A Novel Approach for Triggering the Serverless Function in Serverless Environment

Serverless computing has gained significant popularity in recent years due to its scalability, cost efficiency, and simplified development process. In a serverless environment, functions are the basic units of computation that are executed on-demand, without the need for provisioning and managing servers. However, efficiently triggering serverless functions remains a challenge, as traditional methodologies often suffer from latency, Time limit and scalability issues and the efficient execution and management of serverless functions heavily rely on effective triggering mechanisms. This research paper explores various design considerations and proposes a novel approach for designing efficient triggering mechanisms in serverless environments. By leveraging our proposed methodology, developers can efficiently trigger serverless functions in a variety of scenarios, including event-driven architectures, data processing pipelines, and web application backend

Triggerflow: Trigger-based Orchestration of Serverless Workflows

As more applications are being moved to the Cloud thanks to serverless computing, it is increasingly necessary to support native life cycle execution of those applications in the data center. But existing systems either focus on short-running workflows (like IBM Composer or Amazon Express Workflows) or impose considerable overheads for synchronizing massively parallel jobs (Azure Durable Functions, Amazon Step Functions, Google Cloud Composer). None of them are open systems enabling extensible interception and optimization of custom workflows. We present Triggerflow: an extensible Trigger-based Orchestration architecture for serverless workflows built on top of Knative Eventing and Kubernetes technologies. We demonstrate that Triggerflow is a novel serverless building block capable of constructing different reactive schedulers (State Machines, Directed Acyclic Graphs, Workflow as code). We also validate that it can support high-volume event processing workloads, auto-scale on demand and transparently optimize scientific workflows.Comment: The 14th ACM International Conference on Distributed and Event-based Systems (DEBS 2020

A Data-parallel Approach for Efficient Resource Utilization in Distributed Serverless Deep Learning

Serverless computing is an integral part of the recent success of cloud computing, offering cost and performance efficiency for small and large scale distributed systems. Owing to the increasing interest of developers in integrating distributed computing techniques into deep learning frameworks for better performance, serverless infrastructures have been the choice of many to host their applications. However, this computing architecture bears resource limitations which challenge the successful completion of many deep learning jobs. In our research, an approach is presented to address timeout and memory resource limitations which are two key issues to deep learning on serverless infrastructures. Focusing on Apache OpenWhisk as severless platform, and TensorFlow as deep learning framework, our solution follows an in-depth assessment of the former and failed attempts at tackling resource constraints through system-level modifications. The proposed approach employs data parallelism and ensures the concurrent execution of separate cloud functions. A weighted averaging of intermediate models is afterwards applied to build an ensemble model ready for evaluation. Through a fine-grained system design, our solution executed and completed deep learning workflows on OpenWhisk with a 0% failure rate. Moreover, the comparison with a traditional deployment on OpenWhisk shows that our approach uses 45% less memory and reduces the execution time by 58%

Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration

Future AI applications require performance, reliability and privacy that the existing, cloud-dependant system architectures cannot provide. In this article, we study orchestration in the device-edge-cloud continuum, and focus on AI for edge, that is, the AI methods used in resource orchestration. We claim that to support the constantly growing requirements of intelligent applications in the device-edge-cloud computing continuum, resource orchestration needs to embrace edge AI and emphasize local autonomy and intelligence. To justify the claim, we provide a general definition for continuum orchestration, and look at how current and emerging orchestration paradigms are suitable for the computing continuum. We describe certain major emerging research themes that may affect future orchestration, and provide an early vision of an orchestration paradigm that embraces those research themes. Finally, we survey current key edge AI methods and look at how they may contribute into fulfilling the vision of future continuum orchestration.Comment: 50 pages, 8 figures (Revised content in all sections, added figures and new section