Reducing the byte miss ratio (BMR) in the Content Delivery Network (CDN) caches can help providers save on the cost of paying for traffic. When evicting objects or files of different sizes in the caches of CDNs, it is no longer sufficient to pursue an optimal object miss ratio (OMR) by approximating Belady to ensure an optimal BMR. Our experimental observations suggest that there are multiple request sequence windows. In these windows, a replacement policy prioritizes the eviction of objects with large sizes and ultimately evicts the object with the longest reuse distance, lowering the BMR without increasing the OMR. To accurately capture those windows, we monitor the changes in OMR and BMR using a deep reinforcement learning (RL) model and then implement a BMR-friendly replacement algorithm in these windows. Based on this policy, we propose a Belady and Size Eviction (LRU-BaSE) algorithm that reduces BMR while maintaining OMR. To make LRU-BaSE efficient and practical, we address the feedback delay problem of RL with a two-pronged approach. On the one hand, we shorten the LRU-base decision region based on the observation that the rear section of the cache queue contains most of the eviction candidates. On the other hand, the request distribution on CDNs makes it feasible to divide the learning region into multiple sub-regions that are each learned with reduced time and increased accuracy. In real CDN systems, LRU-BaSE outperforms LRU by reducing “backing to OS” traffic and access latency by 30.05% and 17.07%, respectively, on average. In simulator tests, LRU-BaSE outperforms state-of-the-art cache replacement policies. On average, LRU-BaSE's BMR is 0.63% and 0.33% less than that of Belady and Practical Flow-based Offline Optimal (PFOO), respectively. In addition, compared to Learning Relaxed Belady (LRB), LRU-BaSE can yield relatively stable performance when facing workload drift.
{"title":"Beyond Belady to Attain a Seemingly Unattainable Byte Miss Ratio for Content Delivery Networks","authors":"Peng Wang;Hong Jiang;Yu Liu;Zhelong Zhao;Ke Zhou;Zhihai Huang","doi":"10.1109/TPDS.2024.3452096","DOIUrl":"https://doi.org/10.1109/TPDS.2024.3452096","url":null,"abstract":"Reducing the byte miss ratio (BMR) in the Content Delivery Network (CDN) caches can help providers save on the cost of paying for traffic. When evicting objects or files of different sizes in the caches of CDNs, it is no longer sufficient to pursue an optimal object miss ratio (OMR) by approximating Belady to ensure an optimal BMR. Our experimental observations suggest that there are multiple request sequence windows. In these windows, a replacement policy prioritizes the eviction of objects with large sizes and ultimately evicts the object with the longest reuse distance, lowering the BMR without increasing the OMR. To accurately capture those windows, we monitor the changes in OMR and BMR using a deep reinforcement learning (RL) model and then implement a BMR-friendly replacement algorithm in these windows. Based on this policy, we propose a Belady and Size Eviction (LRU-BaSE) algorithm that reduces BMR while maintaining OMR. To make LRU-BaSE efficient and practical, we address the feedback delay problem of RL with a two-pronged approach. On the one hand, we shorten the LRU-base decision region based on the observation that the rear section of the cache queue contains most of the eviction candidates. On the other hand, the request distribution on CDNs makes it feasible to divide the learning region into multiple sub-regions that are each learned with reduced time and increased accuracy. In real CDN systems, LRU-BaSE outperforms LRU by reducing “backing to OS” traffic and access latency by 30.05% and 17.07%, respectively, on average. In simulator tests, LRU-BaSE outperforms state-of-the-art cache replacement policies. On average, LRU-BaSE's BMR is 0.63% and 0.33% less than that of Belady and Practical Flow-based Offline Optimal (PFOO), respectively. In addition, compared to Learning Relaxed Belady (LRB), LRU-BaSE can yield relatively stable performance when facing workload drift.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"35 11","pages":"1949-1963"},"PeriodicalIF":5.6,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142160006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Top-K sparsification-based compression framework is extensively explored for reducing communication costs in distributed learning. However, we identified several issues with existing Top-K sparsification-based compression methods: ( i ) The limited compressibility of the Top-K parameter's indexes critically restricts the overall communication compression ratio; ( ii ) Several time-consuming compression operations significantly offset the benefits of communication compression; ( iii ) The use of error feedback techniques to maintain model quality results in a high memory footprint consumption. To solve these issues, we propose BIRD, a lightweight tensor-wise Bi-Random sampling strategy with an expectation invariance property. Specifically, BIRD applies a tensor-wise index sharing mechanism that reduces the index proportion by allowing multiple tensor elements to share a single index, thus improving the overall compression ratio. Additionally, BIRD replaces the time-consuming Top-K sorting with a faster Bi-Random sampling strategy based on the aforementioned index sharing mechanism, significantly reducing compression overheads; Moreover, BIRD establishes an expectation invariance property into the Bi-Random sampling to ensure an approximate unbiased representation for the $L_1$ -norm of the sampled tensors, effectively maintaining the model quality without incurring extra memory costs. We further optimize BIRD to BIRD+ by introducing the uniform distribution-based sampling and Gamma correction on the tensor-wise sampling process, achieving a more flexibly adjustment of the sparsity with better convergence performance. Experimental evaluations across multiple conventional distributed learning tasks demonstrate that compared to state-of-the-art approaches, BIRD+ achieves higher communication compression ratios up to 36.2 $times$ and higher computation throughput up to 149.6 $times$ while maintaining the model quality without incurring extra memory costs.
{"title":"BIRD+: Design of a Lightweight Communication Compressor for Resource-Constrained Distribution Learning Platforms","authors":"Donglei Wu;Weihao Yang;Xiangyu Zou;Hao Feng;Dingwen Tao;Shiyi Li;Wen Xia;Binxing Fang","doi":"10.1109/TPDS.2024.3447221","DOIUrl":"10.1109/TPDS.2024.3447221","url":null,"abstract":"The Top-K sparsification-based compression framework is extensively explored for reducing communication costs in distributed learning. However, we identified several issues with existing Top-K sparsification-based compression methods: (\u0000<i>i</i>\u0000) The limited compressibility of the Top-K parameter's indexes critically restricts the overall communication compression ratio; (\u0000<i>ii</i>\u0000) Several time-consuming compression operations significantly offset the benefits of communication compression; (\u0000<i>iii</i>\u0000) The use of error feedback techniques to maintain model quality results in a high memory footprint consumption. To solve these issues, we propose BIRD, a lightweight tensor-wise \u0000<i>Bi-Random sampling</i>\u0000 strategy with an expectation invariance property. Specifically, BIRD applies a tensor-wise \u0000<i>index sharing</i>\u0000 mechanism that reduces the index proportion by allowing multiple tensor elements to share a single index, thus improving the overall compression ratio. Additionally, BIRD replaces the time-consuming Top-K sorting with a faster \u0000<i>Bi-Random sampling</i>\u0000 strategy based on the aforementioned \u0000<i>index sharing</i>\u0000 mechanism, significantly reducing compression overheads; Moreover, BIRD establishes an \u0000<i>expectation invariance</i>\u0000 property into the \u0000<i>Bi-Random sampling</i>\u0000 to ensure an approximate unbiased representation for the \u0000<inline-formula><tex-math>$L_1$</tex-math></inline-formula>\u0000-norm of the sampled tensors, effectively maintaining the model quality without incurring extra memory costs. We further optimize BIRD to BIRD+ by introducing the uniform distribution-based sampling and Gamma correction on the tensor-wise sampling process, achieving a more flexibly adjustment of the sparsity with better convergence performance. Experimental evaluations across multiple conventional distributed learning tasks demonstrate that compared to state-of-the-art approaches, BIRD+ achieves higher communication compression ratios up to 36.2\u0000<inline-formula><tex-math>$times$</tex-math></inline-formula>\u0000 and higher computation throughput up to 149.6\u0000<inline-formula><tex-math>$times$</tex-math></inline-formula>\u0000 while maintaining the model quality without incurring extra memory costs.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"35 11","pages":"2193-2207"},"PeriodicalIF":5.6,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-08-20DOI: 10.1109/TPDS.2024.3446188
Francesco De Pellegrini;Vaibhav Kumar Gupta;Rachid El Azouzi;Serigne Gueye;Cedric Richier;Jeremie Leguay
The average coflow completion time (CCT) is the standard performance metric in coflow scheduling. However, standard CCT minimization may introduce unfairness between the data transfer phase of different computing jobs. Thus, while progress guarantees have been introduced in the literature to mitigate this fairness issue, the trade-off between fairness and efficiency of data transfer is hard to control. This paper introduces a fairness framework for coflow scheduling based on the concept of slowdown, i.e., the performance loss of a coflow compared to isolation. By controlling the slowdown it is possible to enforce a target coflow progress while minimizing the average CCT. In the proposed framework, the minimum slowdown for a batch of coflows can be determined in polynomial time. By showing the equivalence with Gaussian elimination, slowdown constraints are introduced into primal-dual iterations of the CoFair algorithm. The algorithm extends the class of the $sigma$ -order schedulers to solve the fair coflow scheduling problem in polynomial time. It provides a 4-approximation of the average CCT w.r.t. an optimal scheduler. Extensive numerical results demonstrate that this approach can trade off average CCT for slowdown more efficiently than existing state of the art schedulers.
{"title":"Fair Coflow Scheduling via Controlled Slowdown","authors":"Francesco De Pellegrini;Vaibhav Kumar Gupta;Rachid El Azouzi;Serigne Gueye;Cedric Richier;Jeremie Leguay","doi":"10.1109/TPDS.2024.3446188","DOIUrl":"https://doi.org/10.1109/TPDS.2024.3446188","url":null,"abstract":"The average coflow completion time (CCT) is the standard performance metric in coflow scheduling. However, standard CCT minimization may introduce unfairness between the data transfer phase of different computing jobs. Thus, while progress guarantees have been introduced in the literature to mitigate this fairness issue, the trade-off between fairness and efficiency of data transfer is hard to control. This paper introduces a fairness framework for coflow scheduling based on the concept of slowdown, i.e., the performance loss of a coflow compared to isolation. By controlling the slowdown it is possible to enforce a target coflow progress while minimizing the average CCT. In the proposed framework, the minimum slowdown for a batch of coflows can be determined in polynomial time. By showing the equivalence with Gaussian elimination, slowdown constraints are introduced into primal-dual iterations of the CoFair algorithm. The algorithm extends the class of the \u0000<inline-formula><tex-math>$sigma$</tex-math></inline-formula>\u0000-order schedulers to solve the fair coflow scheduling problem in polynomial time. It provides a 4-approximation of the average CCT w.r.t. an optimal scheduler. Extensive numerical results demonstrate that this approach can trade off average CCT for slowdown more efficiently than existing state of the art schedulers.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"35 12","pages":"2347-2360"},"PeriodicalIF":5.6,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142438663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-08-19DOI: 10.1109/TPDS.2024.3439709
Lan Zhang;Anran Li;Hongyi Peng;Feng Han;Fan Huang;Xiang-Yang Li
Federated learning (FL) enables distributed participants to collaboratively train a machine learning model without accessing to their local data. In FL systems, the selection of training samples has a significant impact on model performances, e.g., selecting participants whose datasets have low-quality samples, features would result in low accuracy, unstable models. In this work, we aim to solve the problem that selects a collection of high-quality training samples for a given FL task under a monetary budget. We propose a holistic design to efficiently select high-quality samples while preserve the privacy of participants’ local data, the server’s label set. We propose an efficient hierarchical sample selection mechanism to select relevant clients, their samples before training for horizontal federated learning (HFL). It uses the determinantal point process (DPP) to select both the statistical homogenous, content diverse clients, samples. Besides, we propose a private set intersection (PSI) based scheme to filter relevant features for the target VFL task. Finally, during training, an erroneous-aware importance based selection is proposed to dynamically select important clients, samples to accelerate model convergence. We verify the merits of our proposed solution with extensive experiments on a real AIoT system with 50 clients. The experimental results validate that our solution achieves accurate, efficient selection of high-quality data, consequently an FL model with a faster convergence speed, higher accuracy.
{"title":"Privacy-Preserving Data Selection for Horizontal and Vertical Federated Learning","authors":"Lan Zhang;Anran Li;Hongyi Peng;Feng Han;Fan Huang;Xiang-Yang Li","doi":"10.1109/TPDS.2024.3439709","DOIUrl":"10.1109/TPDS.2024.3439709","url":null,"abstract":"Federated learning (FL) enables distributed participants to collaboratively train a machine learning model without accessing to their local data. In FL systems, the selection of training samples has a significant impact on model performances, e.g., selecting participants whose datasets have low-quality samples, features would result in low accuracy, unstable models. In this work, we aim to solve the problem that selects a collection of high-quality training samples for a given FL task under a monetary budget. We propose a holistic design to efficiently select high-quality samples while preserve the privacy of participants’ local data, the server’s label set. We propose an efficient hierarchical sample selection mechanism to select relevant clients, their samples before training for horizontal federated learning (HFL). It uses the determinantal point process (DPP) to select both the statistical homogenous, content diverse clients, samples. Besides, we propose a private set intersection (PSI) based scheme to filter relevant features for the target VFL task. Finally, during training, an erroneous-aware importance based selection is proposed to dynamically select important clients, samples to accelerate model convergence. We verify the merits of our proposed solution with extensive experiments on a real AIoT system with 50 clients. The experimental results validate that our solution achieves accurate, efficient selection of high-quality data, consequently an FL model with a faster convergence speed, higher accuracy.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"35 11","pages":"2054-2068"},"PeriodicalIF":5.6,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We introduce logical synchrony, a framework that allows distributed computing to be coordinated as tightly as in synchronous systems without the distribution of a global clock or any reference to universal time. We develop a model of events called a logical synchrony network, in which nodes correspond to processors and every node has an associated local clock which generates the events. We construct a measure of logical latency and develop its properties. A further model, called a multiclock network, is then analyzed and shown to be a refinement of the logical synchrony network. We present the bittide mechanism as an instantiation of multiclock networks, and discuss the clock control mechanism that ensures that buffers do not overflow or underflow. Finally we give conditions under which a logical synchrony network has an equivalent synchronous realization.
{"title":"Logical Synchrony and the Bittide Mechanism","authors":"Sanjay Lall;Călin Caşcaval;Martin Izzard;Tammo Spalink","doi":"10.1109/TPDS.2024.3444739","DOIUrl":"https://doi.org/10.1109/TPDS.2024.3444739","url":null,"abstract":"We introduce logical synchrony, a framework that allows distributed computing to be coordinated as tightly as in synchronous systems without the distribution of a global clock or any reference to universal time. We develop a model of events called a logical synchrony network, in which nodes correspond to processors and every node has an associated local clock which generates the events. We construct a measure of logical latency and develop its properties. A further model, called a multiclock network, is then analyzed and shown to be a refinement of the logical synchrony network. We present the bittide mechanism as an instantiation of multiclock networks, and discuss the clock control mechanism that ensures that buffers do not overflow or underflow. Finally we give conditions under which a logical synchrony network has an equivalent synchronous realization.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"35 11","pages":"1936-1948"},"PeriodicalIF":5.6,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10638228","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142159918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-16DOI: 10.1109/TPDS.2024.3445283
Fatemeh Keshavarz-Kohjerdi
In the paired many-to-many $k$ -disjoint path cover ( $k$ -DPC) problem, given a set of $k$ pairs of vertices $(s_{i},t_{i})$ , $1leqslant ileqslant k$ , of a graph $G$ we want to find $k$ simple vertex-disjoint paths whose end-vertices are these $k$ pairs, such that each vertex of $G$ is covered by a path. This problem is a well-known problem in parallel processing and is a generalization of the well-known Hamiltonian $(s,t)$ -path problem, which is equal to 1-DPC. In this paper, we consider the paired many-to-many 2-disjoint path cover problem (2-DPC) in meshes (rectangular grids). We give the necessary conditions for existence of such covers, and present a linear-time algorithm to compute them. Although the paired many-to-many $k$ -disjoint path cover problem is well-known in parallel processing, our motivation to study this problem is its application in solving the Hamiltonian path problem in solid grid graphs. We consider the case where the pairs of vertices are on the outer face of the graph.
{"title":"Paired Many-to-Many 2-Disjoint Path Covers in Meshes","authors":"Fatemeh Keshavarz-Kohjerdi","doi":"10.1109/TPDS.2024.3445283","DOIUrl":"https://doi.org/10.1109/TPDS.2024.3445283","url":null,"abstract":"In the paired many-to-many \u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000-disjoint path cover (\u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000-DPC) problem, given a set of \u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000 pairs of vertices \u0000<inline-formula><tex-math>$(s_{i},t_{i})$</tex-math></inline-formula>\u0000, \u0000<inline-formula><tex-math>$1leqslant ileqslant k$</tex-math></inline-formula>\u0000, of a graph \u0000<inline-formula><tex-math>$G$</tex-math></inline-formula>\u0000 we want to find \u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000 simple vertex-disjoint paths whose end-vertices are these \u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000 pairs, such that each vertex of \u0000<inline-formula><tex-math>$G$</tex-math></inline-formula>\u0000 is covered by a path. This problem is a well-known problem in parallel processing and is a generalization of the well-known Hamiltonian \u0000<inline-formula><tex-math>$(s,t)$</tex-math></inline-formula>\u0000-path problem, which is equal to 1-DPC. In this paper, we consider the paired many-to-many 2-disjoint path cover problem (2-DPC) in meshes (rectangular grids). We give the necessary conditions for existence of such covers, and present a linear-time algorithm to compute them. Although the paired many-to-many \u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000-disjoint path cover problem is well-known in parallel processing, our motivation to study this problem is its application in solving the Hamiltonian path problem in solid grid graphs. We consider the case where the pairs of vertices are on the outer face of the graph.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"35 10","pages":"1854-1866"},"PeriodicalIF":5.6,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-08-14DOI: 10.1109/TPDS.2024.3443424
Mi Zhang;Qihan Kang;Patrick P. C. Lee
Consensus protocols like Paxos and Raft provide data consistency and fault tolerance for distributed services. Log replication in these protocols can be supported by erasure coding, which incurs lower redundancy than full-copy replication and significantly saves network and storage costs for overall performance improvements. However, existing consensus protocols with erasure coding cannot achieve the minimum network and storage costs during log replication. We propose FlexRaft, which dynamically varies the coding scheme used in Raft based on the server status to always achieve the theoretically minimum redundancy ratio, while maintaining the same liveness as in Raft. To address the issue of an inconsistent coding scheme between the leader and its followers, we specify the prerequisite of overwriting a log entry and also allow the leader and its followers to exactly track the coding scheme being used. We further extend FlexRaft into FlexRaft+, which provides a different storage layout to vary the coding scheme through a novel technique called re-encoding-free replication, so as to enable fast server recovery. We prove that both FlexRaft and FlexRaft+ maintain Raft safety. We implement a prototype of FlexRaft and FlexRaft+, atop which we build a distributed key-value store to show its efficacy. Experiments on Alibaba Cloud show that FlexRaft achieves the theoretically minimum network and storage costs in practice, and reduces the commit latency by 44.51% and 19.37% compared with state-of-the-art CRaft and HRaft, respectively. FlexRaft+ further reduces the commit latency when the coding scheme is being varied and improves the server recovery performance.
{"title":"FlexRaft: Exploiting Flexible Erasure Coding for Minimum-Cost Consensus and Fast Recovery","authors":"Mi Zhang;Qihan Kang;Patrick P. C. Lee","doi":"10.1109/TPDS.2024.3443424","DOIUrl":"https://doi.org/10.1109/TPDS.2024.3443424","url":null,"abstract":"Consensus protocols like Paxos and Raft provide data consistency and fault tolerance for distributed services. Log replication in these protocols can be supported by erasure coding, which incurs lower redundancy than full-copy replication and significantly saves network and storage costs for overall performance improvements. However, existing consensus protocols with erasure coding cannot achieve the minimum network and storage costs during log replication. We propose FlexRaft, which dynamically varies the coding scheme used in Raft based on the server status to always achieve the theoretically minimum redundancy ratio, while maintaining the same liveness as in Raft. To address the issue of an inconsistent coding scheme between the leader and its followers, we specify the prerequisite of overwriting a log entry and also allow the leader and its followers to exactly track the coding scheme being used. We further extend FlexRaft into FlexRaft+, which provides a different storage layout to vary the coding scheme through a novel technique called re-encoding-free replication, so as to enable fast server recovery. We prove that both FlexRaft and FlexRaft+ maintain Raft safety. We implement a prototype of FlexRaft and FlexRaft+, atop which we build a distributed key-value store to show its efficacy. Experiments on Alibaba Cloud show that FlexRaft achieves the theoretically minimum network and storage costs in practice, and reduces the commit latency by 44.51% and 19.37% compared with state-of-the-art CRaft and HRaft, respectively. FlexRaft+ further reduces the commit latency when the coding scheme is being varied and improves the server recovery performance.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"35 10","pages":"1826-1840"},"PeriodicalIF":5.6,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-08-14DOI: 10.1109/TPDS.2024.3443083
Peixuan Li;Ping Xie;Qiang Cao
RAID (Redundant Array of Independent Disks) has been widely used to enhance read and write performance of existing storage systems. Existing software RAID do not fully utilize write performance of Serial interface SSDs (Solid State Drive). The most popular software RAID currently is Linux Multiple-Disks (MD), and the latest software RAID is StRAID. We observe that both of these software RAID methods lead to thread contention in multi-threaded mode, especially when applied to Serial interface SSDs. Multiple threads writing to same address can limit write performance. In this paper, we propose a stripe-queued and stripe-threaded merging I/O strategy. First, SSRAID segregates write requests across different stripes using a set of stripe-queues and stripe-threads to prevent interference between them. As a result, write thread contention in SSRAID is eliminated, allowing stripe-threads to maintain the highest efficiency of parallelism. Secondly, SSRAID can merge write requests from the same stripe-queue multiple times through stripe-thread, effectively reducing the number of additional write I/Os. Finally, SSRAID presents a stage buffer based on data merging. During partial stripe-write, write-induced read I/Os on the SSD are transformed into direct access to the stage buffer, effectively reducing write-induced read I/Os. Compared to StRAID, SSRAID improves average sequential write throughput by 86% and reduces average sequential write latency by 61% in the optimal case.
{"title":"SSRAID: A Stripe-Queued and Stripe-Threaded Merging I/O Strategy to Improve Write Performance of Serial Interface SSD RAID","authors":"Peixuan Li;Ping Xie;Qiang Cao","doi":"10.1109/TPDS.2024.3443083","DOIUrl":"https://doi.org/10.1109/TPDS.2024.3443083","url":null,"abstract":"RAID (Redundant Array of Independent Disks) has been widely used to enhance read and write performance of existing storage systems. Existing software RAID do not fully utilize write performance of Serial interface SSDs (Solid State Drive). The most popular software RAID currently is Linux Multiple-Disks (MD), and the latest software RAID is StRAID. We observe that both of these software RAID methods lead to thread contention in multi-threaded mode, especially when applied to Serial interface SSDs. Multiple threads writing to same address can limit write performance. In this paper, we propose a stripe-queued and stripe-threaded merging I/O strategy. First, SSRAID segregates write requests across different stripes using a set of stripe-queues and stripe-threads to prevent interference between them. As a result, write thread contention in SSRAID is eliminated, allowing stripe-threads to maintain the highest efficiency of parallelism. Secondly, SSRAID can merge write requests from the same stripe-queue multiple times through stripe-thread, effectively reducing the number of additional write I/Os. Finally, SSRAID presents a stage buffer based on data merging. During partial stripe-write, write-induced read I/Os on the SSD are transformed into direct access to the stage buffer, effectively reducing write-induced read I/Os. Compared to StRAID, SSRAID improves average sequential write throughput by 86% and reduces average sequential write latency by 61% in the optimal case.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"35 10","pages":"1841-1853"},"PeriodicalIF":5.6,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-08-14DOI: 10.1109/TPDS.2024.3443255
Jiangfei Duan;Xiuhong Li;Ping Xu;Xingcheng Zhang;Shengen Yan;Yun Liang;Dahua Lin
DNN models are becoming increasingly larger to achieve unprecedented accuracy, and the accompanying increased computation and memory requirements necessitate the employment of massive clusters and elaborate parallelization strategies to accelerate DNN training. In order to better optimize the performance and analyze the cost, it is indispensable to model the training throughput of distributed DNN training. However, complex parallelization strategies and the resulting complex runtime behaviors make it challenging to construct an accurate performance model. In this article, we present Proteus, the first standalone simulator to model the performance of complex parallelization strategies through simulation execution. Proteus first models complex parallelization strategies with a unified representation named Strategy Tree . Then, it compiles the strategy tree into a distributed execution graph and simulates the complex runtime behaviors, comp-comm overlap and bandwidth sharing , with a H ierarchical T opo- A ware E xecutor ( HTAE ). We finally evaluate Proteus across a wide variety of DNNs on three hardware configurations. Experimental results show that Proteus achieves 3.0% average prediction error and preserves order for training throughput of various parallelization strategies. Compared to state-of-the-art approaches, Proteus reduces prediction error by up to 133.8%.
{"title":"Proteus: Simulating the Performance of Distributed DNN Training","authors":"Jiangfei Duan;Xiuhong Li;Ping Xu;Xingcheng Zhang;Shengen Yan;Yun Liang;Dahua Lin","doi":"10.1109/TPDS.2024.3443255","DOIUrl":"https://doi.org/10.1109/TPDS.2024.3443255","url":null,"abstract":"DNN models are becoming increasingly larger to achieve unprecedented accuracy, and the accompanying increased computation and memory requirements necessitate the employment of massive clusters and elaborate parallelization strategies to accelerate DNN training. In order to better optimize the performance and analyze the cost, it is indispensable to model the training throughput of distributed DNN training. However, complex parallelization strategies and the resulting complex runtime behaviors make it challenging to construct an accurate performance model. In this article, we present Proteus, the first standalone simulator to model the performance of complex parallelization strategies through simulation execution. Proteus first models complex parallelization strategies with a unified representation named \u0000<italic>Strategy Tree</i>\u0000. Then, it compiles the strategy tree into a distributed execution graph and simulates the complex runtime behaviors, \u0000<italic>comp-comm overlap</i>\u0000 and \u0000<italic>bandwidth sharing</i>\u0000, with a \u0000<underline>H</u>\u0000ierarchical \u0000<underline>T</u>\u0000opo-\u0000<underline>A</u>\u0000ware \u0000<underline>E</u>\u0000xecutor (\u0000<italic>HTAE</i>\u0000). We finally evaluate Proteus across a wide variety of DNNs on three hardware configurations. Experimental results show that Proteus achieves 3.0% average prediction error and preserves order for training throughput of various parallelization strategies. Compared to state-of-the-art approaches, Proteus reduces prediction error by up to 133.8%.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"35 10","pages":"1867-1878"},"PeriodicalIF":5.6,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10636756","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-12DOI: 10.1109/TPDS.2024.3441623
Yuezhi Che;Dazhao Cheng;Xiao Wang;Rujia Wang
The challenges of data privacy and security posed by data outsourcing are becoming increasingly prevalent. Oblivious RAM (ORAM)-based oblivious data storage guarantees data confidentiality through data encryption and access pattern obfuscation. However, it suffers from performance degradation and low throughput. To address these issues, the concurrency of ORAM in a multi-user scenario has been explored. We investigate several existing concurrent oblivious data storage solutions and discover that a trusted proxy is used to serve concurrent accesses between users and storage, with processing locks involved in the proxy to ensure correctness and prevent conflicts. The proxy-based system is inherently prone to pessimistic concurrency control, and as the number of users grows, a proxy might become a performance bottleneck, causing significant delays. In this study, we propose Opca, a novel oblivious data storage framework that enables optimistic concurrent access. Opca refines the proxy design by temporally storing multiple versions of modified data with labeled timestamps, committing only the latest version to the storage during a separate processing period. Opca is implemented and evaluated in different real-world storage backends with a scalable number of users, and its performance is compared to alternative schemes. Opca outperforms the state-of-the-art concurrent oblivious storage system TaoStore, which relies on a similar system setting. Our results show that Opca can improve 3.77x throughput and reduce 73.5% response time.
{"title":"Opca: Enabling Optimistic Concurrent Access for Multiple Users in Oblivious Data Storage","authors":"Yuezhi Che;Dazhao Cheng;Xiao Wang;Rujia Wang","doi":"10.1109/TPDS.2024.3441623","DOIUrl":"https://doi.org/10.1109/TPDS.2024.3441623","url":null,"abstract":"The challenges of data privacy and security posed by data outsourcing are becoming increasingly prevalent. Oblivious RAM (ORAM)-based oblivious data storage guarantees data confidentiality through data encryption and access pattern obfuscation. However, it suffers from performance degradation and low throughput. To address these issues, the concurrency of ORAM in a multi-user scenario has been explored. We investigate several existing concurrent oblivious data storage solutions and discover that a trusted proxy is used to serve concurrent accesses between users and storage, with processing locks involved in the proxy to ensure correctness and prevent conflicts. The proxy-based system is inherently prone to pessimistic concurrency control, and as the number of users grows, a proxy might become a performance bottleneck, causing significant delays. In this study, we propose Opca, a novel oblivious data storage framework that enables optimistic concurrent access. Opca refines the proxy design by temporally storing multiple versions of modified data with labeled timestamps, committing only the latest version to the storage during a separate processing period. Opca is implemented and evaluated in different real-world storage backends with a scalable number of users, and its performance is compared to alternative schemes. Opca outperforms the state-of-the-art concurrent oblivious storage system TaoStore, which relies on a similar system setting. Our results show that Opca can improve 3.77x throughput and reduce 73.5% response time.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"35 11","pages":"1891-1903"},"PeriodicalIF":5.6,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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