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1. Serverless-DFS: Serverless Federated Learning with Dynamic Forest Strategy

   https://doi.org/10.1145/3638837.3638865  

   Deng, Bobin; Zhang, Xinyue; Lo, Dan Chia-Tien (December 2023, ACM)
2. Serverless linear algebra

   https://doi.org/10.1145/3419111.3421287  

   Shankar, Vaishaal; Krauth, Karl; Vodrahalli, Kailas; Pu, Qifan; Recht, Benjamin; Stoica, Ion; Ragan-Kelley, Jonathan; Jonas, Eric; Venkataraman, Shivaram (October 2020, SoCC '20: Proceedings of the 11th ACM Symposium on Cloud Computing)

   null (Ed.)

   Datacenter disaggregation provides numerous benefits to both the datacenter operator and the application designer. However switching from the server-centric model to a disaggregated model requires developing new programming abstractions that can achieve high performance while benefiting from the greater elasticity. To explore the limits of datacenter disaggregation, we study an application area that near-maximally benefits from current server-centric datacenters: dense linear algebra. We build NumPyWren, a system for linear algebra built on a disaggregated serverless programming model, and LAmbdaPACK, a companion domain-specific language designed for serverless execution of highly parallel linear algebra algorithms. We show that, for a number of linear algebra algorithms such as matrix multiply, singular value decomposition, Cholesky decomposition, and QR decomposition, NumPyWren's performance (completion time) is within a factor of 2 of optimized server-centric MPI implementations, and has up to 15% greater compute efficiency (total CPU-hours), while providing fault tolerance. 

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3. Enhancing Observability of Serverless Computing with the Serverless Application Analytics Framework

   https://doi.org/10.1145/3447545.3451173  

   Cordingly, Robert; Heydari, Navid; Yu, Hanfei; Hoang, Varik; Sadeghi, Zohreh; Lloyd, Wes (April 2021, 12th ACM/SPEC International Conference on Performance Engineering)

   To improve the observability of workload performance, resource utilization, and infrastructure underlying serverless Function-as-a-Service (FaaS) platforms, we have developed the Serverless Application Analytics Framework (SAAF). SAAF provides a reusable framework supporting multiple programming languages that developers can leverage to inspect performance, resource utilization, scalability, and infrastructure metrics of function deployments to commercial and open-source FaaS platforms. To automate reproducible FaaS performance experiments, we provide the FaaS Runner as a multithreaded FaaS client. FaaS Runner provides a programmable client that can orchestrate over one thousand concurrent FaaS function calls. The ReportGenerator is then used to aggregate experiment output into CSV files for consumption by popular data analytics tools. SAAF and its supporting tools combined can assess forty-eight distinct metrics to enhance observability of serverless software deployments. In this tutorial paper, we describe SAAF and its supporting tools and provide examples of observability insights that can be derived. 

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4. Deserv: Decentralized Serverless Computing

   Christie, Samuel H.; Chopra, Amit K.; Singh, Munindar P. (January 2021, Proceedings of the 19th IEEE International Conference on Web Services)

   null (Ed.)

   A decentralized application involves multiple autonomous principals, e.g., humans and organizations. Autonomy motivates (1) specifying a decentralized application via a protocol that captures the interactions between the principals, and (2) a programming model that enables each principal to independently (from other principals) construct its own protocol-compliant agent. An agent encodes its principal's decision making and represents it in the application. We contribute Deserv, the first protocol-based programming model for decentralized applications that is suited to the cloud. Specifically, Deserv demonstrates how to leverage function-as-a-service (FaaS), a popular serverless programming model, to implement agents. A notable feature of Deserv is the use declarative protocols to specify interactions. Declarative protocols support implementing stateful agents in a manner that naturally exploits the concurrency and autoscaling benefits offered by serverless computing. 

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5. InfiniStore: Elastic Serverless Cloud Storage

   https://doi.org/10.14778/3587136.3587139  

   Zhang, Jingyuan; Wang, Ao; Ma, Xiaolong; Carver, Benjamin; Newman, Nicholas John; Anwar, Ali; Rupprecht, Lukas; Tarasov, Vasily; Skourtis, Dimitrios; Yan, Feng; et al (March 2023, Proceedings of the VLDB Endowment)

   Cloud object storage such as AWS S3 is cost-effective and highly elastic but relatively slow, while high-performance cloud storage such as AWS ElastiCache is expensive and provides limited elasticity. We present a new cloud storage service called ServerlessMemory, which stores data using the memory of serverless functions. ServerlessMemory employs a sliding-window-based memory management strategy inspired by the garbage collection mechanisms used in the programming language to effectively segregate hot/cold data and provides fine-grained elasticity, good performance, and a pay-per-access cost model with extremely low cost. We then design and implement InfiniStore, a persistent and elastic cloud storage system, which seamlessly couples the function-based ServerlessMemory layer with a persistent, inexpensive cloud object store layer. InfiniStore enables durability despite function failures using a fast parallel recovery scheme built on the auto-scaling functionality of a FaaS (Function-as-a-Service) platform. We evaluate InfiniStore extensively using both microbenchmarking and two real-world applications. Results show that InfiniStore has more performance benefits for objects larger than 10 MB compared to AWS ElastiCache and Anna, and InfiniStore achieves 26.25% and 97.24% tenant-side cost reduction compared to InfiniCache and ElastiCache, respectively. 

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