A Game-Theoretic Based QoS-Aware Capacity Management for Real-Time EdgeIoT Applications

Aliyu, Suleiman Onimisi; Chen, Feng; He, Ying; Yang, Hongji

doi:10.1109/qrs.2017.48

Cited by 6 publications

(5 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed scheme reduces the communication overhead by increasing the participation of fog nodes and updating the variables of fog nodes in parallel. Aliyu et al 108 presented an adaptive resource capacity management (ARCM) approach to optimize the resource capacity for real-time mobile edge computing applications. Moreover, a partition form games (PFG) approach in SDN-based intercloud architecture is adopted for capacity management and load balancing.…”

Section: Resource Scheduling In Fog Computingmentioning

confidence: 99%

A comprehensive survey on software‐defined networking for smart communities

Chaudhary

Aujla

Kumar

et al. 2022

Int J Communication

View full text Add to dashboard Cite

The need to provide services closer to the end-user proximity leads to the exchange of a large volume of data generated from the smart devices deployed at different geo-distributed sites. The massive amount of data generated from the smart devices need to be transmitted, analyzed, and processed. This requires seamless data exchanges among geo-separated nodes, which results in a considerable burden on the underlying network infrastructure and can degrade the performance of any implemented solution. Therefore, a dynamic, agile, and programmable network management paradigm is required. To handle the challenges mentioned above, software-defined networking (SDN) gained much attention from academia, researchers, and industrial sectors.Shifting the computational load from forwarding devices to a logically centralized controller is a dream of every network operator who wants to have complete control and global visibility of the network. Also, the concept of network functions virtualization (NFV) in SDN controller is required to increase resource utilization efficiency. Thus, in this paper, a comprehensive survey on SDN for various smart applications is presented. This survey covers the infrastructural details of SDN hardware and OpenFlow switches, controllers, simulation tools, programming languages, open issues, and challenges in SDN implementation with advanced technologies such as 5G and microservices. In addition, the challenges on the control plane and data plane are highlighted in detail, such as fault tolerance, routing, scheduling of flows, and energy consumption on OpenFlow switches. Finally, various open issues and challenges future scope of SDN are discussed and analyzed in the proposal.

show abstract

Section: Resource Scheduling In Fog Computingmentioning

confidence: 99%

A comprehensive survey on software‐defined networking for smart communities

Chaudhary

Aujla

Kumar

et al. 2022

Int J Communication

View full text Add to dashboard Cite

show abstract

“…Till recent past, the realisation of RT-IoT was not possible due to the communication delays but since the introduction of mobile edge computing and 5G technologies the end-to-end delays and latencies can be ultra-reliable which is necessary for RT-IoT systems [28,29,35]. For a certain upper bound of delay, the challenge is fall back on the implementation of effective tools which can study the scheduling policies and ensure that no tasks will be missed by considering the maximum delay specified by the upper bound [27]. Such simulation tools present an appropriate solution for the development and testing of strategies that aim to identify and fix problems before their application in a real environment [36].…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, 5G technologies have also found an excellent way for the accurate prediction of the delay because of the ultra-low latency the upper bound on delay can be determined. Recent research studies by Aliyu et al, Nunna et al and Condoluci et al propose that 5G can pave the way toward modelling latency and end-to-end communication delays which were a significant barrier in older days [24,[27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Towards the Design of a Formal Verification and Evaluation Tool of Real-Time Tasks Scheduling of IoT Applications

et al. 2019

View full text Add to dashboard Cite

Real-Time Internet of Things (RT-IoT) is a newer technology paradigm envisioned as a global inter-networking of devices and physical things enabling real-time communication over the Internet. The research in Edge Computing and 5G technology is making way for the realisation of future IoT applications. In RT-IoT tasks will be performed in real-time for the remotely controlling and automating of various jobs and therefore, missing their deadline may lead to hazardous situations in many cases. For instance, in the case of safety-critical and mission-critical IoT systems, a missed task could lead to a human loss. Consequently, these systems must be simulated, as a result, and tasks should only be deployed in a real scenario if the deadline is guaranteed to be met. Numerous simulation tools are proposed for traditional real-time systems using desktop technologies, but these relatively older tools do not adapt to the new constraints imposed by the IoT paradigm. In this paper, we design and implement a cloud-based novel architecture for the formal verification of IoT jobs and provide a simulation environment for a typical RT-IoT application where the feasibility of real-time remote tasks is perceived. The proposed tool, to the best of our knowledge, is the first of its kind effort to support not only the feasibility analysis of real-time tasks but also to provide a real environment in which it formally monitors and evaluates different IoT tasks from anywhere. Furthermore, it will also act as a centralised server for evaluating and tracking the real-time scheduled jobs in a smart space. The novelty of the platform is purported by a comparative analysis with the state-of-art solutions against attributes which is vital for any open-source tools in general and IoT in specifics.

show abstract

“…In [7], the authors use coalitional game theory to solve a resource allocation problem in MEC-enabled IoT networks with software-defined network (SDN) capability. In such a network, delay sensitive tasks are offloaded to the edge servers by the IoT applications.…”

Section: Radio and Computational Resource Managementmentioning

confidence: 99%

“…Objective Model MEC type [3] minimize users' energy and latency cost dynamic sequential game quasi-static [4] minimize users' energy and latency cost stochastic game dynamic [5] maximize utilities of users and servers Stackelberg game vehicular [6] minimize unprocessed offloading requests Markov decision process dynamic [7] optimize resource usage and QoS guarantee coalitional game edge IoT [8] minimize overall cost and latency Markov decision process energy harvesting MEC [9] minimize latency auction theory dynamic workload arrival [10] efficient resource allocation deep Q-learning vehicular [11] minimize servers' energy and QoS guarantee minority game random spectrum resources, as well as caching storage. Modeling and analysis of such a general virtualized network under users' quality of experience (QoE) constraints is an interesting research problem.…”

Section: Referencementioning

confidence: 99%

Mobile Edge Computation Offloading Using Game Theory and Reinforcement Learning

Ranadheera¹,

Hossain²

2017

Preprint

View full text Add to dashboard Cite

Due to the ever-increasing popularity of resourcehungry and delay-constrained mobile applications, the computation and storage capabilities of remote cloud has partially migrated towards the mobile edge, giving rise to the concept known as Mobile Edge Computing (MEC). While MEC servers enjoy the close proximity to the end-users to provide services at reduced latency and lower energy costs, they suffer from limitations in computational and radio resources, which calls for fair efficient resource management in the MEC servers. The problem is however challenging due to the ultra-high density, distributed nature, and intrinsic randomness of next generation wireless networks. In this article, we focus on the application of game theory and reinforcement learning for efficient distributed resource management in MEC, in particular, for computation offloading. We briefly review the cutting-edge research and discuss future challenges. Furthermore, we develop a gametheoretical model for energy-efficient distributed edge server activation and study several learning techniques. Numerical results are provided to illustrate the performance of these distributed learning techniques. Also, open research issues in the context of resource management in MEC servers are discussed.

show abstract

A Game-Theoretic Based QoS-Aware Capacity Management for Real-Time EdgeIoT Applications

Cited by 6 publications

References 43 publications

A comprehensive survey on software‐defined networking for smart communities

A comprehensive survey on software‐defined networking for smart communities

Towards the Design of a Formal Verification and Evaluation Tool of Real-Time Tasks Scheduling of IoT Applications

Mobile Edge Computation Offloading Using Game Theory and Reinforcement Learning

Contact Info

Product

Resources

About