Embedding Virtual Network Functions with Backup for Reliable Large-Scale Edge Computing

Service Function Chaining (SFC) is a capability that links multiple network functions to deploy end-to-end network services. By virtualizing these network functions also known as Virtual Network Functions (VNFs), the dependency on traditional hardware can be removed, hence making it easier to deploy dynamic service chains over the cloud environment. Before implementing service chains over a large scale, it is necessary to understand the performance overhead created by each VNF owing to their varied characteristics. This research paper attempts to gain insights on the server and networking overhead encountered when a service chain is deployed on a cloud orchestration tool such as OpenStack. Specifically, this research will measure the CPU utilization, RAM usage and System Load of the server hosting OpenStack. Each VNF will be monitored for its varying performance parameters when subjected to different kinds of traffic. Our focus lies on acquiring performance parameters of the entire system for different service chains and compare throughput, latency, and VNF statistics of the virtual network. Insights obtained from this research can be used in the industry to achieve optimum performance of hardware and network resources while deploying service chains.

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Service-aware Based Virtual Network Functions Deployment Scheme in Edge Computing

2020 22nd International Conference on Advanced Communication Technology (ICACT) ◽

10.23919/icact48636.2020.9061231 ◽

2020 ◽

Cited By ~ 1

Author(s):

Shujun Han ◽

Jun Li ◽

Qian Dong ◽

Yuxiang Ma ◽

Liujing Song

Keyword(s):

Edge Computing ◽

Virtual Network ◽

Network Functions

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Efficient Placement of Service Function Chains in Cloud Computing Environments

Electronics ◽

10.3390/electronics10030323 ◽

2021 ◽

Vol 10 (3) ◽

pp. 323

Author(s):

Marwa A. Abdelaal ◽

Gamal A. Ebrahim ◽

Wagdy R. Anis

Keyword(s):

Energy Consumption ◽

Load Balancing ◽

Response Time ◽

Optimization Problem ◽

Service Providers ◽

Virtual Network ◽

Placement Problem ◽

Service Function ◽

Network Functions ◽

Bandwidth Consumption

The widespread adoption of network function virtualization (NFV) leads to providing network services through a chain of virtual network functions (VNFs). This architecture is called service function chain (SFC), which can be hosted on top of commodity servers and switches located at the cloud. Meanwhile, software-defined networking (SDN) can be utilized to manage VNFs to handle traffic flows through SFC. One of the most critical issues that needs to be addressed in NFV is VNF placement that optimizes physical link bandwidth consumption. Moreover, deploying SFCs enables service providers to consider different goals, such as minimizing the overall cost and service response time. In this paper, a novel approach for the VNF placement problem for SFCs, called virtual network functions and their replica placement (VNFRP), is introduced. It tries to achieve load balancing over the core links while considering multiple resource constraints. Hence, the VNF placement problem is first formulated as an integer linear programming (ILP) optimization problem, aiming to minimize link bandwidth consumption, energy consumption, and SFC placement cost. Then, a heuristic algorithm is proposed to find a near-optimal solution for this optimization problem. Simulation studies are conducted to evaluate the performance of the proposed approach. The simulation results show that VNFRP can significantly improve load balancing by 80% when the number of replicas is increased. Additionally, VNFRP provides more than a 54% reduction in network energy consumption. Furthermore, it can efficiently reduce the SFC placement cost by more than 67%. Moreover, with the advantages of a fast response time and rapid convergence, VNFRP can be considered as a scalable solution for large networking environments.

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Joint optimization of network selection and task offloading for vehicular edge computing

Journal of Cloud Computing Advances Systems and Applications ◽

10.1186/s13677-021-00240-y ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Lujie Tang ◽

Bing Tang ◽

Li Zhang ◽

Feiyan Guo ◽

Haiwu He

Keyword(s):

Large Scale ◽

Vehicular Networks ◽

Access Point ◽

Edge Computing ◽

Network Selection ◽

Network Access ◽

Task Execution ◽

Point Selection ◽

Vehicle Movement ◽

Task Offloading

AbstractTaking the mobile edge computing paradigm as an effective supplement to the vehicular networks can enable vehicles to obtain network resources and computing capability nearby, and meet the current large-scale increase in vehicular service requirements. However, the congestion of wireless networks and insufficient computing resources of edge servers caused by the strong mobility of vehicles and the offloading of a large number of tasks make it difficult to provide users with good quality of service. In existing work, the influence of network access point selection on task execution latency was often not considered. In this paper, a pre-allocation algorithm for vehicle tasks is proposed to solve the problem of service interruption caused by vehicle movement and the limited edge coverage. Then, a system model is utilized to comprehensively consider the vehicle movement characteristics, access point resource utilization, and edge server workloads, so as to characterize the overall latency of vehicle task offloading execution. Furthermore, an adaptive task offloading strategy for automatic and efficient network selection, task offloading decisions in vehicular edge computing is implemented. Experimental results show that the proposed method significantly improves the overall task execution performance and reduces the time overhead of task offloading.

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A Proximal Algorithm for Fork-Choice in Distributed Ledger Technology for Context-Based Clustering on Edge Computing

Engineering Proceedings ◽

10.3390/ecsa-7-08261 ◽

2020 ◽

Vol 2 (1) ◽

pp. 92

Author(s):

Rahim Rahmani ◽

Ramin Firouzi ◽

Sachiko Lim ◽

Mahbub Alam

Keyword(s):

Large Scale ◽

Cluster Head ◽

Edge Computing ◽

Computing Technology ◽

Proximal Algorithm ◽

Data Intensive ◽

Distributed Ledger ◽

Smart Contract ◽

Distributed Ledger Technology ◽

Iot Devices

The major challenges of operating data-intensive of Distributed Ledger Technology (DLT) are (1) to reach consensus on the main chain as a set of validators cast public votes to decide on which blocks to finalize and (2) scalability on how to increase the number of chains which will be running in parallel. In this paper, we introduce a new proximal algorithm that scales DLT in a large-scale Internet of Things (IoT) devices network. We discuss how the algorithm benefits the integrating DLT in IoT by using edge computing technology, taking the scalability and heterogeneous capability of IoT devices into consideration. IoT devices are clustered dynamically into groups based on proximity context information. A cluster head is used to bridge the IoT devices with the DLT network where a smart contract is deployed. In this way, the security of the IoT is improved and the scalability and latency are solved. We elaborate on our mechanism and discuss issues that should be considered and implemented when using the proposed algorithm, we even show how it behaves with varying parameters like latency or when clustering.

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