scholarly journals Efficient Placement of Service Function Chains in Cloud Computing Environments

Electronics ◽  
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.

scholarly journals Path Mapping Approach for Network Function Virtualization Resource Allocation with Network Function Decomposition Support

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1173 ◽  
Author(s):  
Basheer Raddwan ◽  
Khalil AL-Wagih ◽  
Ibrahim A. Al-Baltah ◽  
Mohamed A. Alrshah ◽  
Mohammed A. Al-Maqri
Keyword(s):  
Resource Allocation ◽  
Execution Time ◽  
Service Providers ◽  
Virtual Network ◽  
Network Function Virtualization ◽  
Network Function ◽  
Function Decomposition ◽  
Mapping Approach ◽  
Network Functions ◽  
The Impact

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.

scholarly journals Optimization of mobile device energy consumption in a fog-based mobile computing offloading mechanism

2021 ◽  
Vol 29 (1) ◽  
pp. 53-62
Author(s):  
Anastasia V. Daraseliya ◽  
Eduard S. Sopin
Keyword(s):  
Energy Consumption ◽  
Response Time ◽  
Mobile Devices ◽  
Mobile Device ◽  
Mobile Applications ◽  
Optimization Problem ◽  
Fog Computing ◽  
Computing System ◽  
Battery Life ◽  
Average Response Time

The offloading of computing tasks to the fog computing system is a promising approach to reduce the response time of resource-greedy real-time mobile applications. Besides the decreasing of the response time, the offloading mechanisms may reduce the energy consumption of mobile devices. In the paper, we focused on the analysis of the energy consumption of mobile devices that use fog computing infrastructure to increase the overall system performance and to improve the battery life. We consider a three-layer computing architecture, which consists of the mobile device itself, a fog node, and a remote cloud. The tasks are processed locally or offloaded according to the threshold-based offloading criterion. We have formulated an optimization problem that minimizes the energy consumption under the constraints on the average response time and the probability that the response time is lower than a certain threshold. We also provide the numerical solution to the optimization problem and discuss the numerical results.

scholarly journals Multiple instances mapping of Service Function Chain with parallel Virtual Network Functions

2019 ◽  
Vol 13 ◽  
pp. 174830261986853 ◽  
Author(s):  
Dong Zhang ◽  
Xiang Lin ◽  
Xiang Chen
Keyword(s):  
Queuing Theory ◽  
Virtual Network ◽  
Network Function Virtualization ◽  
Service Function ◽  
Network Function ◽  
Integer Programing ◽  
Service Function Chain ◽  
Network Functions ◽  
Simulation Results ◽  
Mapping Scheme

Network Function Virtualization addresses the defect of traditional middleboxes and enables operators to implement new services through a process named Service Function Chain mapping. Service Function Chain is composed by a sequence of Virtual Network Functions (VNFs) which is deployed in shared platforms. Service Function Chain with parallel VNFs is proposed to reduce the delivery latency. In this paper, a multiple instances mapping scheme named MIM is proposed to resolve the performance bottleneck introduced by the imbalance of parallel VNFs. A integer programing model is established to describe the multiple instances mapping problem based on queuing theory, and a double layer Genetic Algorithm is used to allocate parallel VNFs with multiple instances. Simulation results show that the multiple instances mapping scheme can improve the performance of Service Function Chain with parallel VNFs effectively.

scholarly journals The Value of Simple Heuristics for Virtualized Network Function Placement

2020 ◽  
Vol 12 (10) ◽  
pp. 161
Author(s):  
Zahra Jahedi ◽  
Thomas Kunz
Keyword(s):  
Mathematical Models ◽  
Optimization Model ◽  
Service Providers ◽  
Network Function Virtualization ◽  
Np Hard ◽  
Placement Problem ◽  
Network Function ◽  
Main Challenge ◽  
Wide Range ◽  
Network Functions

Network Function Virtualization (NFV) can lower the CAPEX and/or OPEX for service providers and allow for quick deployment of services. Along with the advantages come some challenges. The main challenge in the use of Virtualized Network Functions (VNF) is the VNFs’ placement in the network. There is a wide range of mathematical models proposed to place the Network Functions (NF) optimally. However, the critical problem of mathematical models is that they are NP-hard, and consequently not applicable to larger networks. In wireless networks, we are considering the scarcity of Bandwidth (BW) as another constraint that is due to the presence of interference. While there exist many efforts in designing a heuristic model that can provide solutions in a timely manner, the primary focus with such heuristics was almost always whether they provide results almost as good as optimal solution. Consequently, the heuristics themselves become quite non-trivial, and solving the placement problem for larger networks still takes a significant amount of time. In this paper, in contrast, we focus on designing a simple and scalable heuristic. We propose four heuristics, which are gradually becoming more complex. We compare their performance with each other, a related heuristic proposed in the literature, and a mathematical optimization model. Our results demonstrate that while more complex placement heuristics do not improve the performance of the algorithm in terms of the number of accepted placement requests, they take longer to solve and therefore are not applicable to larger networks.In contrast, a very simple heuristic can find near-optimal solutions much faster than the other more complicated heuristics while keeping the number of accepted requests close to the results achieved with an NP-hard optimization model.

scholarly journals DEEP LEARNING BASED LOAD BALANCING USING MULTIDIMENSIONAL QUEUING LOAD OPTIMIZATION ALGORITHM FOR CLOUD ENVIRONMENT.

2020 ◽  
Vol 9 (10) ◽  
pp. 156-167
Keyword(s):  
Cloud Computing ◽  
Deep Learning ◽  
Load Balancing ◽  
Response Time ◽  
Success Rate ◽  
Virtual Machines ◽  
Service Providers ◽  
Resource Scheduling ◽  
Cloud Environment ◽  
Load Optimization

Cloud computing becoming one of the most advanced and promising technologies in these days for information technology era. It has also helped to reduce the cost of small and medium enterprises based on cloud provider services. Resource scheduling with load balancing is one of the primary and most important goals of the cloud computing scheduling process. Resource scheduling in cloud is a non-deterministic problem and is responsible for assigning tasks to virtual machines (VMs) by a servers or service providers in a way that increases the resource utilization and performance, reduces response time, and keeps the whole system balanced. So in this paper, we presented a model deep learning based resource scheduling and load balancing using multidimensional queuing load optimization (MQLO) algorithm with the concept of for cloud environment Multidimensional Resource Scheduling and Queuing Network (MRSQN) is used to detect the overloaded server and migrate them to VMs. Here, ANN is used as deep learning concept as a classifier that helps to identify the overloaded or under loaded servers or VMs and balanced them based on their basis parameters such as CPU, memory and bandwidth. In particular, the proposed ANN-based MQLO algorithm has improved the response time as well success rate. The simulation results show that the proposed ANN-based MQLO algorithm has improved the response time compared to the existing algorithms in terms of Average Success Rate, Resource Scheduling Efficiency, Energy Consumption and Response Time.

Optimization of cloud load balancing using fitness function and duopoly theory

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
KS Resma ◽  
GS Sharvani ◽  
Ramasubbareddy Somula
Keyword(s):  
Energy Consumption ◽  
Load Balancing ◽  
Response Time ◽  
Virtual Machine ◽  
Fitness Function ◽  
Resource Requirement ◽  
Content Type ◽  
Cloud Data ◽  
Physical Machine ◽  
Bee Colony

PurposeCurrent industrial scenario is largely dependent on cloud computing paradigms. On-demand services provided by cloud data centre are paid as per use. Hence, it is very important to make use of the allocated resources to the maximum. The resource utilization is highly dependent on the allocation of resources to the incoming request. The allocation of requests is done with respect to the physical machines present in the datacenter. While allocating the tasks to these physical machines, it needs to be allocated in such a way that no physical machine is underutilized or over loaded. To make sure of this, optimal load balancing is very important.Design/methodology/approachThe paper proposes an algorithm which makes use of the fitness functions and duopoly game theory to allocate the tasks to the physical machines which can handle the resource requirement of the incoming tasks. The major focus of the proposed work is to optimize the load balancing in a datacenter. When optimization happens, none of the physical machine is neither overloaded nor under-utilized, hence resulting in efficient utilization of the resources.FindingsThe performance of the proposed algorithm is compared with different existing load balancing algorithms such as round-robin load (RR) ant colony optimization (ACO), artificial bee colony (ABC) with respect to the selected parameters response time, virtual machine migrations, host shut down and energy consumption. All the four parameters gave a positive result when the algorithm is simulated.Originality/valueThe contribution of this paper is towards the domain of cloud load balancing. The paper is proposing a novel approach to optimize the cloud load balancing process. The results obtained show that response time, virtual machine migrations, host shut down and energy consumption are reduced in comparison to few of the existing algorithms selected for the study. The proposed algorithm based on the duopoly function and fitness function brings in an optimized performance compared to the four algorithms analysed.

scholarly journals Uma Abordagem Holística para a Definição de Service Chains Utilizando Click-on-OSv sobre Diferentes Plataformas NFV

2018 ◽  
Author(s):  
Alexandre Huff ◽  
Giovanni Venâncio ◽  
Leonardo da C. Marcuzzo ◽  
Vinícius F. Garcia ◽  
Carlos R. P. dos Santos ◽  
...  
Keyword(s):  
Virtual Network ◽  
Service Function ◽  
Service Function Chain ◽  
Network Functions

A implantação de serviços em redes virtualizadas pode ser feita através da composição de várias funções implementadas como VNFs (Virtual Network Functions). A instanciação de VNFs e o encaminhamento do tráfego em uma ordem definida através das mesmas forma uma SFC (Service Function Chain). Este trabalho propõe um framework para a composição e o gerenciamento do ciclo de vida de SFCs formadas por VNFs programadas sobre o Click-on-OSv. Além disso, a proposta deste artigo amplia este contexto, possibilitando a execução de SFCs em diferentes orquestradores NFV. A abordagem proposta define uma APIúnica para a composição de SFCs que permite abstrair as especificidades dos orquestradores NFV. Um protótipo do framework foi implementado para realizar a composição e o gerenciamento de SFCs formadas por VNFs programadas com Click-on-OSv sobre o orquestrador NFV OpenStack Tacker.

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