The Art of Multiprocessor Programming

Serverless cloud computing handles virtually all the system administration operations needed to make it easier for programmers to use the cloud. It provides an interface that greatly simplifies cloud programming, and represents an evolution that parallels the transition from assembly language to high-level programming languages. This paper gives a quick history of cloud computing, including an accounting of the predictions of the 2009 Berkeley View of Cloud Computing paper, explains the motivation for serverless computing, describes applications that stretch the current limits of serverless, and then lists obstacles and research opportunities required for serverless computing to fulfill its full potential. Just as the 2009 paper identified challenges for the cloud and predicted they would be addressed and that cloud use would accelerate, we predict these issues are solvable and that serverless computing will grow to dominate the future of cloud computing.

Cloud Programming Simplified: A Berkeley View on Serverless Computing

Serverless containers and functions are widely used for deploying and managing software in the cloud. Their popularity is due to reduced cost of operations, improved utilization of hardware, and faster scaling than traditional deployment methods. The economics and scale of serverless applications demand that workloads from multiple customers run on the same hardware with minimal overhead, while preserving strong security and performance isolation. The traditional view is that there is a choice between virtualization with strong security and high overhead, and container technologies with weaker security and minimal overhead. This tradeoff is unacceptable to public infrastructure providers, who need both strong security and minimal overhead. To meet this need, we developed Firecracker, a new open source Virtual Machine Monitor (VMM) specialized for serverless workloads, but generally useful for containers, functions and other compute workloads within a reasonable set of constraints. We have deployed Firecracker in two publically-available serverless compute services at Amazon Web Services (Lambda and Fargate), where it supports millions of production workloads, and trillions of requests per month. We describe how specializing for serverless informed the design of Firecracker, and what we learned from seamlessly migrating Lambda customers to Firecracker.

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Firecracker: Lightweight virtualization for serverless applications

Distributed Computing Through Combinatorial Topology describes techniques for analyzing distributed algorithms based on award winning combinatorial topology research. The authors present a solid theoretical foundation relevant to many real systems reliant on parallelism with unpredictable delays, such as multicore microprocessors, wireless networks, distributed systems, and Internet protocols. Today, a new student or researcher must assemble a collection of scattered conference publications, which are typically terse and commonly use different notations and terminologies. This book provides a self-contained explanation of the mathematics to readers with computer science backgrounds, as well as explaining computer science concepts to readers with backgrounds in applied mathematics. The first section presents mathematical notions and models, including message passing and shared-memory systems, failures, and timing models. The next section presents core concepts in two chapters each: first, proving a simple result that lends itself to examples and pictures that will build up readers' intuition; then generalizing the concept to prove a more sophisticated result. The overall result weaves together and develops the basic concepts of the field, presenting them in a gradual and intuitively appealing way. The book's final section discusses advanced topics typically found in a graduate-level course for those who wish to explore further. Gathers knowledge otherwise spread across research and conference papers using consistent notations and a standard approach to facilitate understandingPresents unique insights applicable to multiple computing fields, including multicore microprocessors, wireless networks, distributed systems, and Internet protocols Synthesizes and distills material into a simple, unified presentation with examples, illustrations, and exercises

Distributed Computing Through Combinatorial Topology

Machine learning (ML) workflows are extremely complex. The typical workflow consists of distinct stages of user interaction, such as preprocessing, training, and tuning, that are repeatedly executed by users but have heterogeneous computational requirements. This complexity makes it challenging for ML users to correctly provision and manage resources and, in practice, constitutes a significant burden that frequently causes over-provisioning and impairs user productivity. Serverless computing is a compelling model to address the resource management problem, in general, but there are numerous challenges to adopt it for existing ML frameworks due to significant restrictions on local resources. This work proposes Cirrus---an ML framework that automates the end-to-end management of datacenter resources for ML workflows by efficiently taking advantage of serverless infrastructures. Cirrus combines the simplicity of the serverless interface and the scalability of the serverless infrastructure (AWS Lambdas and S3) to minimize user effort. We show a design specialized for both serverless computation and iterative ML training is needed for robust and efficient ML training on serverless infrastructure. Our evaluation shows that Cirrus outperforms frameworks specialized along a single dimension: Cirrus is 100x faster than a general purpose serverless system [36] and 3.75x faster than specialized ML frameworks for traditional infrastructures [49].

https://dl.acm.org/doi/pdf/10.1145/3357223.3362711

Cirrus: a Serverless Framework for End-to-end ML Workflows

Serverless computing offers the potential to program the cloud in an autoscaling, pay-as-you go manner. In this paper we address critical gaps in first-generation serverless computing, which place its autoscaling potential at odds with dominant trends in modern computing: notably data-centric and distributed computing, but also open source and custom hardware. Put together, these gaps make current serverless offerings a bad fit for cloud innovation and particularly bad for data systems innovation. In addition to pinpointing some of the main shortfalls of current serverless architectures, we raise a set of challenges we believe must be met to unlock the radical potential that the cloud---with its exabytes of storage and millions of cores---should offer to innovative developers.

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Serverless Computing: One Step Forward, Two Steps Back.

Multi-versioned database systems have the potential to significantly increase the amount of concurrency in transaction processing because they can avoid read-write conflicts. Unfortunately, the increase in concurrency usually comes at the cost of transaction serializability. If a database user requests full serializability, modern multi-versioned systems significantly constrain read-write concurrency among conflicting transactions and employ expensive synchronization patterns in their design. In main-memory multi-core settings, these additional constraints are so burdensome that multi-versioned systems are often significantly outperformed by single-version systems.We propose Bohm, a new concurrency control protocol for main-memory multi-versioned database systems. Bohm guarantees serializable execution while ensuring that reads never block writes. In addition, Bohm does not require reads to perform any bookkeeping whatsoever, thereby avoiding the overhead of tracking reads via contended writes to shared memory. This leads to excellent scalability and performance in multi-core settings. Bohm has all the above characteristics without performing validation based concurrency control. Instead, it is pessimistic, and is therefore not prone to excessive aborts in the presence of contention. An experimental evaluation shows that Bohm performs well in both high contention and low contention settings, and is able to dramatically outperform state-of-the-art multi-versioned systems despite maintaining the full set of serializability guarantees.

/pdf/rethinking-serializable-multiversion-concurrency-control-3ssetveu8p.pdf

Rethinking serializable multiversion concurrency control

Function-as-a-Service (FaaS) platforms and "serverless" cloud computing are becoming increasingly popular. Current FaaS offerings are targeted at stateless functions that do minimal I/O and communication. We argue that the benefits of serverless computing can be extended to a broader range of applications and algorithms. We present the design and implementation of Cloudburst, a stateful FaaS platform that provides familiar Python programming with low-latency mutable state and communication, while maintaining the autoscaling benefits of serverless computing. Cloudburst accomplishes this by leveraging Anna, an autoscaling key-value store, for state sharing and overlay routing combined with mutable caches co-located with function executors for data locality. Performant cache consistency emerges as a key challenge in this architecture. To this end, Cloudburst provides a combination of lattice-encapsulated state and new definitions and protocols for distributed session consistency. Empirical results on benchmarks and diverse applications show that Cloudburst makes stateful functions practical, reducing the state-management overheads of current FaaS platforms by orders of magnitude while also improving the state of the art in serverless consistency.

Cloudburst: Stateful Functions-as-a-Service

Serverless Computing: One Step Forward, Two Steps Back

In order to guarantee recoverable transaction execution, database systems permit a transaction's writes to be observable only at the end of its execution. As a consequence, there is generally a delay between the time a transaction performs a write and the time later transactions are permitted to read it. This delayed write visibility can significantly impact the performance of serializable database systems by reducing concurrency among conflicting transactions.This paper makes the observation that delayed write visibility stems from the fact that database systems can arbitrarily abort transactions at any point during their execution. Accordingly, we make the case for database systems which only abort transactions under a restricted set of conditions, thereby enabling a new recoverability mechanism, early write visibility, which safely makes transactions' writes visible prior to the end of their execution. We design a new serializable concurrency control protocol, piece-wise visibility (PWV), with the explicit goal of enabling early write visibility. We evaluate PWV against state-of-the-art serializable protocols and a highly optimized implementation of read committed, and find that PWV can outperform serializable protocols by an order of magnitude and read committed by 3X on high contention workloads.

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Jose M. Faleiro

Papers

Serverless Computing: One Step Forward, Two Steps Back.

Rethinking serializable multiversion concurrency control

Cloudburst: Stateful Functions-as-a-Service

Serverless Computing: One Step Forward, Two Steps Back

High performance transactions via early write visibility