Weighted Round Robin Algorithm Research Articles

A New WRR Algorithm for an Efficient Load Balancing System in IoT Networks under SDN

The Internet of Things (IoT) connects various smart objects and manages a vast network using diverse technologies, which present numerous challenges. Software-defined networking (SDN) is a system that addresses the challenges of traditional networks and ensures the centralized configuration of network entities to manage network integrity. Furthermore, the uneven distribution of IoT network load results in the depletion of IoT device resources. To address this issue, traffic must be distributed equally, requiring efficient load balancing to be ensured. This requires the development of an efficient architecture for IoT networks. The main goal of this paper is to propose a novel architecture that leverages the potential of SDN, the clustering technique, and a new weighted round-robin (N-WRR) protocol. The objective of this architecture is to achieve load balancing, which is a crucial aspect in the development of IoT networks as it ensures the network’s efficiency. Furthermore, to prevent network congestion and ensure efficient data flow by redistributing traffic from overloaded paths to less burdened ones. The simulation results demonstrate that our N-WRR algorithm achieves highly efficient load balancing compared to the simple weighted round-robin (WRR), and without the application of any load balancing method. Furthermore, our proposed approach enhances throughput, data transfer, and bandwidth availability. This results in an increase in processed requests.

Packet Loss Optimization in Router Forwarding Tasks Based on the Particle Swarm Algorithm

Software-defined networks (SDNs) are computer networks where parameters and devices are configured by software. Recently, artificial intelligence aspects have been used for SDN programs for various applications, including packet classification and forwarding according to the quality of service (QoS) requirements. The main problem is that when packets from different applications pass through computer networks, they have different QoS criteria. To meet the requirements of packets, routers classify these packets, add them to multiple weighting queue systems, and forward them according to their priorities. Multiple queue systems in routers usually use a class-based weighted round-robin (CBWRR) scheduling algorithm with pre-configured fixed weights for each priority queue. The problem is that the intensity of traffic in general and of each packet class occasionally changes. Therefore, in this work, we suggest using the particle swarm optimization algorithm to find the optimal weights for the weighted fair round-robin algorithm (WFRR) by considering the variable densities of the traffic. This work presents a framework to simulate router operations by determining the weights and schedule packets and forwarding them. The proposed algorithm to optimize the weights is compared with the conventional WFRR algorithm, and the results show that the particle swarm optimization for the weighted round-robin algorithm is more efficient than WFRR, especially in high-intensity traffic. Moreover, the average packet-loss ratio does not exceed 7%, and the proposed algorithms are better than the conventional CBWRR algorithm and the related work results.

An Improved Dynamic Smooth Weighted Round-robin Load-balancing Algorithm

Load-balancing is a key component of highly available network infrastructure. Weighted round-robin is one of the important strategies to achieve load-balancing, which is a classic research subject. In recent years, the weighted round-robin algorithm has developed from static weight to dynamic. The Dynamic weighted round-robin algorithm needs further research in time-sensitive of dynamic weights and the rationality of results. Based on Nginx, this paper improves the dynamic weighting algorithm. Firstly, it proposes a method of dynamically calculating weights. The method determines the weight of the server in the cluster according to its theoretical performance and runtime state. Secondly, making the request allocation result more balanced and avoiding continuous centralized scheduling by an improved weighted algorithm. Experiments show that the algorithm proposed in this paper is effective and can improve system performance and load-balancing efficiency.

Fuzzy Based Ant Colony Optimization Scheduling in Cloud Computing

Cloud computing is an Information Technology deployment model established on virtualization. Task scheduling states the set of rules for task allocations to an exact virtual machine in the cloud computing environment. However, task scheduling challenges such as optimal task scheduling performance solutions, are addressed in cloud computing. First, the cloud computing performance due to task scheduling is improved by proposing a Dynamic Weighted Round-Robin algorithm. This recommended DWRR algorithm improves the task scheduling performance by considering resource competencies, task priorities, and length. Second, a heuristic algorithm called Hybrid Particle Swarm Parallel Ant Colony Optimization is proposed to solve the task execution delay problem in DWRR based task scheduling. In the end, a fuzzy logic system is designed for HPSPACO that expands task scheduling in the cloud environment. A fuzzy method is proposed for the inertia weight update of the PSO and pheromone trails update of the PACO. Thus, the proposed Fuzzy Hybrid Particle Swarm Parallel Ant Colony Optimization on cloud computing achieves improved task scheduling by minimizing the execution and waiting time, system throughput, and maximizing resource utilization.

Computation Offloading in Untrusted MEC-Aided Mobile Blockchain IoT Systems

Deploying a mobile edge computing (MEC) server in the mobile blockchain-enabled Internet of things (IoT) system is a promising approach to improve the system performance, however, it imposes a significant challenge on the trust of the MEC server. To address this problem, we first propose an untrusted MEC proof of work (PoW) scheme in mobile blockchain networks where plenty of nonce hash computing demands can be offloaded to the MEC server. Then, we design a nonce ordering algorithm for this scheme to provide fairer computing resource allocation for all mobile IoT devices/users. Specifically, we formulate the user’s nonce selection strategy as a non-cooperative game, where utilities of the individual user are maximized in the untrusted MEC-aided mobile blockchain networks. We also prove the existence of Nash equilibrium and analyze that the cooperation behavior is unsuitable for blockchain-enabled IoT devices by using the repeated game. Finally, we design the blockchain’s difficulty adjustment mechanism to ensure stable block times during a long period of time. Compared with the weighted round-robin algorithm, our proposed nonce ordering algorithm can provide fairer computation resources and optimal nonce selection strategies for all mobile users. Network stability is gained through the proposed blockchain’s difficulty adjustment mechanism. The analysis and optimization results provide valuable design insights for practical mobile blockchain IoT systems.

Improved algorithm for enhance robustness of IPTV based on GEPON

Abstract Ten Gigabit-capable ethernet passive optical network (GEPON) can quickly improve network bandwidth, meet multilevel business needs, and quickly improve the comprehensive competitiveness of the network. It is well known that the 10G EPON access network technology is used to carry IPTV services, VoIP services and high-speed Internet access, known as “triple play”, and IPTV services have been particularly popular in recent years. As the demand for IPTV service increases exponentially, a large amount of data will be generated, occupying a large access bandwidth and causing an impact on other services. Therefore, reasonable scheduling of IPTV service queue is an urgent problem to be solved under multiservice environment at present. Therefore, the design of PON bearing IPTV robustness algorithm to meet user experience is the focus of this paper. This paper proposes a Forward Error Correction with Weight Round Robin algorithm (FWRR), which adopts RS error correction code and Weighted Round Robin algorithm based on IP layer. When IPTV packet is lost at optical line terminal (OLT) end, downlink optical network unit (ONU) can recover the lost data through received packets. This mechanism can effectively solve the problem of packet loss caused by the blocking of OLT end, and realize the scheme of PON carrying IPTV multicast service. Simulations results in OPNET show that compared with weighted round robin (WRR) algorithm and round robin (RR) algorithm, FWRR mechanism can reduce packet loss, delay and jitter, in addition, improve the robustness of IPTV service in GEPON.

FUZZY BASED ANT COLONY OPTIMIZATION SCHEDULING IN CLOUD COMPUTING

Cloud computing is an Information Technology deployment model established on virtualization. Task scheduling states the set of rules for task allocations to an exact virtual machine in the cloud computing environment. However, task scheduling challenges such as optimal task scheduling performance solutions, are addressed in cloud computing. First, the cloud computing performance due to task scheduling is improved by proposing a Dynamic Weighted Round-Robin algorithm. This recommended DWRR algorithm improves the task scheduling performance by considering resource competencies, task priorities, and length. Second, a heuristic algorithm called Hybrid Particle Swarm Parallel Ant Colony Optimization is proposed to solve the task execution delay problem in DWRR based task scheduling. In the end, a fuzzy logic system is designed for HPSPACO that expands task scheduling in the cloud environment. A fuzzy method is proposed for the inertia weight update of the PSO and pheromone trails update of the PACO. Thus, the proposed Fuzzy Hybrid Particle Swarm Parallel Ant Colony Optimization on cloud computing achieves improved task scheduling by minimizing the execution and waiting time, system throughput, and maximizing resource utilization.

A Conceptual Framework Towards Implementing a Cloud-Based Dynamic Load Balancer Using a Weighted Round-Robin Algorithm

Cloud load balancing has become one of the most vital aspects of Cloud computing that has captured the attention of IT organizations and business firms in recent years. Among the issues related to this particular aspect, one such issue which needs to be addressed is the issue of effectively serving the clients' requests among multiple servers using an appropriate load balancer. Previous survey papers discussed various issues of cloud load balancing and accordingly devised various methods and techniques to address those issues with the objectives of reduction of processing time and response time along with optimization of costs. In this article, we have discussed an effective load balancing technique using the weighted Round-Robin algorithm which can process the client requests among multiple servers with minimal response time. Considering all these aspects, a cloud-based dynamic load balancer is being used to solve the problem of load balancing in the cloud infrastructure.

BWManager: Mitigating Denial of Service Attacks in Software-Defined Networks Through Bandwidth Prediction

Software-defined networking (SDN) has emerged as a new networking paradigm that can provide fine-grained network management service. Since the SDN controller makes control decision for the network, it becomes the main target of denial of service (DoS) attacks. In this paper, we propose BWManager to mitigate... which mainly consists mitigate the DoS attacks on the SDN controller with BWManager that mainly consists of four key components: 1) simplified DoS detection module; 2) forecasting engine; 3) priority manager; and 4) scheduler. The simplified DoS detection module calculates a comprehensive judgment score for each switch, which indicates the attacking severity of each switch and is used to decide time slice allocation of the controller. The forecasting engine is the basis of the controller scheduling method and forecasts the bandwidth consumption of users to determine the users’ trust values. The trust values are used by the priority manager to manage multiple buffer queues with different priorities for the users. The scheduler protects the controller and the normal users under DoS attacks by running a weighted Round-Robin algorithm to process flow requests in different priority queues. We evaluate the performance and overhead of BWManager in both hardware and software OpenFlow environments. The results demonstrate that BWManager is effective with a limited overhead.

Weighted Round Robin Load Balancer to Enhance Web Server Cluster in OpenFlow Networks

Web server clusters require a reliable network management for increasing the quality of service (QoS). A load balancer system installed in a software-defined network (SDN) is one method that can improve the performance and availability of web server services. SDN is a dynamic and a programmable network management approach, and one protocol that supports it is OpenFlow. This research aims to design and analyse a model of a load balancer on OpenFlow networks, implementing a Weighted Round Robin (WRR) algorithm. The analysis process is conducted by measuring the value of a QoS web server performance parameters, such as response time, throughput, HTTP success, and loss connection. The results showed the WRR algorithm can be implemented for balancing a network system with dynamic resource allocation. The weight workload of each service can be obtained from the needs and existing network resources. The performance of a load balancer on an OpenFlow network is 57% better than in a traditional one for testing of response time conducted in a high connection. However, the throughput and HTTP success connection decreased by 2% and 10%, respectively, while HTTP loss connection increased by 49%.

Dynamic load balancing scheme on massive file transfer system

In this paper, a dynamic load balancing scheme applied to massive file transfer system is proposed. The scheme is designed to load balance FTP server cluster. Instead of recording connection number, runtime load information of each server is periodically collected and used in combination of static performance parameters collected on server startup to calculate the weight of servers. Improved Weighted Round-Robin algorithm is adopted in this scheme. Importantly, the weight of each server is initialized with static performance parameters and dynamically modified according to the runtime load. Apache Zookeeper cluster is used to receive all information and it will inform director of the runtime load variation and offline behavior of any server. In order to evaluate the effect of this scheme, a contrast experiment with LVS is also conducted.

Load balancing prediction method of cloud storage based on analytic hierarchy process and hybrid hierarchical genetic algorithm.

With the continuous expansion of the cloud computing platform scale and rapid growth of users and applications, how to efficiently use system resources to improve the overall performance of cloud computing has become a crucial issue. To address this issue, this paper proposes a method that uses an analytic hierarchy process group decision (AHPGD) to evaluate the load state of server nodes. Training was carried out by using a hybrid hierarchical genetic algorithm (HHGA) for optimizing a radial basis function neural network (RBFNN). The AHPGD makes the aggregative indicator of virtual machines in cloud, and become input parameters of predicted RBFNN. Also, this paper proposes a new dynamic load balancing scheduling algorithm combined with a weighted round-robin algorithm, which uses the predictive periodical load value of nodes based on AHPPGD and RBFNN optimized by HHGA, then calculates the corresponding weight values of nodes and makes constant updates. Meanwhile, it keeps the advantages and avoids the shortcomings of static weighted round-robin algorithm.

A Load Balancing Mechanism for Video Surveillance System

In recent years, with the development of video surveillance systems, the cluster and load balancing technology need be applied to improve the system performance and the quality of service. In this paper, on the basis of common load balancing algorithms, considering the characteristics of the video surveillance system, design a new load balancing scheduling mechanism, by improving the weighted round robin algorithm and introducing the nodes cooperation strategy. The testing results show that the new mechanism can distribute the loads more reasonably and make use of the server resources more effectively.