Resource demands are crucial parameters for modeling and predicting the performance of software systems. Currently, resource demand estimators are usually executed once for system analysis. However, the monitored system, as well as the resource demand itself, are subject to constant change in runtime environments. These changes additionally impact the applicability, the required parametrization as

Data-driven Learning-enabled Systems are limited by the quality of available training data, particularly when trained offline. For systems that must operate in real-world environments, the space of possible conditions that can occur is vast and difficult to comprehensively predict at design time. Environmental uncertainty arises when run-time conditions diverge from design-time training conditions

In a teacher-student framework, a more experienced agent (teacher) helps accelerate the learning of another agent (student) by suggesting actions to take in certain states. In cooperative multi-agent reinforcement learning (MARL), where agents must cooperate with one another, a student could fail to cooperate effectively with others even by following a teacher’s suggested actions, as the policies of

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

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

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’

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

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

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

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,

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

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

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

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

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

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

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

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