Context-Aware Data and Task Placement in Edge Computing Environments

Breitbach, Martin; Schäfer, Dominik; Edinger, Janick; Becker, Christian

doi:10.1109/percom.2019.8767386

Cited by 64 publications

(30 citation statements)

References 40 publications

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“…As discussed in [46], research in offloading workloads towards ENs focus on caching [12], context-aware web browsing, and video pre-processing [42], while, recently, approaches cope with the resource management problem [16], [46], [34]. They address the challenges of deciding on tasks/data offloading to ENs taking into consideration communication as in [13] and [36]. However, the decision making of the above-mentioned approaches is unaware of the past, current, and future network performance, which is a crucial factor especially in dynamic environments.…”

Section: Related Workmentioning

confidence: 99%

“…Our mechanism supports these desiderata by not only drastically departing from the current approaches being network-context and data-summaries aware, but also leveraging the decision making by being pro-active due to (i) learning the past and forecasting future network context and (ii) radically relying on the evaluation of inherent changes on synopses due to the changing nature of the underlying data; which is a dominant factor in EC environments. And, the fundamental differentiation of our approach with the literature is that the decision making is achieved by fulfilling an incremental stochastic optimization objective, departing from arbitrary hard/threshold-based decision makings found in certain approaches, e.g., [13], [36]. To the best of our knowledge our mechanism is the first network context and synopsis-aware stochastic objective-driven decision making, whose performance and adaptability is thoroughly evaluated and presented in our experimental section.…”

Section: Related Workmentioning

confidence: 99%

“…We provide a performance evaluation of our model, coined Optimal Stopping Decision Model (OSDM) and a comparative assessment with the approaches in [13], [34], and [36].…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Random Decision Model (RDM), which takes a decision when the accumulated synopses quanta are over a pre-defined threshold and is unaware of network performance. RDM is adopted from the random scheduling model in [13]; (ii)…”

Section: ) Models Under Comparisonmentioning

confidence: 99%

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Proactive & Time-Optimized Data Synopsis Management at the Edge

Kolomvatsos

Anagnostopoulos

Koziri

et al. 2020

IEEE Trans. Knowl. Data Eng.

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Internet of Things offers the infrastructure for smooth functioning of autonomous context-aware devices being connected towards the Cloud. Edge Computing (EC) relies between the IoT and Cloud providing significant advantages. One advantage is to perform local data processing (limited latency, bandwidth preservation) with real time communication among IoT devices, while multiple nodes become hosts of the collected data (reported by IoT devices). In this work, we provide a mechanism for the exchange of data synopses (summaries of extracted knowledge) among EC nodes that are necessary to give the knowledge on the data present in EC environments. The overarching aim is to intelligently decide on when nodes should exchange data synopses in light of efficient execution of tasks. We enhance such a decision with a stochastic optimization model based on the Theory of Optimal Stopping. We provide the fundamentals of our model and the relevant formulations on the optimal time to disseminate data synopses to network edge nodes. We report a comprehensive experimental evaluation and comparative assessment related to the optimality achieved by our model and the positive effects on EC.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…We provide a performance evaluation of our model, coined Optimal Stopping Decision Model (OSDM) and a comparative assessment with the approaches in [13], [34], and [36].…”

Section: Performance Evaluationmentioning

confidence: 99%

Section: ) Models Under Comparisonmentioning

confidence: 99%

See 2 more Smart Citations

Proactive & Time-Optimized Data Synopsis Management at the Edge

Kolomvatsos

Anagnostopoulos

Koziri

et al. 2020

IEEE Trans. Knowl. Data Eng.

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show abstract

“…The modern smart devices, from smartphones to home IoT, from industrial applications to smart cities and smart transportation, are ushering in an era of pervasive computing. The massive amounts of data generated by these devices provide a ground for various novel applications, while also introducing novel challenges for data processing and connectivity [1]. Indeed, current cloud computing systems and network infrastructures may not provide enough computing capacity to manage latency and performance requirements set by the modern pervasive computing systems [2].…”

Section: Introductionmentioning

confidence: 99%

Scaling up an Edge Server Deployment

Lovén

Lähderanta

Ruha

et al. 2020

2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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In this article, we study the scaling up of edge computing deployments. In edge computing, deployments are scaled up by adding more computational capacity atop the initial deployment, as deployment budgets allow. However, without careful consideration, adding new servers may not improve proximity to the mobile users, crucial for the Quality of Experience of users and the Quality of Service of the network operators. In this paper, we propose a novel method for scaling up an edge computing deployment by selecting the optimal number of new edge servers and their placement, and re-allocating access points optimally to the old and new edge servers. The algorithm is evaluated with two scenarios, using data on a real-world large-scale wireless network deployment. The evaluation shows that the proposed method is stable on a real city-scale deployment, resulting in optimized Quality of Service for the network operator.

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Hybrid Harris Hawk‐Salp swarm optimization algorithm‐based integrated optimal data placement and task scheduling for improving the user experience in edge computing

Vivekanandan¹,

Gnansekaran²

2021

Concurrency and Computation

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Edge computing permits the computation process at the edge of the network for enhancing the efficiency of the data access. The problem of optimal data block placement entirely depends on the maximization of interdependent factors, which corresponds to an NP-hard problem. In this paper, the Hybrid Harris Hawk-Salp Swarm-based Optimization, Data Placement and Task Scheduling (HHHSS-ODPTS) scheme is proposed for improving the user experience in edge computing. This data placement is devised considering the popularity and user preference of data blocks, storage capacity of edge server, and the ratio of replacement associated with the edge servers. This proposed scheme utilized a 2/3-approximation method for essential mapping of tasks with generated containers of the edge server. The simulation results confirmed that the proposed HHHSS-ODPTS scheme is better at data response time, response time of tasks, number of the replaced data blocks, and hit rate of tasks with different capacity of data storage, number of required data blocks, and data popularity.

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Context-Aware Data and Task Placement in Edge Computing Environments

Cited by 64 publications

References 40 publications

Proactive & Time-Optimized Data Synopsis Management at the Edge

Proactive & Time-Optimized Data Synopsis Management at the Edge

Scaling up an Edge Server Deployment

Hybrid Harris Hawk‐Salp swarm optimization algorithm‐based integrated optimal data placement and task scheduling for improving the user experience in edge computing

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