Store Edge Networked Data (SEND): A Data and Performance Driven Edge Storage Framework

Nicolaescu, Adrian-Cristian; Mastorakis, Spyridon; Psaras, Ioannis

doi:10.1109/infocom42981.2021.9488804

Cited by 18 publications

(3 citation statements)

References 39 publications

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“…These layers are connected through a ring overlay which offers a fast and reliable system, utilizes data replication and provides strong consistency. Nicolaescu et al [14] proposed an in-network storage and management framework at the network edge called SEND, which aims to improve the performance of the network edge and QoS. Raw and processed data data are stored in persistent storage close to users for periods of time longer than cache storage.…”

Section: Related Workmentioning

confidence: 99%

Towards a Distributed Storage Framework for Edge Computing Infrastructures

Makris

Psomakelis

Θεοδωρόπουλος

et al. 2022

Proceedings of the 2nd Workshop on Flexible Resource and Application Management on the Edge

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Due to the continuous development of Internet of Things (IoT), the volume of the data these devices generate are expected to grow dramatically in the future. As a result, managing and processing such massive data amounts at the edge becomes a vital issue. Edge computing moves data and computation closer to the client enabling latency-and bandwidth-sensitive applications, that would not be feasible using cloud and remote processing alone. Nevertheless, implementing an efficient edge-enabled storage system is challenging due to the distributed and heterogeneous nature of the edge and its limited resource capabilities. To this end, we propose a lightweight hybrid distributed edge/cloud storage framework which aims to improve the Quality of Experience (QoE) of the end-users by migrating data "close" to them, thus reducing data transfers delays and network utilization. The proposed edge storage component (𝐸𝑆𝐶) exploits the Dynamic Lifecycle Framework, in order to enable transparent and automated access for containerized applications to remote workloads. The effectiveness of the ESC is evaluated by employing a number of resource utilization and Quality of Service (QoS) metrics.

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Section: Related Workmentioning

confidence: 99%

Towards a Distributed Storage Framework for Edge Computing Infrastructures

Makris

Psomakelis

Θεοδωρόπουλος

et al. 2022

Proceedings of the 2nd Workshop on Flexible Resource and Application Management on the Edge

View full text Add to dashboard Cite

show abstract

“…SDN can dynamically forward the data to corresponding edge data repository from the centralized view. Nicolaescu et al propose to place data according to multiple criteria, e.g., data size, freshness, popularity, etc., and design a store edge networked data (SEND) system to intelligently place different types of data at edge data repositories where it is expected to be required [96]. The system is shown as Fig.…”

Section: Edge Cachingmentioning

confidence: 99%

Edge Intelligence: Empowering Intelligence to the Edge of Network

et al. 2021

Proc. IEEE

126

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Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data is captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protect the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this paper, we present a thorough and comprehensive survey on the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e. edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare and analyse the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, etc. This article provides a comprehensive survey to edge intelligence and its application areas. In addition, we summarise the development of the emerging research fields and the current stateof-the-art and discuss the important open issues and possible theoretical and technical directions.

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“…The problem has been also studied in the context of Mobile Edge Computing (MEC) with DAG-modeled applications in [19], where the authors sought the goal of meeting as many application request deadlines as possible using an online algorithm approximating an NPhard scheduling problem. Finally, in [20] the authors have proposed SEND, an in-network storage management system realized through edge-deployed data repositories, which places intelligently raw and processed data based on locality or popularity criteria. Key difference: Our work is complementary to the above studies because we do not address the allocation of containers/tasks to edge nodes, but rather propose solutions to manage the applications' state in FaaS under a given allocation.…”

Section: Related Workmentioning

confidence: 99%

FaaS Execution Models for Edge Applications

Cicconetti¹,

Conti²,

Passarella³

2021

Preprint

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In this paper, we address the problem of supporting stateful workflows following a Function-as-a-Service (FaaS) model in edge networks. In particular we focus on the problem of data transfer, which can be a performance bottleneck due to the limited speed of communication links in some edge scenarios and we propose three different schemes: a pure FaaS implementation, StateProp, i.e., propagation of the application state throughout the entire chain of functions, and StateLocal, i.e., a solution where the state is kept local to the workers that run functions and retrieved only as needed. We then extend the proposed schemes to the more general case of applications modeled as Directed Acyclic Graphs (DAGs), which cover a broad range of practical applications, e.g., in the Internet of Things (IoT) area. Our contribution is validated via a prototype implementation. Experiments in emulated conditions show that applying the data locality principle reduces significantly the volume of network traffic required and improves the end-to-end delay performance, especially with local caching on edge nodes and low link speeds.

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Store Edge Networked Data (SEND): A Data and Performance Driven Edge Storage Framework

Cited by 18 publications

References 39 publications

Towards a Distributed Storage Framework for Edge Computing Infrastructures

Towards a Distributed Storage Framework for Edge Computing Infrastructures

Edge Intelligence: Empowering Intelligence to the Edge of Network

FaaS Execution Models for Edge Applications

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