed, their adoption in practice has been limited. In this paper, we argue that the NF2 (non-first normal form) algebra, which was originally designed to process nested tables, is a better approach to nested query optimization as it fulfills two key requirements. First, the NF2 algebra can represent all types of nested queries as well as both existing and novel optimization techniques based on its equivalences. Second, performance benefits can be achieved with little changes to existing transformation-based query optimizers as the NF2 algebra is an extension of the relational algebra.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90971329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to the "curse of dimensionality" problem, it is very expensive to process the nearest neighbor (NN) query in high-dimensional spaces; and hence, approximate approaches, such as Locality-Sensitive Hashing (LSH), are widely used for their theoretical guarantees and empirical performance. Current LSH-based approaches target at the L1 and L2 spaces, while as shown in previous work, the fractional distance metrics (Lp metrics with 0 < p < 1) can provide more insightful results than the usual L1 and L2 metrics for data mining and multimedia applications. However, none of the existing work can support multiple fractional distance metrics using one index. In this paper, we propose LazyLSH that answers approximate nearest neighbor queries for multiple Lp metrics with theoretical guarantees. Different from previous LSH approaches which need to build one dedicated index for every query space, LazyLSH uses a single base index to support the computations in multiple Lp spaces, significantly reducing the maintenance overhead. Extensive experiments show that LazyLSH provides more accurate results for approximate kNN search under fractional distance metrics.
由于“维数诅咒”问题，在高维空间中处理最近邻(NN)查询是非常昂贵的;因此，近似方法，如位置敏感哈希(LSH)，因其理论保证和经验性能而被广泛使用。当前基于lsh的方法针对L1和L2空间，而正如之前的工作所示，对于数据挖掘和多媒体应用，分数距离度量(0 < p < 1的Lp度量)可以提供比通常的L1和L2度量更有洞察力的结果。然而，现有的工作都不能支持使用一个索引的多个分数距离度量。在本文中，我们提出了一个具有理论保证的回答多个Lp指标的近似最近邻查询的LazyLSH。与以前需要为每个查询空间构建一个专用索引的LSH方法不同，LazyLSH使用单个基本索引来支持多个Lp空间中的计算，从而大大降低了维护开销。大量的实验表明，在分数距离度量下，LazyLSH提供了更准确的近似kNN搜索结果。
{"title":"LazyLSH: Approximate Nearest Neighbor Search for Multiple Distance Functions with a Single Index","authors":"Yuxin Zheng, Qi Guo, A. Tung, Sai Wu","doi":"10.1145/2882903.2882930","DOIUrl":"https://doi.org/10.1145/2882903.2882930","url":null,"abstract":"Due to the \"curse of dimensionality\" problem, it is very expensive to process the nearest neighbor (NN) query in high-dimensional spaces; and hence, approximate approaches, such as Locality-Sensitive Hashing (LSH), are widely used for their theoretical guarantees and empirical performance. Current LSH-based approaches target at the L1 and L2 spaces, while as shown in previous work, the fractional distance metrics (Lp metrics with 0 < p < 1) can provide more insightful results than the usual L1 and L2 metrics for data mining and multimedia applications. However, none of the existing work can support multiple fractional distance metrics using one index. In this paper, we propose LazyLSH that answers approximate nearest neighbor queries for multiple Lp metrics with theoretical guarantees. Different from previous LSH approaches which need to build one dedicated index for every query space, LazyLSH uses a single base index to support the computations in multiple Lp spaces, significantly reducing the maintenance overhead. Extensive experiments show that LazyLSH provides more accurate results for approximate kNN search under fractional distance metrics.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90201548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We propose the 3c-index that measures the influence degree of researchers by evaluating the links they establish between communities. We evaluate its performance against well known metrics. The results show 3c-index outperforms them in most cases and can be employed as a complementary metric to assess researchers' productivity.
{"title":"Research Contribution as a Measure of Influence","authors":"Lais M. A. Rocha, Mirella M. Moro","doi":"10.1145/2882903.2914834","DOIUrl":"https://doi.org/10.1145/2882903.2914834","url":null,"abstract":"We propose the 3c-index that measures the influence degree of researchers by evaluating the links they establish between communities. We evaluate its performance against well known metrics. The results show 3c-index outperforms them in most cases and can be employed as a complementary metric to assess researchers' productivity.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90325975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sudipto Das, Feng Li, Vivek R. Narasayya, A. König
Relational Database-as-a-Service (DaaS) platforms today support the abstraction of a resource container that guarantees a fixed amount of resources. Tenants are responsible for selecting a container size suitable for their workloads, which they can change to leverage the cloud's elasticity. However, automating this task is daunting for most tenants since estimating resource demands for arbitrary SQL workloads in an RDBMS is complex and challenging. In addition, workloads and resource requirements can vary significantly within minutes to hours, and container sizes vary by orders of magnitude both in the amount of resources as well as monetary cost. We present a solution to enable a DaaS to auto-scale container sizes on behalf of its tenants. Approaches to auto-scale stateless services, such as web servers, that rely on historical resource utilization as the primary signal, often perform poorly for stateful database servers which are significantly more complex. Our solution derives a set of robust signals from database engine telemetry and combines them to significantly improve accuracy of demand estimation for database workloads resulting in more accurate scaling decisions. Our solution raises the abstraction by allowing tenants to reason about monetary budget and query latency rather than resources. We prototyped our approach in Microsoft Azure SQL Database and ran extensive experiments using workloads with realistic time-varying resource demand patterns obtained from production traces. Compared to an approach that uses only resource utilization to estimate demand, our approach results in 1.5x to 3x lower monetary costs while achieving comparable query latencies.
{"title":"Automated Demand-driven Resource Scaling in Relational Database-as-a-Service","authors":"Sudipto Das, Feng Li, Vivek R. Narasayya, A. König","doi":"10.1145/2882903.2903733","DOIUrl":"https://doi.org/10.1145/2882903.2903733","url":null,"abstract":"Relational Database-as-a-Service (DaaS) platforms today support the abstraction of a resource container that guarantees a fixed amount of resources. Tenants are responsible for selecting a container size suitable for their workloads, which they can change to leverage the cloud's elasticity. However, automating this task is daunting for most tenants since estimating resource demands for arbitrary SQL workloads in an RDBMS is complex and challenging. In addition, workloads and resource requirements can vary significantly within minutes to hours, and container sizes vary by orders of magnitude both in the amount of resources as well as monetary cost. We present a solution to enable a DaaS to auto-scale container sizes on behalf of its tenants. Approaches to auto-scale stateless services, such as web servers, that rely on historical resource utilization as the primary signal, often perform poorly for stateful database servers which are significantly more complex. Our solution derives a set of robust signals from database engine telemetry and combines them to significantly improve accuracy of demand estimation for database workloads resulting in more accurate scaling decisions. Our solution raises the abstraction by allowing tenants to reason about monetary budget and query latency rather than resources. We prototyped our approach in Microsoft Azure SQL Database and ran extensive experiments using workloads with realistic time-varying resource demand patterns obtained from production traces. Compared to an approach that uses only resource utilization to estimate demand, our approach results in 1.5x to 3x lower monetary costs while achieving comparable query latencies.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85170142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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