| 1. |
- Eismann, Simon, et al.
(författare)
-
The State of Serverless Applications: Collection, Characterization, and Community Consensus
- 2022
-
Ingår i: IEEE Transactions on Software Engineering. - 0098-5589 .- 1939-3520. ; 48:10, s. 4152-4166
-
Tidskriftsartikel (refereegranskat)abstract
- Over the last five years, all major cloud platform providers have increased their serverless offerings. Many early adopters report significant benefits for serverless-based over traditional applications, and many companies are considering moving to serverless themselves. However, currently there exist only few, scattered, and sometimes even conflicting reports on when serverless applications are well suited and what the best practices for their implementation are. We address this problem in the present study about the state of serverless applications. We collect descriptions of 89 serverless applications from open-source projects, academic literature, industrial literature, and domain-specific feedback. We analyze 16 characteristics that describe why and when successful adopters are using serverless applications, and how they are building them. We further compare the results of our characterization study to 10 existing, mostly industrial, studies and datasets; this allows us to identify points of consensus across multiple studies, investigate points of disagreement, and overall confirm the validity of our results. The results of this study can help managers to decide if they should adopt serverless technology, engineers to learn about current practices of building serverless applications, and researchers and platform providers to better understand the current landscape of serverless applications.
|
|
| 2. |
- Scheuner, Joel, 1991, et al.
(författare)
-
CrossFit: Fine-grained Benchmarking of Serverless Application Performance across Cloud Providers
- 2022
-
Ingår i: Proceedings - 2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing, UCC 2022. ; , s. 51-60
-
Konferensbidrag (refereegranskat)abstract
- Serverless computing emerged as a promising cloud computing paradigm for deploying cloud-native applications but raises new performance challenges. Existing performance evaluation studies focus on micro-benchmarking to measure an individual aspect of serverless functions, such as CPU speed, but lack an in-depth analysis of differences in application performance across cloud providers. This paper presents CrossFit, an approach for detailed and fair cross-provider performance benchmarking of serverless applications based on a providerindependent tracing model. Our case study demonstrates how detailed distributed tracing enables drill-down analysis to explain performance differences between two leading cloud providers, AWS and Azure. The results for an asynchronous application show that trigger time contributes most delay to the end-to-end latency and explains the main performance difference between cloud providers. Our results further reveal how increasing and bursty workloads affect performance stability, median latency, and tail latency.
|
|
| 3. |
- Scheuner, Joel, 1991
(författare)
-
Performance Evaluation of Serverless Applications and Infrastructures
- 2022
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
|
|
| 4. |
- Scheuner, Joel, 1991, et al.
(författare)
-
TriggerBench: A Performance Benchmark for Serverless Function Triggers
- 2022
-
Ingår i: Proceedings - 2022 IEEE International Conference on Cloud Engineering, IC2E 2022. ; , s. 96-103
-
Konferensbidrag (refereegranskat)abstract
- Serverless computing offers a scalable event-based paradigm for deploying managed cloud-native applications. Function triggers are essential building blocks in serverless, as they initiate any function execution. However, function triggering is insufficiently studied and inherently hard to measure given the distributed, ephemeral, and asynchronous nature of event-based function coordination. To address this gap, we present TriggerBench, a cross-provider benchmark for evaluating serverless function triggers based on distributed tracing. We evaluate the trigger latency (i.e., time to transition between two functions) of eight types of triggers in Microsoft Azure and three in AWS. Our results show that all triggers suffer from long tail latency, storage triggers introduce variable multi-second delays, and HTTP triggers are most suitable for interactive applications. Our insights can guide developers in choosing optimal event or messaging triggers for latency-sensitive applications. Researchers can extend TriggerBench to study the latency, scalability, and reliability of further trigger types and cloud providers.
|
|
| 5. |
- Schirmer, Trever, et al.
(författare)
-
Fusionize: Improving Serverless Application Performance through Feedback-Driven Function Fusion
- 2022
-
Ingår i: Proceedings - 2022 IEEE International Conference on Cloud Engineering, IC2E 2022. ; , s. 85-95
-
Konferensbidrag (refereegranskat)abstract
- Serverless computing increases developer productivity by removing operational concerns such as managing hardware or software runtimes. Developers, however, still need to partition their application into functions, which can be error-prone and adds complexity: Using a small function size where only the smallest logical unit of an application is inside a function maximizes flexibility and reusability. Yet, having small functions leads to invocation overheads, additional cold starts, and may increase cost due to double billing during synchronous invocations. In this paper we present Fusionize, a framework that removes these concerns from developers by automatically fusing the application code into a multi-function orchestration with varying function size. Developers only need to write the application code following a lightweight programming model and do not need to worry how the application is turned into functions. Our framework automatically fuses different parts of the application into functions and manages their interactions. Leveraging monitoring data, the framework optimizes the distribution of application parts to functions to optimize deployment goals such as end-to-end latency and cost. Using two example applications, we show that Fusionizecan automatically and iteratively improve the deployment artifacts of the application.
|
|
