Game-theoretic Learning-based QoS Satisfaction in Autonomous Mobile Edge Computing

Apostolopoulos, Pavlos Athanasios; Tsiropoulou, Eirini Eleni; Papavassiliou, Symeon

doi:10.1109/giis.2018.8635770

Cited by 37 publications

(34 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A minority game theoretic approach is introduced in [18], [19] via considering the total number of MEC servers that should be activated to serve the users and allowing the MEC servers to autonomously decide their activation in order to respect the aforementioned constraint by following the theory of minority games. This work has been further extended in [20] via considering the problem of users' association to the MEC servers and introducing a distributed reinforcement learning-based decision making process.…”

Section: A Related Workmentioning

confidence: 99%

Artificial Intelligent Multi-Access Edge Computing Servers Management

2020

Self Cite

View full text Add to dashboard Cite

The advances of multi-access edge computing (MEC) have paved the way for the integration of the MEC servers, as intelligent entities into the Internet of Things (IoT) environment as well as into the 5G radio access networks. In this paper, a novel artificial intelligence-based MEC servers' activation mechanism is proposed, by adopting the principles of Reinforcement Learning (RL) and Bayesian Reasoning. The considered problem enables the MEC servers' activation decision-making, aiming at enhancing the reputation of the overall MEC system, as well as considering the total computing costs to serve efficiently the users' computing demands, guaranteeing at the same time their Quality of Experience (QoE) prerequisites satisfaction. Each MEC server decides in an autonomous manner whether it will be activated or remain in sleep mode by utilizing the theory of Bayesian Learning Automata (BLA). A human-driven peer-review-based evaluation of the edge computing system's provided services is also introduced based on the concept of Bayesian Truth Serum (BTS), which supports the development of a reputation mechanism regarding the MEC servers' provided services. The intelligent MEC servers' autonomous decisions' satisfaction is captured via a holistic utility function, which they aim to maximize in a distributed manner. Finally, detailed numerical results obtained via modeling and simulation, highlight the key operation features and superiority of the proposed framework.

show abstract

Section: A Related Workmentioning

confidence: 99%

Artificial Intelligent Multi-Access Edge Computing Servers Management

2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…A similar problem is addressed in [11], while a multiple MEC servers environment is considered and the end-users have to additionally select to which MEC server they will offload part of their data. The problem of activating the MEC servers based on the end-users computing demands is addressed in [12], where the MEC servers' activation problem is formulated as a minority game and a distributed reinforcement learning algorithm is executed by each MEC server in order to determine if it will be active or not. The concept of applying usage-based pricing policies to the end-users while they exploit the MEC servers' computing capabilities is introduced in [13,14] towards providing incentives to the end-users to consume the MEC servers' computing services in a fair manner.…”

Section: Related Workmentioning

confidence: 99%

“…In this subsection, the optimal pricing of the MEC server's computing services is determined towards maximizing the MEC servers' profit given the offloaded data of the end-users. Combining Equations (7), (10b) and (12), the corresponding optimal pricing problem of the MEC servers can be written as follows.…”

Section: Optimal Pricing Of the Mec Servers Computing Servicesmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Dynamic Data Offloading in a Competitive Mobile Edge Computing Market

et al. 2019

Self Cite

View full text Add to dashboard Cite

Software Defined Networks (SDN) and Mobile Edge Computing (MEC), capable of dynamically managing and satisfying the end-users computing demands, have emerged as key enabling technologies of 5G networks. In this paper, the joint problem of MEC server selection by the end-users and their optimal data offloading, as well as the optimal price setting by the MEC servers is studied in a multiple MEC servers and multiple end-users environment. The flexibility and programmability offered by the SDN technology enables the realistic implementation of the proposed framework. Initially, an SDN controller executes a reinforcement learning framework based on the theory of stochastic learning automata towards enabling the end-users to select a MEC server to offload their data. The discount offered by the MEC server, its congestion and its penetration in terms of serving end-users’ computing tasks, and its announced pricing for its computing services are considered in the overall MEC selection process. To determine the end-users’ data offloading portion to the selected MEC server, a non-cooperative game among the end-users of each server is formulated and the existence and uniqueness of the corresponding Nash Equilibrium is shown. An optimization problem of maximizing the MEC servers’ profit is formulated and solved to determine the MEC servers’ optimal pricing with respect to their offered computing services and the received offloaded data. To realize the proposed framework, an iterative and low-complexity algorithm is introduced and designed. The performance of the proposed approach was evaluated through modeling and simulation under several scenarios, with both homogeneous and heterogeneous end-users.

show abstract

“…Chen et al [25] has proposed an efficient task offloading algorithm to limited access points (APs) for multiple mobile users. Some algorithms [26,27] attempt to efficiently offload tasks to an MEC server considering the efficiency of transmission power and computing intensity. However, the previous studies constrain themselves on some selected computing devices such as MEC servers and APs still in a somewhat centralized manner where resource at the devices is not limited.…”

Section: Related Workmentioning

confidence: 99%

On-Demand Computation Offloading Architecture in Fog Networks

Jin

Lee

2019

Electronics

View full text Add to dashboard Cite

With the advent of the Internet-of-Things (IoT), end-devices have been served as sensors, gateways, or local storage equipment. Due to their scarce resource capability, cloud-based computing is currently a necessary companion. However, raw data collected at devices should be uploaded to a cloud server, taking a significantly large amount of network bandwidth. In this paper, we propose an on-demand computation offloading architecture in fog networks, by soliciting available resources from nearby edge devices and distributing a suitable amount of computation tasks to them. The proposed architecture aims to finish a necessary computation job within a distinct deadline with a reduced network overhead. Our work consists of three elements: (1) resource provider network formation by classifying nodes into stem or leaf depending on network stability, (2) task allocation based on each node’s resource availability and soliciting status, and (3) task redistribution in preparation for possible network and computation losses. Simulation-driven validation in the iFogSim simulator demonstrates that our work achieves a high task completion rate within a designated deadline, while drastically reducing unnecessary network overhead, by selecting only some effective edge devices as computation delegates via locally networked computation.

show abstract

Game-theoretic Learning-based QoS Satisfaction in Autonomous Mobile Edge Computing

Cited by 37 publications

References 5 publications

Artificial Intelligent Multi-Access Edge Computing Servers Management

Artificial Intelligent Multi-Access Edge Computing Servers Management

Intelligent Dynamic Data Offloading in a Competitive Mobile Edge Computing Market

On-Demand Computation Offloading Architecture in Fog Networks

Contact Info

Product

Resources

About