Context: Dashboards play a pivotal role in cloud systems monitoring, as they facilitate the visualization of the Key Performance Indicators (KPIs) that are continuously gathered from the system under observation. To timely and easily identify malfunctions and unexpected behaviors, operators have to configure, design, and maintain dashboards, so that the right set of indicators is properly visualized. Unfortunately, cost-effectively manipulating dashboards is a challenge, also for experts. Objectives: This paper proposes a model-driven approach that supports both the cost-effective definition (generation) and modification (adaptation) of dashboards. Method: The key idea is that a model-driven representation of a dashboard can be more easily manipulated than interacting with the GUI of dashboard management systems. Once a dashboard’s model is defined, the actual dashboard can be generated automatically with model-transformation techniques. Results: Our empirical results with popular Grafana Labs and Dynatrace dashboards show that the interpretability of the dashboards generated automatically is similar to the one of the manually configured dashboards. Moreover, the model-driven customization of the dashboard allows non-expert operators to act more efficiently, sometime as efficient as expert users. Conclusions: Overall results show that the model-driven approach can be used to cost-effectively generate useful dashboards, with an effectiveness close to that of experts.

Edge computing provides resources for IoT workloads at the network edge. Monitoring systems are vital for efficiently managing resources and application workloads by collecting, storing, and providing relevant information about the state of the resources. However, traditional monitoring systems have a centralized architecture for both data plane and control plane, which increases latency, creates a failure bottleneck, and faces challenges in providing quick and trustworthy data in volatile edge environments, especially where infrastructures are often built upon failure-prone, unsophisticated computing and network resources. Thus, we propose DEMon, a decentralized, self-adaptive monitoring system for edge. DEMon leverages the stochastic gossip communication protocol at its core. It develops efficient protocols for information dissemination, communication, and retrieval, avoiding a single point of failure and ensuring fast and trustworthy data access. Its decentralized control enables self-adaptive management of monitoring parameters, addressing the tradeoffs between the quality of service of monitoring and resource consumption. We implement the proposed system as a lightweight and portable container-based system and evaluate it through experiments. We also present a use case demonstrating its feasibility. The results show that DEMon efficiently disseminates and retrieves the monitoring information, addressing the challenges of edge monitoring.

Edge computing has emerged as a crucial paradigm for addressing the growing demands of interconnected devices and large-scale mobile applications by relocating computation and storage services closer to end-users. Edge workloads are inherently volatile and challenging to forecast due to their dependence on factors such as human mobility patterns and geographically-distributed infrastructure, combined with the dynamic nature of edge nodes. Traditional centralized approaches to workload forecasting are inadequate in the context of decentralized and failure-prone edge environments. To address this challenge, this paper investigates workload forecasting using Gossip Learning (GL), an asynchronous peer-to-peer learning protocol. GL allows for the training of forecasting models in a fully-decentralized manner, thereby mitigating single point of failure risks and enhancing overall system robustness. We extended the original protocol across multiple dimensions to improve convergence, reduce communication overhead, and enhance resilience to failures. We evaluated the proposed approach through extensive simulations; the obtained results demonstrate its effectiveness with respect to classical methods, rendering it a promising solution to enhance load balancing and task offloading strategies at the edge, thereby ensuring Quality-of-Service (QoS) and reducing Service Level Agreement (SLA) violations.

Monitoring is a key feature to enhance systems with the capability to anticipate, detect, predict, and mitigate failures, while providing Quality of Service (QoS) monitoring and Service Level Agreements (SLAs) guarantee. Monitoring frameworks can serve these purposes by deploying probes according to many possible patterns that have different features, for instance in terms of efficiency and privacy. So far, these probe deployment patterns have not been systematically defined, analyzed and assessed. Thus, engineers who design and configure their monitoring systems have to take decisions only based on partial knowledge and personal experience. This paper addresses this knowledge gap, by presenting a systematic analysis of 11 probe deployment patterns, their known uses, and implementations. We assess these patterns qualitatively, and quantitatively using both VMs and containers. Results show the targets have negligible resource consumption (e.g., less than 1% CPU usage), while the probe holder consumption is mainly significant in relation to memory consumption, reaching up to 10 GiB in our experiments. Our findings suggest that reusing probes and holders among users can generally enhance efficiency and scalability when direct access to the monitored target is not an option. We generate a set of best practices that can assist engineers in configuring their monitoring systems. Finally, we showcase the application of certain patterns through three practical usage scenarios, which feature diverse technologies and requirements.

The advent of edge devices dedicated to machine learning tasks enabled the execution of AI-based applications that efficiently process and classify the data acquired by the resource-constrained devices populating the Internet of Things. The proliferation of such applications (e.g., critical monitoring in smart cities) demands new strategies to make these systems also sustainable from an energetic point of view. In this paper, we present an energy-aware approach for the design and deployment of self-adaptive AI-based applications that can balance application objectives (e.g., accuracy in object detection and frames processing rate) with energy consumption. We address the problem of determining the set of configurations that can be used to self-adapt the system with a meta-heuristic search procedure that only needs a small number of empirical samples. The final set of configurations are selected using weighted gray relational analysis, and mapped to the operation modes of the self-adaptive application. We validate our approach on an AI-based application for pedestrian detection. Results show that our self-adaptive application can outperform non-adaptive baseline configurations by saving up to 81% of energy while loosing only between 2% and 6 % in accuracy.

Abstract Microservice‐based applications consist of multiple services that can evolve independently. When a service must be updated, it is first tested with in‐house regression test suites. However, the test suites that are executed are usually designed without the exact knowledge about how the services will be accessed and used in the field; therefore, they may easily miss relevant test scenarios, failing to prevent the deployment of faulty services. To address this problem, we introduce ExVivoMicroTest, an approach that analyzes the execution of deployed services at run‐time in the field, in order to generate test cases for future versions of the same services. ExVivoMicroTest implements lightweight monitoring and tracing capabilities, to inexpensively record executions that can be later turned into regression test cases that capture how services are used in the field. To prevent accumulating an excessive number of test cases, ExVivoMicroTest uses a test coverage model that can discriminate the recorded executions between the ones that are worth to be turned into test cases and the ones that should be discarded. The resulting test cases use a mocked environment that fully isolates the service under test from the rest of the system to faithfully reply interactions. We assessed ExVivoMicroTest with the PiggyMetrics and Train Ticket open source microservice applications and studied how different configurations of the monitoring and tracing logic impact on the capability to generate test cases.

Cloud services must be continuously monitored to guarantee that misbehaviors can be timely revealed, compensated, and fixed. While simple applications can be easily monitored and controlled, monitoring non-trivial cloud systems with dynamic behavior requires the operators to be able to rapidly adapt the set of collected indicators. Although the currently available monitoring frameworks are equipped with a rich set of probes to virtually collect any indicator, they do not provide the automation capabilities required to quickly and easily change (i.e., deploy and undeploy) the probes used to monitor a target system. Indeed, changing the collected indicators beyond standard platform-level indicators can be an error-prone and expensive process, which often requires manual intervention. This article presents a Monitoring-as-a-Service framework that provides the capability toautomaticallydeploy and undeploy arbitrary probes based on a user-provided set of indicators to be collected. The life-cycle of the probes is fully governed by the framework, including the detection and resolution of theerroneous statesat deployment time. The framework can be used jointly withexisting monitoring technologies, without requiring the adoption of a specific probing technology. We experimented our framework with cloud systems based on containers and virtual machines, obtaining evidence of the efficiency and effectiveness of the proposed solution.

Monitoring is a critical component in fog environments: it promptly provides insights about the behavior of systems, reveals Service Level Agreements (SLAs) violations, enables the autonomous orchestration of services and platforms, calls for the intervention of operators, and triggers self-healing actions.

Hierarchical Temporal Memory (HTM) is an unsupervised learning algorithm inspired by the features of the neocortex that can be used to continuously process stream data and detect anomalies, without requiring a large amount of data for training nor requiring labeled data. HTM is also able to continuously learn from samples, providing a model that is always up-to-date with respect to observations.These characteristics make HTM particularly suitable for supporting online failure prediction in cloud systems, which are systems with a dynamically changing behavior that must be monitored to anticipate problems. This paper presents the first systematic study that assesses HTM in the context of failure prediction.The results that we obtained considering 72 configurations of HTM applied to 12 different types of faults introduced in the Clearwater cloud system show that HTM can help to predict failures with sufficient effectiveness (F-measure = 0.76), representing an interesting practical alternative to (semi-)supervised algorithms.

Cloud systems are large scalable distributed systems that must be carefully monitored to timely detect problems and anomalies. While a number of cloud monitoring frameworks are available, only a few solutions address the problem of adaptively and dynamically selecting the indicators that must be collected, based on the actual needs of the operator. Unfortunately, these solutions are either limited to infrastructure-level indicators or technology-specific, for instance, they are designed to work with OpenStack but not with other cloud platforms. This paper presents the VARYS monitoring framework, a technology-agnostic Monitoring-as-a-Service solution that can address KPI monitoring at all levels of the Cloud stack, including the application-level. Operators use VARYS to indicate their monitoring goals declaratively, letting the framework to perform all the operations necessary to achieve a requested monitoring configuration automatically. Interestingly, the VARYS architecture is general and extendable, and can thus be used to support increasingly more platforms and probing technologies.