ACM Transactions on Computer Systems (TOCS)

Xiao, Yunming; Gu, Yanqi; Zhao, Yibo; Lin, Sen; Kuzmanovic, Aleksandar

doi: 10.1145/3746130pmid: N/A

In recent years, content hyper-giants have increasingly deployed server infrastructure and services close to end-users within “eyeball” networks. Still, online streaming analytics has largely remained unaffected by this trend. This is despite the fact that most of the “big data” is received in real-time and is most valuable at the time of arrival. The inability to process data at the network edge is caused by a common setting where user profiles, necessary for analytics, are stored deep in the data center backends. This setting also carries privacy concerns as such user profiles are individually identifiable, yet the users are almost blind to what data is associated with their identities and how the data is analyzed. In this article, we revise this arrangement, and plant encrypted semantic cookies at the user end. By redesigning the cookie content without altering existing protocols, semantic cookies enable the capture and pre-processing of user data at edge ISPs or CDNs while preserving user anonymity. Additionally, lightweight cryptographic algorithms like partially homomorphic encryption can protect web providers’ proprietary data from CDNs. We present Snatch, a QUIC-based streaming analytics prototype that achieves up to 200x faster user analytics, with common-case improvements of 10-30x.

Sajal, Sultan Mahmud; Estyak, Md Salman; Hasan, Rubaba; Zhu, Timothy; Urgaonkar, Bhuvan; Sen, Siddhartha

doi: 10.1145/3760774pmid: N/A

Trace replay is a common approach for evaluating systems by rerunning historical traffic patterns, but it’s not always possible to find suitable real-world traces at the desired level of system load. To experiment with different loads, one needs to downscale a trace to decrease the load or upscale a trace to artificially increase the load. This article expands upon our work, TraceUpscaler [92], by considering the interaction of upscaling and downscaling. In addition to evaluating upscaling with traces collected from a subset of the cluster, we also evaluate upscaling with traces that were downscaled with the state-of-the-art downscaling tool, TraceSplitter [91], to demonstrate that the upscaling and downscaling techniques are compatible and do not introduce unexpected artifacts in the scaling. In addition to comparing against prior approaches, we develop a novel upscaling technique, TraceOverlap , based on the idea of overlapping different time periods in a trace, where we identify the most similar time periods to overlap. Our evaluation demonstrates that TraceUpscaler and TraceOverlap are both more accurate in maintaining latency characteristics than prior approaches, with TraceUpscaler matching the original trace latency more closely. Finally, we provide a unified framework, TraceScaler, that combines TraceUpscaler with TraceSplitter to provide experimenters a common tool for their trace scaling needs.

Mahmud, Tabassum; Gatla, Om Rameshwar; Zhang, Duo; Love, Carson; Bumann, Ryan; Girimaji, Varun; Zheng, Mai

doi: 10.1145/3747177pmid: N/A

File systems play an essential role in modern society for managing precious data. To meet diverse needs, they often support many configuration parameters. Such flexibility comes at the price of additional complexity which can lead to subtle configuration-related issues. To address this challenge, we study the configuration-related issues of two major file systems (i.e., Ext4 and XFS) in depth, and identify a prevalent pattern called multilevel configuration dependencies. Based on the study, we build an extensible tool called ConfD to extract the dependencies automatically, and create a set of plugins to address different configuration-related issues. Our experiments on Ext4, XFS and a modern copy-on-write file system (i.e., ZFS) show that ConfD was able to extract 160 configuration dependencies for the file systems with a low false positive rate. Moreover, the dependency-guided plugins can identify various configuration issues (e.g., mishandling of configurations, regression test failures induced by valid configurations). In addition, we also explore the applicability of ConfD on a popular storage engine (i.e., WiredTiger). We hope that this comprehensive analysis of configuration dependencies of storage systems can shed light on addressing configuration-related challenges for the system community in general.

Wei, Xingda; Lu, Fangming; Huang, Zhuobin; Chen, Rong; Wu, Mingyu; Chen, Haibo

doi: 10.1145/3725986pmid: N/A

Serialization and deserialization dominate the state transfer time of serverless workflows, leading to substantial performance penalties when executing various serverless workflow applications. We identify the key reason for serialization and deserialization as a lack of ability to efficiently access the (remote) memory of another function. To this end, we propose RMMap, an OS primitive for remote memory map, which allows a serverless function to directly access the memory of another function, even if it is located remotely. RMMap is the first to completely eliminate serialization and deserialization overhead when transferring states between any pairs of functions in (unmodified) serverless workflows. To make remote memory map efficient and feasible, we co-design it with modern networking (RDMA), OS, language runtime, and serverless platform. Evaluations using real-world serverless workloads show that integrating RMMap with Knative reduces the serverless workflow execution time on Knative by up to 2.6× and improves resource utilizations by 86.3%.

Yang, Zhenkun; Qian, Chen; Teng, Xuwang; Kong, Fanyu; Han, Fusheng; Xu, Quanqing; Hu, Daokun

doi: 10.1145/3768621pmid: N/A

The problem of deadlock detection and resolution in database systems has been studied for decades. Although it has long been a mature feature of classical centralized database systems for many years, its use in distributed database systems remains in its infancy. A simple and fully distributed deadlock detection and resolution algorithm was proposed by Don P. Mitchell and Michael J. Merritt (M&M), but its assumption that each process waits for only one resource at a time prevents it from being generally applicable. The distributed deadlock detection algorithm based on Lock Chain Length (LCL) surpasses the limitations of the M&M algorithm. However, it is less effective in quickly detecting deadlocks that encompass both distributed and local deadlocks. In this article, we introduce LCL+, an advanced and universally applicable algorithm specifically designed for the detection and resolution of resource deadlocks in distributed environments. This algorithm improves the efficiency of identifying distributed deadlocks by accelerating the detection of hybrid deadlocks. Our extensive experiments demonstrate that LCL+ significantly outperforms its predecessor, LCL, in efficiency. In addition, it has been successfully implemented in the OceanBase distributed relational database system. Detailed analyses from multiple perspectives within OceanBase confirm that LCL+ significantly improves the system’s scalability and ensures the provision of high-quality service.

Schroeder, Bianca; Silberstein, Mark

doi: 10.1145/3768140pmid: N/A