We present SwitchVM, an in-switch virtual machine for reconfigurable match-action table programmable switches aimed at providing multi-tenant in-network computing.
{"title":"SwitchVM: Multi-Tenancy for In-Network Computing","authors":"Sajy Khashab, M. Silberstein","doi":"10.1145/3579370.3594756","DOIUrl":"https://doi.org/10.1145/3579370.3594756","url":null,"abstract":"We present SwitchVM, an in-switch virtual machine for reconfigurable match-action table programmable switches aimed at providing multi-tenant in-network computing.","PeriodicalId":180024,"journal":{"name":"Proceedings of the 16th ACM International Conference on Systems and Storage","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123108576","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}
Grisha Weintraub, N. Hadar, E. Gudes, S. Dolev, O. Birk
In recent years there is huge influx of genomic data and a growing need for its analysis, yet existing genomic databases do not allow easy accessibility. We developed a pipeline that continuously pre-processes raw human genetic data. The data is then stored in a cloud data lake and can be accessed via a simple and intuitive web service and API.
{"title":"Analyzing large-scale genomic data with cloud data lakes","authors":"Grisha Weintraub, N. Hadar, E. Gudes, S. Dolev, O. Birk","doi":"10.1145/3579370.3594750","DOIUrl":"https://doi.org/10.1145/3579370.3594750","url":null,"abstract":"In recent years there is huge influx of genomic data and a growing need for its analysis, yet existing genomic databases do not allow easy accessibility. We developed a pipeline that continuously pre-processes raw human genetic data. The data is then stored in a cloud data lake and can be accessed via a simple and intuitive web service and API.","PeriodicalId":180024,"journal":{"name":"Proceedings of the 16th ACM International Conference on Systems and Storage","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117338172","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}
RDMA (Remote Direct Memory Access) is widely studied in building key-value stores to achieve ultra-low latency. In RDMA-based key-value stores, the indexing time takes a large fraction of the overall operation latency as RDMA enables fast data access. However, the single index structure used in existing RDMA-based key-value stores, either hash-based or sorted index, fails to support range queries efficiently while achieving high performance for singlepoint operations. In this paper, we explore the adoption of a hybrid index in the key-value stores based on RDMA, especially under the memory disaggregation architecture, to combine the benefits of a hash table and a sorted index. We propose HStore, an RDMA-based key-value store that uses a hash table for single-point lookups and leverages a skiplist for range queries to index the values stored in the memory pool. Guided by previous work on using RDMA for key-value services, HStore dedicatedly chooses different RDMA verbs to optimize the read and write performance. To efficiently keep the index structures within a hybrid index consistent, HStore asynchronously applies the updates to the sorted index by shipping the update log via two-sided verbs. Compared to state-of-the-art Sherman and Clover, HStore improves the throughput by up to 54.5% and 38.5% respectively under the YCSB benchmark.
RDMA (Remote Direct Memory Access)在构建键值存储以实现超低延迟方面得到了广泛的研究。在基于RDMA的键值存储中，索引时间占总操作延迟的很大一部分，因为RDMA支持快速数据访问。然而，在现有的基于rdma的键值存储中使用的单索引结构(无论是基于哈希还是排序索引)在实现单点操作的高性能的同时，无法有效地支持范围查询。在本文中，我们探讨了在基于RDMA的键值存储中采用混合索引，特别是在内存分解架构下，以结合哈希表和排序索引的优点。我们提出HStore，这是一个基于rdma的键值存储，它使用哈希表进行单点查找，并利用skiplist进行范围查询来索引存储在内存池中的值。在之前使用RDMA进行键值服务的工作的指导下，HStore专门选择了不同的RDMA动词来优化读写性能。为了有效地保持混合索引中的索引结构的一致性，HStore通过双面动词发送更新日志，异步地将更新应用于排序索引。与最先进的Sherman和Clover相比，HStore在YCSB基准下的吞吐量分别提高了54.5%和38.5%。
{"title":"Exploiting Hybrid Index Scheme for RDMA-based Key-Value Stores","authors":"Shukai Han, Mi Zhang, D. Jiang, Jin Xiong","doi":"10.1145/3579370.3594768","DOIUrl":"https://doi.org/10.1145/3579370.3594768","url":null,"abstract":"RDMA (Remote Direct Memory Access) is widely studied in building key-value stores to achieve ultra-low latency. In RDMA-based key-value stores, the indexing time takes a large fraction of the overall operation latency as RDMA enables fast data access. However, the single index structure used in existing RDMA-based key-value stores, either hash-based or sorted index, fails to support range queries efficiently while achieving high performance for singlepoint operations. In this paper, we explore the adoption of a hybrid index in the key-value stores based on RDMA, especially under the memory disaggregation architecture, to combine the benefits of a hash table and a sorted index. We propose HStore, an RDMA-based key-value store that uses a hash table for single-point lookups and leverages a skiplist for range queries to index the values stored in the memory pool. Guided by previous work on using RDMA for key-value services, HStore dedicatedly chooses different RDMA verbs to optimize the read and write performance. To efficiently keep the index structures within a hybrid index consistent, HStore asynchronously applies the updates to the sorted index by shipping the update log via two-sided verbs. Compared to state-of-the-art Sherman and Clover, HStore improves the throughput by up to 54.5% and 38.5% respectively under the YCSB benchmark.","PeriodicalId":180024,"journal":{"name":"Proceedings of the 16th ACM International Conference on Systems and Storage","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128554574","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}
Kfir Toledo, Pravein G. Kannan, Michal Malka, Etai Lev-Ran, K. Barabash, V. Bortnikov
Enterprises often deploy their business applications in multiple clouds as well as in multiple traditional environments. This work focuses on the connectivity aspects of this new way of operating and consuming digital services. We define the related requirements, analyze the challenges, and present ClusterLink, our solution for interconnecting today's and future multi-cloud applications.
{"title":"ClusterLink: A Multi-Cluster Application Interconnect","authors":"Kfir Toledo, Pravein G. Kannan, Michal Malka, Etai Lev-Ran, K. Barabash, V. Bortnikov","doi":"10.1145/3579370.3594747","DOIUrl":"https://doi.org/10.1145/3579370.3594747","url":null,"abstract":"Enterprises often deploy their business applications in multiple clouds as well as in multiple traditional environments. This work focuses on the connectivity aspects of this new way of operating and consuming digital services. We define the related requirements, analyze the challenges, and present ClusterLink, our solution for interconnecting today's and future multi-cloud applications.","PeriodicalId":180024,"journal":{"name":"Proceedings of the 16th ACM International Conference on Systems and Storage","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127054713","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}
Public cloud providers offer a diverse collection of storage types and configurations with different costs and performance SLAs. As a consequence, it is difficult to select the most cost-efficient allocations for storage backends, while satisfying a given workload's performance requirements, when moving data-heavy applications to the cloud. We present Mimir, a tool for automatically finding a cost-efficient virtual storage cluster configuration for a customer's storage workload and performance requirements. Importantly, Mimir considers all block storage types and configurations, and even heterogeneous mixes of them. In our experiments, compared to state-of-the-art approaches that consider only one storage type, Mimir finds configurations that reduce cost by up to 81% for real-application-based key-value store workloads.
{"title":"Mimir: Finding Cost-efficient Storage Configurations in the Public Cloud","authors":"Ho-Joon Park, G. Ganger, George Amvrosiadis","doi":"10.1145/3579370.3594776","DOIUrl":"https://doi.org/10.1145/3579370.3594776","url":null,"abstract":"Public cloud providers offer a diverse collection of storage types and configurations with different costs and performance SLAs. As a consequence, it is difficult to select the most cost-efficient allocations for storage backends, while satisfying a given workload's performance requirements, when moving data-heavy applications to the cloud. We present Mimir, a tool for automatically finding a cost-efficient virtual storage cluster configuration for a customer's storage workload and performance requirements. Importantly, Mimir considers all block storage types and configurations, and even heterogeneous mixes of them. In our experiments, compared to state-of-the-art approaches that consider only one storage type, Mimir finds configurations that reduce cost by up to 81% for real-application-based key-value store workloads.","PeriodicalId":180024,"journal":{"name":"Proceedings of the 16th ACM International Conference on Systems and Storage","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129859207","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}
High performance computing (HPC) cloud, which is a fusion of HPC and cloud, is receiving much attention due to growing demand for large-scale computing. The HPC cloud is targeted for running HPC-derived applications, but it requires solving an HPC-specific challenge, namely operating system (OS) noise. Herein, we focus on the OS noise caused by asynchronous I/O processing and propose offloading it to data processing unit (DPU).
{"title":"Towards Less Operating System Noise: An Approach with Data Processing Unit","authors":"Jun Kato, Koki Kusunoki, Mitsuru Sato","doi":"10.1145/3579370.3594749","DOIUrl":"https://doi.org/10.1145/3579370.3594749","url":null,"abstract":"High performance computing (HPC) cloud, which is a fusion of HPC and cloud, is receiving much attention due to growing demand for large-scale computing. The HPC cloud is targeted for running HPC-derived applications, but it requires solving an HPC-specific challenge, namely operating system (OS) noise. Herein, we focus on the OS noise caused by asynchronous I/O processing and propose offloading it to data processing unit (DPU).","PeriodicalId":180024,"journal":{"name":"Proceedings of the 16th ACM International Conference on Systems and Storage","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126242842","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}
Alon Rashelbach, Ori Rottenstreich, M. Silberstein
We present the Range Query Recursive Model Index (RQRMI) data structure that trades memory accesses for computations in performance-critical systems that employ Range Matching.
{"title":"Neural Networks for Computer Systems","authors":"Alon Rashelbach, Ori Rottenstreich, M. Silberstein","doi":"10.1145/3579370.3594755","DOIUrl":"https://doi.org/10.1145/3579370.3594755","url":null,"abstract":"We present the Range Query Recursive Model Index (RQRMI) data structure that trades memory accesses for computations in performance-critical systems that employ Range Matching.","PeriodicalId":180024,"journal":{"name":"Proceedings of the 16th ACM International Conference on Systems and Storage","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122432380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We present a new fuzzing approach for Iago vulnerabilities in Library OSes for SGX enclaves. Based on the filesystem model, it allows efficiently combining valid and malicious values to reach deeper paths in LibraryOS to identify more potential security vulnerabilities.
{"title":"Fuzzing LibraryOSes for Iago vulnerabilities","authors":"Leonid Dyachkov, Meni Orenbach, M. Silberstein","doi":"10.1145/3579370.3594760","DOIUrl":"https://doi.org/10.1145/3579370.3594760","url":null,"abstract":"We present a new fuzzing approach for Iago vulnerabilities in Library OSes for SGX enclaves. Based on the filesystem model, it allows efficiently combining valid and malicious values to reach deeper paths in LibraryOS to identify more potential security vulnerabilities.","PeriodicalId":180024,"journal":{"name":"Proceedings of the 16th ACM International Conference on Systems and Storage","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121050192","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}
Ronen Schaffer, E. Raichstein, K. Meth, Joel Takvorian, Julien Pinsonneau
Flow Logs Pipeline (a.k.a. FLP) is an observability tool that consumes flow logs from various inputs, transforms them and exports logs to Loki and / or time series metrics to Prometheus. While flow logs encompass a lot of valuable data, observing the network from the level of flow logs is often too low. In many cases, we are interested in observing it from a higher level, the level of connections. In this work, we introduce a new processing stage in FLP that allows aggregating flow logs from the same connection - connection tracking.
{"title":"Smart Network Observability – Connection Tracking","authors":"Ronen Schaffer, E. Raichstein, K. Meth, Joel Takvorian, Julien Pinsonneau","doi":"10.1145/3579370.3594751","DOIUrl":"https://doi.org/10.1145/3579370.3594751","url":null,"abstract":"Flow Logs Pipeline (a.k.a. FLP) is an observability tool that consumes flow logs from various inputs, transforms them and exports logs to Loki and / or time series metrics to Prometheus. While flow logs encompass a lot of valuable data, observing the network from the level of flow logs is often too low. In many cases, we are interested in observing it from a higher level, the level of connections. In this work, we introduce a new processing stage in FLP that allows aggregating flow logs from the same connection - connection tracking.","PeriodicalId":180024,"journal":{"name":"Proceedings of the 16th ACM International Conference on Systems and Storage","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125398166","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}
Peter-Jan Gootzen, Jonas Pfefferle, R. Stoica, A. Trivedi
As we move towards hyper-converged cloud solutions, the efficiency and overheads of distributed file systems at the cloud tenant side (i.e., client) become of paramount importance. Often, the clientside driver of a cloud file system is complex and CPU intensive, deeply coupled with the backend implementation, and requires optimizing multiple intrusive knobs. In this work, we propose to decouple the file system client from its backend implementation by virtualizing it with an off-the-shelf DPU using the Linux virtio-fs software stack. The decoupling allows us to offload the file system client execution to a DPU, which is managed and optimized by the cloud provider, while freeing the host CPU cycles. DPFS, our proposed framework, is 4.4× more host CPU efficient per I/O, delivers comparable performance to a tenant with zero-configuration and without modification of their host software stack, while allowing workload and hardware specific backend optimizations. The DPFS framework and its artifacts are publically available at https://github.com/IBM/DPFS.
{"title":"DPFS: DPU-Powered File System Virtualization","authors":"Peter-Jan Gootzen, Jonas Pfefferle, R. Stoica, A. Trivedi","doi":"10.1145/3579370.3594769","DOIUrl":"https://doi.org/10.1145/3579370.3594769","url":null,"abstract":"As we move towards hyper-converged cloud solutions, the efficiency and overheads of distributed file systems at the cloud tenant side (i.e., client) become of paramount importance. Often, the clientside driver of a cloud file system is complex and CPU intensive, deeply coupled with the backend implementation, and requires optimizing multiple intrusive knobs. In this work, we propose to decouple the file system client from its backend implementation by virtualizing it with an off-the-shelf DPU using the Linux virtio-fs software stack. The decoupling allows us to offload the file system client execution to a DPU, which is managed and optimized by the cloud provider, while freeing the host CPU cycles. DPFS, our proposed framework, is 4.4× more host CPU efficient per I/O, delivers comparable performance to a tenant with zero-configuration and without modification of their host software stack, while allowing workload and hardware specific backend optimizations. The DPFS framework and its artifacts are publically available at https://github.com/IBM/DPFS.","PeriodicalId":180024,"journal":{"name":"Proceedings of the 16th ACM International Conference on Systems and Storage","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122389371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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