Latency-aware VNF Chain Deployment with Efficient Resource Reuse at Network Edge

Jin, Panpan; Fei, Xincai; Zhang, Qixia; Liu, Fangming; Li, Bo

doi:10.1109/infocom41043.2020.9155345

Cited by 111 publications

(24 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Orchestration in the edge of 5G has motivated solutions [11] that benefit from edge servers to asses the mapping and migration of VNF resources upon users' mobility. Additionally, edge computing has popped up the quest of deploying NSs with very strict latency requirements, and recent research as [30], [31], [32], [33], and [34] study solutions about how to allocate VNFs to meet low latency requirements. [30] uses a genetic algorithm to obtain a fixed allocation that minimizes/maximizes latency/availability, [34] provides a stopping theory solution that migrates the allocated VNFs as time passes, such that latency restrictions are not violated.…”

Section: Related Workmentioning

confidence: 99%

“…[30] uses a genetic algorithm to obtain a fixed allocation that minimizes/maximizes latency/availability, [34] provides a stopping theory solution that migrates the allocated VNFs as time passes, such that latency restrictions are not violated. [31] formulates an optimization problem to allocate VNFs demanded by endusers attached to antennas, so as to maximize/minimize resources re/usage, by imposing latency constraints. [32] proposes a deep learning agent that assigns VNFs to servers maximizing the requests' throughput, while they meet latency constraints.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Delay and Reliability-Constrained VNF Placement on Mobile and Volatile 5G Infrastructure

Németh¹,

Molner

Martín‐Pérez

et al. 2022

IEEE Trans. on Mobile Comput.

View full text Add to dashboard Cite

Ongoing research and industrial exploitation of SDN and NFV technologies promise higher flexibility on network automation and infrastructure optimization. Choosing the location of Virtual Network Functions is a central problem in the automation and optimization of the software-defined, virtualization-based next generation of networks such as 5G and beyond. Network services provided for autonomous vehicles, factory automation, e-health and cloud robotics often require strict delay bounds and reliability constraints influenced by the location of its composing Virtual Network Functions. Robots, vehicles and other end-devices provide significant capabilities such as actuators, sensors and local computation which are essential for some services. Moreover, these devices are continuously on the move and might lose network connection or run out of battery, which further challenge service delivery in this dynamic environment. This work tackles the mobility, and battery restrictions; as well as the temporal aspects and conflicting traits of reliable, low latency service deployment over a volatile network, where mobile compute nodes act as an extension of the cloud and edge computing infrastructure. The problem is formulated as a costminimizing Virtual Network Function placement optimization and an efficient heuristic is proposed. The algorithms are extensively evaluated from various aspects by simulation on detailed real-world scenarios.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Delay and Reliability-Constrained VNF Placement on Mobile and Volatile 5G Infrastructure

Németh¹,

Molner

Martín‐Pérez

et al. 2022

IEEE Trans. on Mobile Comput.

View full text Add to dashboard Cite

show abstract

“…Aburukba et al and Lakhan et al [17] suggested mobility-aware workload assignment solutions for the distributed network. The processing delay, communication delay and prorogation delay are also considered during problem formulation and, based on these delays, the studies proposed solutions.…”

Section: Related Workmentioning

confidence: 99%

“…Equation (17) shows the commutative probability of the objective function during the initial assignment of workloads.…”

mentioning

confidence: 99%

Multi-Layer Latency Aware Workload Assignment of E-Transport IoT Applications in Mobile Sensors Cloudlet Cloud Networks

et al. 2021

View full text Add to dashboard Cite

These days, with the emerging developments in wireless communication technologies, such as 6G and 5G and the Internet of Things (IoT) sensors, the usage of E-Transport applications has been increasing progressively. These applications are E-Bus, E-Taxi, self-autonomous car, E-Train and E-Ambulance, and latency-sensitive workloads executed in the distributed cloud network. Nonetheless, many delays present in cloudlet-based cloud networks, such as communication delay, round-trip delay and migration during the workload in the cloudlet-based cloud network. However, the distributed execution of workloads at different computing nodes during the assignment is a challenging task. This paper proposes a novel Multi-layer Latency (e.g., communication delay, round-trip delay and migration delay) Aware Workload Assignment Strategy (MLAWAS) to allocate the workload of E-Transport applications into optimal computing nodes. MLAWAS consists of different components, such as the Q-Learning aware assignment and the Iterative method, which distribute workload in a dynamic environment where runtime changes of overloading and overheating remain controlled. The migration of workload and VM migration are also part of MLAWAS. The goal is to minimize the average response time of applications. Simulation results demonstrate that MLAWAS earns the minimum average response time as compared with the two other existing strategies.

show abstract

“…While a number of current literatures solve the different requirement-driven SFC resource allocation problem [17][18][19][20][21] in the generic NFV environment, few works have taken the security-driven SFC embedding in the cloud datacenter networks into account, which is actually a fairly recent issue. There are some pioneering studies with regard to the SSC-DMP [6,[13][14].…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Resource Efficient Security Service Chain Embedding Oriented to Cloud Datacenter Networks

Qiao

Liu

et al. 2021

IEEE Access

View full text Add to dashboard Cite

An important and promising application for the network function virtualization (NFV) technology is in network security, where can dynamically and flexibly accomplish the chaining virtualized security network functions (VSNFs), e.g., network address translation, antispam and packet filter firewall, etc., and thus inspect, monitor or filter traffic flows in the cloud datacenter networks. However, the traffic flows addressed by the VSNFs mainly depend on the security service requirements from mobile users, such as the network security level, end-to-end latency, and security resource, etc. Considering the dynamic nature of cloud datacenter networks, determining the embedding of VSNFs and routing security service paths that optimizes the security resource utilization is a challenging problem, particularly without violating the end-to-end delay constraints and security service requirements. This can also be called security service chain dynamic embedding problem (SSC-DMP). In this paper, we present an NFV-enabled framework for a system that achieves the SSC dynamic embedding in the cloud datacenter networks. We first formulate an integer linear programming (ILP) model to solve the SSC-DMP exactly in small-scale network topology. Then, in order to reduce the time complexity when applying the large-scale network topology, we propose an efficient SSC dynamic embedding solution that is based on the particle swarm optimization. Extensive simulation results show that the proposed algorithm could significantly outperform the current benchmarks at least 35.2% and 23.1% in terms of resource consumption and end-to-end delay, respectively.

show abstract

Latency-aware VNF Chain Deployment with Efficient Resource Reuse at Network Edge

Cited by 111 publications

References 31 publications

Delay and Reliability-Constrained VNF Placement on Mobile and Volatile 5G Infrastructure

Delay and Reliability-Constrained VNF Placement on Mobile and Volatile 5G Infrastructure

Multi-Layer Latency Aware Workload Assignment of E-Transport IoT Applications in Mobile Sensors Cloudlet Cloud Networks

A Novel Method for Resource Efficient Security Service Chain Embedding Oriented to Cloud Datacenter Networks

Contact Info

Product

Resources

About