Toward Optimal Hybrid Service Function Chain Embedding in Multiaccess Edge Computing

Zheng, Danyang; Peng, Chengzong; Liao, Xueting; Cao, Xiaojun

doi:10.1109/jiot.2019.2957961

Cited by 39 publications

(19 citation statements)

References 38 publications

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“…Subramanya et al [17] converted the SFC placement problem in an MEC-enabled 5G network into an ILP model, where machine learning methods were adopted for network traffic prediction. Zheng et al [18] studied a hybrid SFC embedding problem in the context of MEC and addressed it by a heuristic algorithm based on hybrid SFC embedding auxiliary graph. Yang et al [19] investigated the VNF placement and routing problem in edge clouds and proposed a randomized rounding approximation algorithm to minimize the maximum link load.…”

Section: Vnf Placement Optimizationmentioning

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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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: Vnf Placement Optimizationmentioning

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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“…Constraints sets ( 5), and ( 6) guarantee that the trust requirements of each virtual link and each virtual node are satisfied, while constraints sets (7), and (8) guarantee that the allocated CPU and bandwidth resources do not exceed the residual capacity for each substrate node and link respectively. Constraints sets (9), and ( 10) are the domain constraints corresponding to variable sets x and f respectively. We note that removing constraints (5), and ( 6) from the last model gives the baseline PB-SCE model.…”

Section: A Path-based Modelmentioning

confidence: 99%

“…The literature on the SFC embedding problem is quite rich. Recently, applications of this problem have been explored in mobile edge and fog computing [9] [10] [11], Space-Air-Ground Integrated Networks (SAGIN) [12], 5G core network [13], multi-domain service provisioning [14] [15], cloud data centers [16], etc. Multiple objectives and design requirements are sought when addressing the SFC embedding problem including but not limited to cost minimization [19], resiliency [20], energy consumption minimization [21], privacy [17], security [18], and trust-awareness [1], etc.…”

Section: Related Workmentioning

confidence: 99%

Trust-Aware Service Function Chain Embedding: A Path-Based Approach

Torkzaban

Baras

2020

2020 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)

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“…At the hospital, some critical cases need intensive medical care with immediate blood transfer, sometimes the accidant nearest hospital don't own the blood types need, this condtion can lead to fatal consequences. Technologies such as short-range wireless communications, radio-frequency identification (RFID) and wireless sensor networks (WSN) have permitted the Internet to dive into embedded computing [8][9][10][11]. The internet of things (IoT) becomes a known communication paradigm that focuse connecting various kinds of devices to the Internet, in order to concatinate data acquired by remotely control, sensors, monitor environments, appliances and machines, buildings, and vehicles, and so on [12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Smart ambulance using IoT for blood transfer facilities

Torad¹,

Hossamel-din²

2021

IJEECS

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<span>In case of an accident occurrence, ambulance try to transport wounded and injured people to the nearest hospital, but if the injured persons need an urgent blood transfer, the emergency reception (ER) can check the availability of the needed blood type and quantity. If it is available, the emergency reception accepts the injured person, but if it is not available, the reception reroutes the ambulance to the nearest hospital which have the required blood type and quantity. So, a system created that save that wasted time to save injured people life as possible. So, we create a system that save that wasted time to save injured people life as possible. So, the main contribution in this paper is selecting and determining the nearest hospital that own the proper blood types to save injurjed people lives using dijkstra algorithm. In this study, we use an android application at the ambulance which permit the paramedic to login and select the required blood type and quantity needed after determining the injured person’s blood type and the android application automatically determine the nearest hospital using the best shortest path algorithm (SPA) after checking the database and generate a pin code for the paramedic to deliver it with injured person to the ER and get the needed blood. This process will consequently decrease the used blood quantity from the database.</span>

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Toward Optimal Hybrid Service Function Chain Embedding in Multiaccess Edge Computing

Cited by 39 publications

References 38 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

Trust-Aware Service Function Chain Embedding: A Path-Based Approach

Smart ambulance using IoT for blood transfer facilities

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