Rise of the Planet of Serverless Computing: A Systematic Review

Serverless computing is an emerging cloud computing paradigm, being adopted to develop a wide range of software applications. It allows developers to focus on the application logic in the granularity of function, thereby freeing developers from tedious and error-prone infrastructure management. Meanwhile, its unique characteristic poses new challenges to the development and deployment of serverless-based applications. To tackle these challenges, enormous research efforts have been devoted. This paper provides a comprehensive literature review to characterize the current research state of serverless computing. Specifically, this paper covers 164 papers on 17 research directions of serverless computing, including performance optimization, programming framework, application migration, multi-cloud development, testing and debugging, etc. It also derives research trends, focus, and commonly-used platforms for serverless computing, as well as promising research opportunities

Performance Evaluation of Serverless Applications and Infrastructures

Context. Cloud computing has become the de facto standard for deploying modern web-based software systems, which makes its performance crucial to the efficient functioning of many applications. However, the unabated growth of established cloud services, such as Infrastructure-as-a-Service (IaaS), and the emergence of new serverless services, such as Function-as-a-Service (FaaS), has led to an unprecedented diversity of cloud services with different performance characteristics. Measuring these characteristics is difficult in dynamic cloud environments due to performance variability in large-scale distributed systems with limited observability.Objective. This thesis aims to enable reproducible performance evaluation of serverless applications and their underlying cloud infrastructure.Method. A combination of literature review and empirical research established a consolidated view on serverless applications and their performance. New solutions were developed through engineering research and used to conduct performance benchmarking field experiments in cloud environments.Findings. The review of 112 FaaS performance studies from academic and industrial sources found a strong focus on a single cloud platform using artificial micro-benchmarks and discovered that most studies do not follow reproducibility principles on cloud experimentation. Characterizing 89 serverless applications revealed that they are most commonly used for short-running tasks with low data volume and bursty workloads. A novel trace-based serverless application benchmark shows that external service calls often dominate the median end-to-end latency and cause long tail latency. The latency breakdown analysis further identifies performance challenges of serverless applications, such as long delays through asynchronous function triggers, substantial runtime initialization for coldstarts, increased performance variability under bursty workloads, and heavily provider-dependent performance characteristics. The evaluation of different cloud benchmarking methodologies has shown that only selected micro-benchmarks are suitable for estimating application performance, performance variability depends on the resource type, and batch testing on the same instance with repetitions should be used for reliable performance testing.Conclusions. The insights of this thesis can guide practitioners in building performance-optimized serverless applications and researchers in reproducibly evaluating cloud performance using suitable execution methodologies and different benchmark types

A Dependency Tracking Storage System for Optimistic Execution of Serverless Applications

Serverless computing has become an increasingly popular paradigm for building cloud applications. There has been a recent trend of building stateful applications on top of serverless platforms in the form of workflows composed of individual functions. As functions are short-lived and state is not recoverable across function invocations, these applications typically store state that is used between functions in an external storage system. Such storage systems should enforce concurrency control, as different workflow instances may update overlapping state simultaneously. However, existing concurrency control algorithms typically incur significant latency due to locking or read/write set validation. This is undesirable, since execution latency is an important performance metric for workflow applications as each stage is executed sequentially. Furthermore, they can abort transactions in a manner that is oblivious to application preferences. In this thesis, we present Arbor, a sharded dependency-tracking storage system designed for optimistic execution of serverless workflows while ensuring serializability. Arbor introduces a two-round commit model where submitted client transactions are organized in a dependency graph. Transactions are then processed in batches, off the critical path of client execution, allowing clients to continue executing quickly without having to wait for Arbor to validate each transaction. As Arbor processes transactions, it organizes them into a tree where each branch is a serialized execution and conflicts result in new branches being created. It then commits one branch from this tree and prunes the rest. To minimize re-executions, Arbor chooses the longest branch by default, but application developers can implement their own policies. Pruning branches is simple with Arbor, since it can re-execute the corresponding transactions by invoking the respective functions from the serverless platform. Furthermore, Arbor is designed to be scalable. Data is partitioned by key, but the metadata of its dependency graph is replicated. This design allows single-shard transactions in each batch to be processed independently, while multi-shard transactions are replicated and processed by each shard. Our evaluation on a cluster of machines shows that Arbor’s two-round commit model reduces transaction execution latency by a median value of 1.26x when compared to a system that uses OCC and commits transactions synchronously

New Directions in Cloud Programming

Nearly twenty years after the launch of AWS, it remains difficult for most developers to harness the enormous potential of the cloud. In this paper we lay out an agenda for a new generation of cloud programming research aimed at bringing research ideas to programmers in an evolutionary fashion. Key to our approach is a separation of distributed programs into a PACT of four facets: Program semantics, Availablity, Consistency and Targets of optimization. We propose to migrate developers gradually to PACT programming by lifting familiar code into our more declarative level of abstraction. We then propose a multi-stage compiler that emits human-readable code at each stage that can be hand-tuned by developers seeking more control. Our agenda raises numerous research challenges across multiple areas including language design, query optimization, transactions, distributed consistency, compilers and program synthesis