The growing environmental impact of the ICT sector calls for sustainable computing, including software services designed to minimise their carbon footprint, without compromising on quality of service. In particular, software services should be carbon-aware by design - leveraging alternative configurations to reduce emissions while featuring the required quality of service. In this work, we present a carbon-aware approach to realise dynamically adaptive software services. We propose an optimisation scheme that enables serving incoming requests with different implemented versions of software services, by minimising carbon emissions, combining service adaptability and time-shifting. Finally, to demonstrate the feasibility of our proposal, we introduce CarbonShift, an open-source prototype implementation.

Cloud-Edge application deployment involves placing multiple software components on infrastructural topologies of heterogeneous nodes, ranging from Cloud servers to Internet-of-Things (IoT) edge devices. When multiple versions (or “ flavours ”) of a component are available, application managers must select a flavour for each deployed component, and assign these components to specific nodes, all while considering constraints such as dependencies, quality of service (QoS), budget, operational costs, and carbon emissions. In complex scenarios, finding the optimal deployment is often infeasible for human operators without automated tools to systematically explore the solution space. To address this challenge, we introduce FREEDA, a first constraint optimisation approach for deploying constrained and multi-flavoured applications on Cloud-Edge infrastructure topologies. We demonstrate the practical feasibility of FREEDA through experiments on a variety of realistic Cloud-Edge infrastructural topologies and component architectures. Furthermore, we benchmark FREEDA against Zephyrus, a comparable tool employing the same underlying solving technology. Empirical results show that FREEDA achieves strong scalability across a broad spectrum of realistic configurations and consistently outperforms Zephyrus.

ABSTRACT Context Identifying the possible root causes of observed failures is crucial in microservice applications, as much as explaining how such possible root failures propagated across the microservices forming an application. This can indeed help pick countermeasures avoiding observed failures to happen again, e.g., by introducing circuit breakers or bulkheads avoiding the root failures to propagate and cause those observed. Objective This paper aims at enabling to explain observed failures in microservice applications, either searching for all possible cascading failures or focusing only on those starting in a known root cause. Method We propose a log‐based root cause analysis technique, which declaratively determines the cascading failures that possibly caused an observed failure. We also enable exploiting our proposed technique in practice, by introducing a logging methodology to instrument applications to log their failures and service interactions, and by enabling to analyse such logs through yRCA, a prototype implementation of our proposed root cause analysis technique. Results The practical usability of our proposed technique is assessed by means of a case study and controlled experiments. The case study shows the low effort for instrumenting a third‐party application to produce the logs needed by our technique and its effectiveness in explaining injected failures. The controlled experiments further assess our technique's effectiveness and performances in explaining failures obtained with an existing chaos testbed. Conclusion Our proposed technique can help to identify the cascading failures that possibly caused an observed failure in a microservice application. It can be used to determine all possible cascading failures, or to explain how cascading failures propagated from a known root cause (e.g., identified with some other existing root cause analyser).

Abstract Novel utility computing paradigms rely upon the deployment of multi-service applications to pervasive and highly distributed cloud-edge infrastructure resources. Deciding onto which computational nodes to place services in cloud-edge networks, as per their functional and non-functional constraints, can be formulated as a combinatorial optimisation problem. Most existing solutions in this space are not able to deal with unsatisfiable problem instances, nor preferences, i.e., requirements that DevOps may agree to relax to obtain a solution. In this article, we exploit Answer Set Programming optimisation capabilities to tackle this problem. Experimental results in simulated settings show that our approach is effective on lifelike networks and applications.

Placing virtual machines so to minimize the number of used physical hosts is an utterly important problem in cloud computing and next-generation virtualized networks.This article proposes a declarative reasoning methodology, and its open-source prototype, including four heuristic strategies to tackle this problem.Our proposal is extensively assessed over real data from an industrial case study and compared to state-of-the-art approaches, both in terms of execution times and solution optimality.As a result, our declarative approach determines placements that are only 6% far from optimal, outperforming a state-of-the-art genetic algorithm in terms of execution times, and a first-fit search for optimality of found placements.Last, its pipelining with a mathematical programming solution improves execution times of the latter by one order of magnitude on average, compared to using a genetic algorithm as a primer.

Abstract The problem of managing multi‐service applications on top of Cloud‐Edge networks in a quality of service (QoS)‐aware manner has been thoroughly studied in recent years from a decision‐making perspective. However, only a few studies addressed the problem of actively enforcing such decisions while orchestrating multi‐service applications and considering infrastructure and application variations. In this article, we propose a next‐gen orchestrator prototype based on Docker to achieve the continuous and QoS‐compliant management of multiservice applications on top of geographically distributed Cloud‐Edge resources, in continuity with CI/CD pipelines and infrastructure monitoring tools. Finally, we assess our proposal over a geographically distributed testbed across Italy.

The advent of the Internet of Things has increased the interest in automating mission-critical processes from domains such as smart cities. These applications’ stringent Quality of Service (QoS) requirements motivate their deployment through the Cloud-IoT Continuum, which requires solving the NP-hard problem of placing the application's services onto the infrastructure's devices. Moreover, as the infrastructure and application change over time, the placement needs to continuously adapt to these changes to maintain an acceptable QoS. While continuous reasoning techniques have enabled the creation of tools for these scenarios, they can have some trouble finding a feasible adaptation for abrupt and sharp changes, requiring non-adaptive techniques in those cases. Furthermore, for scenarios with smoother changes, it would be desirable to have faster algorithms to perform this placement. To explore the trade-off of effectiveness and execution times of different methods while ensuring that an application placement is found, we propose Multi-Layered Continuous Reasoning (MLCR) as an autonomic framework to adapt application placements through multiple continuous reasoning-based methods. We also present an MLCR prototype based on three methods: Faustum, MigDADO, and ConDADO. An evaluation in a realistic use case shows that MLCR is faster than traditional methods for application placement and maintains an acceptable QoS.

This article proposes an approach to the data-aware multi-service application placement problem in Cloud-Edge settings. We propose both declarative programming and a Mixed-Integer Linear Programming (MILP) approach to determine eligible placements that minimise operational costs and reduce the number of used nodes to contain the amount of data transfers. After assessing the performance of both approaches, we reconcile them into a methodology that combines the best of the two worlds by exploiting a declarative pre-processing step to boost the MILP solver while determining optimal solutions. We open-sourced the methodology into a prototype that is l0x faster than pure MILP, determines optimal results, and easily accommodates non-numerical constraints on application placements.

Monitoring the proper functioning and performance of an infrastructure spanning multiple Cloud datacentres is challenging. It requires continuously aggregating monitored data across multiple source machines and processing them so to obtain useful alerts and insights. In this article, we propose a simple open-source prototype tool to perform highly customisable fault and performance monitoring across multiple Clouds. Differently from commercial tools, our prototype is simpler to deploy and it can be configured through a declarative approach, by simply specifying data monitoring tasks and aggregation policies. We illustrate such peculiarities over a use case relying on three datacentres under the Italian Research and Education Network Consortium.

This paper proposes a continuum-aware programming model enabling the execution of application workflows across the compute continuum: cloud, fog and edge resources. It simplifies the management of heterogeneous nodes while alleviating the burden of programmers and unleashing innovation. This model optimizes the continuum through advanced development experiences by transforming workflows into autonomous service collaborations. It reduces complexity in positioning/interconnecting services across the continuum. A meta-model introduces high-level workflow descriptions as service networks with defined contracts and quality of service, thus enabling the deployment/management of workflows as first-class entities. It also provides automation based on policies, monitoring and heuristics. Tailored mechanisms orchestrate/manage services across the continuum, optimizing performance, cost, data protection and sustainability while managing risks. This model facilitates incremental development with visibility of design impacts and seamless evolution of applications and infrastructures. In this work, we explore this new computing paradigm showing how it can trigger the development of a new generation of tools to support the compute continuum progress.

Intent-based Networking (IBN) aims at simplifying network configuration and management by using high-level objectives that express the desired state of the network rather than the details of how to implement it. In this article, we propose a declarative methodology and an associated open-source Prolog prototype (i) to model IBN intents related to the provisioning of Virtual Network Function (VNF) chains, and (ii) to process those intents to assemble and place a VNF chain that fulfils them. Our prototype is assessed over a lifelike motivating scenario.

Monitoring resource availability along Cloud-IoT networks in a lightweight and fault-resilient manner is a challenging research problem due to scarce resource availability, infrastructure dynamics, and platform heterogeneity. In this article, we illustrate a thorough experimental assessment of a self-organising and fault-tolerant monitoring service, FogMon, especially targeting Cloud-IoT settings and capable of probing hardware resource, latency and bandwidth. The assessment is carried out over networks made up of 20 to 40 nodes across two testbeds within the Fed4Fire+ federation. As a result of the assessment, we agilely improved and refined FogMon into FogMon 2, which settles at TRL5 and improves on monitoring accuracy and fault-resiliency. Experimental results show how FogMon 2 can promptly and suitably handle different types of infrastructure failure, with an average relative error of 10% on measurements and limited footprint on hardware and network resources.

Abstract The sustainable management of smart environments enabled by the Internet of Things (IoT) requires new methodologies and tools to suitably handle potentially many users and their objectives on cyber-physical systems, e.g. smart lighting, smart A/C. In this article, we propose a declarative framework to model IoT-enabled smart environments. Our methodology permits (i) expressing user roles and hierarchical environments, (ii) declaring customized policies to mediate user objectives into a target state and (iii) determining valid settings for IoT actuators to achieve such a target also reducing energy consumption. An open-source Prolog prototype of the framework is showcased over two lifelike motivating examples and its scalability is assessed at increasing sizes of the managed smart environment.

Placing applications composed as orchestrated serverless functions onto Cloud-loT infrastructures is a chal-lenging problem as it must consider hardware, software, network Quality of Service, and service interactions constraints. In this paper, we propose a novel declarative methodology that handles all of the above, also relying on information-flow analyses and padding techniques to prevent information leaks through side channels. A motivating use case from augmented reality is used to showcase the open-source prototype implementing our proposal.

Abstract Developing and releasing multiservice applications rely upon a pipeline of automation tools known as Continuous Integration/Continuous Deployment. Among those tools, continuous reasoning is exploited by large companies to perform incremental static analyses on their code commits as soon as they are integrated into a shared codebase. In this article, we extend continuous reasoning towards the continuous QoS- and context-aware management of multiservice applications in Cloud-IoT scenarios. We propose a novel continuous reasoning methodology that supports runtime decision on service placement by reacting both to changes in the infrastructure and in the application requirements, and capable of suggesting migrations only for services affected by such changes. The methodology is prototyped in Prolog and assessed through simulations over a realistic use case and over a lifelike motivating scenario at increasing infrastructure sizes. Experimental results show that our approach brings considerable speed-up in comparison with an exhaustive search employing non-incremental reasoning.

Abstract Osmotic computing encompasses emerging Cloud‐Internet of Things (IoT) computing paradigms, by featuring the possibility for application services to adapt into different functionally equivalent flavours, depending on the contextually available resources and on specific requirements of running applications. This article proposes a fully decentralised declarative framework that enables both application and infrastructure operators to declare management policies for the service instances and the nodes they manage, respectively. Policies are composed of a simple and well‐defined set of management operations, declared in Prolog, which trigger based on locally available contextual information on application requests and infrastructure resources. A prototype implementation of the framework is showcased and assessed via simulation over a lifelike Smart Campus use case with multiple applications, at increasing infrastructure sizes and number of mobile users. Experimental results show that the proposed management framework scales to large infrastructure sizes and suits the needs of multiflavoured Osmotic applications in dynamic deployment conditions, by improving the trade‐off between their response times and suitable service usage.

Abstract Deploying Internet of Things (IoT)-enabled virtual network function (VNF) chains to Cloud-Edge infrastructures requires determining a placement for each VNF that satisfies all set deployment requirements as well as a software-defined routing of traffic flows between consecutive functions that meets all set communication requirements. In this article, we present a declarative solution, EdgeUsher, to the problem of how to best place VNF chains to Cloud-Edge infrastructures. EdgeUsher can determine all eligible placements for a set of VNF chains to a Cloud-Edge infrastructure so to satisfy all of their hardware, IoT, security, bandwidth, and latency requirements. It exploits probability distributions to model the dynamic variations in the available Cloud-Edge infrastructure and to assess output eligible placements against those variations.

Smart environments enabled by the Internet of Things aim at improving our daily lives by automatically tuning ambient parameters and by achieving energy savings through self-managing cyber-physical systems. Commercial solutions, however, only permit setting simple target goals on those parameters and do not mediate between conflicting goals among different users and/or system administrators, nor across different IoT verticals. In this article, we propose a declarative approach (and its open-source Prolog prototype) to represent smart environments, user-set goals and customisable mediation policies to reconcile contrasting goals across multiple IoT systems.

Abstract Orchestrating next-gen applications over heterogeneous resources along the Cloud-IoT continuum calls for new strategies and tools to enable scalable and application-specific managements. Inspired by the self-organisation capabilities of bacteria colonies, we propose a declarative, fully decentralised application management solution, targeting pervasive opportunistic Cloud-IoT infrastructures. We present a customisable declarative implementation of the approach and validate its scalability through simulation over motivating scenarios, also considering end-user’s mobility and the possibility to enforce application-specific management policies for different (classes of) applications.

Summary Fog computing aims at extending the cloud towards the Internet of things so to achieve improved quality of service and to empower latency‐sensitive and bandwidth‐hungry applications. The fog calls for novel models and algorithms to distribute multiservice applications in such a way that data processing occurs wherever it is best placed, based on both functional and nonfunctional requirements. This survey reviews the existing methodologies to solve the application placement problem in the fog, while pursuing three main objectives. First, it offers a comprehensive overview on the currently employed algorithms, on the availability of open‐source prototypes and on the size of test use cases. Second, it classifies the literature based on the application and fog infrastructure characteristics that are captured by available models, with a focus on the considered constraints and the optimized metrics. Finally, it identifies some open challenges in application placement in the fog.

Deploying IoT applications through the Fog in a QoS-, context-, and cost-aware manner is challenging due to the heterogeneity, scale and dynamicity of Fog infrastructures. To decide how to allocate app functionalities over the continuum from the IoT to the Cloud, app administrators need to find a trade-off among QoS, resource consumption and cost. In this paper, we present a novel cost model for estimating the cost of deploying IoT applications to Fog infrastructures. We show how the inclusion of the cost model in the FogTorchP open-source prototype permits to determine eligible deployments of multi-component applications to Fog infrastructures and to rank them according to their QoS-assurance, Fog resource consumption and cost. We run the extended prototype on a motivating scenario, showing how it can support IT experts in choosing the deployments that best suit their desiderata.

Deploying composite applications to Fog nodes in a QoS-and context-aware manner is challenging due to the heterogeneity and scale of Fog infrastructures. Application components must be provided with the software and hardware capabilities they need. Communication links that support interactions between components must meet certain QoS (latency and bandwidth). On the other hand, different Fog and Cloud nodes provide different software and hardware capabilities, and actual communication links support different QoS over time. In this paper we present a prototype (FogTorchII) capable of determining deployments of composite applications to Fog infrastructures, which fulfil software, hardware and QoS requirements. FogTorchII exploits Monte Carlo simulations to take into account possible variations of the QoS of communication links. It classifies eligible deployments both in terms of QoS-assurance and of Fog resource consumption. We illustrate the utility of FogTorchII over a motivating example where we compare different possible deployments for a smart agriculture application.

Fog computing aims at extending the Cloud by bringing computational power, storage, and communication capabilities to the edge of the network, in support of the IoT. Segmentation, distribution, and adaptive deployment of functionalities over the continuum from Things to Cloud are challenging tasks, due to the intrinsic heterogeneity, hierarchical structure, and very large scale infrastructure they will have to exploit. In this paper, we propose a simple, yet general, model to support the QoS-aware deployment of multicomponent IoT applications to Fog infrastructures. The model describes operational systemic qualities of the available infrastructure (latency and bandwidth), interactions among software components and Things, and business policies. Algorithms to determine eligible deployments for an application to a Fog infrastructure are presented. A Java tool, FogTorch, based on the proposed model has been prototyped.