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1. Real-time Serverless: Enabling Application Performance Guarantees

   https://doi.org/10.1145/3366623.3368133  

   Nguyen, Hai Duc ; Zhang, Chaojie ; Xiao, Zhujun ; Chien, Andrew A. ( December 2019 , Proceedings of the International Workshop on Serverless Computing)

   Today's serverless provides "function-as-a-service" with dynamic scaling and fine-grained resource charging, enabling new cloud applications. Serverless functions are invoked as a best-effort service. We propose an extension to serverless, called real-time serverless that provides an invocation rate guarantee, a service-level objective (SLO) specified by the application, and delivered by the underlying implementation. Real-time serverless allows applications to guarantee real-time performance. We study real-time serverless behavior analytically and empirically to characterize its ability to support bursty, real-time cloud and edge applications efficiently. Finally, we use a case study, traffic monitoring, to illustrate the use and benefits of real-time serverless, on our prototype implementation. 

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2. Motivating High Performance Serverless Workloads

   Nguyen, Hai Duc ; Yang, Zhifei ; Chien, Andrew A. ( June 2021 , THE 1ST WORKSHOP ON HIGH PERFORMANCE SERVERLESS COMPUTING)

   Foster, Ian ; Chard, Kyle ; Babuji, Yadu (Ed.)

   The historical motivation for serverless comes from internet-of-things, smartphone client server, and the objective of simplifying programming (no provisioning) and scale-down (pay-for-use). These applications are generally low-performance best-effort. However, the serverless model enables flexible software architectures suitable for a wide range of applications that demand high-performance and guaranteed performance. We have studied three such applications - scientific data streaming, virtual/augmented reality, and document annotation. We describe how each can be cast in a serverless software architecture and how the application performance requirements translate into high performance requirements (invocation rate, low and predictable latency) for the underlying serverless system implementation. These applications can require invocations rates as high as millions per second (40 MHz) and latency deadlines below a microsecond (300 ns), and furthermore require performance predictability. All of these capabilities are far in excess of today's commercial serverless offerings and represent interesting research challenges. 

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3. Architectural Implications of Function-as-a-Service Computing

   https://doi.org/10.1145/3352460.3358296  

   Shahrad, Mohammad ; Balkind, Jonathan ; Wentzlaff, David ( October 2019 , Proceedings of the 52nd International Symposium on Microarchitecture (MICRO-52))

   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. 

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4. Balancing Costs and Durability for Serverless Data

   Merenstein, Alex ; Wang, Xinran ; Tarasov, Vasily ; Agarwal, Prajjawal ; Guthridge, Scott ; Thakkar, Kapil ; Wu, Katherine ; Anwar, Ali ; Zadok, Erez ( June 2024 , IEEE)

   Durability features such as replication or erasure coding serve an important role in storage systems, enabling users to store data without fear of loss due to device failures. However, these durability features come with a cost, in terms of storage, network traffic, and computational overheads. For most data, loss is a catastrophic event and so these overheads are acceptable. However, some data tolerates low durability and does not need the high level of durability that most storage systems provide. Identifying the proper level of durability for a piece of data is difficult, especially since it is often not clear how to determine the cost of loss. For some data used in serverless applications, however, this cost is relatively straightforward to calculate: serverless functions are often required to be idempotent, meaning that the data produced by them can be re-created by re-running the function. The cost of losing a piece of data then is merely the cost of re-running the function that originally created the data. In this paper, we explore the tradeoff between the cost of storing data durably and the cost to re-create data. We focus on serverless data because its ability to be recreated makes it possible to assign a cost to its loss. We develop a mathematical model that relates compute costs, storage costs, and application-specific parameters to calculate the cost-optimal placement of data. We also develop an execution framework capable of handling lost data transparently, enabling applications to use lower-durability storage with no additional burden on the developer. Next, we show how different factors such as failure rate and compute costs affect the placement decision. We find that thanks to the relatively short lifetime of serverless data, the probability of data loss even on low-durability storage is fairly low. Finally, we use the model to place data for several applications, including a video-transcoding application and an image-assembly application. We show that our model can predict execution costs within 7% of actual execution costs, and can reduce storage costs by up to 3x while never exceeding baseline costs. 

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5. Reasoning about Uncertainties in Discrete-Time Dynamical Systems using Polynomial Forms

   Sankaranarayanan, Sriram ; Chou, Yi ; Goubault, Eric ; and Putot, Sylvie ( December 2020 , Advances in Neural Information Processing System (NeurIPS))

   null (Ed.)

   In this paper, we propose polynomial forms to represent distributions of state variables over time for discrete-time stochastic dynamical systems. This problem arises in a variety of applications in areas ranging from biology to robotics. Our approach allows us to rigorously represent the probability distribution of state variables over time, and provide guaranteed bounds on the expectations, moments and probabilities of tail events involving the state variables. First we recall ideas from interval arithmetic, and use them to rigorously represent the state variables at time t as a function of the initial state variables and noise symbols that model the random exogenous inputs encountered before time t. Next we show how concentration of measure inequalities can be employed to prove rigorous bounds on the tail probabilities of these state variables. We demonstrate interesting applications that demonstrate how our approach can be useful in some situations to establish mathematically guaranteed bounds that are of a different nature from those obtained through simulations with pseudo-random numbers. 

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