Hang An;Fang Wang;Dan Feng;Xiaomin Zou;Zefeng Liu;Jianshun Zhang
{"title":"A Scalable and Write-Optimized Disaggregated B+-Tree With Adaptive Cache Assistance","authors":"Hang An;Fang Wang;Dan Feng;Xiaomin Zou;Zefeng Liu;Jianshun Zhang","doi":"10.1109/TCC.2024.3437472","DOIUrl":null,"url":null,"abstract":"Disaggregated memory (DM) architecture separates CPU and DRAM into computing/memory resource pools and interconnects them with high-speed networks. Storage systems on DM locate data by distributed index. However, existing distributed indexes either suffer from prohibitive synchronization overhead of write operation or sacrifice the performance of read operation, resulting in low throughput, high tail latency, and challenging trade-off. In this paper, we present Marlin+, a scalable and optimized B+-tree on DM. Marlin+ provides atomic granularity synchronization between write operations via three strategies: 1) a concurrent algorithm that is friendly to IDU operations (Insert, Delete, and Update), enabling different clients to concurrently operate on the same leaf node, 2) shared-exclusive leaf node lock, effectively preventing conflicts between index structure modification operation (SMO) and IDU operations, and 3) critical path compression of write to reduce latency of write operation. Moreover, Marlin+ proposes an adaptive remote address cache to accelerate the access of hot data. Compared to the state-of-the-art schemes based on DM, Marlin achieves 2.21× higher throughput and 83.4% lower P99 latency under YCSB hybrid workloads. Compared to Marlin, Marlin+ improves the throughput by up to 1.58× and reduces the P50 latency by up to 50.5% under YCSB read-intensive workloads.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 4","pages":"1074-1087"},"PeriodicalIF":5.3000,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Cloud Computing","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10621579/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Disaggregated memory (DM) architecture separates CPU and DRAM into computing/memory resource pools and interconnects them with high-speed networks. Storage systems on DM locate data by distributed index. However, existing distributed indexes either suffer from prohibitive synchronization overhead of write operation or sacrifice the performance of read operation, resulting in low throughput, high tail latency, and challenging trade-off. In this paper, we present Marlin+, a scalable and optimized B+-tree on DM. Marlin+ provides atomic granularity synchronization between write operations via three strategies: 1) a concurrent algorithm that is friendly to IDU operations (Insert, Delete, and Update), enabling different clients to concurrently operate on the same leaf node, 2) shared-exclusive leaf node lock, effectively preventing conflicts between index structure modification operation (SMO) and IDU operations, and 3) critical path compression of write to reduce latency of write operation. Moreover, Marlin+ proposes an adaptive remote address cache to accelerate the access of hot data. Compared to the state-of-the-art schemes based on DM, Marlin achieves 2.21× higher throughput and 83.4% lower P99 latency under YCSB hybrid workloads. Compared to Marlin, Marlin+ improves the throughput by up to 1.58× and reduces the P50 latency by up to 50.5% under YCSB read-intensive workloads.
期刊介绍:
The IEEE Transactions on Cloud Computing (TCC) is dedicated to the multidisciplinary field of cloud computing. It is committed to the publication of articles that present innovative research ideas, application results, and case studies in cloud computing, focusing on key technical issues related to theory, algorithms, systems, applications, and performance.