Load Balancing Rate Research Articles

Load Balancing Using Dynamic Ant Colony System Based Fault Tolerance in Grid Computing

Load balancing is often disregarded when implementing fault tolerance capability in grid computing. Effective load balancing ensures that a fair amount of load is assigned to each resource, based on its fitness rather than assigning a majority of tasks to the most fitting resources. Proper load balancing in a fault tolerance system would also reduce the bottleneck at the most fit resources and increase utilization of other resources. This paper presents a fault tolerance algorithm based on ant colony system, that considers load balancing based on resource fitness with resubmission and checkpoint technique, to improve fault tolerance capability in grid computing. Experimental results indicated that the proposed fault tolerance algorithm has better execution time, throughput, makespan, latency, load balancing and success rate.

Reliable and load balance-aware multi-controller deployment in SDN

Software Defined Networking (SDN) provides flexible network management by decoupling control plane and data plane. However, such separation introduces the issues regarding the reliability of the control plane and controller load imbalance in the distributed SDN network, which will cause the low network stability and the poor controller performance. This paper proposes Reliable and Load balance-aware Multi-controller Deployment (RLMD) strategy to address the above problems. Firstly, we establish a multiple-controller network model and define the relevant parameters for RLMD. Then, we design the corresponding algorithms to implement this strategy. By weighing node efficiency and path quality, Controller Placement Selection (CPS) algorithm is introduced to explore the reliable deployments of the controllers. On this basis, we design Multiple Domain Partition (MDP) algorithm to allocate switches for controllers according to node attractability and controller load balancing rate, which could realize the reasonable domain planning. Finally, the simulations show that, compared with the typical strategies, RLMD has the better performance in improving the reliability of the control plane and balancing the distribution of the controller loads.

Hybrid gradient descent cuckoo search (HGDCS) algorithm for resource scheduling in IaaS cloud computing environment

Resource scheduling is a procedure for the distribution of resources over time to perform a required task and a decision making process in cloud computing. Optimal resource scheduling is a great challenge and considered to be an NP-hard problem due to the fluctuating demand of cloud users and dynamic nature of resources. In this paper, we formulate a new hybrid gradient descent cuckoo search (HGDCS) algorithm based on gradient descent (GD) approach and cuckoo search (CS) algorithm for optimizing and resolving the problems related to resource scheduling in Infrastructure as a Service (IaaS) cloud computing. This work compares the makespan, throughput, load balancing and performance improvement rate of existing meta-heuristic algorithms with proposed HGDCS algorithm applicable for cloud computing. In comparison with existing meta-heuristic algorithms, proposed HGDCS algorithm performs well for almost in both cases (Case-I and Case-II) with all selected datasets and workload archives. HGDCS algorithm is comparatively and statistically more effective than ACO, ABC, GA, LCA, PSO, SA and original CS algorithms in term of problem solving ability in accordance with results obtained from simulation and statistical analysis.

EASM: Efficiency-aware switch migration for balancing controller loads in software-defined networking

Distributed multi-controller deployment is a promising method to achieve a scalable and reliable control plane of Software-Defined Networking (SDN). However, it brings a new challenge for balancing loads on the distributed controllers as the network traffic dynamically changes. The unbalanced load distribution on the controllers will increase response delay for processing flows and reduce the controllers'throughput. Switch migration is an effective approach to solve the problem. However, existing schemes focus only on the load balancing performance but ignore migration efficiency, which may result in high migration costs and unnecessary control overheads. This paper proposes Efficiency-Aware Switch Migration (EASM) to balance the controllers'loads and improve migration efficiency. We introduce load difference matrix and trigger factor to measure load balancing on controllers. We also introduce the migration efficiency problem, which considers load balancing rate and migration cost simultaneously to optimally migrate switches. We propose EASM to efficiently solve to the problem. The simulation results show that EASM outperforms baseline schemes by reducing the controller response time by about 21.9%, improving the controller throughput by 30.4% on average, maintaining good load balancing rate, low migration costs and migration time, when the network scale changes.

Load balancing for mitigating hotspot problem in wireless sensor network based on enhanced diversity pollen

ABSTRACTThis paper proposes a load balancing to mitigate the hot spot problem in wireless sensor network (WSN), based on enhancing diversity pollens in Flower pollination algorithm (FPA). The hotspot problem in the WSN is spots near base station (BS) that consume more energy and drain out energy more quickly than other nodes farther from the BS. The spots near BS are hotter than other places due to the heavy traffic from the cluster members and other cluster heads (CH) for relaying data to BS. Enhancing diversity pollens for FPA is one of the solutions to deal smoothly with trapping local extrema for solving the hotspot problem. To evaluate the proposed algorithm, we firstly use a set of benchmark functions to test performance quality, and secondly, we deal with the load balancing problem in WSN. The results compared with some metaheuristic approaches and other related clustering algorithms demonstrate that the proposed method performs better than the others regarding various performance metrics such as the load balancing, execution time, energy consumption, and convergence rate.

A Load-Balanced Multicast Routing Algorithm Using Diversity Rate in CWMNs

Cognitive wireless mesh networks (CWMNs) were developed to improve the utilization ratio of licensed spectrum. Since the spectrum opportunities for users vary over time and location, enhancing the spectrum effectiveness is a goal and also a challenge for CWMNs. Multimedia applications have recently generated much interest in CWMNs supporting quality-of-service (QoS) communications. Multicast routing and spectrum allocation is an important challenge in CWMNs. In this paper, we study to design an effective multicast routing algorithm based on diversity rate with respect to load balancing and the number of transmissions for CWMNs. In this paper, a load balancing wireless links weight computing function and computing algorithm based on diversity rate (DRLB) are proposed, and a load balancing channel and rate allocating algorithm based on diversity rate (DR2CS) is proposed. On this basis, a load balancing joint multicast routing, channel and rate allocation algorithm based on diversity rate with QoS constraints for CWMNs (LMR2D) is proposed. Balancing the load of node and channel, and minimizing the number of transmissions of multicast tree are the objectives of LMR2D. Firstly, LMR2D computes the weight of wireless links using DRLB and Dijkstra for constructing the load balancing multicast tree step by step. Secondly, LMR2D uses DR2CS to allocate channel and rate of channel to links which is based on the wireless broadcast advantage. Simulation results show that LMR2D can achieve the expected goal. It can not only balance the load of node and channel, but also need lower number of transmissions for multicast tree.

Schematic Design of Comprehensive Economic Operation and Optimization Analysis System for Low-voltage Smart Distribution Area

Aimed at the problems that are generally existed in 10kV distribution network area, such as low automation and inaccessible remote monitoring, etc. An advanced distribution economic operation and optimization analysis system was developed based on smart area, which included smart distribution area, IDTT distribution terminal, communication network and main station construction. In aspect of smart distribution area construction, the original distribution area was updated and rebuilt, and based on which, the basic monitoring and analysis function of distribution area was achieved, moreover, a new type of smart distribution area based on ZJP-II-type of distribution terminal was designed. Its design principle and the main station construction scheme were discussed in details, and its main functions were summarized. Based on realtime monitoring, the whole system can make analysis on the area load rate, reliability rate, balance rate, and load increase trend, etc. as well as make timely response to the faults occurred in area, thus can achieve intelligent and refinement management on distribution area and provide guidance of the distribution area updating and rebuilding for the operation management units.

Schematic Design of Comprehensive Economic Operation and Optimization Analysis System for Low-voltage Smart Distribution Area

Aimed at the problems that are generally existed in 10kV distribution network area, such as low automation and inaccessible remote monitoring, etc. An advanced distribution economic operation and optimization analysis system was developed based on smart area, which included smart distribution area, IDTT distribution terminal, communication network and main station construction. In aspect of smart distribution area construction, the original distribution area was updated and rebuilt, and based on which, the basic monitoring and analysis function of distribution area was achieved, moreover, a new type of smart distribution area based on ZJP-II-type of distribution terminal was designed. Its design principle and the main station construction scheme were discussed in details, and its main functions were summarized. Based on realtime monitoring, the whole system can make analysis on the area load rate, reliability rate, balance rate, and load increase trend, etc. as well as make timely response to the faults occurred in area, thus can achieve intelligent and refinement management on distribution area and provide guidance of the distribution area updating and rebuilding for the operation management units.

A Switch Migration-Based Decision-Making Scheme for Balancing Load in SDN

Elastic scaling and load balancing with efficient switch migration are critical to enable the elasticity of software-defined networking (SDN) controllers, but learning how to improve migration efficiency remains a difficult problem. To address this issue, a switch migration-based decision-making (SMDM) scheme is put forward that could be made aware of the load imbalance by a switch migration trigger metric; the migration efficiency model for this scheme is built to make a tradeoff between migration costs and the load balance rate. An efficiency-aware switch migration algorithm based on greedy method is designed to utilize the migration efficiency model and thus guide the choice of possible migration actions. We implement a proof of the scheme and present a numerical evaluation using Mininet emulator to demonstrate the effectiveness of our proposal.

Topological Embedding Feature Based Resource Allocation in Network Virtualization

Virtualization provides a powerful way to run multiple virtual networks on a shared substrate network, which needs accurate and efficient mathematical models. Virtual network embedding is a challenge in network virtualization. In this paper, considering the degree of convergence when mapping a virtual network onto substrate network, we propose a new embedding algorithm based on topology mapping convergence-degree. Convergence-degree means the adjacent degree of virtual network’s nodes when they are mapped onto a substrate network. The contributions of our method are as below. Firstly, we map virtual nodes onto the substrate nodes with the maximum convergence-degree. The simulation results show that our proposed algorithm largely enhances the network utilization efficiency and decreases the complexity of the embedding problem. Secondly, we define the load balance rate to reflect the load balance of substrate links. The simulation results show our proposed algorithm achieves better load balance. Finally, based on the feature of star topology, we further improve our embedding algorithm and make it suitable for application in the star topology. The test result shows it gets better performance than previous works.

Predictive and content-aware load balancing algorithm for peer-service area based IPTV networks

Load balancing is a crucial factor in IPTV delivery networks. Load balancing aims at utilizing the resources efficiently, maximizing the throughput, and minimizing the request rejection rate. The peer-service area is the recent architecture for IPTV delivery networks that overcomes the flaws of the previous architectures. However, it still suffers from the load imbalance problem. This paper investigates the load imbalance problem, and tries to augment the peer-service area architecture to overcome this problem. To achieve the load balancing over the proposed architecture, we suggest a new load-balancing algorithm that considers both the expected and the current load of both contents and servers. The proposed load-balancing algorithm consists of two stages. The first stage is the contents replication according to their expected load, while the second stage is the content-aware request distribution. To test the effectiveness of the proposed algorithm, we have compared it with both the traditional Round Robin algorithm and Cho algorithm. The experimental results depict that the proposed algorithm outperforms the two other algorithms in terms of load balance, throughput, and request rejection rate.

Caching Scheme Research Based on Unstructured Peer-to-Peer Network

For Unstructured P2P (Peer-to-Peer) networks leading to the high-load of “high-degree peers”and the quick deletion of rarely requested contents. This paper proposed a new cache scheme MFU (Most Frequently Used) that replaces the cache on the basis of the number of request messages per unit of time. This algorithm aims to achieve the high-degree peers to delete the popular contents and low-degree ones hold the unpopular contents by setting the threshold properly. Moreover, The author also used a simulation to show the effectivity of the new algorithm. With the traditional LRU, LFU and ARP scheme compared to prove that the advantage of the new replacement policy in load balance and hit rate.

Optimization of Network Fast Flow Based on Anti-ant Colony Optimization

There are fewer research results of optimizing the network fast flow by relative optimal path in the links. This paper presents a conception of link capacity based on the foundation of concept convergence expectation and convergence gradient. Based on the convergence gradient, the relatively optimal path is used to analyze problem in a reverse perspective of ant colony algorithm, which is also called anti-ant colony algorithm. According to the pheromone strength and rate of flow, the link makes a second judgment to decide the route. In this way, the load balance rate of the flow achieved and the congestion of the network can be avoided. Experimental results show that the method achieved link load balancing and the network resources utilization coefficient can be improved.

Production balance of ship erection

A network plan model of ship erection was established based on the network planning technology and the work-package breakdown system. The load-oriented production control method was introduced to build up a throughput diagram model thus it is possible to describe the ship erection process numerically. Based on the digitalized models some cases of production balance of ship erection were studied and three balance indexes were put forward, they are the load balance rate, the input manpower balance rate and the maximum gantry crane operating times. Such an analytic method based on the balance evaluation is the important foundation for digitization and intelligentization of shipyard production management.