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Computers
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Applications in Self-Aware Computing Systems and their Evaluation
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Applications in Self-Aware Computing Systems and their Evaluation

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "Cloud Continuum and Enabled Applications".

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 31675

Special Issue Editors

Dr. Benedikt Eberhardinger

E-Mail Website
Guest Editor
MHP — A Porsche Company, 71638 Ludwigsburg, Germany
Interests: self-organizing systems; software engineering; software architectures; experimental algorithmic; predictive analytics; artificial intelligence; data analytics
Dr. Ilias Gerostathopoulos

E-Mail Website
Guest Editor
Department of Informatics, Technical University Munich, 80333 München, Germany
Interests: self-adaptive systems; software architecture; software engineering; data-driven decisions and experimentation
Dr. Christian Krupitzer

E-Mail Website
Guest Editor
Department of Computer Science, University of Würzburg, 97080 Würzburg, Germany
Interests: self-aware computing systems; self-adaptive systems; cyberphysical systems; Industry 4.0; data analytics
Special Issues, Collections and Topics in MDPI journals
Dr. Claudia Raibulet

E-Mail Website
Guest Editor
Dipartimento di Informatica, Sistemistica e Comunicazione, Universita' degli Studi di Milano-Bicocca, 20126 Milano, Italy
Interests: software engineering; software architectures; design patterns; self-adaptive systems; evaluation of self-adaptive systems; smart systems; context-aware systems

Special Issue Information

Dear colleagues,

During the past decade, many different research communities have explored the aspects of self-awareness in computing systems, each from their own perspective. Relevant work can be found in different areas, including autonomic computing, self-adaptive and self-organizing software and systems, machine learning, artificial intelligence and multi-agent systems, organic computing, context- and situation-aware systems, reflective computing, model-predictive control, as well as work from the models@run-time community. More specifically, self-aware computing systems are understood as having two main properties. They (1) learn models, capturing knowledge about themselves and their environment (such as their structure, design, state, possible actions, and runtime behavior) on an ongoing basis; and (2) reason using the models (to predict, analyze, consider, or plan), which enables them to act based on their knowledge and reasoning (for example, to explore, explain, report, suggest, self-adapt, or impact their environment). They do so in accordance with high-level goals, which can change.

This Special Issue addresses all facets of research in the area of self-aware computing systems, including fundamental science and theory, levels and aspects, architectures for individual and collective systems, methods and algorithms for model learning, self-adaptation in individual and collective systems, transition strategies for increasing self-awareness in existing systems, open challenges and future research directions, as well as applications and case studies. In this Special Issue, particular emphasis will be given to the evaluation of these systems, including objectives, metrics, tools, procedure, methodologies, reference systems, and benchmarks.

Selected papers presented at the Workshop on Self-Aware Computing (SeAC) as well as the Workshop on Evaluations and Measurements in Self-Aware Computing Systems (EMSAC) are invited to submit their extended versions to this Special Issue of the journal Computers. All submitted papers will undergo our standard peer-review procedure. Accepted papers will be published in open access format in Computers and collected together on the Special Issue website.

Conference papers should be cited and noted on the first page of the paper; authors are asked to disclose that it is a conference paper in their cover letter and include a statement on what has been changed compared to the original conference paper. Please note that the submitted extended paper should contain at least 50% new content (e.g., in the form of technical extensions, more in-depth evaluations, or additional use cases) and not exceed 30% copy/paste from the conference paper.

Dr. Benedikt Eberhardinger
Dr. Ilias Gerostathopoulos
Dr. Christian Krupitzer
Dr. Claudia Raibulet
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computers is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Self-aware computing systems
  • Self-adaptive systems
  • Evaluation of adaptive systems
  • Application of adaptive systems
  • Development support for adaptive systems

Published Papers (7 papers)

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Editorial

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6 pages, 171 KiB  
Editorial
Introduction to the Special Issue “Applications in Self-Aware Computing Systems and their Evaluation”
by Christian Krupitzer, Benedikt Eberhardinger, Ilias Gerostathopoulos and Claudia Raibulet
Computers 2020, 9(1), 22; https://doi.org/10.3390/computers9010022 - 21 Mar 2020
Cited by 1 | Viewed by 3775
Abstract
The joint 1st Workshop on Evaluations and Measurements in Self-Aware Computing Systems (EMSAC 2019) and Workshop on Self-Aware Computing (SeAC) was held as part of the FAS* conference alliance in conjunction with the 16th IEEE International Conference on Autonomic Computing (ICAC) and the [...] Read more.
The joint 1st Workshop on Evaluations and Measurements in Self-Aware Computing Systems (EMSAC 2019) and Workshop on Self-Aware Computing (SeAC) was held as part of the FAS* conference alliance in conjunction with the 16th IEEE International Conference on Autonomic Computing (ICAC) and the 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO) in Umeå, Sweden on 20 June 2019. The goal of this one-day workshop was to bring together researchers and practitioners from academic environments and from the industry to share their solutions, ideas, visions, and doubts in self-aware computing systems in general and in the evaluation and measurements of such systems in particular. The workshop aimed to enable discussions, partnerships, and collaborations among the participants. This special issue follows the theme of the workshop. It contains extended versions of workshop presentations as well as additional contributions. Full article
(This article belongs to the Special Issue Applications in Self-Aware Computing Systems and their Evaluation)

Research

Jump to: Editorial

25 pages, 1738 KiB  
Article
To Adapt or Not to Adapt: A Quantification Technique for Measuring an Expected Degree of Self-Adaptation
by Sven Tomforde and Martin Goller
Computers 2020, 9(1), 21; https://doi.org/10.3390/computers9010021 - 18 Mar 2020
Cited by 14 | Viewed by 4464
Abstract
Self-adaptation and self-organization (SASO) have been introduced to the management of technical systems as an attempt to improve robustness and administrability. In particular, both mechanisms adapt the system’s structure and behavior in response to dynamics of the environment and internal or external disturbances. [...] Read more.
Self-adaptation and self-organization (SASO) have been introduced to the management of technical systems as an attempt to improve robustness and administrability. In particular, both mechanisms adapt the system’s structure and behavior in response to dynamics of the environment and internal or external disturbances. By now, adaptivity has been considered to be fully desirable. This position paper argues that too much adaptation conflicts with goals such as stability and user acceptance. Consequently, a kind of situation-dependent degree of adaptation is desired, which defines the amount and severity of tolerated adaptations in certain situations. As a first step into this direction, this position paper presents a quantification approach for measuring the current adaptation behavior based on generative, probabilistic models. The behavior of this method is analyzed in terms of three application scenarios: urban traffic control, the swidden farming model, and data communication protocols. Furthermore, we define a research roadmap in terms of six challenges for an overall measurement framework for SASO systems. Full article
(This article belongs to the Special Issue Applications in Self-Aware Computing Systems and their Evaluation)
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32 pages, 1108 KiB  
Article
Evaluation of Self-Healing Systems: An Analysis of the State-of-the-Art and Required Improvements
by Sona Ghahremani and Holger Giese
Computers 2020, 9(1), 16; https://doi.org/10.3390/computers9010016 - 27 Feb 2020
Cited by 8 | Viewed by 4286
Abstract
Evaluating the performance of self-adaptive systems is challenging due to their interactions with often highly dynamic environments. In the specific case of self-healing systems, the performance evaluations of self-healing approaches and their parameter tuning rely on the considered characteristics of failure occurrences and [...] Read more.
Evaluating the performance of self-adaptive systems is challenging due to their interactions with often highly dynamic environments. In the specific case of self-healing systems, the performance evaluations of self-healing approaches and their parameter tuning rely on the considered characteristics of failure occurrences and the resulting interactions with the self-healing actions. In this paper, we first study the state-of-the-art for evaluating the performances of self-healing systems by means of a systematic literature review. We provide a classification of different input types for such systems and analyse the limitations of each input type. A main finding is that the employed inputs are often not sophisticated regarding the considered characteristics for failure occurrences. To further study the impact of the identified limitations, we present experiments demonstrating that wrong assumptions regarding the characteristics of the failure occurrences can result in large performance prediction errors, disadvantageous design-time decisions concerning the selection of alternative self-healing approaches, and disadvantageous deployment-time decisions concerning parameter tuning. Furthermore, the experiments indicate that employing multiple alternative input characteristics can help with reducing the risk of premature disadvantageous design-time decisions. Full article
(This article belongs to the Special Issue Applications in Self-Aware Computing Systems and their Evaluation)
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21 pages, 437 KiB  
Article
On Implementing Autonomic Systems with a Serverless Computing Approach: The Case of Self-Partitioning Cloud Caches
by Edwin F. Boza, Xavier Andrade, Jorge Cedeno, Jorge Murillo, Harold Aragon, Cristina L. Abad and Andres G. Abad
Computers 2020, 9(1), 14; https://doi.org/10.3390/computers9010014 - 26 Feb 2020
Cited by 3 | Viewed by 4754
Abstract
The research community has made significant advances towards realizing self-tuning cloud caches; notwithstanding, existing products still require manual expert tuning to maximize performance. Cloud (software) caches are built to swiftly serve requests; thus, avoiding costly functionality additions not directly related to the request-serving [...] Read more.
The research community has made significant advances towards realizing self-tuning cloud caches; notwithstanding, existing products still require manual expert tuning to maximize performance. Cloud (software) caches are built to swiftly serve requests; thus, avoiding costly functionality additions not directly related to the request-serving control path is critical. We show that serverless computing cloud services can be leveraged to solve the complex optimization problems that arise during self-tuning loops and can be used to optimize cloud caches for free. To illustrate that our approach is feasible and useful, we implement SPREDS (Self-Partitioning REDiS), a modified version of Redis that optimizes memory management in the multi-instance Redis scenario. A cost analysis shows that the serverless computing approach can lead to significant cost savings: The cost of running the controller as a serverless microservice is 0.85% of the cost of the always-on alternative. Through this case study, we make a strong case for implementing the controller of autonomic systems using a serverless computing approach. Full article
(This article belongs to the Special Issue Applications in Self-Aware Computing Systems and their Evaluation)
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15 pages, 550 KiB  
Article
Self-Adaptive Data Processing to Improve SLOs for Dynamic IoT Workloads
by Peeranut Chindanonda, Vladimir Podolskiy and Michael Gerndt
Computers 2020, 9(1), 12; https://doi.org/10.3390/computers9010012 - 14 Feb 2020
Cited by 4 | Viewed by 4120
Abstract
Internet of Things (IoT) covers scenarios of cyber–physical interaction of smart devices with humans and the environment and, such as applications in smart city, smart manufacturing, predictive maintenance, and smart home. Traditional scenarios are quite static in the sense that the amount of [...] Read more.
Internet of Things (IoT) covers scenarios of cyber–physical interaction of smart devices with humans and the environment and, such as applications in smart city, smart manufacturing, predictive maintenance, and smart home. Traditional scenarios are quite static in the sense that the amount of supported end nodes, as well as the frequency and volume of observations transmitted, does not change much over time. The paper addresses the challenge of adapting the capacity of the data processing part of IoT pipeline in response to dynamic workloads for centralized IoT scenarios where the quality of user experience matters, e.g., interactivity and media streaming as well as the predictive maintenance for multiple moving vehicles, centralized analytics for wearable devices and smartphones. The self-adaptation mechanism for data processing IoT infrastructure deployed in the cloud is horizontal autoscaling. In this paper we propose augmentations to the computation schemes of data processing component’s desired replicas count from the previous work; these augmentations aim to repurpose original sets of metrics to tackle the task of SLO violations minimization for dynamic workloads instead of minimizing the cost of deployment in terms of instance seconds. The cornerstone proposed augmentation that underpins all the other ones is the adaptation of the desired replicas computation scheme to each scaling direction (scale-in and scale-out) separately. All the proposed augmentations were implemented in the standalone self-adaptive agent acting alongside Kubernetes’ HPA such that limitations of timely acquisition of the monitoring data for scaling are mitigated. Evaluation and comparison with the previous work show improvement in service level achieved, e.g., latency SLO violations were reduced from 2.87% to 1.70% in case of the forecasted message queue length-based replicas count computation used both for scale-in and scale-out, but at the same time higher cost of the scaled data processor deployment is observed. Full article
(This article belongs to the Special Issue Applications in Self-Aware Computing Systems and their Evaluation)
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21 pages, 374 KiB  
Article
A Taxonomy of Techniques for SLO Failure Prediction in Software Systems
by Johannes Grohmann, Nikolas Herbst, Avi Chalbani, Yair Arian, Noam Peretz and Samuel Kounev
Computers 2020, 9(1), 10; https://doi.org/10.3390/computers9010010 - 11 Feb 2020
Cited by 3 | Viewed by 4525
Abstract
Failure prediction is an important aspect of self-aware computing systems. Therefore, a multitude of different approaches has been proposed in the literature over the past few years. In this work, we propose a taxonomy for organizing works focusing on the prediction of Service [...] Read more.
Failure prediction is an important aspect of self-aware computing systems. Therefore, a multitude of different approaches has been proposed in the literature over the past few years. In this work, we propose a taxonomy for organizing works focusing on the prediction of Service Level Objective (SLO) failures. Our taxonomy classifies related work along the dimensions of the prediction target (e.g., anomaly detection, performance prediction, or failure prediction), the time horizon (e.g., detection or prediction, online or offline application), and the applied modeling type (e.g., time series forecasting, machine learning, or queueing theory). The classification is derived based on a systematic mapping of relevant papers in the area. Additionally, we give an overview of different techniques in each sub-group and address remaining challenges in order to guide future research. Full article
(This article belongs to the Special Issue Applications in Self-Aware Computing Systems and their Evaluation)
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29 pages, 2288 KiB  
Article
Towards Self-Aware Multirotor Formations
by Dennis Kaiser, Veronika Lesch, Julian Rothe, Michael Strohmeier, Florian Spieß, Christian Krupitzer, Sergio Montenegro and Samuel Kounev
Computers 2020, 9(1), 7; https://doi.org/10.3390/computers9010007 - 7 Feb 2020
Cited by 3 | Viewed by 4594
Abstract
In the present day, unmanned aerial vehicles become seemingly more popular every year, but, without regulation of the increasing number of these vehicles, the air space could become chaotic and uncontrollable. In this work, a framework is proposed to combine self-aware computing with [...] Read more.
In the present day, unmanned aerial vehicles become seemingly more popular every year, but, without regulation of the increasing number of these vehicles, the air space could become chaotic and uncontrollable. In this work, a framework is proposed to combine self-aware computing with multirotor formations to address this problem. The self-awareness is envisioned to improve the dynamic behavior of multirotors. The formation scheme that is implemented is called platooning, which arranges vehicles in a string behind the lead vehicle and is proposed to bring order into chaotic air space. Since multirotors define a general category of unmanned aerial vehicles, the focus of this thesis are quadcopters, platforms with four rotors. A modification for the LRA-M self-awareness loop is proposed and named Platooning Awareness. The implemented framework is able to offer two flight modes that enable waypoint following and the self-awareness module to find a path through scenarios, where obstacles are present on the way, onto a goal position. The evaluation of this work shows that the proposed framework is able to use self-awareness to learn about its environment, avoid obstacles, and can successfully move a platoon of drones through multiple scenarios. Full article
(This article belongs to the Special Issue Applications in Self-Aware Computing Systems and their Evaluation)
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