EdgeDecAp: An auction-based decentralized algorithm for optimizing application placement in edge computing

Smolka, Sven; Wißenberg, Leon; Mann, Zoltán Ádám

doi:10.1016/j.jpdc.2023.01.002

Cited by 7 publications

(4 citation statements)

References 31 publications

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“…Edge computing is a decentralized computing model that brings data processing closer to IoT devices at the "edge" of the network. In this model, data is collected and processed locally on IoT devices or nearby devices like gateways or edge servers [23][24][25][26][27][28][29][30][31]. This offers several advantages, including reduced latency, improved responsiveness, and bandwidth savings, as only relevant data is sent to data centers or the cloud.…”

Section: Iot Computing Modelmentioning

confidence: 99%

IoT APIs: Time Response Optimization in Edge Computing Data Communication for Power Phase Detection System

Nursuwars,

Shofa,

Andang

et al. 2024

E3S Web Conf.

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The IoT-based phase detection system is one of the important innovations in monitoring and managing modern electrical systems. However, challenges arise in presenting real-time data communication in the context of edge computing through the use of APIs. The problem that arises is the length of response time required in the data communication process, which can hamper the efficiency and accuracy of the system. The main objective of this research is to design and implement an effective strategy to reduce response time in API-based IoT data communication in phase detection systems. The method adopted includes a thorough analysis of existing communication processes and the development of optimized algorithms to speed up response times. This research approach involves measuring the response time before and after implementing an optimized algorithm on the client side, which in this case is represented by an Arduino device. Experiments were conducted using realistic data communication scenarios to validate the effectiveness of the proposed approach. The experimental results show that by optimizing the communication algorithm on the client side, the response time in IoT data communications can be significantly reduced. The response time which originally reached 4 seconds, was successfully reduced to only 0.8 seconds after the implementation of an optimized algorithm. This result has the potential to increase the operational efficiency of the system and expand the application of this technology in a variety of applications that require a fast response time.

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Section: Iot Computing Modelmentioning

confidence: 99%

IoT APIs: Time Response Optimization in Edge Computing Data Communication for Power Phase Detection System

Nursuwars,

Shofa,

Andang

et al. 2024

E3S Web Conf.

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“…In this class of solutions, Li et al [29] propose a method for optimally replicating service instances at the Edge. Smolka et al [30] present EdgeDecAp, a decentralized approach for dynamically placing components of Edge applications. Their solution employs local auctions autonomously coordinated among Edge devices to optimize system performance by identifying suitable hosts for both new and active application components.…”

Section: Related Workmentioning

confidence: 99%

Decentralized Replica Management in Latency-Bound Edge Environments for Resource Usage Minimization

Ferrucci,

Mordacchini,

Dazzi

2024

IEEE Access

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The Internet is experiencing a fast expansion at its edges. The wide availability of heterogeneous resources at the Edge is pivotal in the definition and extension of traditional Cloud solutions toward supporting the development of new applications. However, the dynamic and distributed nature of these resources poses new challenges for the optimization of the behavior of the system. New decentralized and self-organizing methods are needed to face the Cloud-Edge scenario's needs and optimize the exploitation of Edge resources. In this paper we propose a distributed and adaptive solution that reduces the number of replicas of application services that are executed throughout the system, all the while ensuring that the latency constraints of applications are met, thus allowing also to meet the end users' QoS requirements. Experimental evaluations through simulation show the effectiveness of the proposed approach.

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“…Game theory is popular for modeling the system, given that task orchestration and offloading are multiplayer decision problems where entities may compete selfishly or inefficiently to maximize their benefit. [6] Auctions are non-cooperative games that have been studied to support many aspects of resource management and pricing for edge and cloud computing [7], comprising task orchestration and offloading [8]. A mechanism based on auctions usually forces the bidders to be strategic [9], i.e.…”

Section: Orchestration Using Auction Mechanismsmentioning

confidence: 99%

Edge Computing Tasks Orchestration: An Energy-Aware Approach

Thomsen,

Dragsbæk Schmidt Thomsen,

Schmidt

et al. 2023

2023 IEEE International Conference on Edge Computing and Communications (EDGE)

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In this paper, we investigate experimentally the use of auctioning as a method for optimizing task orchestration in distributed computing systems by making selfish agents compete to execute computational tasks. Our goal is to find an approach that can improve the performance of these systems, using a deadline, fines, and reward limits in a reverse second-price sealed bid auction, to incentive and control the system, specifically in terms of improving task throughput and power consumption. With improvements to both energy consumption and task throughput, we have developed a promising approach, that is able to scale with the number of machines in the system. Results suggest that this type of auction may be useful for improving the implementation of these systems in a wide range of scenarios.

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EdgeDecAp: An auction-based decentralized algorithm for optimizing application placement in edge computing

Cited by 7 publications

References 31 publications

IoT APIs: Time Response Optimization in Edge Computing Data Communication for Power Phase Detection System

IoT APIs: Time Response Optimization in Edge Computing Data Communication for Power Phase Detection System

Decentralized Replica Management in Latency-Bound Edge Environments for Resource Usage Minimization

Edge Computing Tasks Orchestration: An Energy-Aware Approach

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