Heuristic Edge Server Placement in Industrial Internet of Things and Cellular Networks

Kasi, Shahrukh Khan; Kasi, Mumraiz Khan; Ali, Kamran; Raza, Mohsin; Afzal, Hifza; Lasebae, Aboubaker; Naeem, Bushra; Islam, Saif ul; Rodrigues, Joel J. P. C.

doi:10.1109/jiot.2020.3041805

Cited by 90 publications

(33 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, the literature belonging to efficient deployment strategies of edge servers includes solving edge server placement problem using genetic, simulated annealing, and hill-climbing algorithms [5,6], k-means clustering with quadratic programming [8], queuing theory and vector quantization technique [16], graph theory [12], cost constrained multi-objective problem [10], and integer programming [11] to optimally place mobile edge servers in a wireless network.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In [5], the authors formulate the problem as a constrained multi-objective problem to balance workloads of mobile edge servers and reduce network access delay. To find the optimal solution, the authors utilize genetic, simulated annealing, and hill-climbing algorithms to show the effectiveness of the proposed solution.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Further, the optimal placement strategy should take into consideration the individual mobile edge server's workload, access delay and application specific requirements into consideration. Various solutions have been proposed in literature to solve the edge servers placement problem [5][6][7][8][9][10][11]. However, most of these solutions apply heuristic algorithms or some sort of linear or quadratic optimization technique.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Secure Mobile Edge Server Placement Using Multi-Agent Reinforcement Learning

et al. 2021

Self Cite

View full text Add to dashboard Cite

Mobile edge computing is capable of providing high data processing capabilities while ensuring low latency constraints of low power wireless networks, such as the industrial internet of things. However, optimally placing edge servers (providing storage and computation services to user equipment) is still a challenge. To optimally place mobile edge servers in a wireless network, such that network latency is minimized and load balancing is performed on edge servers, we propose a multi-agent reinforcement learning (RL) solution to solve a formulated mobile edge server placement problem. The RL agents are designed to learn the dynamics of the environment and adapt a joint action policy resulting in the minimization of network latency and balancing the load on edge servers. To ensure that the action policy adapted by RL agents maximized the overall network performance indicators, we propose the sharing of information, such as the latency experienced from each server and the load of each server to other RL agents in the network. Experiment results are obtained to analyze the effectiveness of the proposed solution. Although the sharing of information makes the proposed solution obtain a network-wide maximation of overall network performance at the same time it makes it susceptible to different kinds of security attacks. To further investigate the security issues arising from the proposed solution, we provide a detailed analysis of the types of security attacks possible and their countermeasures.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Secure Mobile Edge Server Placement Using Multi-Agent Reinforcement Learning

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Certainly, if we only solve (7) to obtain a server placement solution, we can still compute a resource pooling factor. However, this resource pooling factor can be much smaller than the optimal value η * .…”

Section: B Handling Out-of-bound Workload With Resource Poolingmentioning

confidence: 99%

“…This setting was also adopted in [10]- [13]. Here, we compute a server placement solution by solving (7) with only one workload vector. (We use historical average workload vector in the evaluation.)…”

Section: B Compare Different Server Placement Policiesmentioning

confidence: 99%

Design of Robust and Efficient Edge Server Placement and Server Scheduling Policies: Extended Version

Zhao¹,

Xiao²,

Cao³

et al. 2021

Preprint

View full text Add to dashboard Cite

We study how to design edge server placement and server scheduling policies under workload uncertainty for 5G networks. We introduce a new metric called resource pooling factor to handle unexpected workload bursts. Maximizing this metric offers a strong enhancement on top of robust optimization against workload uncertainty. Using both real traces and synthetic traces, we show that the proposed server placement and server scheduling policies not only demonstrate better robustness against workload uncertainty than existing approaches, but also significantly reduce the cost of service providers. Specifically, in order to achieve close-to-zero workload rejection rate, the proposed server placement policy reduces the number of required edge servers by about 25% compared with the state-of-theart approach; the proposed server scheduling policy reduces the energy consumption of edge servers by about 13% without causing much impact on the service quality.

show abstract