Abstract: Self-adaptation improves the resilience of software-intensive systems, enabling them to adapt their structure and behavior to run-time changes (e.g., in workload and resource availability). Many of these approaches reason about the best way of adapting by synthesizing adaptation plans online via planning or model checking tools. This method enables the exploration of a rich solution space, but optimal solutions and other guarantees (e.g., constraint satisfaction) are computationally costly, resulting in long planning times during which changes may invalidate plans. An alternative to online planning involves selecting at run time the adaptation best suited to the current system and environment conditions from among a predefined repertoire of adaptation strategies that capture repair and optimization tasks. This method does not incur run-time overhead but requires additional effort from engineers, who have to specify strategies and lack support to systematically assess their quality. In this article, we present MOSAICO, an approach for offline synthesis of adaptation strategy repertoires that makes a novel use of discrete abstractions of the state space to flexibly adapt extra-functional behavior in a scalable manner. The approach supports making trade-offs: (i) among multiple extra-functional concerns, and (ii) between computation time and adaptation quality (varying abstraction resolution). Our results show a remarkable improvement on system qualities in contrast to manually-specified repertoires. More interestingly, moderate increments in abstraction resolution can lead to pronounced quality improvements, whereas high resolutions yield only negligible improvement over medium resolutions.

Abstract: Software systems are increasingly called upon to autonomously manage their goals in changing contexts and environments, and under evolving requirements. In some circumstances, autonomous systems cannot be fully automated but instead cooperate with human operators to maintain and adapt themselves. Furthermore, there are times when a choice should be made between doing a manual or automated repair. Involving operators in self-adaptation should itself be adaptive, and consider aspects such as the training, attention, and ability of operators. Not only do these aspects change from person to person, but they may change with the same person. These aspects make the choice of whether to involve humans non-obvious. Self-adaptive systems should trade-off whether to involve operators, taking these aspects into consideration along with other business qualities it is attempting to achieve. In this chapter, we identify the various roles that operators can perform in cooperating with self-adaptive systems. We focus on humans as effectors - doing tasks which are difficult or infeasible to automate. We describe how we modified our self-adaptive framework, Rainbow, to involve operators in this way, which involved choosing suitable human models and integrating them into the existing utility trade-off decision models of Rainbow. We use probabilistic modeling and quantitative verification to analyze the trade-offs of involving humans in adaptation, and complement our study with experiments to show how different business preferences and modalities of human involvement may result in different outcomes.

Abstract: Many applications have taken advantage of cloud provided autonomic capabilities, commonly auto-scaling, to harness easily available compute capacity to maintain performance against defined quality objectives. This has caused the management complexity of enterprise applications to increase. It is now common for an application to be a collection of autonomic sub-systems. However, combining individual autonomic systems to create an application can lead to behaviors that negatively impact the global aggregate utility of the application and in some cases can be conflicting and self-destructive. Commonly, human administrators address these behaviors as part of a design time analysis of the situation or a run time mitigation of the undesired effects. However, the task of controlling and mitigating undesirable behaviors is complex and error prone. To handle the complexity of managing a collection of autonomic systems we have previously proposed an automated approach to the creation of a higher level autonomic management system, referred to as a Meta-Manager. In this paper, we improve upon prior work with a more streamlined and understandable formal representation of the approach, expand its capabilities to include global knowledge, and test its potential applicability and effectiveness by managing the complexity of a collection of autonomic systems in a case study of a major outage suffered by the Google Cloud Platform.

Abstract: Self-adaptive software systems rely on planning to make adaptation decisions autonomously. Planning is required to produce high-quality adaptation plans in a timely manner; however, quality and timeliness of planning are conflicting in nature. This conflict can be reconciled with hybrid planning, which can combine reactive planning (to quickly provide an emergency response) with deliberative planning that take time but determine a higher-quality plan. While often effective, reactive planning sometimes risks making the situation worse. Hence, a challenge in hybrid planning is to decide whether to invoke reactive planning until the deliberative planning is ready with a high-quality plan. To make this decision, this paper proposes a novel learning-based approach. We demonstrate that this learning-based approach outperforms existing techniques that are based on specifying fixed conditions to invoke reactive planning in two domains: enterprise cloud systems and unmanned aerial vehicles.

Abstract: Modern enterprise IT systems are increasingly becoming compositions of many subsystems each of which is an autonomic system. These individual autonomic systems act independently to maintain their locally defined SLAs but can take actions which are inconsistent with and potentially detrimental to the global system objective. Currently, human administrators intervene to resolve these conflicts but are challenged by complexity in the prediction of current and future states of the constituent systems and their managers, multiple conflicting quality dimensions which may change over time, combinatorially large configuration space across the set of constituent systems, and the time critical nature of the decisions to be made to prevent further degradation. To address these challenges, this paper proposes an approach that enables the creation of a higher level autonomic system, referred to as a meta-manager, that does not subsume the control functions nor does it directly orchestrate the actions of the sub-autonomic managers. Instead, we encapsulate and abstract the behavior of each subsystem as a parameterized adaptation policy which can be adjusted by the meta-manager to tune the adaptive behavior of the subsystem adaptation. We can effectively instantiate this idea by considering each of the subsystems as a player in a stochastic multi-player game against it’s local environment, and synthesize an adaptation strategy using off-the-shelf tools for stochastic game analysis.

Abstract: Self-adaptive software systems make decisions at run time that seek to change their behavior in response to faults, changing environments and attacks. Therefore, having an appropriate planning approach to find an adaptation plan is critical to successful self-adaptation. For many realistic systems, ideally one would like to have a planning approach that is both quick and finds an optimal adaptation plan. However, due to the fundamental trade-off between quality and timeliness of planning, often designers have to compromise between an approach that is quick to find a plan and an approach that is slow but finds an optimal plan. To deal with this trade-off, this work proposes a hybrid planning approach that combines more than one planning approach to bring their benefits together.

Abstract: Self-Adaptive systems are expected to adapt to unanticipated run-time events using imperfect information about their environment. This entails handling the effects of uncertainties in decision-making, which are not always considered as a first-class concern. This paper contributes a formal analysis technique that explicitly considers uncertainty in sensing when reasoning about the best way to adapt. We illustrate our approach on a Denial of Service (DoS) attack scenario and present some preliminary results that show the benefits of uncertainty-aware decision-making with respect to using an uncertainty-ignorant approach.

Abstract: Modern software systems are often compositions of entities that increasingly use self-adaptive capabilities to improve their behavior to achieve systematic quality goals. Self-adaptive managers for each component system attempt to provide locally optimal results, but if they cooperated and potentially coordinated their efforts it might be possible to obtain more globally optimal results. The emergent properties that result from such composition and cooperation of self-adaptive systems are not well understood, difficult to reason about, and present a key challenge in the evolution of modern software systems. For example, the effects of coordination patterns and protocols on emergent properties such as the resiliency of the collectives need to be understood when designing these systems. In this paper we propose that probabilistic model checking of stochastic multiplayer games (SMG) provides a promising approach to analyze, understand, and reason about emergent properties in collectives of adaptive systems (CAS). Probabilistic Model Checking of SMGs is a technique particularly suited to analyzing emergent properties in CAS, since SMG models capture: (i) the uncertainty and variability intrinsic to the CAS and its execution environment in the form of probabilistic and nondeterministic choices, and (ii) the competitive/cooperative aspects of the interplay among the constituent systems of the CAS. Analysis of SMGs allows us to reason about things like the worst case scenarios, which constitutes a new contribution to understanding emergent properties in CAS. We investigate the use of SMGs to show how they can be useful in analyzing the impact of communication topology for collections of fully cooperative systems defending against an external attack.

Abstract: Internet of Things (IoT) allows for cyber-physical applications to be created and composed to provide intelligent support or automation of end-user tasks. For many of such tasks, human participation is crucial to the success and the quality of the tasks. The cyber systems should proactively request help from the humans to accomplish the tasks when needed. However, the outcome of such system-human synergy may be affected by factors external to the systems. Failure to consider those factors when involving human participants in the tasks may result in suboptimal performance and negative experience on the humans. In this paper, we propose an approach for automated generation of control strategies of cyber-human systems. We investigate how explicit modeling of human participant can be used in automated planning to generate cooperative strategy of human and system to achieve a given task, by means of which best and appropriately utilize the human. Specifically, our approach consists of: (1) a formal framework for modeling cooperation between cyber system and human, and (2) a formalization of system-human cooperative task planning as strategy synthesis of stochastic multiplayer game. We illustrate our approach through an example of indoor air quality control in smart homes.

Abstract: Self-adaptive systems overcome many of the limitations of human supervision in complex software-intensive systems

by endowing them with the ability to automatically adapt their

structure and behavior in the presence of runtime changes.

However, adaptation in some classes of systems (e.g., safety- critical) can benefit by receiving information from humans (e.g.,

acting as sophisticated sensors, decision-makers), or by involving

them as system-level effectors to execute adaptations (e.g., when

automation is not possible, or as a fallback mechanism). However,

human participants are influenced by factors external to the

system (e.g., training level, fatigue) that affect the likelihood

of success when they perform a task, its duration, or even if

they are willing to perform it in the first place. Without careful

consideration of these factors, it is unclear how to decide when to

involve humans in adaptation, and in which way. In this paper,

we investigate how the explicit modeling of human participants

can provide a better insight into the trade-offs of involving

humans in adaptation. We contribute a formal framework to

reason about human involvement in self-adaptation, focusing on

the role of human participants as actors (i.e., effectors) during

the execution stage of adaptation. The approach consists of:

(i) a language to express adaptation models that capture factors

affecting human behavior and its interactions with the system,

and (ii) a formalization of these adaptation models as stochastic

multiplayer games (SMGs) that can be used to analyze human- system-environment interactions. We illustrate our approach in

an adaptive industrial middleware used to monitor and manage

sensor networks in renewable energy production plants.

Abstract: Self-Adaptive systems are expected to adapt to unanticipated run-time events using imperfect information about their environment. This entails handling the effects of uncertainties in decision-making, which are not always considered as a first-class concern. This technical report summarizes a set of existing techniques and insights into addressing uncertainty in self-adaptive systems and outlines a future research agenda on uncertainty management in self-adaptive systems. The material in this report is strongly informed by our own research in the area, and is therefore not necessarily representative of other works.

Abstract: Planning approaches in self-adaptation face the fundamental trade-off between quality and timeliness of adaptation plans. Due to this trade-off, today designers often have to compromise between a planning approach that is quick to find a plan and an approach that is slow but finds a quality plan. To deal with this trade-off, researchers have proposed a hybrid planning approach that combines more than one planning approaches to find a balance between quality and timeliness. However, the diversity of planning approaches makes the problem of hybrid planning complex and multi-faceted. This paper advances the theory of hybrid planning by formalizing the central concepts and four subproblems of hybrid planning. This formalization can serve as a foundation for not only understanding and comparing existing hybrid planners, but also for developing new hybrid planners in the future. Moreover, to illustrate practicality of the formal model, the paper uses it to analyze two hybrid planning instantiations that have shown to effective in their respective contexts. Furthermore, grounded on the formal model, the paper formalizes explicit/implicit assumptions about these instantiations that must hold for them to be sound.

Abstract: While security is important to self-adaptive systems research, it is particularly difficult to simulate the characteristics of the most insidious adversary, the advanced persistent threat (APT). The research community requires an APT simulation environment that provides the realism and depth that is necessary for a sufficient simulation, but this should not be too complex, difficult, or expensive to use. Additionally, a successful simulation environment must ensure that malware does not escape into the wild. We present a cyber range for self-adaptive systems researchers to simulate APTs, complete with an example system under test and attack scenario.