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1. InfiniCache: Exploiting Ephemeral Serverless Functions to Build a Cost-Effective Memory Cache

   Wang, Ao; Zhang, Jingyuan; Ma, Xiaolong; Anwar, Ali; Rupprecht, Lukas; Skourtis, Dimitrios; Tarasov, Vasily; Yan, Feng; Cheng, Yue (February 2020, 18th USENIX Conference on File and Storage Technologies)

   Internet-scale web applications are becoming increasingly storage-intensive and rely heavily on in-memory object caching to attain required I/O performance. We argue that the emerging serverless computing paradigm provides a well-suited, cost-effective platform for object caching. We present InfiniCache, a first-of-its-kind in-memory object caching system that is completely built and deployed atop ephemeral serverless functions. InfiniCache exploits and orchestrates serverless functions' memory resources to enable elastic pay-per-use caching. InfiniCache's design combines erasure coding, intelligent billed duration control, and an efficient data backup mechanism to maximize data availability and cost-effectiveness while balancing the risk of losing cached state and performance. We implement InfiniCache on AWS Lambda and show that it: (1) achieves 31 – 96× tenant-side cost savings compared to AWS ElastiCache for a large-object-only production workload, (2) can effectively provide 95.4% data availability for each one hour window, and (3) enables comparative performance seen in a typical in-memory cache. 

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2. OFC: an opportunistic caching system for FaaS platforms

   https://doi.org/10.1145/3447786.3456239  

   Mvondo, Djob; Bacou, Mathieu; Nguetchouang, Kevin; Ngale, Lucien; Pouget, Stéphane; Kouam, Josiane; Lachaize, Renaud; Hwang, Jinho; Wood, Tim; Hagimont, Daniel; et al (April 2021, Proceedings of the Sixteenth European Conference on Computer Systems)

   Cloud applications based on the "Functions as a Service" (FaaS) paradigm have become very popular. Yet, due to their stateless nature, they must frequently interact with an external data store, which limits their performance. To mitigate this issue, we introduce OFC, a transparent, vertically and horizontally elastic in-memory caching system for FaaS platforms, distributed over the worker nodes. OFC provides these benefits cost-effectively by exploiting two common sources of resource waste: (i) most cloud tenants overprovision the memory resources reserved for their functions because their footprint is non-trivially input-dependent and (ii) FaaS providers keep function sandboxes alive for several minutes to avoid cold starts. Using machine learning models adjusted for typical function input data categories (e.g., multimedia formats), OFC estimates the actual memory resources required by each function invocation and hoards the remaining capacity to feed the cache. We build our OFC prototype based on enhancements to the OpenWhisk FaaS platform, the Swift persistent object store, and the RAM-Cloud in-memory store. Using a diverse set of workloads, we show that OFC improves by up to 82 % and 60 % respectively the execution time of single-stage and pipelined functions. 

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3. An Object Storage for Distributed Acoustic Sensing

   https://doi.org/10.1785/0220230172  

   Ni, Yiyu; Denolle, Marine A; Fatland, Rob; Alterman, Naomi; Lipovsky, Bradley P; Knuth, Friedrich (October 2023, Seismological Research Letters)

   Abstract Large-scale processing and dissemination of distributed acoustic sensing (DAS) data are among the greatest computational challenges and opportunities of seismological research today. Current data formats and computing infrastructure are not well-adapted or user-friendly for large-scale processing. We propose an innovative, cloud-native solution for DAS seismology using the MinIO open-source object storage framework. We develop data schema for cloud-optimized data formats—Zarr and TileDB, which we deploy on a local object storage service compatible with the Amazon Web Services (AWS) storage system. We benchmark reading and writing performance for various data schema using canonical use cases in seismology. We test our framework on a local server and AWS. We find much-improved performance in compute time and memory throughout when using TileDB and Zarr compared to the conventional HDF5 data format. We demonstrate the platform with a computing heavy use case in seismology: ambient noise seismology of DAS data. We process one month of data, pairing all 2089 channels within 24 hr using AWS Batch autoscaling. 

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4. PLASMA: programmable elasticity for stateful cloud computing applications

   https://doi.org/10.1145/3342195.3387553  

   Sang, Bo; Roman, Pierre-Louis; Eugster, Patrick; Lu, Hui; Ravi, Srivatsan; Petri, Gustavo (April 2020, EuroSys ’20)

   Developers are always on the lookout for simple solutions to manage their applications on cloud platforms. Major cloud providers have already been offering automatic elasticity management solutions (e.g., AWS Lambda, Azure durable function) to users. However, many cloud applications are stateful --- while executing, functions need to share their state with others. Providing elasticity for such stateful functions is much more challenging, as a deployment/elasticity decision for a stateful entity can strongly affect others in ways which are hard to predict without any application knowledge. Existing solutions either only support stateless applications (e.g., AWS Lambda) or only provide limited elasticity management (e.g., Azure durable function) to stateful applications. PLASMA (Programmable Elasticity for Stateful Cloud Computing Applications) is a programming framework for elastic stateful cloud applications. It includes (1) an elasticity programming language as a second "level" of programming (complementing the main application programming language) for describing elasticity behavior, and (2) a novel semantics-aware elasticity management runtime that tracks program execution and acts upon application features as suggested by elasticity behavior. We have implemented 10+ applications with PLASMA. Extensive evaluation on Amazon AWS shows that PLASMA significantly improves their efficiency, e.g., achieving same performance as a vanilla setup with 25% fewer resources, or improving performance by 40% compared to the default setup. 

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5. Large-Scale Causality Discovery Analytics as a Service

   Wang, Xin; Guo, Pei; Wang, Jianwu (January 2021, 2021 IEEE Big Data Conference)

   Data-driven causality discovery is a common way to understand causal relationships among different components of a system. We study how to achieve scalable data-driven causal- ity discovery on Amazon Web Services (AWS) and Microsoft Azure cloud and propose a causality discovery as a service (CDaaS) framework. With this framework, users can easily re- run previous causality discovery experiments or run causality discovery with different setups (such as new datasets or causality discovery parameters). Our CDaaS leverages Cloud Container Registry service and Virtual Machine service to achieve scal- able causality discovery with different discovery algorithms. We further did extensive experiments and benchmarking of our CDaaS to understand the effects of seven factors (big data engine parameter setting, virtual machine instance number, type, subtype, size, cloud service, cloud provider) and how to best provision cloud resources for our causality discovery service based on certain goals including execution time, budgetary cost and cost-performance ratio. We report our findings from the benchmarking, which can help obtain optimal configurations based on each application’s characteristics. The findings show proper configurations could lead to both faster execution time and less budgetary cost. 

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