scholarly journals Server and network load balancing

2021 ◽  
Author(s):  
Gaurav Gaurav
Keyword(s):  
Fault Tolerance ◽  
Load Balancing ◽  
Network Flows ◽  
Traffic Load ◽  
Network Load ◽  
Handling Technique ◽  
Network Load Balancing ◽  
The Impact ◽  
Tolerance Method ◽  
Overload Traffic

Software Defined Networking (SDN), is an emerging networking technology. This thesis aims to develop a new Server and Network Load balancing scheme in content delivery datacenters using SDN-based architecture. The scheme, called Server and Network Load Balancing (SNLB), tends to distribute the traffic load more evenly across the network. The SNLB achieves even distribution of flows on the links and servers by utilizing real-time network statistics. Furthermore, SNLB classifies the network flows into mice (flows with small bandwidth) and elephant (flows with large bandwidth) flows and performs load balancing on these two classes of flows separately. A detailed comparison of SNLB with Global first fit, Round robin and Load based balancing is presented. Other objectives achieved in this thesis are the designs of overload traffic handling technique and Fault tolerance method. The overload traffic handling technique activates and de-activates servers according to the traffic load; the fault tolerance method can reduce the impact on network performance during the network fault.

scholarly journals Server and network load balancing

2021 ◽  
Author(s):  
Gaurav Gaurav
Keyword(s):  
Fault Tolerance ◽  
Load Balancing ◽  
Network Flows ◽  
Traffic Load ◽  
Network Load ◽  
Handling Technique ◽  
Network Load Balancing ◽  
The Impact ◽  
Tolerance Method ◽  
Overload Traffic

Software Defined Networking (SDN), is an emerging networking technology. This thesis aims to develop a new Server and Network Load balancing scheme in content delivery datacenters using SDN-based architecture. The scheme, called Server and Network Load Balancing (SNLB), tends to distribute the traffic load more evenly across the network. The SNLB achieves even distribution of flows on the links and servers by utilizing real-time network statistics. Furthermore, SNLB classifies the network flows into mice (flows with small bandwidth) and elephant (flows with large bandwidth) flows and performs load balancing on these two classes of flows separately. A detailed comparison of SNLB with Global first fit, Round robin and Load based balancing is presented. Other objectives achieved in this thesis are the designs of overload traffic handling technique and Fault tolerance method. The overload traffic handling technique activates and de-activates servers according to the traffic load; the fault tolerance method can reduce the impact on network performance during the network fault.

Study on Network Load Balancing Algorithm Based on Server Cluster System

2012 ◽  
Vol 182-183 ◽  
pp. 1978-1981 ◽  
Author(s):  
Li Lan ◽  
Chu Huan Qi
Keyword(s):  
Load Balancing ◽  
Load Balance ◽  
Cluster System ◽  
Utilization Efficiency ◽  
Server Cluster ◽  
Use Of Resources ◽  
Network Load ◽  
System Resource ◽  
Network Load Balancing ◽  
Load Balancing Algorithm

The utilization efficiency of system resources is a key issue for cluster system while load balance is an important tool to realize the efficient use of resources. Based on server cluster system, this paper puts forwards an improved self-adaptive algorithm for network load balancing. Simulation results show that the algorithm can improve the utilization efficiency of system resource and reduce the server’s response time so as to achieve the request of real time when dealing with tasks and high availability of system.

Development of a Hybrid Algorithm for User Association and Resource Allocation to Improve Load Balancing and Energy Efficiency in 5G HetNet

2020 ◽  
Vol 19 (1) ◽  
pp. 17-25
Author(s):  
Elvis Obi ◽  
Aliyu Danjuma Usman ◽  
Suleiman Muhammad Sani ◽  
Abdoulie Momodou Sunkary Tekanyi
Keyword(s):  
Resource Allocation ◽  
Energy Efficiency ◽  
Load Balancing ◽  
Bandwidth Allocation ◽  
Hybrid Algorithm ◽  
Control Algorithm ◽  
User Association ◽  
Network Load ◽  
Power Control Algorithm ◽  
Network Load Balancing

This paper presents the development and integration of a power control algorithm into the User Association Algorithm with Optimal Bandwidth Allocation (UAAOBA) to form a Hybrid Algorithm for User Association and Resource Allocation (HAUARA). The power control algorithm updates the transmit power of the Base Stations (BSs) towards a minimum transmit power that satisfies the minimum data rate requirement (1 Gbps) of the User Equipment UEs. The power update is achieved using the Newton Rhapson’s method and it adapts the transmit powers of the BSs to the number of their connected UEs. The developed HAUARA provides an optimal solution for user associations, bandwidth allocation, and transmit powers to UEs concurrently. This maximizes the network energy efficiency by coordinating the load fairness of the network while guaranteeing the quality of service requirement of the UEs. The network energy efficiency performance of the developed HAUARA is compared with that of the UAAOBA. The results show that the developed algorithm has network energy efficiency improvement of 12.36%, 10.58%, and 13.44% with respect to UAAOBA for increase number of macro BS antennas, pico BSs, and femto BSs, respectively. Also, the network load balancing performance of the developed HAUARA is compared with that of the UAAOBA. The results show that the developed algorithm has network load balancing improvement of 12.62%, 10.04%, and 10.34% with respect to UAAOBA for increase number of macro BS antennas, pico BSs, and femto BSs, respectively. This implies that the developed algorithm outperforms the UAAOBA in terms of network energy efficiency and load balancing.

Optimizing network load balancing: an hybridization approach of metaheuristics with column generation

2011 ◽  
Vol 52 (2) ◽  
pp. 959-968 ◽  
Author(s):  
Dorabella Santos ◽  
Amaro de Sousa ◽  
Filipe Alvelos ◽  
Michał Pióro
Keyword(s):  
Load Balancing ◽  
Column Generation ◽  
Network Load ◽  
Network Load Balancing
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