ETS (Efficient, Transparent, and Secured) Self-healing Service for Pervasive Computing Applications

To ensure smooth functioning of numerous handheld devices anywhere anytime, the importance of self-healing mechanism cannot be overlooked. Incorporation of efficient fault detection and recovery in device itself is the quest for long but there is no existing self-healing scheme for devices running in pervasive computing environments that can be claimed as the ultimate solution. Moreover, the highest degree of transparency, security and privacy attainability should also be maintained. ETS Self-healing service, an integral part of our developing middleware named MARKS (Middleware Adaptability for Resource discovery, Knowledge usability, and Self-healing), holds promise for offering all of those functionalities

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

Autonomous Architectural Assembly And Adaptation

An increasingly common solution for systems which are deployed in unpredictable or dangerous environments is to provide the system with an autonomous or selfmanaging capability. This capability permits the software of the system to adapt to the environmental conditions encountered at runtime by deciding what changes need to be made to the system’s behaviour in order to continue meeting the requirements imposed by the designer. The chief advantage of this approach comes from a reduced reliance on the brittle assumptions made at design time. In this work, we describe mechanisms for adapting the software architecture of a system using a declarative expression of the functional requirements (derived from goals), structural constraints and preferences over the space of non-functional properties possessed by the components of the system. The declarative approach places this work in contrast to existing schemes which require more fine-grained, often procedural, specifications of how to perform adaptations. Our algorithm for assembling and re-assembling configurations chooses between solutions that meet both the functional requirements and the structural constraints by comparing the non-functional properties of the selected components against the designer’s preferences between, for example, a high-performance or a highly reliable solution. In addition to the centralised algorithm, we show how the approach can be applied to a distributed system with no central or master node that is aware of the full space of solutions. We use a gossip protocol as a mechanism by which peer nodes can propose what they think the component configuration is (or should be). Gossip ensures that the nodes will reach agreement on a solution, and will do so in a logarithmic number of steps. This latter property ensures the approach can scale to very large systems. Finally, the work is validated on a number of case studies

HUC-HISF: A Hybrid Intelligent Security Framework for Human-centric Ubiquitous Computing

制度:新 ; 報告番号:乙2336号 ; 学位の種類:博士(人間科学) ; 授与年月日:2012/1/18 ; 早大学位記番号:新584

Addressing Resource Variability Through Resource-Driven Adaptation

Software systems execute tasks that depend on different types of resources. However, the variability of resources may interfere with the ability of software systems to execute important tasks. Resource variability can occur due to several reasons including unexpected hardware failures, excess workloads, or lack of materials. For example, in automated warehouses, malfunctioning robots could delay product deliveries causing customer dissatisfaction and, therefore, reducing an enterprise’s sales. Moreover, the unavailability of medical materials hinders the ability of hospitals to perform medically-critical operations causing loss of life. In this thesis, we propose to address the problem of resource variability through resource-driven adaptation, using task models as input for adaptation decisions. The thesis presents the following contributions: • SPARK: a framework for performing proactive and reactive resource-driven adaptation based on multiple task-related criteria. The framework supports different types of depletable and reusable resources that could face variability. SPARK assists with four types of adaptation, namely: (i) execution of a similar task that requires fewer resources, (ii) substitution of resources by alternative ones, (iii) execution of tasks in a different order, and (iv) cancellation of the execution of tasks. • SERIES: a task modelling notation and editor tool that enables software practitioners to create task models that serve as input for SPARK. SERIES supports the representation of task priorities, task variants, task execution types, resource types, and properties representing users’ feedback. SPARK was evaluated in terms of the percentage of executed critical task requests, the average criticality of the executed task requests in comparison to the non-executed ones, overhead, and scalability through two case studies concerned with a medicine consumption system and a manufacturing system. The results of the evaluation showed that SPARK increased the number of executed critical task requests during resource variability. Additionally, the results showed that the time it takes to prepare and apply adaptation plans does not add significant overhead that hinders the ability of software systems to execute tasks in a tolerable waiting time. Furthermore, SPARK was shown to be scalable since the abovementioned time increases polynomially relative to the input size (number of tasks and task variants). SERIES was evaluated through a user study with twenty software practitioners. The results showed that software practitioners performed very well when explaining and creating task models using SERIES. These results were reflected in the task modelling activities that the participants performed as well as in their positive feedback regarding the usability of SERIES and the clarity of its semantic constructs. Overall, we conclude that the research presented in the thesis contributes to addressing resource variability through resource-driven adaptation. We also provide suggestions for future work that can extend this research