Metrics for Self-Adaptive Queuing in Middleware for Internet of Things

Chindanonda, Peeranut; Podolskiy, Vladimir; Gerndt, Michael

doi:10.1109/fas-w.2019.00042

Cited by 3 publications

(6 citation statements)

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“…A comparison of queue size-based autoscaling with approaches from AWS's guide (https://docs. aws.amazon.com/autoscaling/ec2/userguide/as-using-sqs-queue.html), previous work [8], and a master's thesis [9] is shown in Table 3. The first difference is the use of a target tracking scaling policy.…”

Section: Resultsmentioning

confidence: 99%

“…The difference between the approach suggested in AWS's guide and previous work is that in [8] we calculate the metric for each data processor whereas Kubernetes HPA will average metric from every data processor to get the final metric value. On the other hand, AWS's guide computes the final value directly from the total queue size.…”

Section: Resultsmentioning

confidence: 99%

“…The previous work [8] suggests using the estimated waiting time as a metric for autoscaling queue-based data processors that could help in saving computing resources. Additional research [9] was made to find suitable autoscaling mechanisms that prevent SLO violations and allow them to save computing resources.…”

Section: Approachmentioning

confidence: 99%

“…Our previous work proposed and investigated various metrics for automating the scaling of message queuing subsystems such as estimated waiting time (EWT) and mixes of metrics with conventional CPU utilization and processing capacity [8]. The results of the work pointed at particular autoscaling metrics and combinations thereof as being the most promising for IoT scenarios; these papers build on the results of metrics evaluations in [8].…”

Section: Introductionmentioning

confidence: 99%

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

2020

Self Cite

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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: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2020

Self Cite

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“…The first presentation hold by the PhD student Vladimir Podolskiy from the Technical University of Munich, Germany focused on Metrics for Self-Adaptive Queuing in Middleware for Internet of Things co-authored with Peeranut Chindanonda and Michael Gerndt. The paper presented several metrics for automating the scaling of message queuing subsystems and evaluated them on CPU-intensive and blocking I/O-intensive tasks [15].…”

Section: Session 3-seacmentioning

confidence: 99%

Introduction to the Special Issue “Applications in Self-Aware Computing Systems and their Evaluation”

Krupitzer

Eberhardinger²,

Gerostathopoulos

et al. 2020

Computers

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The joint 1st Workshop on Evaluations and Measurements in Self-Aware Computing Systems (EMSAC 2019) and Workshop on Self-Aware Computing (SeAC) was held as part of the FAS* conference alliance in conjunction with the 16th IEEE International Conference on Autonomic Computing (ICAC) and the 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO) in Umeå, Sweden on 20 June 2019. The goal of this one-day workshop was to bring together researchers and practitioners from academic environments and from the industry to share their solutions, ideas, visions, and doubts in self-aware computing systems in general and in the evaluation and measurements of such systems in particular. The workshop aimed to enable discussions, partnerships, and collaborations among the participants. This special issue follows the theme of the workshop. It contains extended versions of workshop presentations as well as additional contributions.

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Dealing with IoT Defiant Components

Fonseca

Denis

Chagas

et al. 2021

2021 IEEE/ACM Joint 9th International Workshop on Software Engineering for Systems-of-Systems and 15th Workshop on Distributed

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A defiant IoT component is a smart device of an IoT system-of-systems (SoS) that should adapt its local behaviour to accomplish the new global requirements of the SoS only in exceptional scenarios. There are several self-adaptive approaches for system-of-systems in the literature. However, to handle defiant components, there is currently only one solution, called cautious adaptation, which relies on the use of AOP-based wrappers. That approach may not be appropriate for IoT systems since the components expose their communication API. Therefore, this paper proposes a new cautious adaptation approach for IoT SoS that adds three external components, the Configurator, the Observer and the Effector, that realize the MAPE control loop to apply behaviour adaptation. The solution is evaluated through a proof of concept using two types of IoT device communication mechanisms that demonstrated that our proposal helps the SoS to achieve its global goals.

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Metrics for Self-Adaptive Queuing in Middleware for Internet of Things

Cited by 3 publications

References 10 publications

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

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

Introduction to the Special Issue “Applications in Self-Aware Computing Systems and their Evaluation”

Dealing with IoT Defiant Components

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