The Grid 2: Blueprint for a New Computing Infrastructure

The Art of Computer Systems Performance Analysis (Techniques for Experimental Design, Measurement, Simulation, and Modeling)

Serverless computing is a rapidly growing cloud application model, popularized by Amazon's Lambda platform. Serverless cloud services provide fine-grained provisioning of resources, which scale automatically with user demand. Function-as-a-Service (FaaS) applications follow this serverless model, with the developer providing their application as a set of functions which are executed in response to a user- or system-generated event. Functions are designed to be short-lived and execute inside containers or virtual machines, introducing a range of system-level overheads. This paper studies the architectural implications of this emerging paradigm. Using the commercial-grade Apache OpenWhisk FaaS platform on real servers, this work investigates and identifies the architectural implications of FaaS serverless computing. The workloads, along with the way that FaaS inherently interleaves short functions from many tenants frustrates many of the locality-preserving architectural structures common in modern processors. In particular, we find that: FaaS containerization brings up to 20x slowdown compared to native execution, cold-start can be over 10x a short function's execution time, branch mispredictions per kilo-instruction are 20x higher for short functions, memory bandwidth increases by 6x due to the invocation pattern, and IPC decreases by as much as 35% due to inter-function interference. We open-source FaaSProfiler, the FaaS testing and profiling platform that we developed for this work.

https://dl.acm.org/doi/pdf/10.1145/3352460.3358296

Architectural Implications of Function-as-a-Service Computing

In April 1956, a refitted oil tanker carried fifty-eight shipping containers from Newark to Houston. From that modest beginning, container shipping developed into a huge industry that made the boom in global trade possible. The Box tells the dramatic story of the container's creation, the decade of struggle before it was widely adopted, and the sweeping economic consequences of the sharp fall in transportation costs that containerization brought about. Published on the fiftieth anniversary of the first container voyage, this is the first comprehensive history of the shipping container. It recounts how the drive and imagination of an iconoclastic entrepreneur, Malcom McLean, turned containerization from an impractical idea into a massive industry that slashed the cost of transporting goods around the world and made the boom in global trade possible. But the container didn't just happen. Its adoption required huge sums of money, both from private investors and from ports that aspired to be on the leading edge of a new technology. It required years of high-stakes bargaining with two of the titans of organized labor, Harry Bridges and Teddy Gleason, as well as delicate negotiations on standards that made it possible for almost any container to travel on any truck or train or ship. Ultimately, it took McLean's success in supplying U.S. forces in Vietnam to persuade the world of the container's potential. Drawing on previously neglected sources, economist Marc Levinson shows how the container transformed economic geography, devastating traditional ports such as New York and London and fueling the growth of previously obscure ones, such as Oakland. By making shipping so cheap that industry could locate factories far from its customers, the container paved the way for Asia to become the world's workshop and brought consumers a previously unimaginable variety of low-cost products from around the globe.

Introduction to The Box: How the Shipping Container Made the World Smaller and the World Economy Bigger

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

In the late-1950s, leasing time on an IBM 704 cost hundreds of dollars per minute. Today, cloud computing, that is, using IT as a service, on-demand and pay-per-use, is a widely used computing paradigm that offers large economies of scale. Born from a need to make platform as a service (PaaS) more accessible, fine-grained, and affordable, serverless computing has garnered interest from both industry and academia. This article aims to give an understanding of these early days of serverless computing: what it is, where it comes from, what is the current status of serverless technology, and what are its main obstacles and opportunities.

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Serverless is More: From PaaS to Present Cloud Computing

Cloud computing enables an entire ecosystem of developing, composing, and providing IT services. An emerging class of cloud-based software architectures, serverless, focuses on providing software architects the ability to execute arbitrary functions with small overhead in server management, as Function-as-a-service (FaaS). However useful, serverless and FaaS suffer from a community problem that faces every emerging technology, which has indeed also hampered cloud computing a decade ago: lack of clear terminology, and scattered vision about the field. In this work, we address this community problem. We clarify the term serverless, by reducing it to cloud functions as programming units, and a model of executing simple and complex (e.g., workflows of) functions with operations managed primarily by the cloud provider. We propose a research vision, where 4 key directions (perspectives) present 17 technical opportunities and challenges.

/pdf/the-spec-cloud-group-s-research-vision-on-faas-and-5fgsl0yuar.pdf

The SPEC cloud group's research vision on FaaS and serverless architectures

As a key part of the serverless computing paradigm, Function-as-a-Service (FaaS) platforms enable users to run arbitrary functions without being concerned about operational issues. However, there are several performance-related issues surrounding the state-of-the-art FaaS platforms that can deter widespread adoption of FaaS, including sizeable overheads, unreliable performance, and new forms of the cost-performance trade-off. In this work we, the SPEC RG Cloud Group, identify six performance-related challenges that arise specifically in this FaaS model, and present our roadmap to tackle these problems in the near future. This paper aims at motivating the community to solve these challenges together.

/pdf/a-spec-rg-cloud-group-s-vision-on-the-performance-challenges-wduiwz8ulq.pdf

A SPEC RG Cloud Group's Vision on the Performance Challenges of FaaS Cloud Architectures

Function-as-a-Service (FaaS) platforms enable users to run arbitrary functions without being concerned about operational issues, while only paying for the consumed resources. Individual functions are often composed into workflows for complex tasks. However, the pay-per-use model and nontransparent reporting by cloud providers make it challenging to estimate the expected cost of a workflow, which prevents informed business decisions. Existing cost-estimation approaches assume a static response time for the serverless functions, without taking input parameters into account. In this paper, we propose a methodology for the cost prediction of serverless workflows consisting of input-parameter sensitive function models and a monte-carlo simulation of an abstract workflow model. Our approach enables workflow designers to predict, compare, and optimize the expected costs and performance of a planned workflow, which currently requires time-intensive experimentation. In our evaluation, we show that our approach can predict the response time and output parameters of a function based on its input parameters with an accuracy of 96.1%. In a case study with two audio-processing workflows, our approach predicts the costs of the two workflows with an accuracy of 96.2%.

Predicting the Costs of Serverless Workflows

Microservices, containers, and serverless computing belong to a trend toward applications composed of many small, self-contained, and automatically managed components. Core to serverless computing, function-as-a-service (FaaS) platforms employ state-of-the-art container technology and microservices-based architectures to enable users to manage complex applications without the need for system-level expertise. Victim of its own success, and partially due to proprietary technology, currently the community has a limited overview of these platforms. To address this, we propose a reference architecture and ecosystem for FaaS platforms. Based on a year-long survey of real-world platforms conducted within the SPEC-RG Cloud Group, we highlight specific components and identify common operational patterns.

Erwin van Eyk

Papers

Serverless is More: From PaaS to Present Cloud Computing

The SPEC cloud group's research vision on FaaS and serverless architectures

A SPEC RG Cloud Group's Vision on the Performance Challenges of FaaS Cloud Architectures

Predicting the Costs of Serverless Workflows

The SPEC-RG Reference Architecture for FaaS: From Microservices and Containers to Serverless Platforms