Dynamic Service Provisioning in the Edge-Cloud Continuum With Bounded Resources

Cohen, Itamar; Chiasserini, Carla-Fabiana; Giaccone, Paolo; Scalosub, Gabriel

doi:10.1109/tnet.2023.3271674

Cited by 10 publications

(2 citation statements)

References 44 publications

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“…Located in close proximity to the end users and connected IoT devices, MEC provides extremely low latency and high bandwidth while always enabling applications to leverage cloud capabilities if necessary. The resulting paradigm shift in computing is centered around the dynamic, intelligent, and yet seamless interconnection of IoT devices, edge, and cloud resources in one computing system to form what is known as a continuum [7,8]. The goal of this synergy is the provision of advanced services and applications to the end users, which is also leveraged by similar advances in the networking field, such as network function virtualization (NFV) [9,10], which decouples network operations from specific hardware equipment, as well as software defined networking (SDN) [11], which enables a holistic and intelligent network management approach.…”

Section: Introductionmentioning

confidence: 99%

A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues

Gkonis,

Giannopoulos,

Trakadas

et al. 2023

Future Internet

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The rapid growth in the number of interconnected devices on the Internet (referred to as the Internet of Things—IoT), along with the huge volume of data that are exchanged and processed, has created a new landscape in network design and operation. Due to the limited battery size and computational capabilities of IoT nodes, data processing usually takes place on external devices. Since latency minimization is a key concept in modern-era networks, edge servers that are in close proximity to IoT nodes gather and process related data, while in some cases data offloading in the cloud might have to take place. The interconnection of a vast number of heterogeneous IoT devices with the edge servers and the cloud, where the IoT, edge, and cloud converge to form a computing continuum, is also known as the IoT-edge-cloud (IEC) continuum. Several key challenges are associated with this new computing systems’ architectural approach, including (i) the design of connection and programming protocols aimed at properly manipulating a huge number of heterogeneous devices over diverse infrastructures; (ii) the design of efficient task offloading algorithms aimed at optimizing services execution; (iii) the support for security and privacy enhancements during data transfer to deal with the existent and even unforeseen attacks and threats landscape; (iv) scalability, flexibility, and reliability guarantees to face the expected mobility for IoT systems; and (v) the design of optimal resource allocation mechanisms to make the most out of the available resources. These challenges will become even more significant towards the new era of sixth-generation (6G) networks, which will be based on the integration of various cutting-edge heterogeneous technologies. Therefore, the goal of this survey paper is to present all recent developments in the field of IEC continuum systems, with respect to the aforementioned deployment challenges. In the same context, potential limitations and future challenges are highlighted as well. Finally, indicative use cases are also presented from an IEC continuum perspective.

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Section: Introductionmentioning

confidence: 99%

A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues

Gkonis,

Giannopoulos,

Trakadas

et al. 2023

Future Internet

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“…Network functions virtualization (NFV) [1] transforms network functions into virtualized network functions (VNF) [2] and deploys them on general-purpose hardware platforms. It provides users with customized network services in the form of service function chain (SFC) [3], provides a new network function service business processing architecture, and improves the flexibility and scalability of network services. In NFV, SFC refers to the logical chaining of service requests for completing network service processing by directing network traffic to be transmitted to the corresponding network functional nodes for processing through the network links in sequence according to the sequential logical order of VNFs in the user-issued SFC request (SFCR) [4].…”

Section: Introductionmentioning

confidence: 99%

An improved resource allocation method for mapping service function chains based on A3C

Huang

Wang

et al. 2023

Preprint

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Network function virtualization(NFV) technology deploys network functions as software functions on a generalised hardware platform and provides customised network services in the form of service function chain (SFC), which improves the flexibility and scalability of network services and reduces network service costs. However, irrational resource allocation during service function chain mapping will cause problems such as low resource utilisation, long service request processing time and low mapping rate. To address the unreasonable problem of service mapping resource allocation, we proposes an improved service function chain mapping resource allocation method (SA3C) based on the Asynchronous advantageous action evaluation algorithm (A3C). Firstly, the SFC mapping model is established, the SFC mapping process is divided into two layers of physical topology resource layer and virtual network function request layer, and the two layers are represented by abstract parameters; Secondly, the relationship between mapping resource allocation and service processing process is analysed, and a mathematical model is established for the minimisation of the processing time of the joint allocation of node computation and link bandwidth communication resources, and the minimisation mathematical model is modelled as a Markov process, defining the ternary containing state, action, and reward; Finally, combining the ternary with the deep reinforcement learning algorithm A3C, using the training master network as a template, and using multi-threading technology to generate multiple sub-networks for parallel training to find the optimal resource allocation strategy. The experimental simulation results show that compared with the Actor-Critic (AC) and Policy Gradient (PG) methods, SA3C algorithm can improve the resource utilisation by 9.85%, reduce the total processing time by 10.72%, and improve the mapping rate by 6.72%, by reasonably allocating node computational resources and link bandwidth communication resources.

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