Evaluating impacts of traffic migration and virtual network functions consolidation on power aware resource allocation algorithms

Recently, Network Function Virtualization (NFV) and Software Defined Networking (SDN) have attracted many mobile operators. For the flexible deployment of Network Functions (NFs) in an NFV environment, NF decompositions and control/user plane separation have been introduced in the literature. That is to map traditional functions into their corresponding Virtual Network Functions (VNFs). This mapping requires the NFV Resource Allocation (NFV-RA) for multi-path service graphs with a high number of virtual nodes and links, which is a complex NP-hard problem that inherited its complexity from the Virtual Network Embedding (VNE). This paper proposes a new path mapping approach to solving the NFV-RA problem for decomposed Network Service Chains (NSCs). The proposed solution has symmetrically considered optimizing an average embedding cost with an enhancement on average execution time. The proposed approach has been compared to two other existing schemes using 6 and 16 scenarios of short and long simulation runs, respectively. The impact of the number of nodes, links and paths of the service requests on the proposed scheme has been studied by solving more than 122,000 service requests. The proposed Integer Linear Programming (ILP) and heuristic schemes have reduced the execution time up to 39.58% and 6.42% compared to existing ILP and heuristic schemes, respectively. Moreover, the proposed schemes have also reduced the average embedding cost and increased the profit for the service providers.

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A Processor-Sharing Scheduling Strategy for NFV Nodes

Journal of Electrical and Computer Engineering ◽

10.1155/2016/3583962 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 9

Author(s):

Giuseppe Faraci ◽

Alfio Lombardo ◽

Giovanni Schembra

Keyword(s):

Resource Allocation ◽

Future Internet ◽

Round Robin ◽

The State ◽

Virtual Network ◽

Network Interface ◽

Processor Sharing ◽

Extensive Simulation ◽

Sharing Strategy ◽

Network Functions

The introduction of the two paradigms SDN and NFV to “softwarize” the current Internet is making management and resource allocation two key challenges in the evolution towards the Future Internet. In this context, this paper proposes Network-Aware Round Robin (NARR), a processor-sharing strategy, to reduce delays in traversing SDN/NFV nodes. The application of NARR alleviates the job of the Orchestrator by automatically working at the intranode level, dynamically assigning the processor slices to the virtual network functions (VNFs) according to the state of the queues associated with the output links of the network interface cards (NICs). An extensive simulation set is presented to show the improvements achieved with respect to two more processor-sharing strategies chosen as reference.

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Optimising resource allocation for virtual network functions in SDN/NFV‐enabled MEC networks

IET Communications ◽

10.1049/cmu2.12183 ◽

2021 ◽

Author(s):

Nahida Kiran ◽

Xuanlin Liu ◽

Sihua Wang ◽

Changchuan Yin

Keyword(s):

Resource Allocation ◽

Virtual Network ◽

Network Functions

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Compare: Comparative Advantage Driven Resource Allocation for Virtual Network Functions

SSRN Electronic Journal ◽

10.2139/ssrn.2745661 ◽

2016 ◽

Author(s):

Bernardo A. Huberman ◽

Puneet Sharma

Keyword(s):

Resource Allocation ◽

Comparative Advantage ◽

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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Minimizing Resource Waste in Heterogeneous Resource Allocation for Data Stream Processing on Clouds

Applied Sciences ◽

10.3390/app11010149 ◽

2020 ◽

Vol 11 (1) ◽

pp. 149

Author(s):

Wu-Chun Chung ◽

Tsung-Lin Wu ◽

Yi-Hsuan Lee ◽

Kuo-Chan Huang ◽

Hung-Chang Hsiao ◽

...

Keyword(s):

Resource Allocation ◽

Cloud Computing ◽

Data Stream ◽

Limited Attention ◽

Heterogeneous Servers ◽

Resource Allocation Algorithm ◽

System Utilization ◽

Allocation Algorithms ◽

Considerable Resource ◽

Heterogeneous Resources

Resource allocation is vital for improving system performance in big data processing. The resource demand for various applications can be heterogeneous in cloud computing. Therefore, a resource gap occurs while some resource capacities are exhausted and other resource capacities on the same server are still available. This phenomenon is more apparent when the computing resources are more heterogeneous. Previous resource-allocation algorithms paid limited attention to this situation. When such an algorithm is applied to a server with heterogeneous resources, resource allocation may result in considerable resource wastage for the available but unused resources. To reduce resource wastage, a resource-allocation algorithm, called the minimizing resource gap (MRG) algorithm, for heterogeneous resources is proposed in this study. In MRG, the gap between resource usages for each server in cloud computing and the resource demands among various applications are considered. When an application is launched, MRG calculates resource usage and allocates resources to the server with the minimized usage gap to reduce the amount of available but unused resources. To demonstrate MRG performance, the MRG algorithm was implemented in Apache Spark. CPU- and memory-intensive applications were applied as benchmarks with different resource demands. Experimental results proved the superiority of the proposed MRG approach for improving the system utilization to reduce the overall completion time by up to 24.7% for heterogeneous servers in cloud computing.

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