Autonomic computing investigates how systems can achieve (user) specified control outcomes on their own, without the intervention of a human operator. Autonomic computing fundamentals have been substantially influenced by those of control theory for closed and open-loop systems. In practice, complex systems may exhibit a number of concurrent and inter-dependent control loops. Despite research into autonomic models for managing computer resources, ranging from individual resources (e.g., web servers) to a resource ensemble (e.g., multiple resources within a data center), research into integrating Artificial Intelligence (AI) and Machine Learning (ML) to improve resource autonomy and performance at scale continues to be a fundamental challenge. The integration of AI/ML to achieve such autonomic and self-management of systems can be achieved at different levels of granularity, from full to human-in-the-loop automation. In this article, leading academics, researchers, practitioners, engineers, and scientists in the fields of cloud computing, AI/ML, and quantum computing join to discuss current research and potential future directions for these fields. Further, we discuss challenges and opportunities for leveraging AI and ML in next generation computing for emerging computing paradigms, including cloud, fog, edge, serverless and quantum computing environments. 

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AI for Next Generation Computing: Emerging Trends and Future Directions

Workflow scheduling is one of the challenging issues in emerging trends of the distributed environment that focuses on satisfying various quality of service (QoS) constraints. The cloud receives the applications as a form of a workflow, consisting of a set of interdependent tasks, to solve the large-scale scientific or enterprise problems. Workflow scheduling in the cloud environment has been studied extensively over the years, and this article provides a comprehensive review of the approaches. This article analyses the characteristics of various workflow scheduling techniques and classifies them based on their objectives and execution model. In addition, the recent technological developments and paradigms such as serverless computing and Fog computing are creating new requirements/opportunities for workflow scheduling in a distributed environment. The serverless infrastructures are mainly designed for processing background tasks such as Internet-of-Things (IoT), web applications, or event-driven applications. To address the ever-increasing demands of resources and to overcome the drawbacks of the cloud-centric IoT, the Fog computing paradigm has been developed. This article also discusses workflow scheduling in the context of these emerging trends of cloud computing.

A Survey on Scheduling Strategies for Workflows in Cloud Environment and Emerging Trends

Function-as-a-Service (FaaS) and serverless applications have proliferated significantly in recent years because of their high scalability, ease of resource management, and pay-as-you-go pricing model. However, cloud users are facing practical problems when they migrate their applications to the serverless pattern, which are the lack of analytical performance and billing model and the trade-off between limited budget and the desired quality of service of serverless applications. In this article, we fill this gap by proposing and answering two research questions regarding the prediction and optimization of performance and cost of serverless applications. We propose a new construct to formally define a serverless application workflow, and then implement analytical models to predict the average end-to-end response time and the cost of the workflow. Consequently, we propose a heuristic algorithm named Probability Refined Critical Path Greedy algorithm (PRCP) with four greedy strategies to answer two fundamental optimization questions regarding the performance and the cost. We extensively evaluate the proposed models by conducting experimentation on AWS Lambda and Step Functions. Our analytical models can predict the performance and cost of serverless applications with more than 98 percent accuracy. The PRCP algorithms can achieve the optimal configurations of serverless applications with 97 percent accuracy on average.

Modeling and Optimization of Performance and Cost of Serverless Applications

Cloud Functions, often called Function‐as‐a‐Service (FaaS), pioneered by AWS Lambda, are an increasingly popular method of running distributed applications. As in other cloud offerings, cloud functions are heterogeneous due to variations in underlying hardware, runtime systems, as well as resource management and billing models. In this paper, we focus on performance evaluation of cloud functions, taking into account heterogeneity aspects. We developed a cloud function benchmarking framework, consisting of one suite based on Serverless Framework and one based on HyperFlow. We deployed the CPU‐intensive benchmarks: Mersenne Twister and Linpack. We measured the data transfer times between cloud functions and storage, and we measured the lifetime of the runtime environment. We evaluated all the major cloud function providers: AWS Lambda, Azure Functions, Google Cloud Functions, and IBM Cloud Functions. We made our results available online and continuously updated. We report on the results of the performance evaluation, and we discuss the discovered insights into resource allocation policies.

Performance evaluation of heterogeneous cloud functions

This paper presents a review of cloud application architectures and its evolution. It reports observations being made during a research project that tackled the problem to transfer cloud applications between different cloud infrastructures. As a side effect, we learned a lot about commonalities and differences from plenty of different cloud applications which might be of value for cloud software engineers and architects. Throughout the research project, we analyzed industrial cloud standards, performed systematic mapping studies of cloud-native application-related research papers, did action research activities in cloud engineering projects, modeled a cloud application reference model, and performed software and domain-specific language engineering activities. Two primary (and sometimes overlooked) trends can be identified. First, cloud computing and its related application architecture evolution can be seen as a steady process to optimize resource utilization in cloud computing. Second, these resource utilization improvements resulted over time in an architectural evolution of how cloud applications are being built and deployed. A shift from monolithic service-oriented architectures (SOA), via independently deployable microservices towards so-called serverless architectures, is observable. In particular, serverless architectures are more decentralized and distributed, and make more intentional use of separately provided services. In other words, a decentralizing trend in cloud application architectures is observable that emphasizes decentralized architectures known from former peer-to-peer based approaches. This is astonishing because, with the rise of cloud computing (and its centralized service provisioning concept), the research interest in peer-to-peer based approaches (and its decentralizing philosophy) decreased. However, this seems to change. Cloud computing could head into the future of more decentralized and more meshed services.

https://www.mdpi.com/2076-3417/8/8/1368/pdf

A Brief History of Cloud Application Architectures

Serverless computing for container-based architectures

The advent of open-source serverless computing frameworks has introduced the ability to bring the Functions-as-a-Service (FaaS) paradigm for applications to be executed on-premises. In particular, data-driven scientific applications can benefit from these frameworks with the ability to trigger scalable computation in response to incoming workloads of files to be processed. This paper introduces an open-source framework to achieve on-premises serverless computing for event-driven data processing applications that features: i) the automated provisioning of an elastic Kubernetes cluster that can grow and shrink, in terms of the number of nodes, on multi-Clouds; ii) the automated deployment of a FaaS framework together with a data storage back-end that triggers events upon file uploads; iii) a service that provides a REST API to orchestrate the creation of such functions and iv) a graphical user interface that provides a unified entry point to interact with the aforementioned services. Together, this provides a framework to deploy a computing platform to create highly-parallel event-driven file-processing serverless applications that execute on customized runtime environments provided by Docker containers that run on an elastic Kubernetes cluster. The usefulness of this framework is exemplified by means of the execution of a data-driven workflow for optimised object detection on video. The workflow is tested under three different workloads which process ten, a hundred and a thousand functions. The results show that the presented architecture is able to process such workloads taking advantage of its elasticity to make a sensible usage of the resources.

On-Premises Serverless Computing for Event-Driven Data Processing Applications

Accelerated serverless computing based on GPU virtualization

Serverless computing has introduced unprecedented levels of scalability and parallelism for the execution of High Throughput Computing tasks. This represents a challenge and an opportunity for different scientific workloads to be adapted to upcoming programming models that simplify the usage of such platforms. In this paper we introduce a serverless model for highly-parallel file-processing applications. We also describe a middleware implementation that supports the execution of customized execution environments based on Docker images on AWS Lambda, the leading serverless computing platform. Moreover, this middleware offers tools to manage the input/output of the serverless infrastructure and the creation of HTTP endpoints in a transparent way to the user. To test the programming model proposed and the middleware, this paper describes two case studies. The first one analyzes medical images with a high degree of parallelism. The second one presents an architecture to process video keyframes. The results from both case studies are discussed and a cost analysis of the medical image architecture comparing different Cloud options is carried out. The results show that the combination of a high-level programming model with the scalable capabilities of AWS Lambda makes it easy for end users to efficiently exploit serverless computing for the optimized and cost-effective execution of loosely-coupled tasks.

Alfonso Pérez

Papers

Serverless computing for container-based architectures

On-Premises Serverless Computing for Event-Driven Data Processing Applications

Accelerated serverless computing based on GPU virtualization

A programming model and middleware for high throughput serverless computing applications

A Self-managed Mesos Cluster for Data Analytics with QoS Guarantees