This paper explores an extended applications' cost function to model the willingness of Edge data centres to accommodate additional users in decentralized edge computing environments. By enhancing the Marginal Computing Cost per User (MCU) concept, we introduce a dynamic cost factor influenced by the number of users currently served. Through extensive simulations conducted on the PureEdgeSim platform, we evaluate the impact of this variable MCU on system performance across various configurations. The results reveal a critical trade-off between cost sensitivity (i.e., collaboration willingness) of Edge data centres and optimization potential. This work offers insights into user allocation strategies in heterogeneous edge systems and sets the stage for future research into non-linear MCU configurations and diverse application workloads.
Testing serverless functions differs significantly from that of conventional software products. Their distributed nature and event-driven architecture make test development inherently complex. Further, the development of functions and their tests often relies on using tools and software development kits managed by cloud providers, resulting in minimal control over the test execution environment and a lack of debugging tools. As such, we have developed the Icarus RESTful, open-source Testing-as-a-Service tool with a transparent and predictable operation, supplying the user with full control over test creation and execution. Icarus does not burden the user with details of a function's deployment across different providers, does not require the user to write any line of testing code and relies solely on using familiar open-source and well-known tools to automatically compose and execute functional and non-functional tests. Its experimental evaluation shows that it scales well with the user and workload increase, rapidly delivering test results to the user.
This extended keynote abstract outlines research on a new paradigm of data sharing designed to recover privacy for data providers, while reducing costs and risks of service providers, who collect personal sensitive data. This paradigm is based on the novel idea of coordinated data sharing within a community, the data collective, which makes automated and scalable collective arrangements of how to share data using decentralized artificial intelligence.
Digital Twins (DTs) are digital representations of physical objects or processes that can be used for their computer-based analysis. This technique has been used in different fields to analyze, simulate, and optimize various scenarios in real time without interfering with the real twin. Using Serverless Computing as a use case, this paper discusses the underlying costs of using DTs when applied to analyze and optimize the management of computational resources for cloud-continuum applications. We argue that, although feasible, using DTs for Digital Systems can be prohibitively expensive. Defining the ideal DT fidelity for a given application is challenging, as this impacts both system management and performance through heavy monitoring as well as the DT running costs.
This study introduces FaaS-Utility, a novel approach aimed at optimizing Function-as-a-Service (FaaS) systems by addressing the critical issue of cold starts, which significantly impede system performance. By introducing a utility function informed by customer preferences and pricing goals, our methodology prioritizes resource allocation to enhance service quality effectively. We implement this strategy within Apache OpenWhisk, demonstrating its integration into a real-world FaaS platform. Our evaluation reveals that the proposed approach notably improves system performance, particularly in over-provisioned states, by reducing latency up to 2.37 times with a maximum additional cost of only 30
