Load Balanced Clustering Research Articles

Optimized and load balanced clustering for wireless sensor networks to increase the lifetime of WSN using MADM approaches

The power utilization has verified as a major problem in wireless sensor networks (WSNs). Many researchers have provided efficient solutions for power utilization. Clustering is one of them that helps in topology control and ensures efficient power utilization. However, the clustering method should be effective that could obtain the best clusters. Comprehensive evolution of clustering protocols for the lifetime of sensor nodes is a unique approach to the total enhancement of the lifetime of WSNs. There are many conflicting factors that affect the efficiency of clustering, i.e. distance between CH and the base station, distance form node to cluster head (CH), maximum residual energy of CHs, etc. The coordination among these factors has a capability to insure the optimal power utilization by reducing power consumption and load balancing among the nodes and CHs. In this paper, we have considered total sixteen such factors and made coordination among them to select the best CHs. Multiple attribute decision-making methods are used to choose best set of CHs from the available alternatives that can fulfill the condition of coordination efficiently. The experimental results validate that the coordination among these sixteen factors put up one of the best demonstration for choosing best CHs.

An Energy Efficient and Load Balancing Clustering Scheme for Wireless Sensor Network (WSN) Based on Distributed Approach

One of the most critical problem and challenging in Wireless Sensor Networks (WSNs) is to reduce energy consumption in order to prolong network lifetime of WSNs. Clustering technique is one of the techniques which have been used to provide energy efficiency.. However Most of clustering schemes select the cluster head either randomly without considered important parameters or based on centralized approach by utilizing the base station which can affect the network scalability. In addition, single hop communication is used by CHs to forward their sensed data to the CH which lead to increases energy consumption of CHs in large scale network. Therefore in this paper clustering scheme is proposed based on distributed approach, different parameters are consider for cluster head selection and also for multi-hop communication. Results shows that the proposed scheme have better performance in term of energy consumption and number of alive sensor nodes

Prediction-Based and Locality-Aware Task Scheduling for Parallelizing Video Transcoding Over Heterogeneous MapReduce Cluster

MapReduce is a popular programming model in cloud computing to deal with the high computational task, such as video transcoding. It splits the video (task) into multiple segments (subtasks) and transcodes them in parallel in cluster. Due to the complexity of video transcoding and the poor performance of heterogeneous MapReduce cluster, scheduling these subtasks to minimize the total transcoding time is still a challenge. In this paper, we propose a prediction-based and locality-aware task scheduling (PLTS) method for parallelizing video transcoding over heterogeneous MapReduce cluster. First, we analyze video decoding and encoding technologies and predict the segment transcoding complexity, which can provide a foundational base for the following scheduling. Second, we attempt to schedule subtasks on machines that contain the related input data, which are referred to as data locality, so as to reduce large-scale data movement and data transfer during the mapping phase. Third, we formulate the scheduling as a job shop scheduling problem and propose a heuristic PLTS algorithm. It combines the benefits of two traditional heuristic scheduling algorithms, Max–Min and Min–Min, to make load balancing in cluster and short the total transcoding time. The experimental results also show the efficiency of our algorithm.

Heuristic Based Coverage Aware Load Balanced Clustering in WSNs and Enablement of IoT

Integration of heterogeneous communication systems is the major objective of Future Internet. Heterogeneous systems such as wired and wireless assert the concept of Internet of Things (IoT). Communication standard protocols based global network of identical addressable interconnected resources conferred to IoT. In this article, it is described how wireless sensor networks (WSN) are one of the keys enabling the IoT. Clustering in WSNs plays an important role since it allows altering the network topology and helps in balancing of traffic loads. Load balancing improves the lifespan and scalability of the WSNs. Here, a heuristic approach, coverage aware load balanced clustering, has been developed in order to increase the lifespan of the WSNs. Moreover, simulation results are demonstrated for a sample network and the results show that the network lifespan has increased and simultaneously energy consumption is reduced compared to the other existing techniques.

3D grid clustering scheme for wireless sensor networks

Advances in wireless sensor networks (WSNs) technology have made a remarkable impact in the society. Since WSNs solely depend on the battery power of the tiny sensor nodes, maximizing the lifespan of the wireless sensor networks is a critical issue in the design and analysis of WSNs. Cluster-based routing prolongs the lifetime of the network. Most of the existing cluster-based routing protocols are applicable for two-dimensional network region. However, 2D deployment is not applicable if sensors are deployed in 3D space such as atmosphere or ocean. The proposed work presents a 3D grid clustering scheme which considers the deployment area as three-dimensional grids. The algorithm constructs optimal and load-balanced clusters at each grid cell with an initial cluster head (CH). As early energy depletion is a major design issue in clustering protocols, the proposed algorithm provides local remedy by using substitution CHs for replacing energy suffering cluster heads and nodes are independently distributed among clusters. The obtained simulation results show that our proposed scheme considerably outpaces the other existing schemes in terms of prolonging the lifespan of WSNs

Energy Efficient Mobile Data Collection in Three Layered Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are widely used for different applications such as monitoring and surveillance in both civilian and military domains. Mobile data collection has significant impact on the performance of WSN. Therefore it is essential to have mechanisms to improve it. In this paper we proposed a three-layered approach that is used to collect data in wireless sensor network. The three layers include sensor layer which is made up of collection of sensors, cluster head layer which is made up of all cluster heads, and the mobile collector layer. The proposed approach also takes care of distributed load balanced clustering. The aim of the proposed approach is to achieve scalable solution with low data collection latency and energy efficiency. While sensors are organized into clusters the distributed algorithm is applied so as to ensure that the efficiency of the network is achieved. We built a prototype application using Microsoft .NET which simulates the proposed approach. The empirical results revealed that the proposed layered approach is energy efficient, scalable and achieves low latency in data collection.

Resource scheduling optimisation algorithm for containerised microservice architecture in cloud computing

Currently, the containerised microservice architecture has aroused great concern. The single application is developed as a suite of small services to facilitate the application deployment, expansion and management. The traditional scheduling of microservice tends to focus on the load balancing of cluster, ignoring the quality of service (QoS). Therefore, this paper proposes a prediction model of component relevance, by adopting the optimised artificial bee colony algorithm (OABC) on the containerised microservice scheduling. Different assessment strategies are adopted according to the differences in the correlation among components. Two-point crossover operator is introduced to improve the exploration ability of the algorithm. The mutation operator is added to enhance the local search ability, and the mutation probability is set to the dynamic value which varies with the number of iterations to speed up the convergence of the algorithm. The experimental results show that the OABC is preferable to the artificial bee colony algorithm (ABC) and the greedy algorithm as to the cluster load balancing and service response time aspects.

Priority Based Multi Sen Car Technique in WSN

In Wireless sensor network (WSN), Clustering is an efficient mechanism used to overcome energy capability problem, routing and load balancing. This paper addresses energy utilization and load distribution between the clusters. The load balanced clustering algorithms (LBC) used to decrease the energy utilization and distribute load into clusters. The SenCar uses multi-user multi-input and multi-output (MU-MIMO) technique which is introduced the multi SenCar to collects the information from each cluster heads and upload the data in the base station. This method achieves more than 50 percent energy saving per node, 80 percent energy saving on cluster heads and also achieves less data collection time compared to the existing system.

Design and Analysis of Clustering Routing Protocol in Wireless Sensor Network of Frozen Food

In order to address the issues of uneven energy dissipation and uniform cluster coverage in wireless sensor networks (WSNs) of frozen food, we developed a load balancing and uniform coverage clustering (LBUCC) algorithm to find an efficient way to generate clusters. Considering node density and the coverage radius of cluster heads, the nearest neighbor clustering algorithm was adopted to cluster the sensor network. On the basis of the number of neighbors and the residual energy of nodes, the LBUCC algorithm ensures the equal distribution of cluster head responsibility among sensor nodes and performs well in periodic data gathering with selected cluster head. As the storage and distribution of frozen food is frequent, the clustering strategy was proposed for dynamic topology in this paper. The LBUCC algorithm was compared with LEACH-C and DHAC algorithms which are well-known in using centralized control algorithm to select cluster head. The simulation results demonstrate that the LBUCC algorithm has longer network lifetime and uniform coverage than the clustering protocols LEACH-C and DHAC do.

Reprint of “LBBSRT: An efficient SDN load balancing scheme based on server response time”

The response time is the most important factor determining user experiences in the service provision model involving server clusters. However, traditional server cluster load balancing scheme are limited by the hardware conditions, and cannot completely exploit the server response times for load balancing. In order to effectively resolve the traditional load balancing schemes, we propose a load balancing scheme based on server response times by using the advantage of SDN flexibility, named LBBSRT. Using the real-time response time of each server measured by the controller for load balancing, we process user requests by obtaining an evenly balanced server loads. Simulation experiments show that our scheme exhibits a better load balancing effect and process requests with a minimum average server response times. In addition, our scheme is easy to implement, and exhibits good scalability and low cost characteristics.

Load-Balanced Clustering Scheme with Sink Mobility for Heterogeneous Wireless Sensor Networks

A hybrid method is proposed to balance the energy consumption of the deployed sensors and that reduces energy holes near the sink by balancing the communication load in this paper. Hilbert Curve have been considered to optimize the network lifetime, a pre-clustering, clustering and sink mobility for fixed sensing points. The cluster heads are selected on the basis of link cost mainly consisting of the residual energy and distance to the sink parameters. An extensive simulations were performed with different network scenarios of the proposed work to enhance the network lifetime. In comparison with the static scheme, the load-balanced energy with sink mobility achieves a uniform distribution of clusters, extends the lifetime of the network and avoids the energy hole problem.

Using a recurrent artificial neural network for dynamic self-adaptation of cluster-based web-server systems

To process huge requests issued from web users, web servers often set up a cluster using switches and gateways where a switch directs users' requests to some gateway. Each gateway, which is connected to some servers, is considered for processing a specific type of request such as fttp or http service. When servers of a gateway are saturated and the gateway is not able to process more requests, adaptation is performed by borrowing a server from another gateway. However, such a reactiveadaptation causes some problems. However, due to problem of the reactive techniques, predictive ones have been paid attention. While a reactive adaptation aims to redress the system after incurring a bottleneck, a predictiveadaptation tries to prevent the system from entering the bottleneck. In this article, we improved our previous predictive framework using a Recurrent Artificial Neural Network (RANN) called Nonlinear Autoregressive with eXogenous (external) inputs (NARX). We employed our new framework for adaptation of a web-based cluster where each cluster is meant for a specific service and self-adaptation is used for load balancing clusters. To show the improvement, we used the case study presented in our previous study.

An Estimation of Distribution Algorithm Based Dynamic Clustering Approach for Wireless Sensor Networks

The design of energy efficiency is a very challenging issue for wireless sensor networks (WSNs). Clustering provides an effective means of tackling the issue. It could reduce energy consumption of the nodes and prolong the network lifetime. However, cluster heads deplete more energy since they bear great load of receiving, aggregation and transmission data than sensor nodes in WSNs. Therefore, the load-balanced clustering is a most significant problem for WSNs with unequal load of the sensor nodes but it is known to be an NP-hard problem. In this paper, we introduce a new model for this problem in which the objective function is to maximize the overall minimum lifetime of the cluster heads. To solve this model, we propose a novel estimation of distribution algorithm based dynamic clustering approach (EDA-MADCA). In EDA-MADCA, a new vector encoding is introduced for representing a complete clustering solution and a probability matrix model is constructed to guide the individual search. In addition, EDA-MADCA merges the EDA based exploration and the local search based exploitation within the memetic algorithm framework. A minimum-lifetime-based local search strategy is presented to avoid invalid search and enhance the local exploitation of the EDA. Experiment results demonstrate that EDA-MADCA can prolong network lifetime, it outperforms the existing DECA algorithm in terms of various performance metrics.

Research on key technologies of technological service and management based on cluster load balancing

Detection of similarity in scientific and technical data finds repetition rate of contents in project notification form for technology plan submitted by each unit. It is conducted through comparison between the same batch and previous batches on declaration book from each declaration unit. Furthermore, it affords early warning, which is an important link in normativeness for project management. Usually, cluster system based on Web servers is adopted to reduce data processing time, where loading balancing of each node is a research hotspot. This paper proposed auto-adaptive load balancing (AALB) algorithm to accomplish load balancing for each node in the cluster by predicting request arrival rate and request size and rapidly adjusting parameter value. The experiment proves that data block distribution is more balanced in each node using balancing strategy for AALB, with less time compared with other load balancing.

HSCA: a novel harmony search based efficient clustering in heterogeneous WSNs

Clustering objective reasons scalability, fault tolerance, data aggregation or fusion, load balancing of cluster heads, stabilized network topology, maximal network lifetime, increased connectivity, reduced routing delay, collision avoidance and utilizing sleeping schemes in wireless sensor networks. Load balanced clustering effectively organize the network into a connected hierarchy. Clustering is a discrete problem that can have more than one solution under different operating constraints. In this scenario, meta-heuristic algorithms are found suitable because they give set of solutions in acceptable time constraints. In the literature, several analytical and meta-heuristic approaches have been developed for load balanced clustering. In this paper, a novel harmony search based energy efficient load balanced clustering algorithm is presented and it is tested on a large sample network. Results demonstrated that the proposed approach has faster convergence and gives reliable and efficient load balanced clustering as compared to conventional harmony search algorithm (HSA) and several other methods in the literature. Moreover, the robustness of the proposed approach is also verified for different cases of fixed and variable parameters of HSA.

Improving Performance of Mobile Ad Hoc Network Using Clustering Schemes

Mobile ad hoc network become nowadays more and more used in different domains, due to its flexibility and low cost of deployment. However, this kind of network still suffering from several problems as the lack of resources. Many solutions are proposed to face these problems, among these solutions there is the clustering approach. This approach tries to partition the network into a virtual group. It is considered as a primordial solution that aims to enhance the performance of the total network, and makes it possible to guarantee basic levels of system performance. In this paper, we study some schemes of clustering such as Dominating-Set-based clustering, Energy-efficient clustering, Low-maintenance clustering, Load-balancing clustering, and Combined-metrics based clustering.

Clubber: removing the bioinformatics bottleneck in big data analyses.

With the advent of modern day high-throughput technologies, the bottleneck in biological discovery has shifted from the cost of doing experiments to that of analyzing results. clubber is our automated cluster-load balancing system developed for optimizing these “big data” analyses. Its plug-and-play framework encourages re-use of existing solutions for bioinformatics problems. clubber’s goals are to reduce computation times and to facilitate use of cluster computing. The first goal is achieved by automating the balance of parallel submissions across available high performance computing (HPC) resources. Notably, the latter can be added on demand, including cloud-based resources, and/or featuring heterogeneous environments. The second goal of making HPCs user-friendly is facilitated by an interactive web interface and a RESTful API, allowing for job monitoring and result retrieval. We used clubber to speed up our pipeline for annotating molecular functionality of metagenomes. Here, we analyzed the Deepwater Horizon oil-spill study data to quantitatively show that the beach sands have not yet entirely recovered. Further, our analysis of the CAMI-challenge data revealed that microbiome taxonomic shifts do not necessarily correlate with functional shifts. These examples (21 metagenomes processed in 172 min) clearly illustrate the importance of clubber in the everyday computational biology environment.

Package balancing k-means algorithm for physical distribution

As the express delivery service is flourishing, it is a challenge to assign a set of tasks to a set of carriers, as it has to aggregate the neighbour tasks to a carrier and keep the loads of carriers balanced simultaneously. K-means clustering algorithm is an effective way to split the delivery tasks. However, it cannot solve the load balancing problem. This paper proposes an extended clustering algorithm - package balancing k-means. It adding the weight metric to the standard k-means algorithm to achieve load balancing. Analyses and experiments show that package balancing k-means can solve the load balancing clustering problem efficiently. Moreover, experiments show that the extended algorithm can achieve faster convergence.

Package balancing k-means algorithm for physical distribution

As the express delivery service is flourishing, it is a challenge to assign a set of tasks to a set of carriers, as it has to aggregate the neighbour tasks to a carrier and keep the loads of carriers balanced simultaneously. K-means clustering algorithm is an effective way to split the delivery tasks. However, it cannot solve the load balancing problem. This paper proposes an extended clustering algorithm - package balancing k-means. It adding the weight metric to the standard k-means algorithm to achieve load balancing. Analyses and experiments show that package balancing k-means can solve the load balancing clustering problem efficiently. Moreover, experiments show that the extended algorithm can achieve faster convergence.

Optimal load balanced clustering in homogeneous wireless sensor networks

SummaryBalancing the load among sensor nodes is a major challenge for the long run operation of wireless sensor networks. When a sensor node becomes overloaded, the likelihood of higher latency, energy loss, and congestion becomes high. In this paper, we propose an optimal load balanced clustering for hierarchical cluster‐based wireless sensor networks. We formulate the network design problem as mixed‐integer linear programming. Our contribution is 3‐fold: First, we propose an energy aware cluster head selection model for optimal cluster head selection. Then we propose a delay and energy‐aware routing model for optimal inter‐cluster communication. Finally, we propose an equal traffic for energy efficient clustering for optimal load balanced clustering. We consider the worst case scenario, where all nodes have the same capability and where there are no ways to use mobile sinks or add some powerful nodes as gateways. Thus, our models perform load balancing and maximize network lifetime with no need for special node capabilities such as mobility or heterogeneity or pre‐deployment, which would greatly simplify the problem. We show that the proposed models not only increase network lifetime but also minimize latency between sensor nodes. Numerical results show that energy consumption can be effectively balanced among sensor nodes, and stability period can be greatly extended using our models.