Joint Computation Offloading, SFC Placement, and Resource Allocation for Multi-Site MEC Systems

Nguyen, Phuong-Duy; Le, Long Bao

doi:10.1109/wcnc45663.2020.9120597

Cited by 11 publications

(10 citation statements)

References 10 publications

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“…If the computing capacity on each cloudlet was sufficient, an ILP solution was proposed to maximize the requests admission number; otherwise, two heuristic algorithms were developed to maximize the network throughput. Nguyen et al [9] investigated the joint optimization problem of computation offloading, SFC placement and resource allocation in NFV-enabled MEC, where a decomposition approach was employed to minimize the weighted normalized energy consumption and computing cost. Kim et al [10] designed a dual-resource NFV-enabled MEC system, with the multi-path and sending rate control problem studied.…”

Section: Computation Offloading and Vnf Placement Joint Optimizationmentioning

confidence: 99%

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On Jointly Optimizing Partial Offloading and SFC Mapping: A Cooperative Dual-agent Deep Reinforcement Learning Approach

Wang¹,

Xing²,

Song³

et al. 2022

Preprint

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Multi-access edge computing (MEC) and network function virtualization (NFV) are promising technologies to support emerging IoT applications, especially those computation-intensive. In NFV-enabled MEC environment, service function chain (SFC), i.e., a set of ordered virtual network functions (VNFs), can be mapped on MEC servers. Mobile devices (MDs) can offload computation-intensive applications, which can be represented by SFCs, fully or partially to MEC servers for remote execution. This paper studies the partial offloading and SFC mapping joint optimization (POSMJO) problem in an NFV-enabled MEC system, where an incoming task can be partitioned into two parts, one for local execution and the other for remote execution. The objective is to minimize the average cost in the long term which is a combination of execution delay, MD's energy consumption, and usage charge for edge computing. This problem consists of two closely related decision-making steps, namely task partition and VNF placement, which is highly complex and quite challenging. To address this, we propose a cooperative dual-agent deep reinforcement learning (CDADRL) algorithm, where we design a framework enabling interaction between two agents. Simulation results show that the proposed algorithm outperforms three combinations of deep reinforcement learning algorithms in terms of cumulative and average episodic rewards and it overweighs a number of baseline algorithms with respect to execution delay, energy consumption, and usage charge.

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Section: Computation Offloading and Vnf Placement Joint Optimizationmentioning

confidence: 99%

“…The European Telecommunications Standards Institute (ETSI) introduced an MEC reference architecture in [8], where MEC was deployed as part of an NFV environment. In this architecture, a mobile edge application is represented by a set of ordered VNFs called service function chain (SFC) [9].…”

Section: Introductionmentioning

confidence: 99%

On Jointly Optimizing Partial Offloading and SFC Mapping: A Cooperative Dual-agent Deep Reinforcement Learning Approach

Wang¹,

Xing²,

Song³

et al. 2022

Preprint

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“…Two types of underlying infrastructure are assumed by these papers. According to our Platform components dimension, the majority of the works focus on the more complex cloud-edge-terminal scenario, [99], [100], [106], [107], [142], [145], [147], [149], [150], [153], some others target the edge-terminal setup, [109], [111], [116], [156], while [139] presents a multi-edge scenario.…”

Section: Multiple Components With Connectionmentioning

confidence: 99%

“…Strategies of the cooperating nodes are also analyzed and the authors propose a solution based on their optimization. Other research works [99], [107], [109], [116], [139], [149], [150] jointly handle both the users' and operators' aspects and besides offloading decisions, the resource allocation is also considered. For example, in [149] researchers consider to integrate optimization of multi-tier offloading with resource allocation.…”

Section: Multiple Components With Connectionmentioning

confidence: 99%

“…Convex optimization [56], [112] Convex and quasi-convex optimization [101] Geometric programming [60] Interior-point method Linear programming [60], [102], [116] Benders decomposition [86] LP with fractal solution rounding Stochastic control [108] Sine cosine algorithm Other heuristic solution [68], [81], [93], [112], [139], [154] n/a [113]…”

Section: A Problem Formulationmentioning

confidence: 99%

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Survey on Placement Methods in the Edge and Beyond

Sonkoly

Czentye

Szalay

et al. 2021

IEEE Commun. Surv. Tutorials

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Edge computing is a (r)evolutionary extension of traditional cloud computing. It expands central cloud infrastructure with execution environments close to the users in terms of latency in order to enable a new generation of cloud applications. This paradigm shift has opened the door for telecommunications operators, mobile and fixed network vendors: they have joined the cloud ecosystem as essential stakeholders considerably influencing the future success of the technology. A key problem in edge computing is the optimal placement of computational units (virtual machines, containers, tasks or functions) of novel distributed applications. These components are deployed to a geographically distributed virtualized infrastructure and heterogeneous networking technologies are invoked to connect them while respecting quality requirements. The optimal hosting environment should be selected based on multiple criteria by novel scheduler algorithms which can cope with the new challenges of distributed cloud architecture where networking aspects cannot be ignored. The research community has dedicated significant efforts to this topic during recent years and a vast number of theoretical results have been published addressing different variants of the related mathematical problems. However, a comprehensive survey focusing on the technical and analytical aspects of the placement problem in various edge architectures is still missing. This survey provides a comprehensive summary and a structured taxonomy of the vast research on placement of computational entities in emerging edge infrastructures. Following the given taxonomy, the research papers are analyzed and categorized according to several dimensions, such as the capabilities of the underlying platforms, the structure of the supported services, the problem formulation, the applied mathematical methods, the objectives and constraints incorporated in the optimization problems, and the complexity of the proposed methods. We summarize the gained insights and important lessons learned, and finally, we reveal some important research gaps in the current literature.

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New Improved Multi-Objective Gorilla Troops Algorithm for Dependent Tasks Offloading problem in Multi-Access Edge Computing

et al. 2023

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Computational offloading allows lightweight battery-operated devices such as IoT gadgets and mobile equipment to send computation tasks to nearby edge servers to be completed, which is a challenging problem in the multi-access edge computing (MEC) environment. Numerous conflicting objectives exist in this problem; for example, the execution time, energy consumption, and computation cost should all be optimized simultaneously. Furthermore, offloading an application that consists of dependent tasks is another important issue that cannot be neglected while addressing this problem. Recent methods are single objective, computationally expensive, or ignore task dependency. As a result, we propose an improved Gorilla Troops Algorithm (IGTA) to offload dependent tasks in the MEC environments with three objectives: 1-Minimizing the execution latency of the application, 2-energy consumption of the light devices, 3-the used cost of the MEC resources. Furthermore, it is supposed that each MEC supports many charge levels to provide more flexibility to the system. Additionally, we have extended the operation of the standard Gorilla Troops Algorithm (GTO) by adopting a customized crossover operation to improve its search strategy. A Max-To-Min (MTM) load-balancing strategy was also implemented in IGTA to improve the offloading operation. Relative to GTO, IGTA has reduced latency by 33%, energy consumption by 93%, and cost usage by 34.5%. We compared IGTA with other Optimizers in this problem, and the results showed the superiority of IGTA.

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Joint Computation Offloading, SFC Placement, and Resource Allocation for Multi-Site MEC Systems

Cited by 11 publications

References 10 publications

On Jointly Optimizing Partial Offloading and SFC Mapping: A Cooperative Dual-agent Deep Reinforcement Learning Approach

On Jointly Optimizing Partial Offloading and SFC Mapping: A Cooperative Dual-agent Deep Reinforcement Learning Approach

Survey on Placement Methods in the Edge and Beyond

New Improved Multi-Objective Gorilla Troops Algorithm for Dependent Tasks Offloading problem in Multi-Access Edge Computing

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