Systematic Cloud-Based Optimization: Twin-Fold Moth Flame Algorithm for VM Deployment and Load-Balancing

Cloud computing has gained significant recognition due to its ability to provide a broad range of online services and applications.Nevertheless,existing commercial cloud computing models demonstrate an appropriate design by concentrating computational assets,such as preservation and server infrastructure,in a limited number of large-scale worldwide data facilities.Optimizing the deployment of virtual machines(VMs)is crucial in this scenario to ensure system dependability,performance,and minimal latency.A significant barrier in the present scenario is the load distribution,particularly when striving for improved energy consumption in a hypothetical grid computing framework.This design employs load-balancing techniques to allocate different user workloads across several virtual machines.To address this challenge,we propose using the twin-fold moth flame technique,which serves as a very effective optimization technique.Developers intentionally designed the twin-fold moth flame method to consider various restrictions,including energy efficiency,lifespan analysis,and resource expenditures.It provides a thorough approach to evaluating total costs in the cloud computing environment.When assessing the efficacy of our suggested strategy,the study will analyze significant metrics such as energy efficiency,lifespan analysis,and resource expenditures.This investigation aims to enhance cloud computing techniques by developing a new optimization algorithm that considers multiple factors for effective virtual machine placement and load balancing.The proposed work demonstrates notable improvements of 12.15%,10.68%,8.70%,13.29%,18.46%,and 33.39%for 40 count data of nodes using the artificial bee colony-bat algorithm,ant colony optimization,crow search algorithm,krill herd,whale optimization genetic algorithm,and improved Lévy-based whale optimization algorithm,respectively.

AN ADVANCED DYNAMIC FEEDBACK AND RANDOM DISPATCHING LOAD-BALANCING ALGORITHM FOR GMLC IN 3G

Based on the system architecture and software structure of GMLC (Gateway Mobile Location Center) in 3G (third generation), a new dynamic load-balancing algorithm is proposed. It bases on dynamic feedback and imports the increment for admitting new request into the load forecast. It dynamically adjusts the dispatching probability according to the remainder process capability of each node. Experiments on the per- formance of algorithm have been carried out in GMLC and the algorithm is compared with Pick-KX algorithm and DFB (Dynamic FeedBack) algorithm in average throughput and average response time. Experiments re- sults show that the average throughput of the proposed algorithm is about five percents higher than that of the other two algorithms and the average response time is four percents higher under high system loading condi- tion.

A LIMITED RESOURCE VECTOR LOAD-BALANCING ALGORITHM FOR SOFTSWITCH-BASED HETEROGENEOUS CLUSTERED MEDIA SERVER

Based on the system feature of softswitch-based heterogeneous clustered media server, this paper proposed a limited resource vector load-balancing algorithm. The purpose of the algorithm was to balance the load of clusters by utilizing all system resources effectively and to avoid violent shaking of the system per- formance. A lot of simulations on the Petri net model of load balance system are conducted and the algorithm is compared with some traditional algorithms on balancing ability for heterogeneity, system throughput, re- quest response time and performance stability. The results of simulations show that the algorithm achieves system higher performance and it has excellent ability to deal with the heterogeneity of clustered media server.

Research on pricing game strategy for load-balancing in VANET

In vehicle Ad-hoc netwok （VANET）, traffic load is often unevenly distributed among access points （APs）. Such load imbalance hampers the network from fully utilizing the network capacity. To alleviate such imbalance, the paper introduces a novel pricing game model. The research scene is at the intersection when the traffic light is green. As vehicles are highly mobile and the network typology changes dynamically, the paper divides the green light time into equal slots and calculates APs＇ prices with the presented pricing game in each time slot. The whole process is a repeated game model. The final equilibrium solution set is APs＇ pricing strategy, and the paper claim that this equilibrium solution set can affect vehicles＇ selection and ensure APs＇ load-balancing. Simulation results based on a realistic vehicular traffic model demonstrate the effectiveness of the game method.

A cross-layer design and ant-colony optimization based load-balancing routing protocol for ad-hoc networks

In order to periodically reassess the status of the alternate path route(APR)set and to improve the efficiency of alternate path construction existing in most current alter-nate path routing protocols,we present a cross-layer design and ant-colony optimization based load-balancing routing protocol for ad-hoc networks(CALRA)in this paper.In CALRA,the APR set maintained in nodes is aged and reas-sessed by the inherent mechanism of pheromone evaporation of ant-colony optimization algorithm,and load balance of network is achieved by ant-colony optimization combining with cross-layer synthetic optimization.The efficiency of APR set construction is improved by bidirectional and hop-by-hop routing update during routing discovery and routing maintenance process.Moreover,ants in CALRA deposit simulated pheromones as a function of multiple parameters corresponding to the information collected by each layer of each node visited,such as the distance from their source node,the congestion degree of the visited nodes,the current pheromones the nodes possess,the velocity of the nodes,and so on,and provide the information to the visiting nodes to update their pheromone tables by endowing the different parameters corresponding to different information and different weight values,which provides a new method to improve the congestion problem,the shortcut problem,the convergence rate and the heavy overheads commonly existed in existing ant-based routing protocols for ad-hoc networks.The performance of the algorithm is measured by the packet delivery rate,good-put ratio(routing overhead),and end-to-end delay.Simulation results show that CALRA performs well in decreasing the route overheads,balancing traffic load,as well as increasing the packet delivery rate,etc.

Load-Balance Policy in Two Level-Cluster File System

In this paper, we explored a load-balancing algorithm in a cluster file system contains two levels of metadata-server, primary-level server quickly distributestasks to second-level servers depending on the closest load-balancing information. At the same time, we explored a method which accurately reflect I/O traffic and storage of storage-node： computing the heat-value of file, according to which we realized a more logical storage allocation. According to the experiment result, we conclude that this new algorithm shortens the executing time of tasks and improves the system performance compared with other load algorithm.

A load-balanced minimum energy routing algorithm for Wireless Ad Hoc Sensor Networks

Wireless Ad Hoc Sensor Networks (WSNs) have received considerable academia research attention at present. The energy-constraint sensor nodes in WSNs operate on limited batteries, so it is a very important issue to use energy efficiently and reduce power consumption. To maximize the network lifetime, it is essential to prolong each individual node’s lifetime through minimizing the transmission energy consumption, so that many minimum energy routing schemes for traditional mobile ad hoc network have been developed for this reason. This paper presents a novel minimum energy routing algorithm named Load-Balanced Minimum Energy Routing (LBMER) for WSNs considering both sensor nodes’ energy consumption status and the sensor nodes’ hierarchical congestion levels, which uses mixture of energy balance and traffic balance to solve the problem of “hot spots” of WSNs and avoid the situation of “hot spots” sensor nodes using their energy at much higher rate and die much faster than the other nodes. The path router established by LBMER will not be very congested and the traffic will be distributed evenly in the WSNs. Simulation results verified that the LBMER performance is better than that of Min-Hop routing and the existing minimum energy routing scheme MTPR (Total Transmission Power Routing).

A Load-Balanced Crosspoint-Queued Switch Fabric

The fast growth of Internet has cre-ated the need for high-speed switches. Re-cently, the crosspoint-queue switch has at-tracted attention because of its scalability and high performance. However, the Cross-point-Queue switch does not perform well under non-uniform traffic. To overcome this limitation, the Load-Balanced Cross-point-Queued switch architecture has been proposed. In this architecture, a load-balance stage is placed ahead of the Cross-point-Queued stage. The load-balance stage transforms the incoming non-uniform traffic into nearly uniform traffic at the input port of the second stage. To avoid out-of-order cells, this stage employs flow-based queues in each crosspoint buffer. Analysis and simulation results reveal that under non-uniform traffic, this new switch architecture achieves a delay performance similar to that of the Out-put-Queued switch without the need for inter- nal acceleration. In addition, its throughput is much better than that of the pure cross- point-queued switch. Finally, it can achieve the same packet loss rate as the cross- point-queue switch, while using a buffer size that is only 65% of that used by the cross- point-queue switch.

Genetic-Chicken Swarm Algorithm for Minimizing Energy in Wireless Sensor Network

Wireless Sensor Network(WSN)technology is the real-time applica-tion that is growing rapidly as the result of smart environments.Battery power is one of the most significant resources in WSN.For enhancing a power factor,the clustering techniques are used.During the forward of data in WSN,more power is consumed.In the existing system,it works with Load Balanced Cluster-ing Method(LBCM)and provides the lifespan of the network with scalability and reliability.In the existing system,it does not deal with end-to-end delay and deliv-ery of packets.For overcoming these issues in WSN,the proposed Genetic Algo-rithm based on Chicken Swarm Optimization(GA-CSO)with Load Balanced Clustering Method(LBCM)is used.Genetic Algorithm generates chromosomes in an arbitrary method then the chromosomes values are calculated using Fitness Function.Chicken Swarm Optimization(CSO)helps to solve the complex opti-mization problems.Also,it consists of chickens,hens,and rooster.It divides the chicken into clusters.Load Balanced Clustering Method(LBCM)maintains the energy during communication among the sensor nodes and also it balances the load in the gateways.The proposed GA-CSO with LBCM improves the life-span of the network.Moreover,it minimizes the energy consumption and also bal-ances the load over the network.The proposed method outperforms by using the following metrics such as energy efficiency,ratio of packet delivery,throughput of the network,lifetime of the sensor nodes.Therefore,the evaluation result shows the energy efficiency that has achieved 83.56%and the delivery ratio of the packet has reached 99.12%.Also,it has attained linear standard deviation and reduced the end-to-end delay as 97.32 ms.

Trusted Cluster-Based Communication for Wireless Sensor Network Using Meta-Heuristic Algorithms

The mobile transient and sensor network’s routing algorithm detects available multi-hop paths between source and destination nodes.However,some methods are not as reliable or trustworthy as expected.Therefore,finding a reliable method is an important factor in improving communication security.For further enhancement of protected communication,we suggest a trust cluster based secure routing(TCSR)framework for wireless sensor network(WSN)using optimization algorithms.First,we introduce an efficient cluster formation using a modified tug of war optimization(MTWO)algorithm,which provides loadbalanced clusters for energy-efficient data transmission.Second,we illustrate the optimal head selection using multiple design constraints received signal strength,congestion rate,data loss rate,and throughput of the node.Those parameters are optimized by a butterfly optimal deep neural network(BO-DNN),which provides first-level security towards the selection of the best head node.Third,we utilize the lightweight signcryption to encrypt the data between two nodes during data transmission,which provides second-level security.The model provides an estimation of the trust level of each route to help a source node to select the most secure one.The nodes of the network improve reliability and security by maintaining the reliability component.Simulation results showed that the proposed scheme achieved 45.6%of delivery ratio.

An Enhanced Hadoop Heartbeat Mechanism for MapReduce Task Scheduler Using Dynamic Calibration

MapReduce is a popular program- ming model for processing large-scale datasets in a distributed environment and is a funda- mental component of current cloud comput- ing and big data applications. In this paper, a heartbeat mechanism for MapReduce Task Scheduler using Dynamic Calibration （HMTS- DC） is proposed to address the unbalanced node computation capacity problem in a het- erogeneous MapReduce environment. HMTS- DC uses two mechanisms to dynamically adapt and balance tasks assigned to each com- pute node： 1） using heartbeat to dynamically estimate the capacity of the compute nodes, and 2） using data locality of replicated data blocks to reduce data transfer between nodes. With the first mechanism, based on the heart- beats received during the early state of the job, the task scheduler can dynamically estimate the computational capacity of each node. Us- ing the second mechanism, unprocessed Tasks local to each compute node are reassigned and reserved to allow nodes with greater capacities to reserve more local tasks than their weaker counterparts. Experimental results show that HMTS-DC performs better than Hadoop and Dynamic Data Placement Strategy （DDP） in a dynamic environment. Furthermore, an en- hanced HMTS-DC （EHMTS-DC） is proposed bv incorporatin historical data. In contrastto the ＂slow start＂ property of HMTS-DC, EHMTS-DC relies on the historical computation capacity of the slave machines. The experimental results show that EHMTS-DC outperforms HMTS-DC in a dynamic environment.

Multi-path routing algorithm in WSN using an improvedparticle swarm optimization

To slove the problems of constrained energy and unbalanced load of wireless sensor network(WSN)nodes,a multipath load balancing routing algorithm based on neighborhood subspace cooperation is proposed.The algorithm adopts the improved particle swarm optimization(PSO)algorithm,takes the shortest distance and minimum energy consumption as optimization target and divides the nodes in one-hop neighborhood near the base station area into different regions.Furthermore,the algorithm designs a fitness function to find the best node in each region as a relay node and forward the data in parallel through the different paths of the relay nodes.The simulation results show that the proposed algorithm can reduce energy consumption and average end-to-end delay,balance network load and prolong network lifetime effectively.

Cooperative game theoretic load balancing scheme with admission control in IEEE 802.11 WLANs

A load-balancing scheme for IEEE 802.11 WLANs based on cooperative game theory is presented.A coalition among the access points（APs） with overlapping coverage is formed to share the network load through a game.Firstly, the candidate APs submit their load-competing strategies（i.e., the amount of user traffic they can admit in an AC/game period） to the control AP.Secondly, the control AP solves the game by the method of shapley value, which is the maximum traffic allocated to each AP in an AC/game period.Finally, the game is repeated periodically to distribute the traffic load among the APs.Simulation results show that the proposed game can balance the network load effectively compared with the IEEE 802.11 standard balancing solution.

ANALYSIS AND DESIGN OF THE STABLE PARALLEL PACKET SWITCH

This paper analyzes the Parallel Packet Switch(PPS) architecture and studies how to guarantee its performance. Firstly a model of Stable PPS (SPPS) is proposed. The constraints of traffic scheduling algorithms, the number of switching layers and internal speedup, for both bufferless and buffered SPPS architecture, are theoretically analyzed. Based on these results, an example of designing a scalable SPPS with 1.28T capacity is presented, and practical considerations on implementing the scheduling algorithm are discussed. Simulations are carried out to investigate the validity and delay performance of the SPPS architecture.

Parallel Switch System with QoS Guarantee for Real-Time Traffic

This paper studies the load-balancing algorithm and quality of service （QoS） control mechanism in a 320Gb/s switch system, which incorporates four packet-level parallel switch planes. Eight priorities for both unicast and multicast traffic are implemented, and the highest priority with strict QoS guarantee is designed for real-time traffic. Through performance analysis under multi-prlorlty burst traffic, we demonstrate that the load-balancing algorithm is efficient, and the switch system not only provides excellent performance to real-time traffic, but also efficiently allocates bandwidth among other traffic of lower priorities. As a result, this parallel switch system is more scalable towards next generation core routers with QoS guarantee, as well as ensures in-order delivery of IP packets.