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1. Executing Microservice Applications on Serverless, Correctly

   https://doi.org/10.1145/3571206  

   Kallas, Konstantinos ; Zhang, Haoran ; Alur, Rajeev ; Angel, Sebastian ; Liu, Vincent ( January 2023 , Proceedings of the ACM on Programming Languages)

   While serverless platforms substantially simplify the provisioning, configuration, and management of cloud applications, implementing correct services on top of these platforms can present significant challenges to programmers. For example, serverless infrastructures introduce a host of failure modes that are not present in traditional deployments. Individual serverless instances can fail while others continue to make progress, correct but slow instances can be killed by the cloud provider as part of resource management, and providers will often respond to such failures by re-executing requests. For functions with side-effects, these scenarios can create behaviors that are not observable in serverful deployments. In this paper, we propose mu2sls, a framework for implementing microservice applications on serverless using standard Python code with two extra primitives: transactions and asynchronous calls. Our framework orchestrates user-written services to address several challenges, such as failures and re-executions, and provides formal guarantees that the generated serverless implementations are correct. To that end, we present a novel service specification abstraction and formalization of serverless implementations that facilitate reasoning about the correctness of a given application’s serverless implementation. This formalization forms the basis of the mu2sls prototype, which we then use to develop a few real-world microservice applications and show that themore »performance of the generated serverless implementations achieves significant scalability (3-5× the throughput of a sequential implementation) while providing correctness guarantees in the context of faults, re-execution, and concurrency.« less
2. Tracking Causal Order in AWS Lambda Applications

   https://doi.org/10.1109/IC2E.2018.00027  

   Lin, Wei-Tsung ; Krintz, Chandra ; Wolski, Rich ; Zhang, Michael ; Cai, Xiaogang ; Li, Tongjun ; Xu, Weijin ( April 2018 , IEEE International Conference on Cloud Engineering (IC2E))

   Serverless computing is a new cloud programming and deployment paradigm that is receiving wide-spread uptake. Serverless offerings such as Amazon Web Services (AWS) Lambda, Google Functions, and Azure Functions automatically execute simple functions uploaded by developers, in response to cloud-based event triggers. The serverless abstraction greatly simplifies integration of concurrency and parallelism into cloud applications, and enables deployment of scalable distributed systems and services at very low cost. Although a significant first step, the serverless abstraction requires tools that software engineers can use to reason about, debug, and optimize their increasingly complex, asynchronous applications. Toward this end, we investigate the design and implementation of GammaRay, a cloud service that extracts causal dependencies across functions and through cloud services, without programmer intervention. We implement GammaRay for AWS Lambda and evaluate the overheads that it introduces for serverless micro-benchmarks and applications written in Python.
3. Self-Learning Threshold-Based Load Balancing

   https://doi.org/10.1287/ijoc.2021.1100  

   Goldsztajn, Diego ; Borst, Sem C. ; van Leeuwaarden, Johan S. ; Mukherjee, Debankur ; Whiting, Philip A. ( January 2022 , INFORMS Journal on Computing)

   We consider a large-scale service system where incoming tasks have to be instantaneously dispatched to one out of many parallel server pools. The user-perceived performance degrades with the number of concurrent tasks and the dispatcher aims at maximizing the overall quality of service by balancing the load through a simple threshold policy. We demonstrate that such a policy is optimal on the fluid and diffusion scales, while only involving a small communication overhead, which is crucial for large-scale deployments. In order to set the threshold optimally, it is important, however, to learn the load of the system, which may be unknown. For that purpose, we design a control rule for tuning the threshold in an online manner. We derive conditions that guarantee that this adaptive threshold settles at the optimal value, along with estimates for the time until this happens. In addition, we provide numerical experiments that support the theoretical results and further indicate that our policy copes effectively with time-varying demand patterns. Summary of Contribution: Data centers and cloud computing platforms are the digital factories of the world, and managing resources and workloads in these systems involves operations research challenges of an unprecedented scale. Due to the massive size,more »complex dynamics, and wide range of time scales, the design and implementation of optimal resource-allocation strategies is prohibitively demanding from a computation and communication perspective. These resource-allocation strategies are essential for certain interactive applications, for which the available computing resources need to be distributed optimally among users in order to provide the best overall experienced performance. This is the subject of the present article, which considers the problem of distributing tasks among the various server pools of a large-scale service system, with the objective of optimizing the overall quality of service provided to users. A solution to this load-balancing problem cannot rely on maintaining complete state information at the gateway of the system, since this is computationally unfeasible, due to the magnitude and complexity of modern data centers and cloud computing platforms. Therefore, we examine a computationally light load-balancing algorithm that is yet asymptotically optimal in a regime where the size of the system approaches infinity. The analysis is based on a Markovian stochastic model, which is studied through fluid and diffusion limits in the aforementioned large-scale regime. The article analyzes the load-balancing algorithm theoretically and provides numerical experiments that support and extend the theoretical results.« less
4. STOIC: Serverless TeleOperable Hybrid Cloud for Machine Learning Applications on Edge Device

   Zhang, M ; Krintz, C ; Wolski, R. ( March 2020 , International Workshop on Smart Edge Computing and Networking)

   Serverless computing is a promising new event- driven programming model that was designed by cloud vendors to expedite the development and deployment of scalable web services on cloud computing systems. Using the model, developers write applications that consist of simple, independent, stateless functions that the cloud invokes on-demand (i.e. elastically), in response to system-wide events (data arrival, messages, web requests, etc.). In this work, we present STOIC (Serverless TeleOperable HybrId Cloud), an application scheduling and deployment system that extends the serverless model in two ways. First, it uses the model in a distributed setting and schedules application functions across multiple cloud systems. Second, STOIC sup- ports serverless function execution using hardware acceleration (e.g. GPU resources) when available from the underlying cloud system. We overview the design and implementation of STOIC and empirically evaluate it using real-world machine learning applications and multi-tier (e.g. edge-cloud) deployments. We find that STOIC’s combined use of edge and cloud resources is able to outperform using either cloud in isolation for the applications and datasets that we consider.
5. Scalog: Seamless Reconfiguration and Total Order in a Scalable Shared Log

   Ding, Cong ; Chu, David ; Zhao, Evan ; Li, Xiang ; Alvisi, Lorenzo ; van Renesse, Robbert ( February 2020 , 17th USENIX Symposium on Networked Systems Design and Implementation)

   The shared log paradigm is at the heart of modern distributed applications in the growing cloud computing industry. Often, application logs must be stored durably for analytics, regulations, or failure recovery, and their smooth operation depends closely on how the log is implemented. Scalog is a new implementation of the shared log abstraction that offers an unprecedented combination of features for continuous smooth delivery of service: Scalog allows applications to customize data placement, supports reconfiguration with no loss in availability, and recovers quickly from failures. At the same time, Scalog provides high throughput and total order. The paper describes the design and implementation of Scalog and presents examples of applications running upon it. To evaluate Scalog at scale, we use a combination of real experiments and emulation. Using 4KB records, a 10 Gbps infrastructure, and SSDs, Scalog can totally order up to 52 million records per second.