h-Horizon Sequential Look-ahead Greedy Algorithm for VNF-FG Embedding

Ebrahimzadeh, Amin; Promwongsa, Nattakorn; Afrasiabi, Seyedeh Negar; Mouradian, Carla; Li, Wubin; Recse, Ákos; Szabó, Rita; Glitho, Roch

doi:10.1109/nfv-sdn53031.2021.9665063

Cited by 10 publications

(1 citation statement)

References 8 publications

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“…During the second step (swap), the algorithm finds pairs of accepted GEMs whose swap will be feasible in terms of maximum delay, bandwidth, and resources, and the overall delay will be decreased (ln. [33][34][35][36][37][38][39][40][41][42][43]. If such a pair is found, it updates the placement allocation and the corresponding resource availability (i.e., bandwidth, memory, CPU, and storage) (ln.…”

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confidence: 99%

MAP-MIND: An Offline Algorithm for Optimizing Game Engine Module Placement in Cloud Gaming

Lotfimahyari,

De Giovanni,

Gadia

et al. 2024

IEEE Access

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Online gaming has seen a significant surge in popularity, becoming a dominant form of entertainment worldwide. This growth has necessitated the evolution of game servers from centralized to distributed models, leading to the emergence of distributed game engines. These engines allow for the distribution of game engine modules (GEMs) across multiple servers, improving scalability and performance. However, this distribution presents a new challenge: the game engine module placement problem. This problem involves strategically placing GEMs to maximize the number of accepted placement requests while minimizing the delay experienced by players, a critical factor in enhancing the gaming experience. The problem can be formulated as an Integer Linear Programming (ILP) model, which provides an optimal solution but suffers from high computational complexity, making it impractical for real-world applications. To address this challenge, this paper introduces two novel heuristic algorithms, MAP-MIND and MAP-MIND*. The MAP-MIND algorithm demonstrates superior performance, achieving near-optimal delay and more than 92% GEM request acceptance in the worst heterogeneous scenarios. The MAP-MIND* algorithm, while slightly under-performing MAP-MIND in terms of delay, proves to be significantly faster, making it a viable alternative for real-world applications with equal GEM request acceptance. The tradeoff between the two algorithms offers a flexible approach to GEM placement, balancing performance and computational efficiency.INDEX TERMS Cloud gaming, distributed game engines, placement algorithm

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confidence: 99%

MAP-MIND: An Offline Algorithm for Optimizing Game Engine Module Placement in Cloud Gaming

Lotfimahyari,

De Giovanni,

Gadia

et al. 2024

IEEE Access

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Optimizing Placement and Scheduling for VNF by a Multi-objective Optimization Genetic Algorithm

Thien,

Wu,

Bekhit

et al. 2024

Int J Comput Intell Syst

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Virtual network functions (VNFs) have gradually replaced the implementation of traditional network functions. Through efficient placement, the VNF placement technology strives to operate VNFs consistently to the greatest extent possible within restricted resources. Thus, VNF mapping and scheduling tasks can be framed as an optimization problem. Existing research efforts focus only on optimizing the VNFs scheduling or mapping. Besides, the existing methods focus only on one or two objectives. In this work, we proposed addressing the problem of VNFs scheduling and mapping. This work proposed framing the problem of VNFs scheduling and mapping as a multi-objective optimization problem on three objectives, namely (1) minimizing line latency of network link, (2) reducing the processing capacity of each virtual machine, and (3) reducing the processing latency of virtual machines. Then, the proposed VNF-NSGA-III algorithm, an adapted variation of the NSGA-III algorithm, was used to solve this multi-objective problem. Our proposed algorithm has been thoroughly evaluated through a series of experiments on homogeneous and heterogeneous data center environments. The proposed method was compared to several heuristic and recent meta-heuristic methods. The results reveal that the VNF-NSGA-III outperformed the comparison methods.

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Cost-Aware Topological Decomposition of Virtual Network Function Forwarding Graphs

Azhdari,

Ebrahimzadeh,

Afrasiabi

et al. 2023

GLOBECOM 2023 - 2023 IEEE Global Communications Conference

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h-Horizon Sequential Look-ahead Greedy Algorithm for VNF-FG Embedding

Cited by 10 publications

References 8 publications

MAP-MIND: An Offline Algorithm for Optimizing Game Engine Module Placement in Cloud Gaming

MAP-MIND: An Offline Algorithm for Optimizing Game Engine Module Placement in Cloud Gaming

Optimizing Placement and Scheduling for VNF by a Multi-objective Optimization Genetic Algorithm

Cost-Aware Topological Decomposition of Virtual Network Function Forwarding Graphs

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