An ICN-based Approach for Service Caching in Edge/Fog Environments

Ben-Ammar, Hamza; Ghamri‐Doudane, Yacine

doi:10.1109/globecom42002.2020.9322305

Cited by 7 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…al. (2020) [12] introduced an Information-Centric Networking (ICN)-based approach for service caching in Edge/Fog environments, focusing on the service placement problem. The proposed 3Q caching strategy efficiently handles caching of service instances and source codes with minimal overhead, leveraging ICN principles.…”

Section: Literature Reviewmentioning

confidence: 99%

“…These studies, however, presume that just computing and storage resources are needed for task execution, ignoring caching of services. Service caching: the combined optimization of computational offloading and caching of various services in Fog computing environment, where specific tasks rely on specific services, has not been thoroughly studied in earlier research [11,12]. They deal with the challenges of implementing certain services at Fog nodes, that demand storage and memory resources.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Caching and Offloading Technique for Dew-assisted Mobile IoTs: A Distributed Deep Reinforcement Learning Framework

Mahapatra,

Pradhan,

Majhi

et al. 2024

Preprint

View full text Add to dashboard Cite

Cloud computing including mobile IoT devices infrastructure have grown to be a key component of future high-performance computing networks because they can supply distributed, hierarchical, and fine-grained resources. The secret is to combine the optimization of compute offloading and service caching. However, dynamic tasks, heterogeneous resources, and coupled decisions pose three major obstacles to the joint service caching and compute offloading dilemma. In this paper, we study how Dew-assisted mobile IoT devices and Fog-Cloud computing networks can benefit from the simultaneous processing for caching of various services and computational offloading. We formulate the optimization problem specifically as minimizing the NP-hard long-term average service delay. We provide thorough theoretical analysis and divide the topic into two smaller issues, i.e., processing for computation offloading and service caching. To answer the defined problem, where several Dew-assisted mobile IoT devices and a Cloud VM jointly determine the caching-action and offloading-action, respectively, we developed a unique Distributed-Deep Reinforcement Learning (DDRL) technique. The suggested framework beats multiple existing techniques regarding the average service delay across several scenarios, according to the findings of trace-driven simulations. When compared to methods based on reinforcement learning, our framework delivers an impressive 39% reduction in the average service delay and an approximate 37% improvement in convergence based on a practical real-world environment.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Caching and Offloading Technique for Dew-assisted Mobile IoTs: A Distributed Deep Reinforcement Learning Framework

Mahapatra,

Pradhan,

Majhi

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Authors in [98] propose an ICN converged heterogeneous network framework to optimise service availability by allocating virtual edge resources. Ben-Ammar et al [105] propose a strategy that handles the caching of both the service instances as well as their source codes (or software images) while an ICN-based Edge architecture is deployed. Additionally, their strategy does not require any explicit cooperation among nodes that helps to keep the overhead as low as possible.…”

Section: ) Information-centric Networking (Icn)mentioning

confidence: 99%

“…• Edge server: The second group consists of entities that can only cover part of the network. Some of these entities are (MEC-enabled) base station [13], [46], [84], [105], [118], [162], [168], [171], [172], control node [187], edge cloud [10], [137], [206], [208], edge servers [3], [83], [194], and in-network computing provider [160].…”

Section: ) Managing Entitymentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Service Placement in Multi-Access Edge Computing: A Systematic Literature Review

et al. 2022

View full text Add to dashboard Cite

The advent of new cloud-based applications such as mixed reality, online gaming, autonomous driving, and healthcare has introduced infrastructure management challenges to the underlying service network. Multi-access edge computing (MEC) extends the cloud computing paradigm and leverages servers near end-users at the network edge to provide a cloud-like environment. The optimum placement of services on edge servers plays a crucial role in the performance of such service-based applications. Dynamic service placement problem addresses the adaptive configuration of application services at edge servers to facilitate end-users and those devices that need to offload computation tasks. While reported approaches in the literature shed light on this problem from a particular perspective, a panoramic study of this problem reveals the research gaps in the big picture. This paper introduces the dynamic service placement problem and outline its relations with other problems such as task scheduling, resource management, and caching at the edge. We also present a systematic literature review of existing dynamic service placement methods for MEC environments from networking, middleware, applications, and evaluation perspectives. In the first step, we review different MEC architectures and their enabling technologies from a networking point of view. We also introduce different cache deployment solutions in network architectures and discuss their design considerations. The second step investigates dynamic service placement methods from a middleware viewpoint. We review different service packaging technologies and discuss their trade-offs. We also survey the methods and identify eight research directions that researchers follow. Our study categorises the research objectives into six main classes, proposing a taxonomy of design objectives for the dynamic service placement problem. We also investigate the reported methods and devise a solutions taxonomy comprising six criteria. In the third step, we concentrate on the application layer and introduce the applications that can take advantage of dynamic service placement. The fourth step investigates evaluation environments used to validate the solutions, including simulators and testbeds. We introduce real-world datasets such as edge server locations, mobility traces, and service requests used to evaluate the methods. We compile a list of open issues and challenges categorised by various viewpoints in the last step.

show abstract

Task Scheduling for ICN-Based Computing First Network: A Deep Reinforcement Learning Approach

Zou

Xie

Ren

et al. 2022

2022 IEEE 8th International Conference on Computer and Communications (ICCC)

View full text Add to dashboard Cite

An ICN-based Approach for Service Caching in Edge/Fog Environments

Cited by 7 publications

References 19 publications

Intelligent Caching and Offloading Technique for Dew-assisted Mobile IoTs: A Distributed Deep Reinforcement Learning Framework

Intelligent Caching and Offloading Technique for Dew-assisted Mobile IoTs: A Distributed Deep Reinforcement Learning Framework

Dynamic Service Placement in Multi-Access Edge Computing: A Systematic Literature Review

Task Scheduling for ICN-Based Computing First Network: A Deep Reinforcement Learning Approach

Contact Info

Product

Resources

About