4,953 research outputs found
Self-organising agent communities for autonomic resource management
The autonomic computing paradigm addresses the operational challenges presented by increasingly complex software systems by proposing that they be composed of many autonomous components, each responsible for the run-time reconfiguration of its own dedicated hardware and software components. Consequently, regulation of the whole software system becomes an emergent property of local adaptation and learning carried out by these autonomous system elements. Designing appropriate local adaptation policies for the components of such systems remains a major challenge. This is particularly true where the system’s scale and dynamism compromise the efficiency of a central executive and/or prevent components from pooling information to achieve a shared, accurate evidence base for their negotiations and decisions.In this paper, we investigate how a self-regulatory system response may arise spontaneously from local interactions between autonomic system elements tasked with adaptively consuming/providing computational resources or services when the demand for such resources is continually changing. We demonstrate that system performance is not maximised when all system components are able to freely share information with one another. Rather, maximum efficiency is achieved when individual components have only limited knowledge of their peers. Under these conditions, the system self-organises into appropriate community structures. By maintaining information flow at the level of communities, the system is able to remain stable enough to efficiently satisfy service demand in resource-limited environments, and thus minimise any unnecessary reconfiguration whilst remaining sufficiently adaptive to be able to reconfigure when service demand changes
A feedback-based decentralised coordination model for distributed open real-time systems
Moving towards autonomous operation and management of increasingly
complex open distributed real-time systems poses very significant challenges.
This is particularly true when reaction to events must be done in a timely and
predictable manner while guaranteeing Quality of Service (QoS) constraints
imposed by users, the environment, or applications. In these scenarios, the
system should be able to maintain a global feasible QoS level while allowing
individual nodes to autonomously adapt under different constraints of
resource availability and input quality.
This paper shows how decentralised coordination of a group of autonomous
interdependent nodes can emerge with little communication, based on the
robust self-organising principles of feedback. Positive feedback is used to
reinforce the selection of the new desired global service solution, while negative
feedback discourages nodes to act in a greedy fashion as this adversely
impacts on the provided service levels at neighbouring nodes.
The proposed protocol is general enough to be used in a wide range of
scenarios characterised by a high degree of openness and dynamism where coordination
tasks need to be time dependent. As the reported results demonstrate,
it requires less messages to be exchanged and it is faster to achieve a
globally acceptable near-optimal solution than other available approaches
A Deep Recurrent Q Network Towards Self-adapting Distributed Microservices Architecture (in press)
One desired aspect of microservices architecture is the ability to self-adapt its own architecture and behaviour in response to changes in the operational environment. To achieve the desired high levels of self-adaptability, this research implements the distributed microservices architectures model, as informed by the MAPE-K model. The proposed architecture employs a multi adaptation agents supported by a centralised controller, that can observe the environment and execute a suitable adaptation action. The adaptation planning is managed by a deep recurrent Q-network (DRQN). It is argued that such integration between DRQN and MDP agents in a MAPE-K model offers distributed microservice architecture with self-adaptability and high levels of availability and scalability. Integrating DRQN into the adaptation process improves the effectiveness of the adaptation and reduces any adaptation risks, including resources over-provisioning and thrashing. The performance of DRQN is evaluated against deep Q-learning and policy gradient algorithms including: i) deep q-network (DQN), ii) dulling deep Q-network (DDQN), iii) a policy gradient neural network (PGNN), and iv) deep deterministic policy gradient (DDPG). The DRQN implementation in this paper manages to outperform the above mentioned algorithms in terms of total reward, less adaptation time, lower error rates, plus faster convergence and training times. We strongly believe that DRQN is more suitable for driving the adaptation in distributed services-oriented architecture and offers better performance than other dynamic decision-making algorithms
A survey on engineering approaches for self-adaptive systems (extended version)
The complexity of information systems is increasing in recent years, leading to increased effort for maintenance and configuration. Self-adaptive systems (SASs) address this issue. Due to new computing trends, such as pervasive computing, miniaturization of IT leads to mobile devices with the emerging need for context adaptation. Therefore, it is beneficial that devices are able to adapt context. Hence, we propose to extend the definition of SASs and include
context adaptation. This paper presents a taxonomy of self-adaptation and a survey on engineering SASs. Based on the taxonomy and the survey, we motivate a new perspective on SAS including context adaptation
AgentChat: Multi-Agent Collaborative Logistics for Carbon Reduction
Heavy Good Vehicles (HGVs) are the second largest source of greenhouse gas
emissions in transportation, after cars and taxis. However, HGVs are
inefficiently utilised, with more than one-third of their weight capacity not
being used during travel. We, thus, in this paper address collaborative
logistics, an effective pathway to enhance HGVs' utilisation and reduce carbon
emissions. We investigate a multi-agent system approach to facilitate
collaborative logistics, particularly carrier collaboration. We propose a
simple yet effective multi-agent collaborative logistics (MACL) framework,
representing key stakeholders as intelligent agents. Furthermore, we utilise
the MACL framework in conjunction with a proposed system architecture to create
an integrated collaborative logistics testbed. This testbed, consisting of a
physical system and its digital replica, is a tailored cyber-physical system or
digital twin for collaborative logistics. Through a demonstration, we show the
utility of the testbed for studying collaborative logistics.Comment: This paper includes 12 pages, 14 figures, and has been submitted to
IEEE for possible publicatio
Decentralized planning for self-adaptation in multi-cloud environment
The runtime management of Internet of Things (IoT) oriented applications deployed in multi-clouds is a complex issue due to the highly heterogeneous and dynamic execution environment. To effectively cope with such an environment, the cross-layer and multi-cloud effects should be taken into account and a decentralized self-adaptation is a promising solution to maintain and evolve the applications for quality assurance. An important issue to be tackled towards realizing this solution is the uncertainty effect of the adaptation, which may cause negative impact to the other layers or even clouds. In this paper, we tackle such an issue from the planning perspective, since an inappropriate planning strategy can fail the adaptation outcome. Therefore, we present an architectural model for decentralized self-adaptation to support the cross-layer and multi-cloud environment. We also propose a planning model and method to enable the decentralized decision making. The planning is formulated as a Reinforcement Learning problem and solved using the Q-learning algorithm. Through simulation experiments, we conduct a study to assess the effectiveness and sensitivity of the proposed planning approach. The results show that our approach can potentially reduce the negative impact on the cross-layer and multi-cloud environment
Modelling and Simulation Approaches for Local Energy Community Integrated Distribution Networks
Due to the absence of studies of local energy communities (LECs) where the grid is represented, it is very difficult to infer implications of increased LEC integration for the distribution grid as well as for the wider society. Therefore, this paper aims to investigate holistic modelling and simulation approaches of LECs. To conduct a quantifiable assessment of different control architectures, LEC types and market frameworks, a flexible and comprehensive LEC modelling and simulation approach is needed. Modelling LECs and the environment they operate in involves a holistic approach consisting of different layers: market, controller, and grid. The controller layer is relevant both for the overall energy management system of the LEC and the controllers of single components in a LEC. In this paper, the different LEC modelling approaches in the reviewed literature are presented, several multilayered concepts for LECs are proposed, and a case study is presented to illustrate a holistic simulation where the different layers interact.Modelling and Simulation Approaches for Local Energy Community Integrated Distribution NetworkspublishedVersio
Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms
The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications
SACRE: Supporting contextual requirements' adaptation in modern self-adaptive systems in the presence of uncertainty at runtime
Runtime uncertainty such as unpredictable resource unavailability, changing
environmental conditions and user needs, as well as system intrusions or faults
represents one of the main current challenges of self-adaptive systems.
Moreover, today's systems are increasingly more complex, distributed,
decentralized, etc. and therefore have to reason about and cope with more and
more unpredictable events. Approaches to deal with such changing requirements
in complex today's systems are still missing. This work presents SACRE (Smart
Adaptation through Contextual REquirements), our approach leveraging an
adaptation feedback loop to detect self-adaptive systems' contextual
requirements affected by uncertainty and to integrate machine learning
techniques to determine the best operationalization of context based on sensed
data at runtime. SACRE is a step forward of our former approach ACon which
focus had been on adapting the context in contextual requirements, as well as
their basic implementation. SACRE primarily focuses on architectural decisions,
addressing self-adaptive systems' engineering challenges. Furthering the work
on ACon, in this paper, we perform an evaluation of the entire approach in
different uncertainty scenarios in real-time in the extremely demanding domain
of smart vehicles. The real-time evaluation is conducted in a simulated
environment in which the smart vehicle is implemented through software
components. The evaluation results provide empirical evidence about the
applicability of SACRE in real and complex software system domains.Comment: 45 pages, journal article, 14 figures, 9 tables, CC-BY-NC-ND 4.0
licens
Artificial Collective Intelligence Engineering: a Survey of Concepts and Perspectives
Collectiveness is an important property of many systems--both natural and
artificial. By exploiting a large number of individuals, it is often possible
to produce effects that go far beyond the capabilities of the smartest
individuals, or even to produce intelligent collective behaviour out of
not-so-intelligent individuals. Indeed, collective intelligence, namely the
capability of a group to act collectively in a seemingly intelligent way, is
increasingly often a design goal of engineered computational systems--motivated
by recent techno-scientific trends like the Internet of Things, swarm robotics,
and crowd computing, just to name a few. For several years, the collective
intelligence observed in natural and artificial systems has served as a source
of inspiration for engineering ideas, models, and mechanisms. Today, artificial
and computational collective intelligence are recognised research topics,
spanning various techniques, kinds of target systems, and application domains.
However, there is still a lot of fragmentation in the research panorama of the
topic within computer science, and the verticality of most communities and
contributions makes it difficult to extract the core underlying ideas and
frames of reference. The challenge is to identify, place in a common structure,
and ultimately connect the different areas and methods addressing intelligent
collectives. To address this gap, this paper considers a set of broad scoping
questions providing a map of collective intelligence research, mostly by the
point of view of computer scientists and engineers. Accordingly, it covers
preliminary notions, fundamental concepts, and the main research perspectives,
identifying opportunities and challenges for researchers on artificial and
computational collective intelligence engineering.Comment: This is the author's final version of the article, accepted for
publication in the Artificial Life journal. Data: 34 pages, 2 figure
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