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1. CausalMesh: A Causal Cache for Stateful Serverless Computing

   Zhang, Haoran; Mu, Shuai; Angel, Sebastian; Liu, Vincent (August 2024, Proceedings of the VLDB Endowment)

   Stateful serverless workflows consist of multiple serverless functions that access state on a remote database. Developers sometimes add a cache layer between the serverless runtime and the database to improve I/O latency. However, in a serverless environment, functions in the same workflow may be scheduled to different nodes with different caches, which can cause non-intuitive anomalies. This paper presents CausalMesh, a novel approach to causally consistent caching in serverless computing. CausalMesh is the first cache system that supports coordination-free and abort-free read-/write operations and read transactions when clients roam among multiple servers. CausalMesh also supports read-write transactional causal consistency in the presence of client roaming, but at the cost of abort-freedom. Our evaluation shows that CausalMesh has lower latency and higher throughput than existing proposals. 

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2. CausalMesh: A Causal Cache for Stateful Serverless Computing

   https://doi.org/10.1145/3733620.3733631  

   Zhang, Haoran; Mu, Shuai; Angel, Sebastian; Liu, Vincent (April 2025, ACM SIGMOD Record)

   Stateful serverless workflows consist of multiple serverless functions that access state on a remote database. Developers sometimes add a cache layer between the serverless runtime and the database to improve I/O latency. However, in a serverless environment, functions in the same workflow may be scheduled to different nodes with different caches, which can cause non-intuitive anomalies. This paper presents CausalMesh, a novel approach to causally consistent caching in serverless computing. CausalMesh is the first cache system that supports coordination-free and abort-free read/write operations and read transactions when clients roam among multiple servers. CausalMesh also supports read-write transactional causal consistency in the presence of client roaming but at the cost of abort-freedom. Our evaluation shows that CausalMesh has lower latency and higher throughput than existing proposals. 

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3. Boki: Towards Data Consistency and Fault Tolerance with Shared Logs in Stateful Serverless Computing

   https://doi.org/10.1145/3653072  

   Jia, Zhipeng; Witchel, Emmett (September 2024, ACM transactions on computer systems)

   Boki is a new serverless runtime that exports a shared log API to serverless functions. Boki shared logs enable stateful serverless applications to manage their state with durability, consistency, and fault tolerance. Boki shared logs achieve high throughput and low latency. The key enabler is themetalog, a novel mechanism that allows Boki to address ordering, consistency and fault tolerance independently. The metalog orders shared log records with high throughput and it provides read consistency while allowing service providers to optimize the write and read path of the shared log in different ways. To demonstrate the value of shared logs for stateful serverless applications, we build Boki support libraries that implement fault-tolerant workflows, durable object storage, and message queues. Our evaluation shows that shared logs can speed up important serverless workloads by up to 4.2 ×. 

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4. Boki: Stateful Serverless Computing with Shared Logs

   https://doi.org/10.1145/3477132.3483541  

   Jia, Zhipeng; Witchel, Emmett (October 2021, Proceedings of the ACM SIGOPS 28th Symposium on Operating Systems Principles)

   Boki is a new serverless runtime that exports a shared log API to serverless functions. Boki shared logs enable stateful serverless applications to manage their state with durability, consistency, and fault tolerance. Boki shared logs achieve high throughput and low latency. The key enabler is the metalog, a novel mechanism that allows Boki to address ordering, consistency and fault tolerance independently. The metalog orders shared log records with high throughput and it provides read consistency while allowing service providers to optimize the write and read path of the shared log in different ways. To demonstrate the value of shared logs for stateful serverless applications, we build Boki support libraries that implement fault-tolerant workflows, durable object storage, and message queues. Our evaluation shows that shared logs can speed up important serverless workloads by up to 4.7x. 

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5. Enabling Timely and Persistent Deletion in LSM-Engines

   https://doi.org/10.1145/3599724  

   Sarkar, Subhadeep; Papon, Tarikul Islam; Staratzis, Dimitris; Zhu, Zichen; Athanassoulis, Manos (September 2023, ACM Transactions on Database Systems)

   Data-intensive applications have fueled the evolution oflog-structured merge (LSM)based key-value engines that employ theout-of-placeparadigm to support high ingestion rates with low read/write interference. These benefits, however, come at the cost oftreating deletes as second-class citizens. A delete operation inserts atombstonethat invalidates older instances of the deleted key. State-of-the-art LSM-engines do not provide guarantees as to how fast a tombstone will propagate topersist the deletion. Further, LSM-engines only support deletion on the sort key. To delete on another attribute (e.g., timestamp), the entire tree is read and re-written, leading to undesired latency spikes and increasing the overall operational cost of a database. Efficient and persistent deletion is key to support: (i) streaming systems operating on a window of data, (ii) privacy with latency guarantees on data deletion, and (iii)en massecloud deployment of data systems. Further, we document that LSM-based key-value engines perform suboptimally in the presence of deletes in a workload. Tombstone-driven logical deletes, by design, are unable to purge the deleted entries in a timely manner, and retaining the invalidated entries perpetually affects the overall performance of LSM-engines in terms of space amplification, write amplification, and read performance. Moreover, the potentially unbounded latency for persistent deletes brings in critical privacy concerns in light of the data privacy protection regulations, such as theright to be forgottenin EU’s GDPR, theright to deletein California’s CCPA and CPRA, anddeletion rightin Virginia’s VCDPA. Toward this, we introduce the delete design space for LSM-trees and highlight the performance implications of the different classes of delete operations. To address these challenges, in this article, we build a new key-value storage engine,Lethe⁺, that uses a very small amount of additional metadata, a set of new delete-aware compaction policies, and a new physical data layout that weaves the sort and the delete key order. We show thatLethe⁺supports any user-defined threshold for the delete persistence latency offeringhigher read throughput(1.17× -1.4×) andlower space amplification(2.1× -9.8×), with a modest increase in write amplification (between 4% and 25%) that can be further amortized to less than 1%. In addition,Lethe⁺supports efficient range deletes on asecondary delete keyby dropping entire data pages without sacrificing read performance or employing a costly full tree merge. 

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