Proc. VLDB Endow.最新文献_第2页

Solver-In-The-Loop Cluster Resource Management for Database-as-a-Service 面向数据库即服务的环内求解器集群资源管理

Proc. VLDB Endow. Pub Date : 2023-09-01 DOI: 10.14778/3625054.3625062

A. König, Yi Shan, Karan Newatia, Luke Marshall, Vivek R. Narasayya

{"title":"Solver-In-The-Loop Cluster Resource Management for Database-as-a-Service","authors":"A. König, Yi Shan, Karan Newatia, Luke Marshall, Vivek R. Narasayya","doi":"10.14778/3625054.3625062","DOIUrl":"https://doi.org/10.14778/3625054.3625062","url":null,"abstract":"In Database-as-a-Service (DBaaS) clusters, resource management is a complex optimization problem that assigns tenants to nodes, subject to various constraints and objectives. Tenants share resources within a node, however, their resource demands can change over time and exhibit high variance. As tenants may accumulate large state, moving them to a different node becomes disruptive, making intelligent placement decisions crucial to avoid service disruption. Placement decisions need to account for dynamic changes in tenant resource demands, different causes of service disruption, and various placement constraints, giving rise to a complex search space. In this paper, we show how to bring combinatorial solvers to bear on this problem, formulating the objective of minimizing service disruption as an optimization problem amenable to fast solutions. We implemented our approach in the Service Fabric cluster manager codebase. Experiments show significant reductions in constraint violations and tenant moves, compared to the previous state-of-the-art, including the unmodified Service Fabric cluster manager, as well as recent research on DBaaS tenant placement.","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139346916","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}

引用次数: 0

Catalyst: Optimizing Cache Management for Large In-memory Key-value Systems 催化剂：优化大型内存键值系统的缓存管理

Proc. VLDB Endow. Pub Date : 2023-09-01 DOI: 10.14778/3625054.3625068

Kefei Wang, Feng Chen

{"title":"Catalyst: Optimizing Cache Management for Large In-memory Key-value Systems","authors":"Kefei Wang, Feng Chen","doi":"10.14778/3625054.3625068","DOIUrl":"https://doi.org/10.14778/3625054.3625068","url":null,"abstract":"In-memory key-value cache systems, such as Memcached and Redis, are essential in today's data centers. A key mission of such cache systems is to identify the most valuable data for caching. To achieve this, the current system design keeps track of each key-value item's access and attempts to make accurate estimation on its temporal locality. All it aims is to achieve the highest cache hit ratio. However, as cache capacity quickly increases, the overhead of managing metadata for a massive amount of small key-value items rises to an unbearable level. Put it simply, the current fine-grained, heavy-cost approach cannot continue to scale. In this paper, we have performed an experimental study on the scalability challenge of the current key-value cache system design and quantitatively analyzed the inherent issues related to the metadata operations for cache management. We further propose a key-value cache management scheme, called Catalyst , based on a highly efficient metadata structure, which allows us to make effective caching decisions in a scalable way. By offloading non-essential metadata operations to GPU, we can further dedicate the limited CPU and memory resources to the main service operations for improved throughput and latency. We have developed a prototype based on Memcached. Our experimental results show that our scheme can significantly enhance the scalability and improve the cache system performance by a factor of up to 4.3.","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139344141","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}

引用次数: 0

AMNES: Accelerating the computation of data correlation using FPGAs AMNES：利用 FPGA 加速数据相关性计算

Proc. VLDB Endow. Pub Date : 2023-09-01 DOI: 10.14778/3625054.3625056

Monica Chiosa, Thomas B. Preußer, Michaela Blott, Gustavo Alonso

引用次数: 0

ZIP: Lazy Imputation during Query Processing ZIP：查询处理过程中的懒惰估算

Proc. VLDB Endow. Pub Date : 2023-09-01 DOI: 10.14778/3617838.3617841

Yiming Lin, S. Mehrotra

引用次数: 0

Flexible Resource Allocation for Relational Database-as-a-Service 关系数据库即服务的灵活资源分配

Proc. VLDB Endow. Pub Date : 2023-09-01 DOI: 10.14778/3625054.3625058

Pankaj Arora, Surajit Chaudhuri, Sudipto Das, Junfeng Dong, Cyril George, Ajay Kalhan, A. König, Willis Lang, Changsong Li, Feng Li, Jiaqi Liu, Lukas M. Maas, Akshay Mata, Ishai Menache, Justin Moeller, Vivek R. Narasayya, Matthaios Olma, Morgan Oslake, Elnaz Rezai, Yi Shan, Manoj Syamala, Shize Xu, Vasileios Zois

{"title":"Flexible Resource Allocation for Relational Database-as-a-Service","authors":"Pankaj Arora, Surajit Chaudhuri, Sudipto Das, Junfeng Dong, Cyril George, Ajay Kalhan, A. König, Willis Lang, Changsong Li, Feng Li, Jiaqi Liu, Lukas M. Maas, Akshay Mata, Ishai Menache, Justin Moeller, Vivek R. Narasayya, Matthaios Olma, Morgan Oslake, Elnaz Rezai, Yi Shan, Manoj Syamala, Shize Xu, Vasileios Zois","doi":"10.14778/3625054.3625058","DOIUrl":"https://doi.org/10.14778/3625054.3625058","url":null,"abstract":"Oversubscription is an essential cost management strategy for cloud database providers, and its importance is magnified by the emerging paradigm of serverless databases. In contrast to general purpose techniques used for oversubscription in hypervisors, operating systems and cluster managers, we develop techniques that leverage our understanding of how DBMSs use resources and how resource allocations impact database performance. Our techniques are designed to flexibly redistribute resources across database tenants at the node and cluster levels with low overhead. We have implemented our techniques in a commercial cloud database service: Azure SQL Database. Experiments using microbenchmarks, industry-standard benchmarks and real-world resource usage traces show that using our approach, it is possible to tightly control the impact on database performance even with a relatively high degree of oversubscription.","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139346437","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}

引用次数: 0

Host Profit Maximization: Leveraging Performance Incentives and User Flexibility 主机利润最大化：利用绩效激励和用户灵活性

Proc. VLDB Endow. Pub Date : 2023-09-01 DOI: 10.14778/3617838.3617843

Xueqin Chang, Xiangyu Ke, Lu Chen, Congcong Ge, Ziheng Wei, Yunjun Gao

{"title":"Host Profit Maximization: Leveraging Performance Incentives and User Flexibility","authors":"Xueqin Chang, Xiangyu Ke, Lu Chen, Congcong Ge, Ziheng Wei, Yunjun Gao","doi":"10.14778/3617838.3617843","DOIUrl":"https://doi.org/10.14778/3617838.3617843","url":null,"abstract":"The social network host has knowledge of the network structure and user characteristics and can earn a profit by providing merchants with viral marketing campaigns. We investigate the problem of host profit maximization by leveraging performance incentives and user flexibility. To incentivize the host's performance, we propose setting a desired influence threshold that would allow the host to receive full payment, with the possibility of a small bonus for exceeding the threshold. Unlike existing works that assume a user's choice is frozen once they are activated, we introduce the Dynamic State Switching model to capture \"comparative shopping\" behavior from an economic perspective, in which users have the flexibilities to change their minds about which product to adopt based on the accumulated influence and propaganda strength of each product. In addition, the incentivized cost of a user serving as an influence source is treated as a negative part of the host's profit. The host profit maximization problem is NP-hard, submodular, and non-monotone. To address this challenge, we propose an efficient greedy algorithm and devise a scalable version with an approximation guarantee to select the seed sets. As a side contribution, we develop two seed allocation algorithms to balance the distribution of adoptions among merchants with small profit sacrifice. Through extensive experiments on four real-world social networks, we demonstrate that our methods are effective and scalable.","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139344906","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}

引用次数: 0

Efficient Dynamic Weighted Set Sampling and Its Extension 高效动态加权集合采样及其扩展

Proc. VLDB Endow. Pub Date : 2023-09-01 DOI: 10.14778/3617838.3617840

Fangyuan Zhang, Mengxu Jiang, Sibo Wang

{"title":"Efficient Dynamic Weighted Set Sampling and Its Extension","authors":"Fangyuan Zhang, Mengxu Jiang, Sibo Wang","doi":"10.14778/3617838.3617840","DOIUrl":"https://doi.org/10.14778/3617838.3617840","url":null,"abstract":"Given a weighted set S of n elements, weighted set sampling (WSS) samples an element in S so that each element a i ; is sampled with a probability proportional to its weight w ( a i ). The classic alias method pre-processes an index in O ( n ) time with O ( n ) space and handles WSS with O (1) time. Yet, the alias method does not support dynamic updates. By minor modifications of existing dynamic WSS schemes, it is possible to achieve an expected O (1) update time and draw t independent samples in expected O ( t ) time with linear space, which is theoretically optimal. But such a method is impractical and even slower than a binary search tree-based solution. How to support both efficient sampling and updates in practice is still challenging. Motivated by this, we design BUS , an efficient scheme that handles an update in O (1) amortized time and draws t independent samples in O (log n + t) time with linear space. A natural extension of WSS is the weighted independent range sampling (WIRS) , where each element in S is a data point from R. Given an arbitrary range Q = [ℓ, r ] at query time, WIRS aims to do weighted set sampling on the set S Q of data points falling into range Q. We show that by integrating the theoretically optimal dynamic WSS scheme mentioned above, it can handle an update in O (log n ) time and can draw t independent samples for WIRS in O (log n + t ) time, the same as the state-of-the-art static algorithm. Again, such a solution by integrating the optimal dynamic WSS scheme is still impractical to handle WIRS queries. We further propose WIRS-BUS to integrate BUS to handle WIRS queries, which handles each update in O (log n ) time and draws t independent samples in O (log 2 n + t ) time with linear space. Extensive experiments show that our BUS and WIRS-BUS are efficient for both sampling and updates.","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139343953","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}

引用次数: 0

Optimizing Data Pipelines for Machine Learning in Feature Stores 在特征库中优化机器学习的数据管道

Proc. VLDB Endow. Pub Date : 2023-09-01 DOI: 10.14778/3625054.3625060

Rui Liu, Kwanghyun Park, Fotis Psallidas, Xiaoyong Zhu, Jinghui Mo, Rathijit Sen, Matteo Interlandi, Konstantinos Karanasos, Yuanyuan Tian, Jesús Camacho-Rodríguez

引用次数: 0

FedGTA: Topology-aware Averaging for Federated Graph Learning FedGTA：拓扑感知平均法用于联盟图学习

Proc. VLDB Endow. Pub Date : 2023-09-01 DOI: 10.14778/3617838.3617842

Xunkai Li, Zhengyu Wu, Wentao Zhang, Yinlin Zhu, Ronghua Li, Guoren Wang

{"title":"FedGTA: Topology-aware Averaging for Federated Graph Learning","authors":"Xunkai Li, Zhengyu Wu, Wentao Zhang, Yinlin Zhu, Ronghua Li, Guoren Wang","doi":"10.14778/3617838.3617842","DOIUrl":"https://doi.org/10.14778/3617838.3617842","url":null,"abstract":"Federated Graph Learning (FGL) is a distributed machine learning paradigm that enables collaborative training on large-scale subgraphs across multiple local systems. Existing FGL studies fall into two categories: (i) FGL Optimization, which improves multi-client training in existing machine learning models; (ii) FGL Model, which enhances performance with complex local models and multi-client interactions. However, most FGL optimization strategies are designed specifically for the computer vision domain and ignore graph structure, presenting dissatisfied performance and slow convergence. Meanwhile, complex local model architectures in FGL Models studies lack scalability for handling large-scale subgraphs and have deployment limitations. To address these issues, we propose Federated Graph Topology-aware Aggregation (FedGTA), a personalized optimization strategy that optimizes through topology-aware local smoothing confidence and mixed neighbor features. During experiments, we deploy FedGTA in 12 multi-scale real-world datasets with the Louvain and Metis split. This allows us to evaluate the performance and robustness of FedGTA across a range of scenarios. Extensive experiments demonstrate that FedGTA achieves state-of-the-art performance while exhibiting high scalability and efficiency. The experiment includes ogbn-papers100M, the most representative large-scale graph database so that we can verify the applicability of our method to large-scale graph learning. To the best of our knowledge, our study is the first to bridge large-scale graph learning with FGL using this optimization strategy, contributing to the development of efficient and scalable FGL methods.","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139346945","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}

引用次数: 0

DecLog: Decentralized Logging in Non-Volatile Memory for Time Series Database Systems DecLog：时间序列数据库系统非易失性内存中的分散式日志记录

Proc. VLDB Endow. Pub Date : 2023-09-01 DOI: 10.14778/3617838.3617839

Bolong Zheng, Yongyong Gao, J. Wan, Lingsen Yan, Long Hu, Bo Liu, Yunjun Gao, Xiaofang Zhou, Christian S. Jensen

{"title":"DecLog: Decentralized Logging in Non-Volatile Memory for Time Series Database Systems","authors":"Bolong Zheng, Yongyong Gao, J. Wan, Lingsen Yan, Long Hu, Bo Liu, Yunjun Gao, Xiaofang Zhou, Christian S. Jensen","doi":"10.14778/3617838.3617839","DOIUrl":"https://doi.org/10.14778/3617838.3617839","url":null,"abstract":"Growing demands for the efficient processing of extreme-scale time series workloads call for more capable time series database management systems (TSDBMS). Specifically, to maintain consistency and durability of transaction processing, systems employ write-ahead logging (WAL) whereby transactions are committed only after the related log entries are flushed to disk. However, when faced with massive I/O, this becomes a throughput bottleneck. Recent advances in byte-addressable Non-Volatile Memory (NVM) provide opportunities to improve logging performance by persisting logs to NVM instead. Existing studies typically track complex transaction dependencies and use barrier instructions of NVM to ensure log ordering. In contrast, few studies consider the heavy-tailed characteristics of time series workloads, where most transactions are independent of each other. We propose DecLog, a decentralized NVM-based logging system that enables concurrent logging of TSDBMS transactions. Specifically, we propose data-driven log sequence numbering and relaxed ordering strategies to track transaction dependencies and resolve serialization issues. We also propose a parallel logging method to persist logs to NVM after being compressed and aligned. An experimental study on the YCSB-TS benchmark offers insight into the performance properties of DecLog, showing that it improves throughput by up to 4.6× while offering lower recovery time in comparison to the open source TSDBMS Beringei.","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139346928","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}

引用次数: 0