Serverless Computing Research Articles

The Rise of Serverless Architectures: Security Challenges and Best Practices

The field of serverless computing has had significant growth and recognition in the past decade. This emerging area has garnered attention because to its notable impact on cost reduction, latency reduction, scalability improvement, and elimination of server-side management, among other benefits. Nevertheless, there is still a dearth of comprehensive study that would facilitate developers and academics in gaining a more profound comprehension of the importance of serverless computing in many scenarios. Therefore, it is imperative to provide scholarly study data that has been published within this particular field. This study conducted a comprehensive analysis of 275 scholarly articles retrieved from reputable literature sources, with the aim of extracting valuable insights pertaining to serverless computing. Subsequently, the acquired data underwent analysis in order to address many study inquiries pertaining to the contemporary advancements in serverless computing, encompassing its fundamental principles, available platforms, and patterns of utilization, among other relevant aspects. In addition, we analyze the current obstacles confronting serverless computing and explore potential avenues for future research to facilitate its deployment and utilization.

Faas‐sim: A trace‐driven simulation framework for serverless edge computing platforms

AbstractThis paper presentsfaas‐sim, a simulation framework tailored to serverless edge computing platforms. In serverless computing, platform operators are tasked with efficiently managing distributed computing infrastructure completely abstracted from application developers. To that end, platform operators and researchers need tools to design, build, and evaluate resource management techniques that efficiently use of infrastructure while optimizing application performance. This challenge is exacerbated in edge computing scenarios, where, compared to cloud computing, there is a lack of reference architectures, design tools, or standardized benchmarks.faas‐simbridges this gap by providing (a) a generalized model of serverless systems that builds on the function‐as‐a‐service abstraction, (b) a simulator that uses trace data from real‐world edge computing testbeds and representative workloads, and (c) a network topology generator to model and simulate distributed and heterogeneous edge‐cloud systems. We present the conceptual design, implementation, and a thorough evaluation offaas‐sim. By running experiments on both real‐world test beds and replicating them usingfaas‐sim, we show that the simulator provides accurate results and reasonable simulation performance. We have profiled a wide range of edge computing infrastructure and workloads, focusing on typical edge computing scenarios such as edge AI inference or data processing. Moreover, we present several instances where we have successfully usedfaas‐simto either design, optimize, or evaluate serverless edge computing systems.

Challenges and Opportunities in Sustainable Serverless Computing

Serverless computing has rapidly emerged as a popular deployment model. However, its energy and carbon implications are unclear and require exploration. This paper takes a look at the fundamental distinguishing attributes of serverless functions, and shows how some of them make energy-efficiency challenging. The programming model and deployment requirements of serverless functions makes them terribly energy inefficient---consuming more than 15× energy compared to conventional web services. On the bright side, FaaS is still actively expanding, and there is also an opportunity for rethinking FaaS resource management and deployment models, and make carbon efficiency a primary consideration. We present a few such techniques: moving functions to energy-friendly locations in distributed edge clouds; machine learning based modeling and control; and function-level demand response that combines ideas from approximate computing. Together, these represent a possible path forward to making serverless cloud computing sustainable.

SD-SRF: An Intelligent Service Deployment Scheme for Serverless-operated Cloud-Edge Computing in 6G Networks

With the development of serverless computing, developers can implement and deploy business applications as a combination of stateless functions. Although originally proposed for cloud computing, serverless computing is gradually applied to cloud-edge systems for service deployment to provide users with high-quality, low-latency services. However, optimized service deployment in 6G networks is a very challenging issue because of the vast number of deployable devices in the network, and its permutations are highly exponential. In this paper, we propose optimized service deployment schemes for online and offline, respectively, to minimize the overall latency at a lower cost. (1) First, a SD-SRF algorithm based on the greedy algorithm is proposed to optimize the service deployment for a multi-layer edge network, which consists of two phases: SR and SF. (a) Services are deployed in the nearest ancestor devices in the routing tree of all such service requests with the least cost of deployment. (b) When the deployment cost of moving some service replicas to devices in the lower layer is less than the benefit, the service will fall. (2) However, the offline algorithm relies on the availability of prior information heavily, such as request arrival pattern and number, which is difficult to obtain. Therefore, this paper proposes a PPO-MSD algorithm for optimal deployment online, where a Markov decision process (MDP) is modeled. Extensive simulation results show that PPO-MSD outperforms existing algorithms in terms of overall delay and utility, and its performance is close to the optimal ones obtained by SD-SRF, with the SD-SRF and PPO-MSD algorithms reducing the delay on average by 32.40% and 9.91%.

MLLess: Achieving cost efficiency in serverless machine learning training

Function-as-a-Service (FaaS) has raised a growing interest in how to “tame” serverless computing to enable domain-specific use cases such as data-intensive applications and machine learning (ML), to name a few. Recently, several systems have been implemented for training ML models. Certainly, these research articles are significant steps in the correct direction. However, they do not completely answer the nagging question of when serverless ML training can be more cost-effective compared to traditional “serverful” computing. To help in this endeavor, we propose MLLess, a FaaS-based ML training prototype built atop IBM Cloud Functions. To boost cost-efficiency, MLLess implements two innovative optimizations tailored to the traits of serverless computing: on one hand, a significance filter, to make indirect communication more effective, and on the other hand, a scale-in auto-tuner, to reduce cost by benefiting from the FaaS sub-second billing model (often per 100 ms). Our results certify that MLLess can be 15X faster than serverful ML systems [27] at a lower cost for sparse ML models that exhibit fast convergence such as sparse logistic regression and matrix factorization. Furthermore, our results show that MLLess can easily scale out to increasingly large fleets of serverless workers.

ESMA: Towards elevating system happiness in a decentralized serverless edge computing framework

Due to the rapid growth in the adoption of numerous technologies, such as smartphones and the Internet of Things (IoT), edge and serverless computing have started gaining momentum in today's computing infrastructure. It has led to the production of huge amounts of data and has also resulted in increased network traffic, which if not managed well can cause network congestion. To address this and maintain the quality of service (QoS), in this work, a novel dispatch (destination selection) algorithm called Egalitarian Stable Matching Algorithm (ESMA) for faster data processing has been developed while also considering the best use of server resources in a decentralized Serverless-Edge environment. This will allow us to effectively utilize the enormous volumes of data that are generated. The proposed algorithm has been able to achieve lower overall dissatisfaction scores for the entire system. Individually, the client's happiness as well as the server's happiness have improved over the baseline. Moreover, there has been a drop of 25.7% in the total execution time and the total network resources consumed are lower as compared to the baseline algorithm as well as random-allocation algorithm.

Real-time air quality monitoring in Bull Trench Kiln-based Brick industry by calibrating sensor readings and utilizing the Serverless Computing

Bull Trench Kiln-based Brick industries are major contributors of Particulate Matter Pollutants (PM2.5 and PM10), which leads to deterioration of air quality, and hence needs to be monitored. As the static air monitoring stations require huge infrastructure and are sparsely located, it is necessary to find alternate ways using recent technologies like IoT, Machine Learning, and Serverless Computing. IoT devices being low cost and portable could be deployed near brick industries, however, they are sensitive to fluctuating ambient air temperature or humidity and hence have to be calibrated and there is a need for powerful back-end infrastructures for storage and analyses of data. The existing approach of transmitting IoT-based data to the cloud is time-consuming, energy inefficient, prone to scaling problems, has poor resource utilization, and is expensive. In this study, the IoT data are calibrated using ML Classifier algorithms and an Automatic Function Triggerer - Function-as-a-Service (AFT-FaaS) method is proposed, which is based on event-driven, Serverless method using AWS Lambda along with the Three-Tier Serverless architecture (Edge Tier, Fog Tire, and Cloud Tier) to deal with the challenges of delay, under-resource allocation, and to reduce the expenditures. The data transmitted to the cloud using the proposed AFT-FaaS method are indexed in ElasticSearch and Kibana is used for the analysis and visualization of data. While performing calibration, Random Forest Classifier was chosen as it had accuracy of above 98%. Finally, an in-depth pricing analysis is performed to better understand the difference between different configurations deployed for Serverless and non-Serverless Computing methods, which shows there is a significant reduction (7 times) of expenses incurred while using Serverless Computing than the non-Serverless Computing methods.

Serverless Computing: Advantages, Limitations and Use Cases

Serverless computing, also known simply as "serverless," represents a significant transformation in how applications are designed, deployed, and executed. Contrary to traditional methods where developers manage servers, serverless allows them to focus only on coding, trusting cloud providers with infrastructure responsibilities. This paradigm shift results in dynamic resource allocation, where users are billed based on actual resource consumption rather than pre-allocated capacities. The article elucidates serverless computing's nature, highlighting its core components - Function as a Service (FaaS) and Backend as a Service (BaaS). The benefits of serverless are underscored, including cost efficiency, inherent scalability, rapid development, and reduced operational demands. Yet, it is not without limitations. Concerns such as "cold starts," potential vendor lock-in, restricted customization, and specific security vulnerabilities are discussed. Practical serverless applications include web applications, data processing, IoT backends, chatbots, and ephemeral tasks. In conclusion, while serverless computing heralds a new age in cloud technology, businesses are encouraged to discerningly evaluate its pros and cons, mainly as the landscape evolves. The future is serverless, prompting organizations to determine their readiness for this revolution.

SNDVI: a new scalable serverless framework to compute NDVI

Farmers and agronomists require crop health metrics to monitor plantations and detect problems like diseases or droughts at an early stage. This enables them to implement measures to address crop problems. The use of multispectral images and cloud computing is conducive to obtaining such metrics. Drones and satellites capture extensive multispectral image datasets, while the cloud facilitates the storage of these images and provides execution services for extracting crop health metrics, such as the Normalized Difference Vegetation Index (NDVI). The use of the Cloud to compute NDVI poses new research challenges, such as determining which cloud technology offers the optimal balance of execution time and monetary cost. In this article, we present Serverless NDVI (SNDVI), a new framework based on serverless computing for NDVI computation. The objective of SNDVI is to minimize the monetary costs and computing times associated with using a Public Cloud while processing NDVI from large datasets. One of SNDVI's key contributions is to crop the dataset into subsegments to leverage Lambda's ability to run up to 1,000 NDVI computing functions in parallel on each subsegment. We deployed SNDVI using Amazon Lambda and conducted two experiments to analyze and validate its performance. Both experiments focused on two key metrics: (i) execution time and (ii) monetary costs. The first experiment involved executing SNDVI to extract NDVI from a multispectral dataset. The objective was to evaluate the overall SNDVI functionality, assess its performance, and verify the quality of SNDVI output. In the second experiment, we conducted a benchmarking analysis comparing SNDVI with an EC2-based NDVI computing architecture. Results from the first experiment demonstrated that the processing times for the entire SNDVI execution ranged from 9 to 15 seconds, with a total cost (including storage) of 4.19 USD. Results from the second experiment revealed that the monetary costs of EC2 and Lambda were similar, but the computing time for SNDVI was 411 times faster than the EC2 architecture. In conclusion, the investigation reported in this paper demonstrates that SNDVI successfully achieves its goals and that Serverless Computing presents a promising native serverless alternative to traditional cloud services for NDVI computation.

Serverless Computing: What It Is, and What It Is Not?

Dispelling the confusion around serverless computing by capturing its essential and conceptual characteristics.

FaaSLight : General Application-level Cold-start Latency Optimization for Function-as-a-Service in Serverless Computing

Serverless computing is a popular cloud computing paradigm that frees developers from server management. Function-as-a-Service (FaaS) is the most popular implementation of serverless computing, representing applications as event-driven and stateless functions. However, existing studies report that functions of FaaS applications severely suffer from cold-start latency. In this article, we propose an approach, namely, FaaSLight , to accelerating the cold start for FaaS applications through application-level optimization. We first conduct a measurement study to investigate the possible root cause of the cold-start problem of FaaS. The result shows that application code loading latency is a significant overhead. Therefore, loading only indispensable code from FaaS applications can be an adequate solution. Based on this insight, we identify code related to application functionalities by constructing the function-level call graph and separate other code (i.e., optional code) from FaaS applications. The separated optional code can be loaded on demand to avoid the inaccurate identification of indispensable code causing application failure. In particular, a key principle guiding the design of FaaSLight is inherently general, i.e., platform - and language-agnostic . In practice, FaaSLight can be effectively applied to FaaS applications developed in different programming languages (Python and JavaScript), and can be seamlessly deployed on popular serverless platforms such as AWS Lambda and Google Cloud Functions, without having to modify the underlying OSes or hypervisors, nor introducing any additional manual engineering efforts to developers. The evaluation results on real-world FaaS applications show that FaaSLight can significantly reduce the code loading latency (up to 78.95%, 28.78% on average), thereby reducing the cold-start latency. As a result, the total response latency of functions can be decreased by up to 42.05% (19.21% on average). Compared with the state-of-the-art, FaaSLight achieves a 21.25× improvement in reducing the average total response latency.

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 article provides a comprehensive literature review to characterize the current research state of serverless computing. Specifically, this article covers 164 articles on 17 research directions of serverless computing, including performance optimization, programming framework, application migration, multi-cloud development, testing and debugging, and so on. It also derives research trends, focus, and commonly-used platforms for serverless computing, as well as promising research opportunities.

Workflow Scheduling in Serverless Edge Computing for the Industrial Internet of Things: A Learning Approach

Serverless edge computing is seen as a promising enabler to execute differentiated Industrial Internet of Things (IIoT) applications without managing the underlying servers and clusters. In IIoT serverless edge computing, IIoT workflow scheduling for cloud-edge collaborative processing is closely related to the service quality of users. However, serverless functions decomposed by IIoT applications are limited in their deployment at the edge due to the resource-constrained nature of edge infrastructures. Additionally, the scheduling of complex IIoT applications supported by serverless computing is more challenging. Therefore, considering the limited function deployment and the complex dependencies of serverless workflows, we model the workflow application as Directed Acyclic Graph (DAG) and formulate the scheduling problem as a multi-objective optimization problem. A Dueling Double Deep Q-Network (D3QN)-based solution is proposed to make scheduling decisions under dynamically changing systems. Extensive simulation experiments are conducted to validate the superiority of the proposed scheme.

Reproducible and Portable Big Data Analytics in the Cloud

Cloud computing has become a major approach to help reproduce computational experiments. Yet there are still two main difficulties in reproducing batch based big data analytics (including descriptive and predictive analytics) in the cloud. The first is how to automate end-to-end scalable execution of analytics including distributed environment provisioning, analytics pipeline description, parallel execution, and resource termination. The second is that an application developed for one cloud is difficult to be reproduced in another cloud, a.k.a. vendor lock-in problem. To tackle these problems, we leverage serverless computing and containerization techniques for automated scalable execution and reproducibility, and utilize the adapter design pattern to enable application portability and reproducibility across different clouds. We propose and develop an open-source toolkit that supports 1) fully automated end-to-end execution and reproduction via a single command, 2) automated data and configuration storage for each execution, 3) flexible client modes based on user preferences, 4) execution history query, and 5) simple reproduction of existing executions in the same environment or a different environment. We did extensive experiments on both AWS and Azure using four big data analytics applications that run on virtual CPU/GPU clusters. The experiments show our toolkit can achieve good execution performance, scalability, and efficient reproducibility for cloud-based big data analytics.

WebAssembly as an Enabler for Next Generation Serverless Computing

WebAssembly as an Enabler for Next Generation Serverless Computing

Efficiency in the serverless cloud paradigm: A survey on the reusing and approximation aspects

SummaryServerless computing along with Function‐as‐a‐Service (FaaS) is forming a new computing paradigm that is anticipated to found the next generation of cloud systems. The popularity of this paradigm is due to offering a highly transparent infrastructure that enables user applications to scale in the granularity of their functions. Since these often small and single‐purpose functions are managed on shared computing resources behind the scene, a great potential for computational reuse and approximate computing emerges that if unleashed, can remarkably improve the efficiency of serverless cloud systems—both from the user's QoS and system's (energy consumption and incurred cost) perspectives. Accordingly, the goal of this survey study is to, first, unfold the internal mechanics of serverless computing and, second, explore the scope for efficiency within this paradigm via studying function reuse and approximation approaches and discussing the pros and cons of each one. Next, we outline potential future research directions within this paradigm that can either unlock new use cases or make the paradigm more efficient.

Serverless Architecture for Data Processing and Detecting Anomalies with the Mars Express MARSIS Instrument

The Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) on board Mars Express has been sampling the topside ionosphere of Mars since mid-2005. The analysis of the main reflection (nadir) of the ionosphere through the ionograms provided by the MARSIS instrument is typically performed manually due to the high noise level in the lower frequencies. This task, which involves pattern recognition, turns out to be unfeasible for the >2 million ionograms available at the European Planetary Science Archive. In the present contribution, we propose a modular architecture based on serverless computing (a paradigm that stands on the cloud) for optimal processing of these ionograms. In particular, we apply serverless computing to detect oblique echoes in the ionosphere, which are nonnadir reflections produced when MARSIS is sounding regions above or nearby crustal magnetic fields, where the ionosphere loses the spherical symmetry. Oblique echoes are typically observed at similar frequencies to the nadir reflections but at different times delays, sometimes even overlaying the nadir reflection. Oblique echoes are difficult to analyze with the standard technique due to their nonconstant and highly variable appearance, but they harbor essential information on the state of the ionosphere over magnetized regions. In this work we compare the proposed serverless architecture with two local alternatives while processing a representative data subset and finally provide a study by means of cost and performance.

A Fast Cold-Start Solution: Container Space Reuse Based on Resource Isolation

With the emergence of cloud-native computing, serverless computing has become a popular way to deploy intensive applications due to its scalability and flexibility, and it has been increasingly applied in the field of big data processing on service platforms. Currently, the development momentum of cloud-native computing is strong, and serverless computing has become more attractive to the growing number of Internet services. However, how to more effectively address the issues of container resource usage and service startup time for serverless computing remains a huge challenge when exploring its potential. Our research is based on the complete life cycle of serverless functions and improves the performance of serverless computing by changing the original method of exchanging space for time or time for space. We focus on how to shorten the cold-start time of serverless computing while maximizing the usage of container resources. The research innovation is the dynamic control of functions and container pools, which mainly includes three aspects. First, we create a container pool with the classification identification based on the usage rate of functions. Then, we use namespace technology to achieve container resource reuse in the security isolated state. Next, we adaptively match the correspondence between functions and reusable container resources through system resource monitoring. Finally, the test results prove that converting the remaining space resources of the container into a prewarm container for new functions can effectively reduce the resource waste caused by idle function-containers, and container resource reuse can further shorten the cold-start time while ensuring the safety and isolation of functions. Compared to other open-source serverless platforms, our solution can reduce the cold-start time of general function calls to less than 20 ms and improve the ability to alleviate cold starts by 90% without enabling container prewarming.

Serverless and IaC

Abstract: The objective of this research paper is to investigate the potential advantages of integrating Terraform and Serverless Computing to construct a scalable and efficient cloud infrastructure. Terraform, an infrastructure-as-code open-source tool, and Serverless Computing, a new paradigm that enables developers to run code without worrying about the underlying infrastructure, are briefly described along with their benefits. The paper then explores how these two can be combined to build a dynamic and robust infrastructure while also addressing the difficulties that arise when constructing a Serverless Computing infrastructure with Terraform, proposing solutions to overcome them. Finally, the study concludes that integrating Terraform and Serverless Computing can help organizations create an efficient, adaptable, and scalable infrastructure while decreasing expenses and enhancing developer productivity.

Massive Parallel Alignment of RNA-seq Reads in Serverless Computing

In recent years, the use of Cloud infrastructures for data processing has proven useful, with a computing potential that is not affected by the limitations of a local infrastructure. In this context, Serverless computing is the fastest-growing Cloud service model due to its auto-scaling methodologies, reliability, and fault tolerance. We present a solution based on in-house Serverless infrastructure, which is able to perform large-scale RNA-seq data analysis focused on the mapping of sequencing reads to a reference genome. The main contribution was bringing the computation of genomic data into serverless computing, focusing on RNA-seq read-mapping to a reference genome, as this is the most time-consuming task for some pipelines. The proposed solution handles massive parallel instances to maximize the efficiency in terms of running time. We evaluated the performance of our solution by performing two main tests, both based on the mapping of RNA-seq reads to Human GRCh38. Our experiments demonstrated a reduction of 79.838%, 90.079%, and 96.382%, compared to the local environments with 16, 8, and 4 virtual cores, respectively. Furthermore, serverless limitations were investigated.