IoT Clusters for Enhancing Multimedia Applications

Coelho, Jorge; Nogueira, Luís

doi:10.3390/s22239077

Cited by 1 publication

(4 citation statements)

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“…The primary objective of our evaluations was to gauge the performance enhancement achieved by our framework when deployed in open and dynamic environments, compared to its previous iteration, which employed a traditional QoS optimization approach [17,51]. In Section 6.1, we delve into the performance profiles of the proposed anytime algorithms, while Section 6.2 addresses the computational overhead incurred by these anytime algorithms in comparison to our prior traditional QoS optimization method [17,51].…”

Section: Discussionmentioning

confidence: 99%

“…Recent research has investigated collaborative approaches to service execution in edge computing environments, leveraging techniques such as task offloading, load balancing, and resource sharing among edge nodes to optimize performance and resource utilization [15,16]. In [17], we presented a framework for exploring the spare capacity of IoT devices for a clustered execution of multimedia applications. The framework allows the dynamic creation of clusters of devices that use their spare resources in a joint effort to provide a service with a given quality level.…”

Section: Related Workmentioning

confidence: 99%

“…These constraints encompass various aspects of service performance, reliability, availability, and other relevant metrics that directly impact the user experience or system operation [17]. With a comprehensive QoS characterization tailored to a specific application domain, both users and service providers can delineate service prerequisites and proposals to establish mutual agreements on service delivery.…”

Section: Definition 2 (Service)mentioning

confidence: 99%

“…Throughout the paper, we have emphasized the notion that complex scenarios may hinder the possibility of computing optimal resource allocations and proposed anytime algorithms capable of trading-off deliberation time for the quality of achieved results. However, it is crucial to analyze the computational cost required by this approach compared to traditional versions of those algorithms [17,51].…”

Section: Overheadmentioning

confidence: 99%

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Exploiting Anytime Algorithms for Collaborative Service Execution in Edge Computing

Nogueira,

Coelho,

Pereira

2024

Computers

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The diversity and scarcity of resources across devices in heterogeneous computing environments can impact their ability to meet users’ quality-of-service (QoS) requirements, especially in open real-time environments where computational loads are unpredictable. Despite this uncertainty, timely responses to events remain essential to ensure desired performance levels. To address this challenge, this paper introduces collaborative service execution, enabling resource-constrained IoT devices to collaboratively execute services with more powerful neighbors at the edge, thus meeting non-functional requirements that might be unattainable through individual execution. Nodes dynamically form clusters, allocating resources to each service and establishing initial configurations that maximize QoS satisfaction while minimizing global QoS impact. However, the complexity of open real-time environments may hinder the computation of optimal local and global resource allocations within reasonable timeframes. Thus, we reformulate the QoS optimization problem as a heuristic-based anytime optimization problem, capable of interrupting and quickly adapting to environmental changes. Extensive simulations demonstrate that our anytime algorithms rapidly yield satisfactory initial service solutions and effectively optimize the solution quality over iterations, with negligible overhead compared to the benefits gained.

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