Pub Date : 2023-08-01DOI: 10.14778/3611540.3611559
Chunhui Shen, Qianyu Ouyang, Feibo Li, Zhipeng Liu, Longcheng Zhu, Yujie Zou, Qing Su, Tianhuan Yu, Yi Yi, Jianhong Hu, Cen Zheng, Bo Wen, Hanbang Zheng, Lunfan Xu, Sicheng Pan, Bin Wu, Xiao He, Ye Li, Jian Tan, Sheng Wang, Dan Pei, Wei Zhang, Feifei Li
Internet services supported by large-scale distributed systems have become essential for our daily life. To ensure the stability and high quality of services, diverse metric data are constantly collected and managed in a time-series database to monitor the service status. However, when the number of metrics becomes massive, existing time-series databases are inefficient in handling high-rate data ingestion and queries hitting multiple metrics. Besides, they all lack the support of machine learning functions, which are crucial for sophisticated analysis of large-scale time series. In this paper, we present Lindorm TSDB, a distributed time-series database designed for handling monitoring metrics at scale. It sustains high write throughput and low query latency with massive active metrics. It also allows users to analyze data with anomaly detection and time series forecasting algorithms directly through SQL. Furthermore, Lindorm TSDB retains stable performance even during node scaling. We evaluate Lindorm TSDB under different data scales, and the results show that it outperforms two popular open-source time-series databases on both writing and query, while executing time-series machine learning tasks efficiently.
{"title":"Lindorm TSDB: A Cloud-Native Time-Series Database for Large-Scale Monitoring Systems","authors":"Chunhui Shen, Qianyu Ouyang, Feibo Li, Zhipeng Liu, Longcheng Zhu, Yujie Zou, Qing Su, Tianhuan Yu, Yi Yi, Jianhong Hu, Cen Zheng, Bo Wen, Hanbang Zheng, Lunfan Xu, Sicheng Pan, Bin Wu, Xiao He, Ye Li, Jian Tan, Sheng Wang, Dan Pei, Wei Zhang, Feifei Li","doi":"10.14778/3611540.3611559","DOIUrl":"https://doi.org/10.14778/3611540.3611559","url":null,"abstract":"Internet services supported by large-scale distributed systems have become essential for our daily life. To ensure the stability and high quality of services, diverse metric data are constantly collected and managed in a time-series database to monitor the service status. However, when the number of metrics becomes massive, existing time-series databases are inefficient in handling high-rate data ingestion and queries hitting multiple metrics. Besides, they all lack the support of machine learning functions, which are crucial for sophisticated analysis of large-scale time series. In this paper, we present Lindorm TSDB, a distributed time-series database designed for handling monitoring metrics at scale. It sustains high write throughput and low query latency with massive active metrics. It also allows users to analyze data with anomaly detection and time series forecasting algorithms directly through SQL. Furthermore, Lindorm TSDB retains stable performance even during node scaling. We evaluate Lindorm TSDB under different data scales, and the results show that it outperforms two popular open-source time-series databases on both writing and query, while executing time-series machine learning tasks efficiently.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135003303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A classic design of cloud-native databases adopts an architecture that consists of one read/write (RW) node and one or more read-only (RO) nodes. In such a design, the propagation of write-ahead logs (WALs) from the RW node to the RO node(s) is typically performed asynchronously. Consequently, system designers either have to accept a loose consistency guarantee, where a read from the RO node may return stale data, or tolerate significant performance degradation in terms of read latency, as it then needs to wait for the log to be propagated and applied. Most commercial cloud-native databases, such as Amazon Aurora, choose performance over strong consistency. As a result, it makes RO nodes useless for many applications requiring read-after-write consistency (a form of strong consistency), and the support for serverless databases (i.e., allowing the RO nodes to be scaled out automatically) is impossible as they require a single endpoint. This paper proposes PolarDB-SCC (PolarDB-Strongly Consistent Cluster), a cloud-native database architecture that guarantees strongly consistent reads with very low latency. The core idea is to eliminate unnecessary waits and reduce the necessary wait time on RO nodes while still supporting strong consistency. To achieve this, it tracks the RW node's modification timestamp at three progressively finer-grained levels. We further design a Linear Lamport timestamp to reduce the RO node's timestamp fetching operations and leverage the RDMA network for all the data transferring ( e.g. , timestamp fetching and log shipment) to minimize network overhead and extra CPU usage. Our evaluation shows that PolarDB-SCC does not incur any noticeable overhead for ensuring strongly consistent reads compared with the eventually consistent (stale) read policy. To the best of our knowledge, PolarDB-SCC is the first "read-write splitting" cloud-native database that supports strongly consistent read with negligible overhead. Compared with a straightforward read-wait design, PolarDB-SCC improves throughput by up to 4.51× and reduces median latency by up to 3.66× in SysBench's read-write workload. PolarDB-SCC is already commercially available at Alibaba Cloud.
{"title":"PolarDB-SCC: A Cloud-Native Database Ensuring Low Latency for Strongly Consistent Reads","authors":"Xinjun Yang, Yingqiang Zhang, Hao Chen, Chuan Sun, Feifei Li, Wenchao Zhou","doi":"10.14778/3611540.3611562","DOIUrl":"https://doi.org/10.14778/3611540.3611562","url":null,"abstract":"A classic design of cloud-native databases adopts an architecture that consists of one read/write (RW) node and one or more read-only (RO) nodes. In such a design, the propagation of write-ahead logs (WALs) from the RW node to the RO node(s) is typically performed asynchronously. Consequently, system designers either have to accept a loose consistency guarantee, where a read from the RO node may return stale data, or tolerate significant performance degradation in terms of read latency, as it then needs to wait for the log to be propagated and applied. Most commercial cloud-native databases, such as Amazon Aurora, choose performance over strong consistency. As a result, it makes RO nodes useless for many applications requiring read-after-write consistency (a form of strong consistency), and the support for serverless databases (i.e., allowing the RO nodes to be scaled out automatically) is impossible as they require a single endpoint. This paper proposes PolarDB-SCC (PolarDB-Strongly Consistent Cluster), a cloud-native database architecture that guarantees strongly consistent reads with very low latency. The core idea is to eliminate unnecessary waits and reduce the necessary wait time on RO nodes while still supporting strong consistency. To achieve this, it tracks the RW node's modification timestamp at three progressively finer-grained levels. We further design a Linear Lamport timestamp to reduce the RO node's timestamp fetching operations and leverage the RDMA network for all the data transferring ( e.g. , timestamp fetching and log shipment) to minimize network overhead and extra CPU usage. Our evaluation shows that PolarDB-SCC does not incur any noticeable overhead for ensuring strongly consistent reads compared with the eventually consistent (stale) read policy. To the best of our knowledge, PolarDB-SCC is the first \"read-write splitting\" cloud-native database that supports strongly consistent read with negligible overhead. Compared with a straightforward read-wait design, PolarDB-SCC improves throughput by up to 4.51× and reduces median latency by up to 3.66× in SysBench's read-write workload. PolarDB-SCC is already commercially available at Alibaba Cloud.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135003304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.14778/3611540.3611607
Yuchen Peng, Ke Chen, Lidan Shou, Dawei Jiang, Gang Chen
Data analysts nowadays are keen to have analytical capabilities involving deep learning (DL). Collaborative queries, which employ relational operations to process structured data and DL models to process unstructured data, provide a powerful facility for DL-based in-database analysis. The classical approach to support collaborative queries in relational databases is to integrate DL models with user-defined functions (UDFs) in a general-purpose language (e.g., C++) to process unstructured data. This approach suffers from suboptimal performance as the opaque UDFs preclude the generation of an optimal query plan. A recent work, DL2SQL, addresses the problem of collaborative query optimization by first converting DL computations into SQL subqueries and then using a classical relational query optimizer to optimize the entire collaborative query. However, the DL2SQL approach compromises usability by requiring data analysts to manually manage DL-related data and tune query performance. To this end, this paper introduces AQUA, an analytical database designed for efficient collaborative query processing. Built on DL2SQL, AQUA automates translations from collaborative queries into SQL queries. To enhance usability, AQUA introduces two techniques: 1) a declarative scheme for DL-related data management, and 2) DL-specific optimizations for collaborative query processing, eliminating the burden of manual data management and performance tuning from the data analysts. We demonstrate the key contributions of AQUA via a web APP that allows the audience to perform collaborative queries on the CIFAR-10 dataset.
{"title":"AQUA: Automatic Collaborative Query Processing in Analytical Database","authors":"Yuchen Peng, Ke Chen, Lidan Shou, Dawei Jiang, Gang Chen","doi":"10.14778/3611540.3611607","DOIUrl":"https://doi.org/10.14778/3611540.3611607","url":null,"abstract":"Data analysts nowadays are keen to have analytical capabilities involving deep learning (DL). Collaborative queries, which employ relational operations to process structured data and DL models to process unstructured data, provide a powerful facility for DL-based in-database analysis. The classical approach to support collaborative queries in relational databases is to integrate DL models with user-defined functions (UDFs) in a general-purpose language (e.g., C++) to process unstructured data. This approach suffers from suboptimal performance as the opaque UDFs preclude the generation of an optimal query plan. A recent work, DL2SQL, addresses the problem of collaborative query optimization by first converting DL computations into SQL subqueries and then using a classical relational query optimizer to optimize the entire collaborative query. However, the DL2SQL approach compromises usability by requiring data analysts to manually manage DL-related data and tune query performance. To this end, this paper introduces AQUA, an analytical database designed for efficient collaborative query processing. Built on DL2SQL, AQUA automates translations from collaborative queries into SQL queries. To enhance usability, AQUA introduces two techniques: 1) a declarative scheme for DL-related data management, and 2) DL-specific optimizations for collaborative query processing, eliminating the burden of manual data management and performance tuning from the data analysts. We demonstrate the key contributions of AQUA via a web APP that allows the audience to perform collaborative queries on the CIFAR-10 dataset.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135003650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the ongoing evolution of the OceanBase database system, it is essential to enhance its adaptability to small-scale enterprises. The OceanBase database system has demonstrated its stability and effectiveness within the Ant Group and other commercial organizations, besides through the TPC-C and TPC-H tests. In this paper, we have designed a stand-alone and distributed integrated architecture named Paetica to address the overhead caused by the distributed components in the stand-alone mode, with respect to the OceanBase system. Paetica enables adaptive configuration of the database that allows OceanBase to support both serial and parallel executions in stand-alone and distributed scenarios, thus providing efficiency and economy. This design has been implemented in version 4.0 of the OceanBase system, and the experiments show that Paetica exhibits notable scalability and outperforms alternative stand-alone or distributed databases. Furthermore, it enables the transition of OceanBase from primarily serving large enterprises to truly catering to small and medium enterprises, by employing a single OceanBase database for the successive stages of enterprise or business development, without the requirement for migration. Our experiments confirm that Paetica has achieved linear scalability with the increasing CPU core number within the stand-alone mode. It also outperforms MySQL and Greenplum in the Sysbench and TPC-H evaluations.
{"title":"OceanBase Paetica: A Hybrid Shared-Nothing/Shared-Everything Database for Supporting Single Machine and Distributed Cluster","authors":"Zhifeng Yang, Quanqing Xu, Shanyan Gao, Chuanhui Yang, Guoping Wang, Yuzhong Zhao, Fanyu Kong, Hao Liu, Wanhong Wang, Jinliang Xiao","doi":"10.14778/3611540.3611560","DOIUrl":"https://doi.org/10.14778/3611540.3611560","url":null,"abstract":"In the ongoing evolution of the OceanBase database system, it is essential to enhance its adaptability to small-scale enterprises. The OceanBase database system has demonstrated its stability and effectiveness within the Ant Group and other commercial organizations, besides through the TPC-C and TPC-H tests. In this paper, we have designed a stand-alone and distributed integrated architecture named Paetica to address the overhead caused by the distributed components in the stand-alone mode, with respect to the OceanBase system. Paetica enables adaptive configuration of the database that allows OceanBase to support both serial and parallel executions in stand-alone and distributed scenarios, thus providing efficiency and economy. This design has been implemented in version 4.0 of the OceanBase system, and the experiments show that Paetica exhibits notable scalability and outperforms alternative stand-alone or distributed databases. Furthermore, it enables the transition of OceanBase from primarily serving large enterprises to truly catering to small and medium enterprises, by employing a single OceanBase database for the successive stages of enterprise or business development, without the requirement for migration. Our experiments confirm that Paetica has achieved linear scalability with the increasing CPU core number within the stand-alone mode. It also outperforms MySQL and Greenplum in the Sysbench and TPC-H evaluations.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135003654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.14778/3611540.3611542
Alex Depoutovitch, Chong Chen, Per-Ake Larson, Jack Ng, Shu Lin, Guanzhu Xiong, Paul Lee, Emad Boctor, Samiao Ren, Lengdong Wu, Yuchen Zhang, Calvin Sun
A single-master database has limited update capacity because a single node handles all updates. A multi-master database potentially has higher update capacity because the load is spread across multiple nodes. However, the need to coordinate updates and ensure durability can generate high network traffic. Reducing network load is particularly important in a cloud environment where the network infrastructure is shared among thousands of tenants. In this paper, we present Taurus MM, a shared-storage multi-master database optimized for cloud environments. It implements two novel algorithms aimed at reducing network traffic plus a number of additional optimizations. The first algorithm is a new type of distributed clock that combines the small size of Lamport clocks with the effective support of distributed snapshots of vector clocks. The second algorithm is a new hybrid page and row locking protocol that significantly reduces the number of lock requests sent over the network. Experimental results on a cluster with up to eight masters demonstrate superior performance compared to Aurora multi-master and CockroachDB.
{"title":"Taurus MM: Bringing Multi-Master to the Cloud","authors":"Alex Depoutovitch, Chong Chen, Per-Ake Larson, Jack Ng, Shu Lin, Guanzhu Xiong, Paul Lee, Emad Boctor, Samiao Ren, Lengdong Wu, Yuchen Zhang, Calvin Sun","doi":"10.14778/3611540.3611542","DOIUrl":"https://doi.org/10.14778/3611540.3611542","url":null,"abstract":"A single-master database has limited update capacity because a single node handles all updates. A multi-master database potentially has higher update capacity because the load is spread across multiple nodes. However, the need to coordinate updates and ensure durability can generate high network traffic. Reducing network load is particularly important in a cloud environment where the network infrastructure is shared among thousands of tenants. In this paper, we present Taurus MM, a shared-storage multi-master database optimized for cloud environments. It implements two novel algorithms aimed at reducing network traffic plus a number of additional optimizations. The first algorithm is a new type of distributed clock that combines the small size of Lamport clocks with the effective support of distributed snapshots of vector clocks. The second algorithm is a new hybrid page and row locking protocol that significantly reduces the number of lock requests sent over the network. Experimental results on a cluster with up to eight masters demonstrate superior performance compared to Aurora multi-master and CockroachDB.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135003928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.14778/3611540.3611610
Nico Schäfer, Damjan Gjurovski, Angjela Davitkova, Sebastian Michel
While existing data management solutions try to keep up with novel data formats and features, a myriad of valuable functionality is often only accessible via programming language libraries. Particularly for machine learning tasks, there is a wealth of pre-trained models and easy-to-use libraries that allow a wide audience to harness state-of-the-art machine learning. We propose the demonstration of a highly modularized data processor for semi-structured data that can be extended by means of plain Python scripts. Next to commonly supported user-defined functions, the deep decomposition allows augmenting the core engine with additional index structures, customized import and export routines, and custom aggregation functions. For several use cases, we detail how user-defined modules can be quickly realized and invite the audience to write and apply custom code, to tailor provided code snippets that we bring along to own preferences to solve data analytics tasks involving sentiment analysis of Twitter tweets.
{"title":"To UDFs and Beyond: Demonstration of a Fully Decomposed Data Processor for General Data Wrangling Tasks","authors":"Nico Schäfer, Damjan Gjurovski, Angjela Davitkova, Sebastian Michel","doi":"10.14778/3611540.3611610","DOIUrl":"https://doi.org/10.14778/3611540.3611610","url":null,"abstract":"While existing data management solutions try to keep up with novel data formats and features, a myriad of valuable functionality is often only accessible via programming language libraries. Particularly for machine learning tasks, there is a wealth of pre-trained models and easy-to-use libraries that allow a wide audience to harness state-of-the-art machine learning. We propose the demonstration of a highly modularized data processor for semi-structured data that can be extended by means of plain Python scripts. Next to commonly supported user-defined functions, the deep decomposition allows augmenting the core engine with additional index structures, customized import and export routines, and custom aggregation functions. For several use cases, we detail how user-defined modules can be quickly realized and invite the audience to write and apply custom code, to tailor provided code snippets that we bring along to own preferences to solve data analytics tasks involving sentiment analysis of Twitter tweets.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134996890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.14778/3611540.3611621
Chen Qian, Shiwei Liang, Zhaoyang Wang, Yin Lou
In many applications the training data do not always contain sufficient information to produce high-quality decision rules for standard (end-to-end) rule mining algorithms, and human experts have to incorporate domain knowledge during rule induction in order to get meaningful results. In this work we present Fanglue, a home-grown system inside Alipay, for interactive decision rule crafting. Fanglue is a distributed in-memory system and is highly responsive when processing large-scale datasets. In addition, Fanglue extends the standard representation of a decision rule by introducing disjunctive clauses. Having disjunctive clauses can improve the coverage and robustness of a decision rule, especially for fraud prevention in Fintech applications.
{"title":"Fanglue: An Interactive System for Decision Rule Crafting","authors":"Chen Qian, Shiwei Liang, Zhaoyang Wang, Yin Lou","doi":"10.14778/3611540.3611621","DOIUrl":"https://doi.org/10.14778/3611540.3611621","url":null,"abstract":"In many applications the training data do not always contain sufficient information to produce high-quality decision rules for standard (end-to-end) rule mining algorithms, and human experts have to incorporate domain knowledge during rule induction in order to get meaningful results. In this work we present Fanglue, a home-grown system inside Alipay, for interactive decision rule crafting. Fanglue is a distributed in-memory system and is highly responsive when processing large-scale datasets. In addition, Fanglue extends the standard representation of a decision rule by introducing disjunctive clauses. Having disjunctive clauses can improve the coverage and robustness of a decision rule, especially for fraud prevention in Fintech applications.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134997928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.14778/3611540.3611566
Zhicheng Pan, Yihang Wang, Yingying Zhang, Sean Bin Yang, Yunyao Cheng, Peng Chen, Chenjuan Guo, Qingsong Wen, Xiduo Tian, Yunliang Dou, Zhiqiang Zhou, Chengcheng Yang, Aoying Zhou, Bin Yang
Predictive autoscaling is a key enabler for optimizing cloud resource allocation in Alibaba Cloud's computing platforms, which dynamically adjust the Elastic Compute Service (ECS) instances based on predicted user demands to ensure Quality of Service (QoS). However, user demands in the cloud are often highly complex, with high uncertainty and scale-sensitive temporal dependencies, thus posing great challenges for accurate prediction of future demands. These in turn make autoscaling challenging---autoscaling needs to properly account for demand uncertainty while maintaining a reasonable trade-off between two contradictory factors, i.e., low instance running costs vs. low QoS violation risks. To address the above challenges, we propose a novel predictive autoscaling framework MagicScaler , consisting of a Multi-scale attentive Gaussian process based predictor and an uncertainty-aware scaler. First, the predictor carefully bridges the best of two successful prediction methodologies---multi-scale attention mechanisms, which are good at capturing complex, multi-scale features, and stochastic process regression, which can quantify prediction uncertainty, thus achieving accurate demand prediction with quantified uncertainty. Second, the scaler takes the quantified future demand uncertainty into a judiciously designed loss function with stochastic constraints, enabling flexible trade-off between running costs and QoS violation risks. Extensive experiments on three clusters of Alibaba Cloud in different Chinese cities demonstrate the effectiveness and efficiency of MagicScaler , which outperforms other commonly adopted scalers, thus justifying our design choices.
{"title":"MagicScaler: Uncertainty-Aware, Predictive Autoscaling","authors":"Zhicheng Pan, Yihang Wang, Yingying Zhang, Sean Bin Yang, Yunyao Cheng, Peng Chen, Chenjuan Guo, Qingsong Wen, Xiduo Tian, Yunliang Dou, Zhiqiang Zhou, Chengcheng Yang, Aoying Zhou, Bin Yang","doi":"10.14778/3611540.3611566","DOIUrl":"https://doi.org/10.14778/3611540.3611566","url":null,"abstract":"Predictive autoscaling is a key enabler for optimizing cloud resource allocation in Alibaba Cloud's computing platforms, which dynamically adjust the Elastic Compute Service (ECS) instances based on predicted user demands to ensure Quality of Service (QoS). However, user demands in the cloud are often highly complex, with high uncertainty and scale-sensitive temporal dependencies, thus posing great challenges for accurate prediction of future demands. These in turn make autoscaling challenging---autoscaling needs to properly account for demand uncertainty while maintaining a reasonable trade-off between two contradictory factors, i.e., low instance running costs vs. low QoS violation risks. To address the above challenges, we propose a novel predictive autoscaling framework MagicScaler , consisting of a Multi-scale attentive Gaussian process based predictor and an uncertainty-aware scaler. First, the predictor carefully bridges the best of two successful prediction methodologies---multi-scale attention mechanisms, which are good at capturing complex, multi-scale features, and stochastic process regression, which can quantify prediction uncertainty, thus achieving accurate demand prediction with quantified uncertainty. Second, the scaler takes the quantified future demand uncertainty into a judiciously designed loss function with stochastic constraints, enabling flexible trade-off between running costs and QoS violation risks. Extensive experiments on three clusters of Alibaba Cloud in different Chinese cities demonstrate the effectiveness and efficiency of MagicScaler , which outperforms other commonly adopted scalers, thus justifying our design choices.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134998134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.14778/3611540.3611638
Yejin Choi
Scale appears to be the winning recipe in today's leaderboards. And yet, extreme-scale neural models are (un)surprisingly brittle and make errors that are often nonsensical and even counterintuitive. In this talk, I will argue for the importance of knowledge, especially commonsense knowledge, as well as inference-time reasoning algorithms, and demonstrate how smaller models developed in academia can still have an edge over larger industry-scale models, if powered with knowledge and/or reasoning algorithms.
{"title":"Common Sense: The Dark Matter of Language and Intelligence (VLDB 2023 Keynote)","authors":"Yejin Choi","doi":"10.14778/3611540.3611638","DOIUrl":"https://doi.org/10.14778/3611540.3611638","url":null,"abstract":"Scale appears to be the winning recipe in today's leaderboards. And yet, extreme-scale neural models are (un)surprisingly brittle and make errors that are often nonsensical and even counterintuitive. In this talk, I will argue for the importance of knowledge, especially commonsense knowledge, as well as inference-time reasoning algorithms, and demonstrate how smaller models developed in academia can still have an edge over larger industry-scale models, if powered with knowledge and/or reasoning algorithms.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135002982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.14778/3611540.3611552
Shafi Ahmad, Dillidorai Arumugam, Srdan Bozovic, Elnata Degefa, Sailesh Duvvuri, Steven Gott, Nitish Gupta, Joachim Hammer, Nivedita Kaluskar, Raghav Kaushik, Rakesh Khanduja, Prasad Mujumdar, Gaurav Malhotra, Pankaj Naik, Nikolas Ogg, Krishna Kumar Parthasarthy, Raghu Ramakrishnan, Vlad Rodriguez, Rahul Sharma, Jakub Szymaszek, Andreas Wolter
Modern data estates are spread across data located on premises, on the edge and in one or more public clouds, spread across various sources like multiple relational databases, file and storage systems, and no-SQL systems, both operational and analytic; this phenomenon is referred to as data sprawl. Data administrators who wish to enforce compliance across the entire organization have to inventory their data, identify what parts of it are sensitive, and govern the sensitive data appropriately --- across the entirety of their sprawling data estate. Today, governance of data is completely siloed; each of the data subsystems has its own (and varied) governance features. Policies applied to sensitive data are applied piece-meal by iterating over all the data sources in a custom language specific to each source. This makes data governance cumbersome, error-prone (because a given policy must be manually enforced across different subsystems, inconsistencies can easily arise), and expensive. This paper presents Microsoft Purview , a service for unified governance of the entire data estate of an organization from a single central pane of glass. The Purview service consists of three parts: (1) a Data Map or metadata catalog that is populated by automated scanning of data sources in the organization, (2) a system to store and manage sensitivity classification of data, and (3) a policy system that enables data security officers to author and implement policies that span the entire organization, e.g., a policy that says, "Non-full-time employees should be denied access to data classified as PII (Personally Identifiable Information.") Purview transforms data governance across a complex data estate by offering the ability to govern centrally and automating data discovery, classification and policy enforcement. While other commercial catalog systems also build a global catalog, Purview is unique in its support for policies. It is also distinguished by covering both structured and unstructured data, thanks to its deep integration with Office 365 and its governance framework; indeed, "Microsoft Purview" represents a new unified offering that combines Office 365 governance and what was formerly a service for governing structured data called "Azure Purview". By integrating with Office 365's Rights Management Service, Purview offers central governance over structured data stored in databases and stores, reports in systems such as Power BI, as well as document data stored in Office 365. The Purview vision is to make the metadata in the Data Map increasingly richer through further automation and curation support and to use this 360 degree view of the data estate to support a wide range of governance policies, ranging from access control to lifecycle management (e.g., retention, deletion, restricting data movement). This paper covers the design and implementation challenges in building the Purview service for Attribute-Based Access Control (ABAC) policies, focusing speci
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