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Information Reuse and Stochastic Search: Managing Uncertainty in Self-*Systems ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2021-02-01 Cody Kinneer; David Garlan; Claire Le Goues
Many software systems operate in environments of change and uncertainty. Techniques for self-adaptation allow these systems to automatically respond to environmental changes, yet they do not handle changes to the adaptive system itself, such as the addition or removal of adaptation tactics. Instead, changes in a self-adaptive system often require a human planner to redo an expensive planning process
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SecRET: Secure Range-based Localization with Evidence Theory for Underwater Sensor Networks ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2021-01-19 Sudip Misra; Tamoghna Ojha; Madhusoodhanan P
Node localization is a fundamental requirement in underwater sensor networks (UWSNs) due to the ineptness of GPS and other terrestrial localization techniques in the underwater environment. In any UWSN monitoring application, the sensed information produces a better result when it is tagged with location information. However, the deployed nodes in UWSNs are vulnerable to many attacks, and hence, can
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PSINES: Activity and Availability Prediction for Adaptive Ambient Intelligence ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2020-12-31 Julien Cumin; Grégoire Lefebvre; Fano Ramparany; James L. Crowley
Autonomy and adaptability are essential components of ambient intelligence. For example, in smart homes, proactive acting and occupants advising, adapted to current and future contexts of living, are essential to go beyond limitations of previous domotic services. To reach such autonomy and adaptability, ambient systems need to automatically grasp their users’ ambient context. In particular, users’
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Finding the Largest Successful Coalition under the Strict Goal Preferences of Agents ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2020-09-13 Zhaopin Su; Guofu Zhang; Feng Yue; Jindong He; Miqing Li; Bin Li; Xin Yao
Coalition formation has been a fundamental form of resource cooperation for achieving joint goals in multiagent systems. Most existing studies still focus on the traditional assumption that an agent has to contribute its resources to all the goals, even if the agent is not interested in the goal at all. In this article, a natural extension of the traditional coalitional resource games (CRGs) is studied
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UAVs vs. Pirates: An Anticipatory Swarm Monitoring Method Using an Adaptive Pheromone Map ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2020-08-04 Ruiwen Zhang; Tom Holvoet; Bifeng Song; Yang Pei
For the rising hazard of pirate attacks, unmanned aerial vehicle (UAV) swarm monitoring is a promising countermeasure. Previous monitoring methods have deficiencies in either adaptivity to dynamic events or simple but effective path coordination mechanisms, and they are inapplicable to the large-area, low-target-density, and long-duration persistent counter-piracy monitoring. This article proposes
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Human Feedback as Action Assignment in Interactive Reinforcement Learning ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2020-08-04 Syed Ali Raza; Mary-Anne Williams
Teaching by demonstrations and teaching by assigning rewards are two popular methods of knowledge transfer in humans. However, showing the right behaviour (by demonstration) may appear more natural to a human teacher than assessing the learner’s performance and assigning a reward or punishment to it. In the context of robot learning, the preference between these two approaches has not been studied
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A Bike-sharing Optimization Framework Combining Dynamic Rebalancing and User Incentives ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2020-02-25 Federico Chiariotti; Chiara Pielli; Andrea Zanella; Michele Zorzi
Bike-sharing systems have become an established reality in cities all across the world and are a key component of the Smart City paradigm. However, the unbalanced traffic patterns during rush hours can completely empty some stations, while filling others, and the service becomes unavailable for further users. The traditional approach to solve this problem is to use rebalancing trucks, which take bikes
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Improving Scalability and Reward of Utility-Driven Self-Healing for Large Dynamic Architectures ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2020-02-25 Sona Ghahremani; Holger Giese; Thomas Vogel
Self-adaptation can be realized in various ways. Rule-based approaches prescribe the adaptation to be executed if the system or environment satisfies certain conditions. They result in scalable solutions but often with merely satisfying adaptation decisions. In contrast, utility-driven approaches determine optimal decisions by using an often costly optimization, which typically does not scale for large
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Argumentation-Based Reasoning about Plans, Maintenance Goals, and Norms ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2020-02-10 Zohreh Shams; Marina De Vos; Nir Oren; Julian Padget
In a normative environment, an agent’s actions are directed not only by its goals but also by the norms activated by its actions and those of other actors. The potential for conflict between agent goals and norms makes decision making challenging, in that it requires looking ahead to consider the longer-term consequences of which goal to satisfy or which norm to comply with in face of conflict. We
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Human-centric Data Dissemination in the IoP ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2020-02-10 Matteo Mordacchini; Marco Conti; Andrea Passarella; Raffaele Bruno
Data management using Device-to-Device (D2D) communications and opportunistic networks (ONs) is one of the main focuses of human-centric pervasive Internet services. In the recently proposed “Internet of People” paradigm, accessing relevant data dynamically generated in the environment nearby is one of the key services. Moreover, personal mobile devices become proxies of their human users while exchanging
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Controlling Interactions with Libraries in Android Apps Through Runtime Enforcement ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2019-12-17 Oliviero Riganelli; Daniela Micucci; Leonardo Mariani
Android applications are executed on smartphones equipped with a variety of resources that must be properly accessed and controlled, otherwise the correctness of the executions and the stability of the entire environment might be negatively affected. For example, apps must properly acquire, use, and release microphones, cameras, and other multimedia devices, otherwise the behavior of the apps that
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Self-awareness in Software Engineering ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2019-12-17 Abdessalam Elhabbash; Maria Salama; Rami Bahsoon; Peter Tino
Background: Self-awareness has been recently receiving attention in computing systems for enriching autonomous software systems operating in dynamic environments. Objective: We aim to investigate the adoption of computational self-awareness concepts in autonomic software systems and motivate future research directions on self-awareness and related problems. Method: We conducted a systemic literature
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Supporting Dynamic Workflows with Automatic Extraction of Goals from BPMN ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2019-12-17 Luca Sabatucci; Massimo Cossentino
Organizations willing to employ workflow technology have to be prepared to undertake a significant investment of time and effort due to the exceptionally dynamic nature of the business environment. Today, it is unlikely that processes are modeled once to be repeatedly executed without any changes. Goal-oriented dynamic workflows are a promising approach to provide flexibility to the execution of business
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Collective Adaptation through Multi-Agents Ensembles ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2019-12-17 Antonio Bucchiarone
Modern software systems are becoming more and more socio-technical systems composed of distributed and heterogeneous agents from a mixture of people, their environment, and software components. These systems operate under continuous perturbations due to the unpredicted behaviors of people and the occurrence of exogenous changes in the environment. In this article, we introduce a notion of ensembles
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Knowledge Management for Self-Organised Resource Allocation ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2019-09-18 David Burth Kurka; Jeremy Pitt; Josiah Ober
Many instances of socio-technical systems in the digital society and digital economy require some form of self-governance. Examples include community energy systems, peer production systems, participatory sensing applications, and shared management of communal living areas or workspace. Such systems have several features in common, of which three are that they are rule-oriented, self-organising, and
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Mutual Influence-aware Runtime Learning of Self-adaptation Behavior ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2019-09-18 Stefan Rudolph; Sven Tomforde; Jörg Hähner
Self-adaptation has been proposed as a mechanism to counter complexity in control problems of technical systems. A major driver behind self-adaptation is the idea to transfer traditional design-time decisions to runtime and into the responsibility of systems themselves. To deal with unforeseen events and conditions, systems need creativity—typically realized by means of machine learning capabilities
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Runtime Monitoring and Resolution of Probabilistic Obstacles to System Goals ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2019-09-18 Antoine Cailliau; Axel Van Lamsweerde
Software systems are deployed in environments that keep changing over time. They should therefore adapt to changing conditions to meet their requirements. The satisfaction rate of these requirements depends on the rate at which adverse conditions prevent their satisfaction. Obstacle analysis is a goal-oriented form of risk analysis for requirements engineering (RE), whereby obstacles to system goals
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Designing Robot Teams for Distributed Construction, Repair, and Maintenance ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2019-09-18 Todd Wareham
Designing teams of autonomous robots that can create target structures or repair damage to those structures on either a one-off or ongoing basis is an important problem in distributed robotics. However, it is not known if a team design algorithm for any of these tasks can both have low runtime and produce teams that will always perform their specified tasks quickly and correctly. In this article, we
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A Measure of Added Value in Groups ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2019-07-27 Bedoor K. Alshebli; Tomasz P. Michalak; Oskar Skibski; Michael Wooldridge; Talal Rahwan
The intuitive notion of added value in groups represents a fundamental property of biological, physical, and economic systems: how the interaction or cooperation of multiple entities, substances, or other agents can produce synergistic effects. However, despite the ubiquity of group formation, a well-founded measure of added value has remained elusive. Here, we propose such a measure inspired by the
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TSLAM ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2019-07-27 Wenjuan Li; Jian Cao; Shiyou Qian; Rajkumar Buyya
With the rapid development of cloud computing, various types of cloud services are available in the marketplace. However, it remains a significant challenge for cloud users to find suitable services for two major reasons: (1) Providers are unable to offer services in complete accordance with their declared Service Level Agreements, and (2) it is difficult for customers to describe their requirements
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An Innovative Approach for Ad Hoc Network Establishment in Disaster Environments by the Deployment of Wireless Mobile Agents ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2019-07-27 Xing Su; Minjie Zhang; Quan Bai
In disasters, many stationary tasks, such as saving survivors in debris, extinguishing fire of buildings, and so on, need first responders to complete on site. In such circumstances, wireless mobile robots are usually employed to search for tasks and establish ad hoc networks to assist first responders. Due to the unknown and complexity of environments and limited capabilities of wireless mobile robots
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SimCA* ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2019-07-27 Stepan Shevtsov; Danny Weyns; Martina Maggio
Self-adaptation provides a principled way to deal with software systems’ uncertainty during operation. Examples of such uncertainties are disturbances in the environment, variations in sensor readings, and changes in user requirements. As more systems with strict goals require self-adaptation, the need for formal guarantees in self-adaptive systems is becoming a high-priority concern. Designing self-adaptive
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Improving Data-Analytics Performance Via Autonomic Control of Concurrency and Resource Units ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2019-03-28 Gil Jae Lee; José A. B. Fortes
Many big-data processing jobs use data-analytics frameworks such as Apache Hadoop (currently also known as YARN). Such frameworks have tunable configuration parameters set by experienced system administrators and/or job developers. However, tuning parameters manually can be hard and time-consuming because it requires domain-specific knowledge and understanding of complex inter-dependencies among parameters
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Probabilistic Policy Reuse for Safe Reinforcement Learning ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2019-03-28 Javier García; Fernando Fernández
This work introduces Policy Reuse for Safe Reinforcement Learning, an algorithm that combines Probabilistic Policy Reuse and teacher advice for safe exploration in dangerous and continuous state and action reinforcement learning problems in which the dynamic behavior is reasonably smooth and the space is Euclidean. The algorithm uses a continuously increasing monotonic risk function that allows for
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SOD ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2019-03-28 Marco Brocanelli; Xiaorui Wang
A major concern for today’s smartphones is their much faster battery drain than traditional feature phones, despite their greater battery capacities. The difference is mainly contributed by those more powerful but also much more power-consuming smartphone components, such as the multi-core application processor and the high-definition (HD) display. While the application processor must be active when
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Adaptive Behavior Modeling in Logistic Systems with Agents and Dynamic Graphs ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2019-03-28 Thibaut Démare; Cyrille Bertelle; Antoine Dutot; Dominique Fournier
Inside a logistic system, actors of the logistics have to interact to manage a coherent flow of goods. They also must deal with the constraints of their environment. The article’s first goal is to study how macro properties (such as global performance) emerge from the dynamic and local behaviors of actors and the structure of the territory. The second goal is to understand which local parameters affect
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Decentralized Collective Learning for Self-managed Sharing Economies ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2018-11-26 Evangelos Pournaras; Peter Pilgerstorfer; Thomas Asikis
The Internet of Things equips citizens with a phenomenal new means for online participation in sharing economies. When agents self-determine options from which they choose, for instance, their resource consumption and production, while these choices have a collective systemwide impact, optimal decision-making turns into a combinatorial optimization problem known as NP-hard. In such challenging computational
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SDN Flow Entry Management Using Reinforcement Learning ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2018-11-26 Ting-Yu Mu; Ala Al-Fuqaha; Khaled Shuaib; Farag M. Sallabi; Junaid Qadir
Modern information technology services largely depend on cloud infrastructures to provide their services. These cloud infrastructures are built on top of Datacenter Networks (DCNs) constructed with high-speed links, fast switching gear, and redundancy to offer better flexibility and resiliency. In this environment, network traffic includes long-lived (elephant) and short-lived (mice) flows with pa
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Understanding Crowdsourcing Systems from a Multiagent Perspective and Approach ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2018-11-26 Jiuchuan Jiang; Bo An; Yichuan Jiang; Donghui Lin; Zhan Bu; Jie Cao; Zhifeng Hao
Crowdsourcing has recently been significantly explored. Although related surveys have been conducted regarding this subject, each has mainly consisted of a review of a single aspect of crowdsourcing systems or on the application of crowdsourcing in a specific application domain. A crowdsourcing system is a comprehensive set of multiple entities, including various elements and processes. Multiagent
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Adaptive Process Migrations in Coupled Applications for Exchanging Data in Local File Cache ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2018-11-26 Jianwei Liao; Zhigang Cai; Francois Trahay; Jun Zhou; Guoqiang Xiao
Many problems in science and engineering are usually emulated as a set of mutually interacting models, resulting in a coupled or multiphysics application. These component models show challenges originating from their interdisciplinary nature and from their computational and algorithmic complexities. In general, these models are independently developed and maintained, so that they commonly employ the
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Flexible and Efficient Decision-Making for Proactive Latency-Aware Self-Adaptation ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2018-05-21 Gabriel A. Moreno; Javier Cámara; David Garlan; Bradley Schmerl
Proactive latency-aware adaptation is an approach for self-adaptive systems that considers both the current and anticipated adaptation needs when making adaptation decisions, taking into account the latency of the available adaptation tactics. Since this is a problem of selecting adaptation actions in the context of the probabilistic behavior of the environment, Markov decision processes (MDPs) are
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Effective Capacity Modulation as an Explicit Control Knob for Public Cloud Profitability ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2018-05-21 Cheng Wang; Bhuvan Urgaonkar; George Kesidis; Aayush Gupta; Lydia Y. Chen; Robert Birke
We explore the efficacy of dynamic effective capacity modulation (i.e., using virtualization techniques to offer lower resource capacity than that advertised by the cloud provider) as an explicit control knob for a cloud provider's profit maximization complementing the more well-studied approach of dynamic pricing. Our focus is on emerging cloud ecosystems wherein we expect tenants to modify their
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Self-Organizing Control Mechanism Based on Collective Decision-Making for Information Uncertainty ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2018-05-21 Naomi Kuze; Daichi Kominami; Kenji Kashima; Tomoaki Hashimoto; Masayuki Murata
Because of the rapid growth in the scale and complexity of information networks, self-organizing systems are increasingly being used to realize novel network control systems that are highly scalable, adaptable, and robust. However, the uncertainty of information (with regard to incompleteness, vagueness, and dynamics) in self-organizing systems makes it difficult for them to work appropriately in accordance
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Engineering Self-Adaptive Software Systems ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2018-05-21 Konstantinos Angelopoulos; Alessandro V. Papadopoulos; Vítor E. Silva Souza; John Mylopoulos
Self-adaptive software systems monitor their operation and adapt when their requirements fail due to unexpected phenomena in their environment. This article examines the case where the environment changes dynamically over time and the chosen adaptation has to take into account such changes. In control theory, this type of adaptation is known as Model Predictive Control and comes with a well-developed
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Model-Based Response Planning Strategies for Autonomic Intrusion Protection ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2018-05-21 Stefano Iannucci; Sherif Abdelwahed
The continuous increase in the quantity and sophistication of cyberattacks is making it more difficult and error prone for system administrators to handle the alerts generated by intrusion detection systems (IDSs). To deal with this problem, several intrusion response systems (IRSs) have been proposed lately. IRSs extend the IDSs by providing an automatic response to the detected attack. Such a response
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Adaptive Opportunistic Airborne Sensor Sharing ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2018-05-21 Jacob Beal; Kyle Usbeck; Joseph Loyall; Mason Rowe; James Metzler
Airborne sensor platforms are becoming increasingly significant for both civilian and military operations; yet, at present, their sensors are typically idle for much of their flight time, e.g., while the sensor-equipped platform is in transit to and from the locations of sensing tasks. The sensing needs of many other potential information consumers might thus be served by sharing such sensors, thereby
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Viable Algorithmic Options for Designing Reactive Robot Swarms ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2018-05-21 Todd Wareham; Andrew Vardy
A central problem in swarm robotics is to design a controller that will allow the member robots of the swarm to collectively perform a given task. Of particular interest in massively distributed applications are reactive controllers with severely limited computational and sensory abilities. In this article, we give the results of the first computational complexity analysis of the reactive swarm design
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Software Adaptation in Wireless Sensor Networks ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2018-01-31 Mikhail Afanasov; Luca Mottola; Carlo Ghezzi
We present design concepts, programming constructs, and automatic verification techniques to support the development of adaptive Wireless Sensor Network (WSN) software. WSNs operate at the interface between the physical world and the computing machine and are hence exposed to unpredictable environment dynamics. WSN software must adapt to these dynamics to maintain dependable and efficient operation
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On Ordering Multi-Robot Task Executions within a Cyber Physical System ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2018-01-31 Tushar Semwal; Shashi Shekhar Jha; Shivashankar B. Nair
With robots entering the world of Cyber Physical Systems (CPS), ordering the execution of allocated tasks during runtime becomes crucial. This is so because, in the real world, there can be several physical tasks that use shared resources that need to be executed concurrently. In this article, we propose a mechanism to solve this issue of ordering task executions within a CPS that inherently handles
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MARC ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2018-01-31 Matteo Ferroni; Andrea Corna; Andrea Damiani; Rolando Brondolin; John D. Kubiatowicz; Donatella Sciuto; Marco D. Santambrogio
Autonomicity is a golden feature when dealing with a high level of complexity. This complexity can be tackled partitioning huge systems in small autonomous modules, i.e., agents. Each agent then needs to be capable of extracting knowledge from its environment and to learn from it, in order to fulfill its goals: this could not be achieved without proper modeling techniques that allow each agent to gaze
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A Stepwise Auto-Profiling Method for Performance Optimization of Streaming Applications ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2018-01-31 Xunyun Liu; Amir Vahid Dastjerdi; Rodrigo N. Calheiros; Chenhao Qu; Rajkumar Buyya
Data stream management systems (DSMSs) are scalable, highly available, and fault-tolerant systems that aggregate and analyze real-time data in motion. To continuously perform analytics on the fly within the stream, state-of-the-art DSMSs host streaming applications as a set of interconnected operators, with each operator encapsulating the semantic of a specific operation. For parallel execution on
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Efficient and Robust Emergence of Norms through Heuristic Collective Learning ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2018-01-31 Jianye Hao; Jun Sun; Guangyong Chen; Zan Wang; Chao Yu; Zhong Ming
In multiagent systems, social norms serves as an important technique in regulating agents’ behaviors to ensure effective coordination among agents without a centralized controlling mechanism. In such a distributed environment, it is important to investigate how a desirable social norm can be synthesized in a bottom-up manner among agents through repeated local interactions and learning techniques.
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Hierarchical Optimal Control Method for Controlling Large-Scale Self-Organizing Networks ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2018-01-31 Naomi Kuze; Daichi Kominami; Kenji Kashima; Tomoaki Hashimoto; Masayuki Murata
Self-organization has the potential for high scalability, adaptability, flexibility, and robustness, which are vital features for realizing future networks. The convergence of self-organizing control, however, is slow in some practical applications in comparison with control by conventional deterministic systems using global information. It is therefore important to facilitate the convergence of self-organizing
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Performance and Cost Considerations for Providing Geo-Elasticity in Database Clouds ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2018-01-31 Tian Guo; Prashant Shenoy
Online applications that serve global workload have become a norm and those applications are experiencing not only temporal but also spatial workload variations. In addition, more applications are hosting their backend tiers separately for benefits such as ease of management. To provision for such applications, traditional elasticity approaches that only consider temporal workload dynamics and assume
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From DevOps to BizOps ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2018-01-31 Marios Fokaefs; Cornel Barna; Marin Litoiu
Virtualization of resources in cloud computing has enabled developers to commission and recommission resources at will and on demand. This virtualization is a coin with two sides. On one hand, the flexibility in managing virtual resources has enabled developers to efficiently manage their costs; they can easily remove unnecessary resources or add resources temporarily when the demand increases. On
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Evolved Control of Natural Plants ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2017-10-09 Daniel Nicolas Hofstadler; Mostafa Wahby; Mary Katherine Heinrich; Heiko Hamann; Payam Zahadat; Phil Ayres; Thomas Schmickl
Mixing societies of natural and artificial systems can provide interesting and potentially fruitful research targets. Here we mix robotic setups and natural plants in order to steer the motion behavior of plants while growing. The robotic setup uses a camera to observe the plant and uses a pair of light sources to trigger phototropic response, steering the plant to user-defined targets. An evolutionary
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Defining Emergent Software Using Continuous Self-Assembly, Perception, and Learning ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2017-10-09 Roberto Rodrigues Filho; Barry Porter
Architectural self-organisation, in which different configurations of software modules are dynamically assembled based on the current context, has been shown to be an effective way for software to self-optimise over time. Current approaches to this rely heavily on human-led definitions: models, policies, and processes to control how self-organisation works. We present the case for a paradigm shift
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Self-Adaptation to Device Distribution in the Internet of Things ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2017-10-09 Jacob Beal; Mirko Viroli; Danilo Pianini; Ferruccio Damiani
A key problem when coordinating the behaviour of spatially situated networks, like those typically found in the Internet of Things (IoT), is adaptation to changes impacting network topology, density, and heterogeneity. Computational goals for such systems, however, are often dependent on geometric properties of the continuous environment in which the devices are situated rather than the particulars
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SASO 2016 ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2017-10-09 Giacomo Cabri; Gauthier Picard; Niranjan Suri
The IEEE International Conference on Self-Adapting and Self-Organizing Systems (SASO) is the main forum for studying and discussing the foundations of a principled approach to engineering systems, networks, and services based on self-adaptation and self-organization. Over the past decade, it has consolidated as the primary scientific conference for sharing ideas on algorithms, technologies, tools,
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Electronic Social Capital for Self-Organising Multi-Agent Systems ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2017-10-09 Patricio E. Petruzzi; Jeremy Pitt; Dídac Busquets
It is a recurring requirement in open systems, such as networks, distributed systems, and socio-technical systems, that a group of agents must coordinate their behaviour for the common good. In those systems—where agents are heterogeneous—unexpected behaviour can occur due to errors or malice. Agents whose practices free-ride the system can be accepted to a certain level; however, not only do they
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Hyper-Learning Algorithms for Online Evolution of Robot Controllers ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2017-10-09 Fernando Silva; Luís Correia; Anders Lyhne Christensen
A long-standing goal in artificial intelligence and robotics is synthesising agents that can effectively learn and adapt throughout their lifetime. One open-ended approach to behaviour learning in autonomous robots is online evolution, which is part of the evolutionary robotics field of research. In online evolution approaches, an evolutionary algorithm is executed on the robots during task execution
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Integrating Reinforcement Learning with Multi-Agent Techniques for Adaptive Service Composition ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2017-05-29 Hongbign Wang; Xin Chen; Qin Wu; Qi Yu; Xingguo Hu; Zibin Zheng; Athman Bouguettaya
Service-oriented architecture is a widely used software engineering paradigm to cope with complexity and dynamics in enterprise applications. Service composition, which provides a cost-effective way to implement software systems, has attracted significant attention from both industry and research communities. As online services may keep evolving over time and thus lead to a highly dynamic environment
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Prediction-Based Multi-Agent Reinforcement Learning in Inherently Non-Stationary Environments ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2017-05-29 Andrei Marinescu; Ivana Dusparic; Siobhán Clarke
Multi-agent reinforcement learning (MARL) is a widely researched technique for decentralised control in complex large-scale autonomous systems. Such systems often operate in environments that are continuously evolving and where agents’ actions are non-deterministic, so called inherently non-stationary environments. When there are inconsistent results for agents acting on such an environment, learning
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Budget-Driven Scheduling of Scientific Workflows in IaaS Clouds with Fine-Grained Billing Periods ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2017-05-29 Maria A. Rodriguez; Rajkumar Buyya
With the advent of cloud computing and the availability of data collected from increasingly powerful scientific instruments, workflows have become a prevailing mean to achieve significant scientific advances at an increased pace. Scheduling algorithms are crucial in enabling the efficient automation of these large-scale workflows, and considerable effort has been made to develop novel heuristics tailored
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On Service Migrations in the Cloud for Mobile Accesses ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2017-05-29 Yang Wang; Bharadwaj Veeravalli; Chen-Khong Tham; Shuibing He; Chengzhong Xu
We study the problem of dynamically migrating a service in the cloud to satisfy an online sequence of mobile batch-request demands in a cost-effective way. The service may have single or multiple replicas, each running on a virtual machine. As the origin of mobile accesses frequently changes over time, this problem is particularly important for time-bounded services to achieve enhanced Quality of Service
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Feature Construction for Controlling Swarms by Visual Demonstration ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2017-05-29 Karan K. Budhraja; John Winder; Tim Oates
Agent-based modeling is a paradigm of modeling dynamic systems of interacting agents that are individually governed by specified behavioral rules. Training a model of such agents to produce an emergent behavior by specification of the emergent (as opposed to agent) behavior is easier from a demonstration perspective. While many approaches involve manual behavior specification via code or reliance on
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Autonomous Mobile Sensor Placement in Complex Environments ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2017-05-29 Novella Bartolini; Tiziana Calamoneri; Stefano Ciavarella; Thomas La Porta; Simone Silvestri
In this article, we address the problem of autonomously deploying mobile sensors in an unknown complex environment. In such a scenario, mobile sensors may encounter obstacles or environmental sources of noise, so that movement and sensing capabilities can be significantly altered and become anisotropic. Any reduction of device capabilities cannot be known prior to their actual deployment, nor can it
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Tight Analysis of a Collisionless Robot Gathering Algorithm ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2017-05-04 Gokarna Sharma; Costas Busch; Supratik Mukhopadhyay; Charles Malveaux
We consider the fundamental problem of gathering a set of n robots in the Euclidean plane which have a physical extent and hence they cannot share their positions with other robots. The objective is to determine a minimum time schedule to gather the robots as close together as possible around a predefined gathering point avoiding collisions. This problem has applications in many real world scenarios
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Topology Management-Based Distributed Camera Actuation in Wireless Multimedia Sensor Networks ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2017-05-04 Goutam Mali; Sudip Misra
Wireless Multimedia Sensor Networks (WMSNs) involving camera and Scalar Sensor (SS) nodes provide precise information of events occurring in the monitored region by transmitting video packets. In WMSNs, it is necessary to provide coverage of events occurring in the monitored region for longer durations of time. The Camera Sensor (CS) nodes provide the coverage of an event and transmit the video data
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e-Sampling ACM Trans. Auton. Adapt. Syst. (IF 1.775) Pub Date : 2017-05-04 Md Zakirul Alam Bhuiyan; Jie Wu; Guojun Wang; Tian Wang; Mohammad Mehedi Hassan
Sampling rate adaptation is a critical issue in many resource-constrained networked systems, including Wireless Sensor Networks (WSNs). Existing algorithms are primarily employed to detect events such as objects or physical changes at a high, low, or fixed frequency sampling usually adapted by a central unit or a sink, therefore requiring additional resource usage. Additionally, this algorithm potentially