Mobile Edge Cloud Network Design Optimization

Ceselli, Alberto; Premoli, Marco; Secci, Stefano

doi:10.1109/tnet.2017.2652850

Cited by 206 publications

(136 citation statements)

References 21 publications

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“…We have shown that all the conditions of a non-wasteful and frugal ME can be inferred from the KKT conditions of (27)- (31). As a result, the optimal solutions to (27) Since this problem has a strictly concave objective function, it has a unique optimal utility vector u * .…”

Section: B Proof Of Theorem 34mentioning

confidence: 99%

“…Note that constraints (28)- (29) imply that any optimal solution X * to this problem is a non-wasteful allocation. To prove the second part of the theorem, we need to show that from the conditions of an ME, non-wastefulness, and frugality, we can reconstruct the problem (27)- (31).…”

Section: B Centralized Solutionmentioning

confidence: 99%

“…The utilities of each buyer at the equilibria may not be unique (e.g., wasteful ME in Proposition 3.3 and non-wasteful ME in Theorem 3.4). It is also worth noting that there can be other market equilibria which are not the optimal solutions to either the problem (22)- (24) or the problem (27)- (31).…”

Section: B Centralized Solutionmentioning

confidence: 99%

“…In summary, a non-wasteful and frugal ME (p * , X * ) is an optimal solution to (27)- (31). Since this is a convex optimization problem, efficient techniques such as the interiorpoint method can be used to solve it effectively [13], [51].…”

Section: B Centralized Solutionmentioning

confidence: 99%

“…Define λ i,j,r , µ i , p j,r , and γ i,j,r as the dual variables associated with (28), (29), (30), and (31), respectively. Consider the problem (27)- (31). The Lagragian is µ i ≥ 0, ∀i; p j,r ≥ 0, ∀j, r; γ i,j,r ≥ 0, ∀i, j, r. (45) and primal feasibility conditions.…”

Section: B Proof Of Theorem 34mentioning

confidence: 99%

See 4 more Smart Citations

A Market-Based Framework for Multi-Resource Allocation in Fog Computing

Nguyen

Bhargava

2019

IEEE/ACM Trans. Networking

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Fog computing is transforming the network edge into an intelligent platform by bringing storage, computing, control, and networking functions closer to end-users, things, and sensors. How to allocate multiple resource types (e.g., CPU, memory, bandwidth) of capacity-limited heterogeneous fog nodes to competing services with diverse requirements and preferences in a fair and efficient manner is a challenging task. To this end, we propose a novel market-based resource allocation framework in which the services act as buyers and fog resources act as divisible goods in the market. The proposed framework aims to compute a market equilibrium (ME) solution at which every service obtains its favorite resource bundle under the budget constraint while the system achieves high resource utilization. This work extends the General Equilibrium literature by considering a practical case of satiated utility functions. Also, we introduce the notions of non-wastefulness and frugality for equilibrium selection, and rigorously demonstrate that all the non-wasteful and frugal ME are the optimal solutions to a convex program. Furthermore, the proposed equilibrium is shown to possess salient fairness properties including envy-freeness, sharing-incentive, and proportionality. Another major contribution of this work is to develop a privacy-preserving distributed algorithm, which is of independent interest, for computing an ME while allowing market participants to obfuscate their private information. Finally, extensive performance evaluation is conducted to verify our theoretical analyses.

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Section: B Proof Of Theorem 34mentioning

confidence: 99%

Section: B Centralized Solutionmentioning

confidence: 99%

Section: B Centralized Solutionmentioning

confidence: 99%

Section: B Centralized Solutionmentioning

confidence: 99%

Section: B Proof Of Theorem 34mentioning

confidence: 99%

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A Market-Based Framework for Multi-Resource Allocation in Fog Computing

Nguyen

Bhargava

2019

IEEE/ACM Trans. Networking

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Enhancing live virtual machine migration process via optimized resource allocation in next generation mobile edge network: A hybrid evolutionary approach

Roy

Midya

Majumder

et al. 2020

Int J Communication

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SummaryThe evolution of 5th Generation wireless technology introduced Mobile Edge Computing, where edge servers are placed at the edge of the network, and are associated with evolved Node Base Stations (eNBs). This enables mobile users to offload their resource‐intensive tasks to these servers and improve network performance by reducing end‐to‐end delay. However, frequent user mobility leads to frequent re‐planning of network and increases network load. This demands dynamic Virtual Machine (VM) migration in Mobile Edge paradigm for an improved Quality of Service (QoS). For an enhanced VM migration process, an optimal pair of migrating VMs and destination edge servers needs to be chosen. In this paper, we propose an optimized decision‐making policy that chooses such optimal pairs. Several decision parameters such as average wait time, processing delay, migration delay, transmission power, and processing power are modeled. A profit function is developed using these modeled decision parameters that chooses the optimal pairs. This function is maximized using the proposed hybrid evolutionary algorithm, which combines the advantages of PSO and GA. The pairs are chosen in such a manner, that the selection guarantees high network throughput, reduced service delay, and energy consumption which is reflected in the simulation.

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An improved Top‐K algorithm for edge servers deployment in smart city

Qin

Xie

et al. 2021

Trans Emerging Tel Tech

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In smart city IoT applications, the deployment of edge servers has problems such as unbalanced servers load and low servers utilization. Therefore, we study the edge servers deployment problem in mobile edge computing environments for smart cities through an improved Top‐K algorithm in this paper. This algorithm comprehensively considering the distance between base stations and edge servers, the weight ratio of base stations in the base station cluster, the coverage of edge servers, and the upper limit of computing tasks, which aims to reduce the access delay of tasks and deployment cost of edge servers, balance the load among edge servers, improve quality of user experience (QoE), and quality of service (QoS) of the smart city. Firstly, deploy an edge server at the base station with the most tasks and divide base station clusters according to the minimum distance strategy. Then, the location of edge servers adjusted according to the cumulative sum of the weight ratio of base station tasks and distance product in each base station cluster. Finally, the simulation results show that the deployment strategy in this paper is better than other methods in terms of server utilization, load balancing and cost, and is slightly better than other algorithms in terms of delay.

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Mobile Edge Cloud Network Design Optimization

Cited by 206 publications

References 21 publications

A Market-Based Framework for Multi-Resource Allocation in Fog Computing

A Market-Based Framework for Multi-Resource Allocation in Fog Computing

Enhancing live virtual machine migration process via optimized resource allocation in next generation mobile edge network: A hybrid evolutionary approach

An improved Top‐K algorithm for edge servers deployment in smart city

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