DeltaIoT: A Self-Adaptive Internet of Things Exemplar

Iftikhar, M. Usman; Ramachandran, Gowri Sankar; Bollansee, Pablo; Weyns, Danny; Hughes, Danny

doi:10.1109/seams.2017.21

Cited by 64 publications

(52 citation statements)

References 20 publications

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“…This contribution focuses on the application part of the IoT and incorporates various types of adaptive, detecting and observing agents. Admitting that there are multiple objectives and methods for self-adaptation in IoT, a program to evaluate methods for self-adaptation in IoT named DeltaIoT was introduced [14]. R. Seiger et al propose to utilize self-adaptive MAPE-K loop to attain the consistency between the physical world's state and its digital representation [15].…”

Section: Related Workmentioning

confidence: 99%

Self-Adaptive Data Processing to Improve SLOs for Dynamic IoT Workloads

2020

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Internet of Things (IoT) covers scenarios of cyber-physical interaction of smart devices with humans and the environment and, such as applications in smart city, smart manufacturing, predictive maintenance, and smart home. Traditional scenarios are quite static in the sense that the amount of supported end nodes, as well as the frequency and volume of observations transmitted, does not change much over time. The paper addresses the challenge of adapting the capacity of the data processing part of IoT pipeline in response to dynamic workloads for centralized IoT scenarios where the quality of user experience matters, e.g., interactivity and media streaming as well as the predictive maintenance for multiple moving vehicles, centralized analytics for wearable devices and smartphones. The self-adaptation mechanism for data processing IoT infrastructure deployed in the cloud is horizontal autoscaling. In this paper we propose augmentations to the computation schemes of data processing component's desired replicas count from the previous work; these augmentations aim to repurpose original sets of metrics to tackle the task of SLO violations minimization for dynamic workloads instead of minimizing the cost of deployment in terms of instance seconds. The cornerstone proposed augmentation that underpins all the other ones is the adaptation of the desired replicas computation scheme to each scaling direction (scale-in and scale-out) separately. All the proposed augmentations were implemented in the standalone self-adaptive agent acting alongside Kubernetes' HPA such that limitations of timely acquisition of the monitoring data for scaling are mitigated. Evaluation and comparison with the previous work show improvement in service level achieved, e.g., latency SLO violations were reduced from 2.87% to 1.70% in case of the forecasted message queue length-based replicas count computation used both for scale-in and scale-out, but at the same time higher cost of the scaled data processor deployment is observed.

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Section: Related Workmentioning

confidence: 99%

Self-Adaptive Data Processing to Improve SLOs for Dynamic IoT Workloads

2020

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“…DeltaIoT [5]) seamlessly. However, integrating a new IoT-Device is currently not only linked to a significant manual integration effort but also relies on shared knowledge bases for control loops.…”

Section: Integrating An Evolutionarymentioning

confidence: 99%

A Conceptual Architecture for Enabling Future Self-Adaptive Service Systems

Burzlaff

Bartelt

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Dynamic integration methods for unknown data sources and services at system design time are currently primarily driven by technological standards. Hence, little emphasis is being placed on integration methods. However, the combination of heterogeneous data sources and services offered by devices across domains is hard to standardize. In this paper, we will shed light on the interplay of self-adaptive system architectures as well as bottom-up, incremental integration methods relying on formal knowledge bases. An incremental integration method has direct influences on both the system architecture itself and the way these systems are engineered and operated during design and runtime. Our findings are evaluated in the context of a case study that uses an adapted bus architecture including two tool prototypes. In addition, we illustrate conceptually how control loops such as MAPE-K can be enriched with machine-readable integration knowledge.

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“…Distributed and interconnected software systems are increasingly deployed in application domains characterised by dynamic environments, evolving requirements, and unpredictable failures. Examples of such systems include network infrastructures [1], smart cities [2], and traffic ecosystems [3] with autonomous vehicles [4]. During operation, these systems might encounter several unexpected scenarios including variations in system performance, sudden changes in system workload and component failures.…”

Section: Introductionmentioning

confidence: 99%

“…Although learning has been explored in many self-adaptive applications, e.g. [2], [13], [14], the use of different types of learning models in CSAS is still a handcrafted process that relies heavily on domain expertise. More specifically, the adoption of learning involves an understanding of the application particularities as well as the requirements that these particularities impose on the designed learning model.…”

Section: Introductionmentioning

confidence: 99%

On Learning in Collective Self-Adaptive Systems: State of Practice and a 3D Framework

D'Angelo

Gerasimou

Ghahremani

et al. 2019

2019 IEEE/ACM 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)

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Collective self-adaptive systems (CSAS) are distributed and interconnected systems composed of multiple agents that can perform complex tasks such as environmental data collection, search and rescue operations, and discovery of natural resources. By providing individual agents with learning capabilities, CSAS can cope with challenges related to distributed sensing and decision-making and operate in uncertain environments. This unique characteristic of CSAS enables the collective to exhibit robust behaviour while achieving system-wide and agent-specific goals. Although learning has been explored in many CSAS applications, selecting suitable learning models and techniques remains a significant challenge that is heavily influenced by expert knowledge. We address this gap by performing a multifaceted analysis of existing CSAS with learning capabilities reported in the literature. Based on this analysis, we introduce a 3D framework that illustrates the learning aspects of CSAS considering the dimensions of autonomy, knowledge access, and behaviour, and facilitates the selection of learning techniques and models. Finally, using example applications from this analysis, we derive open challenges and highlight the need for research on collaborative, resilient and privacy-aware mechanisms for CSAS.

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DeltaIoT: A Self-Adaptive Internet of Things Exemplar

Cited by 64 publications

References 20 publications

Self-Adaptive Data Processing to Improve SLOs for Dynamic IoT Workloads

Self-Adaptive Data Processing to Improve SLOs for Dynamic IoT Workloads

A Conceptual Architecture for Enabling Future Self-Adaptive Service Systems

On Learning in Collective Self-Adaptive Systems: State of Practice and a 3D Framework

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