Harald Psaier

A survey on self-healing systems: approaches and systems

Present large-scale information technology environments are complex, heterogeneous compositions often affected by unpredictable behavior and poor manageability. This fostered substantial research on designs and techniques that enhance these systems with an autonomous behavior. In this survey, we focus on the self-healing branch of the research and give an overview of the current existing approaches. The survey is introduced by an outline of the origins of self-healing. Based on the principles of autonomic computing and self-adapting system research, we identify self-healing systems’ fundamental principles. The extracted principles support our analysis of the collected approaches. In a final discussion, we summarize the approaches’ common and individual characteristics. A comprehensive tabular overview of the researched material concludes the survey.

Auction-based crowdsourcing supporting skill management

Crowdsourcing is a promising approach for enterprises to maintain a flexible workforce that is able to solve parts of business processes formerly processed in-house. Companies perceive crowdsourcing as a concept that allows receiving solutions quickly and at little cost. Similar to cloud computing where computing power is provided on demand, the crowd promises a flexible on-demand workforce. However, businesses realize that these benefits entail a lack of quality control. The main difference compared to traditional approaches in business process execution is that tasks or activities cannot be directly assigned to employees but are posted to the crowdsourcing platform. Its members can choose deliberately which tasks to book and work on. In fact, crowdsourcing is heavily affected by the loose-coupling of workers to crowdsourcers and the dynamics of the environment. Hence, it remains a major challenge to guarantee high-quality processing of tasks within the prescribed time limit. A further obstacle for adoption of crowdsourcing in enterprises is the fact that it is hard to specify a fair monetary reward in advance. The concepts introduced in this work allow to smoothly integrate new workers, to keep them motivated, and to help them develop and improve skills needed in the system. We present a crowdsourcing marketplace that matches complex tasks, requiring multiple skills, to suitable workers. The key to ensuring high quality lies in skilled members whose capabilities can be estimated correctly. To that end, we present auction mechanisms that help to correctly estimate workers and to evolve skills that are needed in the system. Crowdsourcers do not need to predefine exact prices but only maximum prices they are willing to pay since the actual rewards for tasks are formed by supply and demand. Extensive experiments show that our approach leads to improved crowdsourcing, in most cases.

Runtime Behavior Monitoring and Self-Adaptation in Service-Oriented Systems

Mixed service-oriented systems composed of human actors and software services build up complex interaction networks. Without any coordination, such systems may exhibit undesirable properties due to unexpected behavior. Also, communications and interactions in such networks are not preplanned by top-down composition models. Consequently, the management of service-oriented applications is difficult due to changing interaction and behavior patterns that possibly contradict and result in faults from varying conditions and misbehavior in the network. In this paper we present a self-adaptation approach that regulates local interactions to maintain desired system functionality. To prevent degraded or stalled systems, adaptations operate by link modification or substitution of actors based on similarity and trust metrics. Unlike a security perspective on trust, we focus on the notion of socially inspired trust. We design an architecture based on two separate independent frameworks. One providing a real Web service test bed extensible for dynamic adaptation actions. The other is our self-adaptation framework including all modules required by systems with self-* properties. In our experiments we study a trust and similarity based adaptation approach by simulating dynamic interactions in the real Web services test bed.

Stimulating skill evolution in market-based crowdsourcing

Crowdsourcing has emerged as an important paradigm in human problem-solving techniques on the Web. One application of crowdsourcing is to outsource certain tasks to the crowd that are difficult to implement in software. Another potential benefit of crowdsourcing is the on-demand allocation of a flexible workforce. Businesses may outsource tasks to the crowd based on temporary workload variations. A major challenge in crowdsourcing is to guarantee high-quality processing of tasks. We present a novel crowdsourcing marketplace that matches tasks to suitable workers based on auctions. The key to ensuring high quality lies in skilled members whose capabilities can be estimated correctly. We present a novel auction mechanism for skill evolution that helps to correctly estimate workers and to evolve skills that are needed. Evaluations show that this leads to improved crowdsourcing.

Crowdsourcing tasks to social networks in BPEL4People

Human-interactions are a substantial part of today’s business processes. In service-oriented systems this has led to specifications such as WS-HumanTask and BPEL4People which aim at standardizing the interaction protocol between software processes and humans. These specifications received considerable attention from major industry players due to their extensibility and interoperability. Recently, crowdsourcing has emerged as a new paradigm for leveraging a human workforce using Web technologies. We argue that crowdsourcing techniques and platforms could benefit from XML-based standards such as WS-HumanTask and BPEL4People as these specifications allow for extensibility and cross-platform operation. However, most efforts to model human interactions using BPEL4People focus on relatively static role models for selecting the right person to interact with. Thus, BPEL4People is not well suited for specifying and executing processes involving crowdsourcing of tasks to online communities. Here, we extend BPEL4People with non-functional properties that allow to cope with the inherent dynamics of crowdsourcing processes. Such properties include human capabilities and the level of skills. We discuss the formation of social networks that are particularly beneficial for processing extended BPEL4People tasks. Furthermore, we present novel approaches for the automated assignment of tasks to a social group. The feasibility of our approach is shown through a proof of concept implementation of various concepts as well as simulations and experiments to evaluate our ranking and selection approach.

Resource and Agreement Management in Dynamic Crowdcomputing Environments

Open Web-based and social platforms dramatically influence models of work. Today, there is an increasing interest in outsourcing tasks to crowd sourcing environments that guarantee professional processing. The challenge is to gain the customers confidence by organizing the crowds mixture of capabilities and structure to become reliable. This work outlines the requirements for a reliable management in crowd computing environments. For that purpose, distinguished crowd members act as responsible points of reference. These members mediate the crowds workforce, settle agreements, organize activities, schedule tasks, and monitor behavior. At the center of this work we provide a hard/soft constraints scheduling algorithm that integrates existing agreement models for service-oriented systems with crowd computing environments. We outline an architecture that monitors the capabilities of crowd members, triggers agreement violations, and deploys counteractions to compensate service quality degradation.

Behavior Monitoring in Self-Healing Service-Oriented Systems

Web services and service-oriented architecture (SOA) have become the de facto standard for designing distributed and loosely coupled applications. Many service-based applications demand for a mix of interactions between humans and Software-Based Services (SBS). An example is a process model comprising SBS and services provided by human actors. Such applications are difficult to manage due to changing interaction patterns, behavior, and faults resulting from varying conditions in the environment. To address these complexities, we introduce a self-healing approach enabling recovery mechanisms to avoid degraded or stalled systems. The presented work extends the notion of self-healing by considering a mixture of human and service interactions observing their behavior patterns. We present the design and architecture of the VieCure framework supporting fundamental principles for autonomic self-healing strategies. We validate our self-healing approach through simulations.

Adaptive Query Routing on Distributed Context - The COSINE Framework

Context-awareness has become a desired key feature of todays mobile systems, yet, its realization still remains a challenge. On the one hand, mobile computing provides great potential for adaptations based on sensed contextual information. On the other hand, the lack of dependability in mobile networks hampers an efficient provision of this information to requesting clients. In this paper we present COSINE, a context management framework for mobile environments. COSINE has been developed on the principles of peer-to-peer computing and establishes context sharing infrastructures consisting of loosely coupled Web services. The services represent modular entities of the applied context model and manage the retrieval, aggregation, query, and provision of context data. Clients access the distributed information transparently via proxies and a self-adaptive routing of queries provides increased fault-tolerance, which is essential in mobile environments.

Towards Social Crowd Environments Using Service-Oriented Architectures

Abstract Crowdsourcing has emerged as an important paradigm of human problem solving techniques on the Web. More often than noticed, organizations outsource tasks to humans which cannot be processed by software. In this work we demonstrate the application of service-oriented architectures (SOA) for enterprise crowdsourcing. Crowdsourcing applications typically utilize the capabilities of people in open and dynamic Web-based systems. We extend this concept and introduce knowledge crowds where crowd members collaborate in context of joint tasks. Interactions in such environments are performed using software services. Such highly dynamic socio-technical environments, however, demand for flexible interaction models due to varying interaction styles of people and services. Our main contribution centers around the convergence of process artifacts and emerging social structures. Here, we present the implementation of a real world example, a human assisted image processing service that is provided by a knowledge crowd. We discuss the foundational building blocks for realizing the design, execution, and adaptation of service-oriented crowdsourcing applications. Zusammenfassung Crowdsourcing hat sich als wichtiges Paradigma etabliert, um menschliche Fähigkeiten zur Problemlösung über das Web anzubieten. Häufiger als allgemein bekannt lagern Organisationen Aufgaben auf externe Personen aus, insbesondere jene, die schwer von Software gelöst werden können. In dieser Arbeit demonstrieren wir die Anwendung von Service-orientierten Architekturen (SOA) für Crowdsourcing-Unternehmen. Crowdsourcing-Anwendungen nutzen typischerweise die Fähigkeiten von Menschen mittels offener und dynamischer Web-basierter Systeme. Wir erweitern dieses Konzept um die Einführung von Wissensbasierten Crowds, also Systemen in denen Mitglieder im Kontext von gemeinsamen Aufgaben kollaborieren. Interaktionen in solchen Umgebungen werden mittels Software-Services realisiert. Solch hoch-dynamische sozio-technische Umgebungen verlangen, insbesondere aufgrund wechselnder Interaktionsmöglichkeiten und -mustern, jedoch nach flexiblen Interaktionsmodellen. Unser Beitrag konzentriert sich auf die Konvergenz von Prozessartefakten und entstehenden sozialen Strukturen. Wir präsentieren die Umsetzung eines realen Anwendungsfalles, konkret ein Service zur Personen-gestützten Bildverarbeitung, welches von einer Wissensbasierten Crowd angeboten wird. Wir diskutieren die grundlegenden Konzepte für die Realisierung, die Laufzeit und periodische Anpassung einer solchen Service-orientierten Crowdsourcing-Anwendung.

Adaptive provisioning of human expertise in service-oriented systems

Web-based collaborations have become essential in todays business environments. Due to the availability of various SOA frameworks, Web services emerged as the de facto technology to realize flexible compositions of services. While most existing work focuses on the discovery and composition of software based services, we highlight concepts for a people-centric Web. Knowledge-intensive environments clearly demand for provisioning of human expertise along with sharing of computing resources or business data through software-based services. To address these challenges, we introduce an adaptive approach allowing humans to provide their expertise through services using SOA standards, such as WSDL and SOAP. The seamless integration of humans in the SOA loop triggers numerous social implications, such as evolving expertise and drifting interests of human service providers. Here we propose a framework that is based on interaction monitoring techniques enabling adaptations in SOA-based socio-technical systems.