ABSTRACT The concept of a service‐level agreement (SLA), a contractual arrangement between providers and customers, is firmly established in the software engineering field. However, introducing enhanced dynamism to this service contract, in one or more directions to be subsequently specified, constitutes a challenge, an important step for the Computer Engineering community, and, in our case, an anticipated contribution to the fogSLAAntillas (fogSLA) collaborative project. Consequently, this paper seeks to improve the understanding, classification, and articulation of the terminology associated with dynamic SLAs as it has evolved in the literature. The discussion extends to methodological and practical options for integrating this concept within the fogSLA project framework. On this occasion, we share our experience with dynamic SLA in the context of scheduling pods for a Kubernetes cluster. Thus, the mentioned practical work is specifically devoted to the concept of dynamicity in SLA within the Cloud‐Fog‐Edge continuum. This survey commences with a literature review and subsequently explores potential research directions applicable to the fogSLA project and to the broader Cloud, Fog, and Edge Computing communities. Some of these paths are already well defined, but would require community endorsement to attain the status of standards in the future. Therefore, we propose novel ideas and extend beyond a mere survey of the existing literature. The main contributions of this paper are, first, to align with the goal of qualitative research by deepening the understanding of the dynamic SLA topic and the potential challenges it entails, and second, to demonstrate how we have practically tackled the problem of dynamic SLAs within a project involving industrial partners, thereby indicating a real demand for this concept.

Traditional system monitoring relies on fixed scraping intervals, which by definition do not adapt to metrics dynamics. For this reason, monitoring tends to waste resources when collecting repeatedly metrics values which do not change over time or follow a regular trend. We introduce in this paper a monitoring pace scheduler that dynamically adjusts monitoring frequency based on observed metric patterns. Our approach shortens scraping intervals during high activity and extends them during stable phases, optimizing compute, network, and storage resources. Experimental results, when applied to computing resources or energy metrics, show that our method reduces bandwidth size by up to 37.6% and storage space by over 50% while preserving the original metric trend. Comparing further our method with competitors shows the better adaptability and resources footprint of our algorithm. The open source monitoring pace scheduler ensures accurate data collection with a reduced resources overhead, making it particularly effective for environments with fluctuating workloads or with a critical use of computing and network resources, such as telecom operators.

While the Cloud-Edge continuum scheduling domain is well studied, and proposes numerous approaches, from high-level modeling to dynamic scheduling, monitoring means are rarely debated. Solving the ever-growing number of metrics, which can exist in different units and update frequencies, support various system scales and topologies, relies upon classical 2D features (made of charts, gauges, maps and lists) without proposing visual abstractions directed towards multi-layered architectures and distributed infrastructures. Moreover, the need for monitoring in modern Edge systems and applications exceeds system metrics, as other informational dimensions can be considered, such as the ones collected through web services or IoT devices and sensors in the user spaceWe propose with Palindrome.js a 3D monitoring probe, which enables multidimensional visual modeling and analysis. Through sets of metrics called layers, Palindrome.js builds in 3D a structured visual abstraction for any problem modeled with metrics or KPIs. We present in this article the solution initial background, the related works, its design principles, discuss experimental results, and conclude with our observations and future outcomes.

Fog Computing is a paradigm aiming to decentralize the Cloud by geographically distributing away computation, storage and network resources as well as related services. This notably reduces bottlenecks and data movement. However, managing Fog resources is a major challenge because the targeted systems are large, geographically distributed, unreliable and very dynamic. Cloud systems are generally managed via centralized autonomic controllers automatically optimizing both application QoS and resource usage. To leverage the self-management of Fog resources, we propose to orchestrate a fleet of autonomic controllers in a decentralized manner, each with a local view of its own resources. In this paper, we present our SeMaFoR (Self-Management of Fog Resources) vision that aims at collaboratively operating Fog resources. SeMaFoR is a generic approach made of three cornerstones: an Architecture Description Language for the Fog, a collaborative and consensual decision-making process, and an automatic coordination mechanism for reconfiguration.