OverSketch: Approximate Matrix Multiplication for the Cloud

We propose OverSketch, an approximate algorithm for distributed matrix multiplication in serverless computing. OverSketch leverages ideas from matrix sketching and high-performance computing to enable cost-efficient multiplication that is resilient to faults and straggling nodes pervasive in low-cost serverless architectures. We establish statistical guarantees on the accuracy of OverSketch and empirically validate our results by solving a large-scale linear program using interior-point methods and demonstrate a 34% reduction in compute time on AWS Lambda.Comment: Published in Proc. IEEE Big Data 2018. Updated version provides details of distributed sketching and highlights other advantages of OverSketc

Function-as-a-Service Performance Evaluation: A Multivocal Literature Review

Function-as-a-Service (FaaS) is one form of the serverless cloud computing paradigm and is defined through FaaS platforms (e.g., AWS Lambda) executing event-triggered code snippets (i.e., functions). Many studies that empirically evaluate the performance of such FaaS platforms have started to appear but we are currently lacking a comprehensive understanding of the overall domain. To address this gap, we conducted a multivocal literature review (MLR) covering 112 studies from academic (51) and grey (61) literature. We find that existing work mainly studies the AWS Lambda platform and focuses on micro-benchmarks using simple functions to measure CPU speed and FaaS platform overhead (i.e., container cold starts). Further, we discover a mismatch between academic and industrial sources on tested platform configurations, find that function triggers remain insufficiently studied, and identify HTTP API gateways and cloud storages as the most used external service integrations. Following existing guidelines on experimentation in cloud systems, we discover many flaws threatening the reproducibility of experiments presented in the surveyed studies. We conclude with a discussion of gaps in literature and highlight methodological suggestions that may serve to improve future FaaS performance evaluation studies.Comment: improvements including postprint update

Randomized Polar Codes for Anytime Distributed Machine Learning

We present a novel distributed computing framework that is robust to slow compute nodes, and is capable of both approximate and exact computation of linear operations. The proposed mechanism integrates the concepts of randomized sketching and polar codes in the context of coded computation. We propose a sequential decoding algorithm designed to handle real valued data while maintaining low computational complexity for recovery. Additionally, we provide an anytime estimator that can generate provably accurate estimates even when the set of available node outputs is not decodable. We demonstrate the potential applications of this framework in various contexts, such as large-scale matrix multiplication and black-box optimization. We present the implementation of these methods on a serverless cloud computing system and provide numerical results to demonstrate their scalability in practice, including ImageNet scale computations

Rateless Codes for Near-Perfect Load Balancing in Distributed Matrix-Vector Multiplication

Large-scale machine learning and data mining applications require computer systems to perform massive matrix-vector and matrix-matrix multiplication operations that need to be parallelized across multiple nodes. The presence of straggling nodes -- computing nodes that unpredictably slowdown or fail -- is a major bottleneck in such distributed computations. Ideal load balancing strategies that dynamically allocate more tasks to faster nodes require knowledge or monitoring of node speeds as well as the ability to quickly move data. Recently proposed fixed-rate erasure coding strategies can handle unpredictable node slowdown, but they ignore partial work done by straggling nodes thus resulting in a lot of redundant computation. We propose a \emph{rateless fountain coding} strategy that achieves the best of both worlds -- we prove that its latency is asymptotically equal to ideal load balancing, and it performs asymptotically zero redundant computations. Our idea is to create linear combinations of the $m$ rows of the matrix and assign these encoded rows to different worker nodes. The original matrix-vector product can be decoded as soon as slightly more than $m$ row-vector products are collectively finished by the nodes. We conduct experiments in three computing environments: local parallel computing, Amazon EC2, and Amazon Lambda, which show that rateless coding gives as much as $3\times$ speed-up over uncoded schemes

Serverless Computing for Scientific Applications

Serverless computing has become an important model in cloud computing and influenced the design of many applications. Here, we provide our perspective on how the recent landscape of serverless computing for scientific applications looks like. We discuss the advantages and problems with serverless computing for scientific applications, and based on the analysis of existing solutions and approaches, we propose a science-oriented architecture for a serverless computing framework that is based on the existing designs. Finally, we provide an outlook of current trends and future directions