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In the current enterprise competition, the core of the management information system is the financial management information system. Building a more comprehensive and efficient financial management information system can effectively establish a more efficient innovative enterprise and promote the transformation and development of the enterprise. This paper first designs a set of financial management system templates, including general ledger, accounts receivable, and accounts payable. Second, according to the characteristics of wireless sensor network technology, this paper proposes a financial management system based on the wireless sensor network and designs a load balancing topology control algorithm (LHTCA algorithm), which balances the energy consumption of network nodes and helps to improve the network life cycle. Finally, the management system is tested and validated. The test results show that the financial management system can realize the electronicization of financial data and paperless vouchers, improve the efficiency of financial work, and promote the sharing and integration of resources. [ABSTRACT FROM AUTHOR]

A novel random biased-genetic algorithm (NRB-GA) load-balancing algorithm that exhibits the characteristics of both genetic algorithms and biased random algorithms is designed and developed to improve the processing time and response time metrics of the cloud computing environment. The NRB-GA is designed to discover a virtual machine with fewer loads by applying a genetic algorithm with a fitness function that is inversely proportional to the average load over a period of time for each virtual machine and with biased parent selection to maximize the fitness values of offspring. The developed NRB-GA load-balancing algorithm is evaluated by analysing its performance for various simulated scenarios in a cloud computing environment with different user bases and data center configurations. The analysis of the experimental results of NRB-GA indicates that the average response time is reduced by 27.22%, 21.15%, and 22.34%, and the processing time is reduced by 25.73%, 16.14%, and 18.82% for one, two, and three data centers, respectively. It is evident that the proposed NRB-GA algorithm for load balancing outperforms other existing algorithms significantly. [ABSTRACT FROM AUTHOR]

To realize load balancing of cloud computing platforms in big data processing, the method of finding the optimal load balancing physical host in the algorithm cycle is adopted at present. This optimal load balancing strategy that overly focuses on the current deployment problem has certain limitations. It will make the system less efficient and the user's waiting time unnecessarily prolonged. This paper proposes a task assignment method for long-term resource load balancing of cloud platforms based on artificial intelligence and big data (TABAI). The maximum posterior probability for each physical host is calculated using Bayesian theory. Euler's formula is used to calculate the similarity between the host with the largest posterior probability and other hosts as a threshold. The hosts are classified according to the threshold to determine the optimal cluster and then form the final set of candidate physical hosts. It improves the resource utilization and external service capability of the cloud platform by combining cluster analysis with Bayes' theorem to achieve global load balancing in the time dimension. The experimental results show that: TABAI has a smaller processing time than the traditional load balancing multi-task assignment method. When the time is >600 s, the standard deviation of TABAI decreases to a greater extent, and it has stronger external service capabilities. [ABSTRACT FROM AUTHOR]

Cloud services are accessed from different geographical locations where client migration or switching from one server to another based on the loads is a common phenomenon. One of the most critical challenges the cloud data centers face is managing the loads over geographically dispersed data centers and their virtual machines (VMs). VMs need to be balanced with the varied loads or dynamics of traffic. There are possibilities of the highest loads to be tolerated by the VMs over the cloud servers without crashing. Load balancing issues are managed by load balancing algorithms. Load balancing algorithms have varied issues of efficiency due to certain parameters like the capability of the lowest resource utilization, response time, higher overhead while checking the idle or normal nodes, and many others. Throttled load balancing algorithm manages loads of the virtual machines by dividing the virtual machines into two segments, that is, "available" and "free." To do this, the throttled algorithm uses a single component to assign the virtual machines and other tasks. The throttled algorithm utilizes only the first VMs available, the next, and so on. These strategic issues most often degrade the performance of the applied load balancing algorithm. Such issues create a curiosity to enhance this algorithm's performance for efficiently managing the dynamic loads of the cloud VMs. This research paper proposes a component-based throttled load balancing algorithm with VM reader, free VM holder, and free VM manager components. The VM reader component reads all available VMs. The free VM component holds free VMs temporarily until they are moved to the free VM manager component. For the performance test, the cloud analyst simulation tool was used. Based on the comparative analysis with the other five popularly used load balancing algorithms, the component-based algorithm's performance is significantly enhanced. The proposed algorithm resulted in 325.30-microsecond response time and 27.12-microsecond processing time by the closest data center service broker policy. The newly proposed "component-based throttled load balancing algorithm" is found to be better than the existing throttled algorithm and the other five selected algorithms in terms of response time, processing time, and resource utilization. [ABSTRACT FROM AUTHOR]

The central network architecture takes the information content as the processing object and can better meet the user access needs. However, the traditional network node load balancing and caching strategy has been difficult to adapt to the development of the central network architecture. Therefore, this article proposed a load balancing and cache management strategy based on the central network in order to provide a reference for the architecture and application of the central network. Based on the analysis of the existing network architecture, this article expounded the networking form and characteristics of the central network system. By analyzing the load scheduling requirements of network nodes, combined with the transmission characteristics of resource requests in the central network, a cache node load balancing method suitable for the requirements of the central network was proposed based on the existing load balancing algorithms. In addition, by analyzing the cache management strategy and its impact on the performance of the network system, a cache node information forwarding model with resource requests as the object in the central network environment was given. Finally, through experimental test and comparative analysis, the results showed that the method proposed in this article had certain feasibility and effectiveness. Compared with other typical strategies, the load balancing and caching strategy proposed in this article can better meet the user needs of the central network. [ABSTRACT FROM AUTHOR]

Load balancing is of great significance to extend the longevity of wireless sensor networks, due to the inherent imbalanced energy overhead in such networks. However, existing solutions cannot balance the load distribution in partially disconnected wireless sensor networks. For example, if a network is partitioned into several segments with different area sizes, some areas have much more traffic load than other areas. In this article, we propose a load-balanced routing scheme, which aims to balance energy consumption within each segment and among different segments. First, we adopt unequal transmission distances to build initial routing for intrasegment load balancing. Second, we adopt the genetic algorithm to build extra routing between different segments for intersegment load balancing. The unique character of our work is twofold. On one hand, we investigate partitioned wireless sensor networks where there are several isolated segments. On the other hand, we pursue load balancing from a global perspective rather than from a local one. Some simulations verify the effectiveness and the advantages of our scheme in terms of extra deployment cost, system longevity, and load balancing degree. [ABSTRACT FROM AUTHOR]

An important challenge facing cloud computing is how to correctly and effectively handle and serve millions of users' requests. Efficient task scheduling in cloud computing can intuitively affect the resource configuration and operating cost of the entire system. However, task and resource scheduling in a cloud computing environment is an NP-hard problem. In this paper, we propose a three-layer scheduling model based on whale-Gaussian cloud. In the second layer of the model, a whale optimization strategy based on the Gaussian cloud model (GCWOAS2) is used for multiobjective task scheduling in a cloud computing which is to minimize the completion time of the task via effectively utilizing the virtual machine resources and to keep the load balancing of each virtual machine, reducing the operating cost of the system. In the GCWOAS2 strategy, an opposition-based learning mechanism is first used to initialize the scheduling strategy to generate the optimal scheduling scheme. Then, an adaptive mobility factor is proposed to dynamically expand the search range. The whale optimization algorithm based on the Gaussian cloud model is proposed to enhance the randomness of search. Finally, a multiobjective task scheduling algorithm based on Gaussian whale-cloud optimization (GCWOA) is presented, so that the entire scheduling strategy can not only expand the search range but also jump out of the local maximum and obtain the global optimal scheduling strategy. Experimental results show that compared with other existing metaheuristic algorithms, our strategy can not only shorten the task completion time but also balance the load of virtual machine resources, and at the same time, it also has a better performance in resource utilization. [ABSTRACT FROM AUTHOR]

Due to the purpose of this study that reducing power consumption in the cloud network is based on load balancing, the fitness function measures the load balance between cloud network and servers (the hosts). This technique is appropriate for handling the resource optimization challenges, due to the ability to convert the load balancing problem into an optimization problem (reducing imbalance cost). In this research, combining the results of the particle swarm genetic optimization (PSGO) algorithm and using a combination of advantages of these two algorithms lead to the improvement of the results and introducing a suitable solution for load balancing operation, because in the proposed approach (LBPSGORA), instead of randomly assigning the initial population in the genetic algorithm, the best result is procured by putting the initial population. The LBPSGORA method is compared with PSO, GA, and hybrid GA-PSO. The execution cost, load balancing, and makespan have been evaluated and our method has performed better than similar methods. [ABSTRACT FROM AUTHOR]

This article, titled "Research on Load Balancing and Caching Strategy for Central Network," has been retracted by Hindawi due to evidence of systematic manipulation of the publication process. The investigation revealed discrepancies in scope, description of the research, and availability of data, as well as inappropriate citations and incoherent content. Hindawi cannot vouch for the reliability of the article's content and regrets that these issues were not identified before publication. The corresponding author has been given the opportunity to respond to the retraction. [Extracted from the article]

Most supercomputers are shipped with both a CPU and a GPU. With the powerful parallel computing capability of GPUs, heterogeneous computing architecture produces new challenges for system software development and application design. Because of the significantly different architectures and programming models of CPUs and GPUs, conventional optimization techniques for CPUs may not work well in a heterogeneous multi-CPU and multi-GPU system. We present a heterogeneous parallel LU factorization algorithm for heterogeneous architectures. According to the different performances of the processors in the system, any given matrix is partitioned into different sizes of basic column blocks. Then, a static task allocation strategy is used to distribute the basic column blocks to corresponding processors uniformly. The idle time is minimized by optimized sizes and the number of basic column blocks. Right-looking ahead technology is also used in systems configured with one CPU core to one GPU to decrease the wait time. Experiments are conducted to test the performance of synchronization and load balancing, communication cost, and scalability of the heterogeneous parallel LU factorization in different systems and compare it with the related matrix algebra algorithm on a heterogeneous system configured with multiple GPUs and CPUs. [ABSTRACT FROM AUTHOR]

In order to implement an optimized solution for cell range expansion (CRE) and enhanced intercell interference coordination (eICIC) schemes in long-term evolution-advanced (LTE-A) heterogeneous cellular networks (HCNs) and to realize good load-balancing performance in existing LTE-A systems, a practical tessellation-based algorithm is proposed. In this algorithm, a globalized cell-specific bias optimization and a localized almost blank subframe (ABS) ratio update are proposed. The proposed scheme does not require major changes to existing protocols. Thus, it can be implemented in existing LTE-A systems with any legacy user equipment (UE) with only a partial update to the BSs and core networks. From simulation results, it is shown that the tessellation formed by the proposed approach is quite consistent with the optimal one for various realistic scenarios. Thus, the proposed scheme can provide a much better load-balancing capability compared with the conventional common bias scheme. Owing to the improved load-balancing capability, the user rate distribution of the proposed scheme is much better than that obtained from the conventional scheme and is even indistinguishable from that of the ideal joint user association scheme. [ABSTRACT FROM AUTHOR]

Many algorithms can uniformly distribute data to storage nodes in a storage system. However, it cannot avoid load imbalance because data has different popularity. To resolve this issue, we propose a novel dynamic replication scheme, namely, Active Replica Management (ARM). ARM actively establishes optimal number of copies for hotspot data according to data access behaviors and then efficiently distributes copies to other storage nodes based on current amount of copies related to hotspot data. To improve storage utilization, ARM automatically and gradually dereplicates the useless copies of hotspot data when they become nonhotspot data. ARM resolves load imbalance by allocating dynamic copies to adequate storage nodes, and hence it can prevent partial storage nodes from overburdening. Simulation results demonstrate that ARM is an efficient scheme with excellent performance on load balancing, significantly closer to Optimal Load Balancing (OLB). In addition, ARM’s performance outperforms both Static Load Balancing (SLB) and No Replica schemes. [ABSTRACT FROM AUTHOR]

Simulating binary black hole (BBH) systems are a computationally intensive problem and it can lead to great scientific discovery. How to explore more parallelism to take advantage of the large number of computing resources of modern supercomputers is the key to achieve high performance for BBH simulations. In this paper, we propose a scalable MPM (Mesh based Parallel Method) which can explore both the inter- and intramesh level parallelism to improve the performance of BBH simulation. At the same time, we also leverage GPU to accelerate the performance. Different kinds of performance tests are conducted on Blue Waters. Compared with the existing method, our MPM can improve the performance from 5x speedup (compared with the normalized speed of 32 MPI processes) to 8x speedup. For the GPU accelerated version, our MPM can improve the performance from 12x speedup to 28x speedup. Experimental results also show that when only enough CPU computing resource or limited GPU computing resource is available, our MPM can employ two special scheduling mechanisms to achieve better performance. Furthermore, our scalable GPU acceleration MPM can achieve almost ideal weak scaling up to 2048 GPU computing nodes which enables our software to handle even larger BBH simulations efficiently. [ABSTRACT FROM AUTHOR]

Load balancing technology can effectively exploit potential enormous compute power available on distributed systems and achieve scalability. Communication delay overhead on distributed system, which is time-varying and is usually ignored or assumed to be deterministic for traditional load balancing strategies, can greatly degrade the load balancing performance. Considering communication delay overhead and its time-varying feature, a hierarchical load balancing strategy based on generalized neural network (HLBSGNN) is presented for large distributed systems. The novelty of the HLBSGNN is threefold: (1) the hierarchy with optimized communication is employed to reduce load balancing overhead for large distributed computing systems, (2) node computation rate and communication delay randomness imposed by the communication medium are considered, and (3) communication and migration overheads are optimized via forecasting delay. Comparisons with traditional strategies, such as centralized, distributed, and random delay strategies, indicate that the HLBSGNN is more effective and efficient. [ABSTRACT FROM AUTHOR]

The recent advent of novel multi- and many-core architectures forces application programmers to deal with hardware-specific implementation details and to be familiar with software optimisation techniques to benefit from new high-performance computing machines. Extra care must be taken for communication-intensive algorithms, which may be a bottleneck for forthcoming era of exascale computing. This paper aims to present a high-level stencil framework implemented for the EULerian or LAGrangian model (EULAG) that efficiently utilises multi- and many-cores architectures. Only an efficient usage of both many-core processors (CPUs) and graphics processing units (GPUs) with the flexible data decomposition method can lead to the maximum performance that scales the communication-intensive Generalized Conjugate Residual (GCR) elliptic solver with preconditioner. [ABSTRACT FROM AUTHOR]

Transmission links in wireless sensor networks (WSNs) are prone to failure because of breakdown, movement, and energy depletion of nodes. Aiming at node-intensive environment, this paper proposes a highly reliable forwarding model based on virtual nodes (HRVN) in tree-based topology networks. A virtual node (VN) consists of several neighboring physical sensor nodes, which have common parent node and children nodes. Normally, the physical sensor nodes in the same VN forward packets in a load-balancing scheme. When a sensor node in a VN is in failure, the others would take over its forwarding work immediately. Moreover, the internal changes of a VN are transparent to its parent and children nodes. The experiments prove the model can provide load-balance and fast recovery from failure with low cost. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) is currently seen as one of the most promising future network technologies, which can realize the separation between control and data planes. Furthermore, the increasing complexity in future wireless networks (i.e., 5G, wireless sensor networks) renders the control and coordination of networks a challenging task. Future wireless networks need good separation of control and data planes and call for SDN method to handle the explosive increase of mobile data traffic. Relying on a single controller in future wireless networks imposes a potential scalability problem. To tackle this problem, the thought of using multiple controllers to manage the large wide-area wireless network has been proposed, where the load balance problem of multicontroller needs to be resolved. In this paper, we propose a multicontroller load balancing approach called HybridFlow in software-defined wireless networks, which adopts the method of distribution and centralization and designs a double threshold approach to evenly allocate the load. Simulation results reveal that the proposed approach can significantly relieve the working load on the super controller and reduces the load jitter of multicontroller load in a single cluster compared with the BalanceFlow method. [ABSTRACT FROM AUTHOR]

Compound comparison is an important task for the computational chemistry. By the comparison results, potential inhibitors can be found and then used for the pharmacy experiments. The time complexity of a pairwise compound comparison is O(n2), where n is the maximal length of compounds. In general, the length of compounds is tens to hundreds, and the computation time is small. However, more and more compounds have been synthesized and extracted now, even more than tens of millions. Therefore, it still will be time-consuming when comparing with a large amount of compounds (seen as a multiple compound comparison problem, abbreviated to MCC). The intrinsic time complexity of MCC problem is O(k2n2) with k compounds of maximal length n. In this paper, we propose a GPU-based algorithm for MCC problem, called CUDA-MCC, on single- and multi-GPUs. Four LINGO-based load-balancing strategies are considered in CUDA-MCC in order to accelerate the computation speed among thread blocks on GPUs. CUDA-MCC was implemented by C+OpenMP+CUDA. CUDA-MCC achieved 45 times and 391 times faster than its CPU version on a single NVIDIA Tesla K20m GPU card and a dual-NVIDIA Tesla K20m GPU card, respectively, under the experimental results. [ABSTRACT FROM AUTHOR]

Regarding the energy shortage problem of wireless sensor networks (WSNs), various schemes and protocols are proposed to prolong the network lifetime. Data communication is undoubtedly the most important determinant for this energy scarce network type. In this paper, we propose a sophisticated architecture comprising data reduction, load balance, and topology control. Data reduction is ensured by the parameter spatial correlation proximity range (SCPR) that can be adjusted statically at the setup phase or dynamically revised depending on the necessities in the network. Four-layer virtual architecture is applied for implementing topology control. Furthermore, network area is partitioned into fixed-size hexagonal clusters. Depending on the regions in which the clusters take place, cluster heads (CHs) are elected from the respective subregions of the clusters. Load balance is achieved by considering residual energies and distances to the sink during both CH election and data transmission stages. Aggregated data in each cluster is transmitted towards the sink by using a load balancing single-hop intercluster routing protocol instead of direct transmission as offered in LEACH. Simulation results demonstrate that the proposed architecture ECMTADR shows almost 50% better performance in terms of energy conservation and network lifetime when it is compared to LEACH and HEED. [ABSTRACT FROM AUTHOR]

The load balancing technology is widely used in current enterprise network to provide high quality and reliable service. Conventional load balancing technology is often achieved by specific hardware that is usually very expensive and lacks sufficient flexibility. Meanwhile, it is easy to become a single point of failure and would be restricted in virtualization environments. Thus, we propose a load balancing algorithm based on server running state, which can calculate comprehensive loading according to the CPU utilization, memory utilization, and network traffic of the servers. Furthermore, a load balancing solution based on software defined networks (SDN) technology is applied in this paper, and it is designed and implemented in OpenFlow network. We combine network management and server state monitor in this scheme, in which the OpenFlow switches forward the request to the least comprehensive loading server by modifying the packet. [ABSTRACT FROM AUTHOR]

The rollover accidents induced by severe maneuvers are very dangerous and mostly happen to vehicles with elevated center of gravity, such as heavy-duty trucks and pickup trucks. Unfortunately, it is hard for drivers of those vehicles to predict and prevent the trend of the maneuver-induced (untripped) rollover ahead of time. In this study, a lateral load transfer ratio which reflects the load distribution of left and right tires is used to indicate the rollover criticality. An antiroll controller is designed with smooth sliding mode control technique for vehicles, in which an active antiroll suspension is installed. A simplified second order roll dynamic model with additive sector bounded uncertainties is used for control design, followed by robust stability analysis. Combined with the vehicle dynamics simulation package TruckSim, MATLAB/Simulink is used for simulating experiment. The results show that the applied controller can improve the roll stability under some typical steering maneuvers, such as Fishhook and J-turn. This direct antiroll control method could be more effective for untripped rollover prevention when driver deceleration or steering is too late. It could also be extended to handle tripped rollovers. [ABSTRACT FROM AUTHOR]

We propose a multitree based fast failover scheme for Ethernet networks. In our system, only few spanning trees are used to carry working traffic in the normal state. As a failure happens, the nodes adjacent to the failure redirect traffic to the preplanned backup VLAN trees to realize fast failure recovery. In the proposed scheme, a new leaf constraint is enforced on the backup trees. It enables the network being able to provide 100% survivability against any single link and any single node failure. Besides fast failover, we also take load balancing into consideration. We model an Ethernet network as a twolayered graph and propose an Integer Linear Programming (ILP) formulation for the problem. We further propose a heuristic algorithm to provide solutions to large networks. The simulation results show that the proposed scheme can achieve high survivability while maintaining load balancing at the same time. In addition, we have implemented the proposed scheme in an FPGA system. The experimental results show that it takes only few μsec to recover a network failure. This is far beyond the 50 msec requirement used in telecommunication networks for network protection. [ABSTRACT FROM AUTHOR]

While multicore architectures are used in the whole product range from server systems to handheld computers, the deployed software still undergoes the slow transition from sequential to parallel. This transition, however, is gaining more and more momentum due to the increased availability of more sophisticated parallel programming environments. Combined with the ever increasing complexity of multicore architectures, this results in a scheduling problem that is different from what it has been, because concurrently executing parallel programs and features such as non-uniform memory access, shared caches, or simultaneous multithreading have to be considered.In this paper, we compare different ways of scheduling multiple parallel applications on multicore architectures. Due to emerging parallel programming environments, we primarily consider applications where the parallelism degree can be changed on the fly. We propose TACO, a topology-aware scheduling scheme that combines equipartitioning and coscheduling, which does not suffer from the drawbacks of the individual concepts. Additionally, TACO is conceptually compatible with contention-aware scheduling strategies. We find that topology-awareness increases performance for all evaluated workloads. The combination with coscheduling is more sensitive towards the executed workloads and NUMA effects. However, the gained versatility allows new use cases to be explored, which were not possible before. [ABSTRACT FROM AUTHOR]

Challenge of efficient protocol design for energy constrained wireless sensor networks is addressed through application specific cross-layer designs. This design approach along with strong design assumptions limits application of protocols in universal scenarios and affects their practicality. With proliferation of embedded mobile sensors in consumer devices, a changed application paradigm requires generic protocols capable of managing greater device heterogeneousness and mobility. In this paper, we propose a novel lifetime maximization protocol for mobile sensor networks with uncontrolled mobility considering residual energy, traffic load, and mobility of a node. The protocol being generic is equally applicable to heterogeneous, homogenous, static, and mobile sensor networks. It can handle event driven as well as continuous traffic flow applications. Simulation results show that proposed scheme outperforms minimum hop routing and greedy forwarding in terms of network lifetime, data packet latency, and load balance while maintaining comparable throughput. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) can benefit from ad hoc networking technology characterized by multihop wireless connectivity and infrastructure less framework. These features make them suitable for next-generation networks to support several applications such M2M applications for smart cities and public safety scenarios. Pivotal design requirements for these scenarios are energy efficiency, since many of these devices will be battery powered placing a fundamental limit on network, and specifically node lifetime. Moreover, the way in which traffic is managed also influences network lifetime, since there is a high probability for some nodes to become overloaded by packet forwarding operations in order to support neighbour data exchange. These issues imply the need for energy efficient and load balanced routing approaches that can manage the network load and not only provide reduced energy consumption on the network but also prolong the network lifetime providing robust and continuous. This work proposes a new energy efficient and traffic balancing routing approach that can provide a weighted and flexible trade-off between energy consumption and load dispersion. Simulation results show that the proposed protocol achieves high energy efficiency, decreases the percentage of failed nodes due to lack of battery power, and extends the lifespan of the network. [ABSTRACT FROM AUTHOR]

Software-as-a-service (SaaS) has emerged as a new computing paradigm to provide reliable software on demand. With such an inspiring motivation, sensor cloud system can benefit from this infrastructure. Generally, sharing database and schema is the most commonly used data storage model in the cloud. However, the data storage of tenants in the cloud is approaching schema null and evolution issues. To address these limitations, this paper proposes multitenant multiple wide tables with vertical scalability by analyzing the features of multitenant data. To solve schema null issue, extended vertical part is used to trim down the amount of schema null values. To reduce probability of schema evolution, wide table is divided into multiple clusters that we called multiple wide tables. This design reaches the balance between tenant customizing and its performance. Besides, the partition and correctness of multiple wide tables with vertical scalability are discussed in detail. The experimental results indicate that the solution of our multiple wide tables with vertical scalability is superior to single wide table, and single wide table with vertical scalability in the aspects of spatial intensity and read performance. [ABSTRACT FROM AUTHOR]

In mobile ad hoc networks the congestion is a major issue,which affects the overall performance of the networks. The load balancing in the network alongside the congestion is another major problem in mobile ad hoc network (MANET) routing due to difference in link cost of the route. Most of the existing routing protocols provide solutions to load balancing or congestion adaptivity separately. In this paper, a congestion adaptive routing along with load balancing, that is, load balanced congestion adaptive routing (LBCAR), has been proposed. Transferring of load from congested nodes to less busy nodes and involvement of other nodes in transmission that can take part in route can improve the overall network life. In the proposed protocol two metrics, traffic load density and link cost associated with a routing path, have been used to determine the congestion status. The route with low traffic load density and maximum life time is selected for packet transmission using this protocol. Performance of the network using LBCAR has been analyzed and compared with congestion adaptive routing protocol (CRP) for packet delivery ratio, average end-to-end delay, and normalized routing overhead. [ABSTRACT FROM AUTHOR]

With the pervasive use of mobile devices and increasingly computational ability, more concrete and deeper collaborations among mobile users are becoming possible and needed. However, most of the studies fail to consider load balancing requirement among mobile users. When tasks are unevenly distributed, the processing time as well as energy consumption will be extremely high on some devices, which will inevitably counterweight the benefits from incentive mechanism and task scheduling scheme. In this work, we propose eCOTS (Efficient and Cooperative Task Sharing for Large-scale Smart City Sensing Application).We leverage the "balls and bins" theory for task assignment, where d mobile users in contact range are investigated, and select the least loaded one among the d users. It has been proved that such simple case can effectively reduce the largest queueing length from ϑ(log n/ log log n) to ϑ(log log n/ log d). Simulation and real-trace driven studies have shown that, eCOTS can effectively improve the balancing effects in typical network scenarios, even the energy level and computational capability are diverse. In simulation study, eCOTS can reduce the gap between the maximum and minimum queueing lengths up to 5x and over 2x in real trace data evaluations. [ABSTRACT FROM AUTHOR]

A wireless network consists of a large number of nodes with limited resources. The large number of event data will be generated over a period of time in a wireless network. Also, faced with high resource demand variability and with misfit resource renting policies, the current practices is to overprovision for each game tens of owned data. In a wireless network, the agent node may become easily overloaded by the high demand from the players for updates. So, we propose a hybrid load balancing scheme for games in wireless networks which is based on allocating load on wireless mobile nodes proportionally to each of the agent's capacities. This proposed scheme is a combined dynamic provisioning algorithm based on greedy graph and user-oriented load balancing scheme for maintaining the performance and stability of distributed system in wireless networks. We address the key functions for our proposed scheme and simulate the efficiency of our proposed scheme using mathematical analyses. We then testify that the proposal has best performance compared with previous researches. [ABSTRACT FROM AUTHOR]

A wireless sensor network consisting of resources, size, and cost-limited sensors is used in many military and civil applications. This paper proposes an energy-efficient clustering algorithm that extends the lifetime of sensor networks. The proposed clustering algorithm is an extended hierarchical clustering protocol that minimizes the overall amount of consumed energy in the network. The proposed approach dynamically updates clusters and distributes the load on the heavily loaded cluster heads among different nodes. It also balances the remaining energy on nodes in the network, which leads to prolonged network lifetime. The performance is evaluated in terms of network lifetime, average energy consumption, and standard deviation of residual energy. [ABSTRACT FROM AUTHOR]

Wireless sensor networks are evolving with increasingly device heterogeneity, while the problem of effectively composing the platform-specific functionalities provided by heterogeneous sensor nodes to achieve specific goals still remains a challenge. Because of the constrained resources and unreliable communication in sensor networks, traditional service composition techniques in web services with adequate resources are insufficient. In this paper, focusing on the limited energy of sensor nodes, we propose an energy-aware service composition framework for developing various applications in heterogeneous wireless sensor networks. With both the energy-aware metrics: energy-aware load-balancing factor and overall energy consumption, we formulate the process of energy-aware sensor service composition into a combinatorial optimization problem; furthermore, an improved discrete particle swarm optimization (IDPSO) algorithm with inertia weights adjustment and extreme perturbation scheme is proposed to solve the combinatorial optimization problem. The experiment results have shown that the performance of the service route given by IDPSO is approximately equal to the best service route searched out by the exhaustive algorithm. Meanwhile, our proposed energy-aware service composition method is able to reduce the energy consumption and prolong the lifetime of the sensor network when providing stable service composition for various applications. [ABSTRACT FROM AUTHOR]

Wireless sensor networks have been widely studied for decades. There are many research areas that have been explored such as service availability and energy efficiency. Load balancing is one of the key techniques which can be used to solve the tradeoff between the both sides. In this paper, we propose a novel real-time load balancing technique which does not require the knowledge of static traffic demands and is based on control theory. We first formulate the packet forwarding problem into a classic control model and then derive the controller model. Then, we propose an algorithm to implement the controller and perform the simulation. The simulation results show that, our algorithm can balance the network traffic in real time and decreases maximum link utilization (MLU) sharply. Our algorithm reduces MLU by 78.4% compared to the shortest path first (SPF) routing and by 53.8% compared to another packet scheduling algorithm which was proposed in a previous work. [ABSTRACT FROM AUTHOR]

In recent years, countries all over the world consider the future internet as the country's strategic development directions, so projects about future internet have been launched by these countries. Load balancing algorithms and job allocations are main research problems in areas of resourcemanagement of future internet. In this paper, we introduce a load balancing model for future internet. We formulate the static load balancing problem in the model proposed above as noncooperative game among users and cooperative game among processors. Based on this model, we derive a load balancing algorithm for computing center. Finally, we execute the algorithm presented in this paper with another three algorithms for comparison purpose. The advantages of our algorithm are better scalability to the model, improving system performance, and low cost on maintaining system information. [ABSTRACT FROM AUTHOR]

A quantum random walk optimization model and algorithm in network cluster server traffic control and task scheduling is proposed. In order to solve the problem of server load balancing, we research and discuss the distribution theory of energy field in quantum mechanics and apply it to data clustering. We introduce the method of random walk and illuminate what the quantum random walk is. Here, we mainly research the standard model of one-dimensional quantum random walk. For the data clustering problem of high dimensional space, we can decompose one m-dimensional quantum random walk into m onedimensional quantum random walk. In the end of the paper, we compare the quantum random walk optimization method with GA (genetic algorithm), ACO (ant colony optimization), and SAA (simulated annealing algorithm). In the same time, we prove its validity and rationality by the experiment of analog and simulation. [ABSTRACT FROM AUTHOR]

A quantum optimization scheme in network cluster server task scheduling is proposed. We explore and research the distribution theory of energy field in quantum mechanics; specially, we apply it to data clustering. We compare the quantum optimization method with genetic algorithm (GA), ant colony optimization (ACO), simulated annealing algorithm (SAA). At the same time, we prove its validity and rationality by analog simulation and experiment. [ABSTRACT FROM AUTHOR]

With the substantial increase of vehicles on road, driving safety and transportation efficiency have become increasingly concerned focus from drivers, passengers, and governments. Wireless networks constructed by vehicles and infrastructures provide abundant information to share for the sake of both enhanced safety and network efficiency. This paper presents the systematic research to enhance the vehicle safety by wireless communication, in the aspects of information acquisition through vehicle sensing, vehicle-to-vehicle (V2V) routing protocol for the highly dynamic vehicle network, vehicle-to-infrastructure (V2I) routing protocol for a tradeoff in real-time performance and load balance, and hardware implementation of V2V system with on-road test. Simulations and experimental result validate the feasibility of the algorithms and communication system. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) consist of multiple, distributed nodes each with limited resources. With their strict resource constraints and application-specific characteristics, WSNs contain many challenging tradeoffs. This paper proposes a bioinspired load balancing approach, based on pheromone signalling mechanisms, to solve the tradeoff between service availability and energy consumption. We explore the performance consequences of the pheromone-based load balancing approach using (1) a system-level simulator, (2) deployment of real sensor testbeds to provide a competitive analysis of these evaluation methodologies. The effectiveness of the proposed algorithm is evaluated with different scenario parameters and the required performance evaluation techniques are investigated on case studies based on sound sensors. [ABSTRACT FROM AUTHOR]

Mobile cloud computing (MCC) enables the mobile devices to offload their applications to the cloud and thus greatly enriches the types of applications on mobile devices and enhances the quality of service of the applications. Under various circumstances, researchers have put forward several MCC architectures. However, how to reduce the response latency while efficiently utilizing the idle service capacities of the mobile devices still remains a challenge. In this paper, we firstly give a definition of MCC and divide the recently proposed architectures into four categories. Secondly, we present a Hybrid Local Mobile Cloud Model (HLMCM) by extending the Cloudlet architecture. Then, after formulating the application scheduling problems in HLMCM and bringing forward the Hybrid Ant Colony algorithm based Application Scheduling (HACAS) algorithm, we finally validate the efficiency of the HACAS algorithm by simulation experiments. [ABSTRACT FROM AUTHOR]

Disjoint routing is used to find the disjoint paths between a source and a destination subject to QoS requirements. Disjoint QoS routing is an effective strategy to achieve robustness, load balancing, congestion reduction, and an increased throughput in computer networks. For multiple additive constraints, disjoint QoS routing is an NP-complete class that cannot be exactly solved in polynomial time. In the paper, the disjoint QoS routing problem was formulated as a 0-1 integer linear programming. The complicating constraints were included in the objective function using an adaptive penalty function. The special model with a totally unimodular constraint coefficient matrix was constructed and could be solved rapidly as a linear programming. An efficient algorithm using an adaptive penalty function and 0-1 integer linear programming for the disjoint QoS routing problems was designed. The proposed algorithm could obtain the optimal solution, considerably reducing the computational time consumption and improving the computational efficiency. Theoretical analysis and simulation experiments were performed to evaluate the proposed algorithm performance. Through the establishment of random network topologies using Matlab, the average running time, the optimal objective value, and the success rate were evaluated based on the optimal values obtained in Cplex. The simulation experiments validated the effectiveness of the proposed heuristic algorithm. [ABSTRACT FROM AUTHOR]

This paper studies uncertainty and its effect on systemresponse displacement. The paper also describes how IT2MFs (interval type- 2membership functions) differentiate fromT1MFs (type-1 membership functions) by adding uncertainty. The effect of uncertainty is modeled clearly by introducing a technique that describes how uncertainty causes membership degree reduction and changing the fuzzy word meanings in fuzzy logic controllers (FLCs). Several criteria are discussed for themeasurement of the imbalance rate of internal uncertainty and its effect on system behavior. Uncertainty removal is introduced to observe the effect of uncertainty on the output. The theorem of uncertainty avoidance is presented for describing the role of uncertainty in interval type-2 fuzzy systems (IT2FSs). Another objective of this paper is to derive a novel uncertainty measure for IT2MFs with lower complexity and clearer presentation. Finally, for proving the affectivity of novel interpretation of uncertainty in IT2FSs, several investigations are done. [ABSTRACT FROM AUTHOR]

Grid computing is a collection of computational and data resources, providing the means to support both computational intensive applications and data intensive applications. In order to improve the overall performance and efficient utilization of the resources, an efficient load balanced scheduling algorithm has to be implemented. The scheduling approach also needs to consider user demand to improve user satisfaction. This paper proposes a dynamic hierarchical load balancing approach which considers load of each resource and performs load balancing. It minimizes the response time of the jobs and improves the utilization of the resources in grid environment. By considering the user demand of the jobs, the scheduling algorithm also improves the user satisfaction. The experimental results show the improvement of the proposed load balancing method. [ABSTRACT FROM AUTHOR]

A novel compensator topology is proposed for three-phase unbalanced loads in the distribution networks. The negative and zero sequence compensation networks are proposed using delta-connected and star-connected capacitor banks. The compensation networks are paralleled with the unbalanced load to achieve a perfect balancing of active power and a complete compensation of reactive power of the unbalanced load. Intelligent grouping compound switches are implemented to control the capacitors, where, therefore, each capacitor can be switched between phases or phase and ground. Wireless sensors are also applied to acquire the load quantities. The compensation method proposed is based on symmetrical component theory. In addition, the load compensation based on instantaneous symmetrical component theory with positive sequence online extraction is proposed under unbalanced and distorted source voltages. The proposed load-balancing method and device are applied to Liaoning, Neimenggu, and Jilin provinces. The application results are provided to validate the performance of the proposed compensator topology and compensation method. [ABSTRACT FROM AUTHOR]

In-network data storage and retrieval is one of the most important problems in wireless sensor networks. Many schemes have been proposed to solve this problem. However, most of them do not take the frequencies of event and query into consideration. In fact, the frequencies of event and query are very important factors in real applications of in-network data storage and retrieval. In this paper, we introduce a virtual-ring-based data storage and retrieval scheme, which is called VRS, to solve the problem. VRS divides the whole sensor network field into some virtual rings. According to the frequencies, one of the virtual rings is selected out as the rendezvous ring, which plays the role of a bridge between the information consumers and the information producers. Extensive experiments have been done to evaluate the performance of the proposed scheme, VRS. Simulation results show that VRS outperforms the existing work in load balance, delay of data storage and retrieval, and the lifetime of the sensor networks. [ABSTRACT FROM AUTHOR]

To collect the data and transmit them on the base station is the main task of the sensor nodes in wireless sensor networks (WSNs). This transmission requires a specific routing algorithm. To prolong the network lifetime, sensor nodes need a secure load-balancing route. In this paper, we investigate the facts of ensuring secure sensed data in a balanced energy network backbone, and propose energy-efficient secure path algorithm (ESPA) for WSNs. It is a process of ensuring sensed data authenticity and integrity under a well-structured energy-efficient routing. To overcome the limitation caused by the symmetric key cryptography algorithms for securing data in WSNs, we proposed a mechanism for checking data integrity in a balanced energy network backbone. On this basis, we use the combined version of distance energy aware routing and a checking data integrity method for WSNs. ESPA provides a better performance in maximizing the network lifetime. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, clustering expedites many desirable functions such as load balancing, energy savings, and distributed key management. For secure clustering, it is very important to find compromised nodes and remove them during the initial cluster formation process. If some nodes are compromised and survive the censorship process, they can make some nodes have a different cluster view and can split a cluster into multiple clusters to deteriorate cluster quality as a whole. To resolve these problems, we propose a robust scheme against such attacks in this paper. First, our scheme generates large-sized clusters where any two nodes are at most two hops away from each other to raise the quality of clusters. Second, our scheme employs the verification of two-hop distant nodes to preserve the quality of the large-sized clusters and refrains from splitting the clusters. Last, our scheme prefers broadcast transmissions to save the energy of nodes. Security analysis proves that our scheme can identify compromised nodes and preserves the cluster membership agreement against the compromised nodes. In addition, simulation results prove that our scheme generates fewer clusters and is more secure and energy efficient than the scheme producing only small-sized clusters. [ABSTRACT FROM AUTHOR]

This paper presents a mathematical description of Weighted Round Robin service strategy, on which basis all the perspective QoS tools that serve to manage congestion in converged packet networks work. On the basis of the presented mathematical model, it is possible to suitably configure the operational parameters (maximum queue length, distribution of the available transfer capacity) of these tools according to required values of packet delays. The implementation of the analytical model is demonstrated on a real network segment using advanced network data traffic emulator. [ABSTRACT FROM AUTHOR]

The energy consumption of nodes is strictly limited in wireless sensor networks. The traditional direct delivery routing protocol is employed in the paging network of high-rise building construction elevators, which results in the high energy consumption and low successful delivery rate. A load-balancing equal opportunity routing protocol for wireless paging network is proposed in this work. The load-balancing equal opportunity routing protocol is a data transfer technique which maintains the network survivability by selecting the optimal path with both the lower energy consumption and the balanced network load. A load-balancing scheme gives equal opportunities for package transmission. A pager node delivers the packages by selecting a neighbor node with higher energy. The neighbor nodes in the coverage of the source node have an equal opportunity for routing. The sink node in the paging network broadcasts routing messages when moving along the track during the path selecting. The proposed load-balancing equal opportunity routing protocol is implemented and evaluated in a high-rise building construction elevator. The experimental results show that the average energy consumption of nodes is lowered with a balanced network load. It is estimated that successful delivery rate is increased by 55% but with a higher latency, and the network lifetime is prolonged. [ABSTRACT FROM AUTHOR]

Wireless sensor networks have become integral components of modern and smart environments. The main challenge for such important data-acquisition tools is the limited amount of available energy. In integrated networks in which cloud systems act as a self-regulatory controller, distributing the computational load among available partitions with rich energy will positively influence the lifetime of the whole network. This article investigates the application of a modified version of multinomial logistic regression model that incorporates spatiotemporal aspects of data collected from smart environments. The contribution of this research is to propose an energy-efficient load balancing strategy based on the proposed prediction model for the purpose of enhancing the lifetime of wireless infrastructure. Our proposed algorithm grows linearly in terms of time complexity. Extensive experiments have been performed to measure the prediction error rate and the energy consumption. The results showed that the proposed model significantly reduces the error rate and distinctly maximizes the lifetime of wireless sensor networks. [ABSTRACT FROM AUTHOR]

In this article, to monitor the fields with square and circular geometries, three energy-efficient routing protocols are proposed for underwater wireless sensor networks. First one is sparsity-aware energy-efficient clustering, second one is circular sparsity-aware energy-efficient clustering, and the third one is circular depth–based sparsity-aware energy-efficient clustering routing protocol. All three protocols are proposed to minimize the energy consumption of sparse regions, whereas sparsity search algorithm is proposed to find sparse regions and density search algorithm is used to find dense regions of the network field. Moreover, clustering is performed in dense regions to minimize redundant transmissions of a data packet, while sink mobility is exploited to collect data from sensor nodes with an objective of minimum energy consumption. A depth threshold ( d th ) value is also used to minimize number of hops between source and destination for less energy consumption. Simulation results show that our schemes perform better than their counter-part schemes (depth-based routing and energy-efficient depth-based routing) in terms of energy efficiency. [ABSTRACT FROM AUTHOR]

Satellite networks provide complete connectivity and worldwide data transmission capability for constructing the Internet of Things. However, because of the varying Internet of Things traffic density, satellite networks may endure imbalanced traffic requirements and frequent link congestion. To effectively resolve these problems and optimally transmit Internet of Things data, a novel hybrid global-local load balancing routing scheme for Low Earth Orbit satellite networks is proposed in this article. Hybrid global-local load balancing routing scheme enables satellites to route Internet of Things traffic through global planning and local real-time adjustments in two steps. In hybrid global-local load balancing routing scheme, given the predictive nature of Internet of Things traffic distribution and Low Earth Orbit satellite networks, the inter-satellite traffic demand is decomposed into a predictable long-range baseline and unpredictable short-range fluctuations. A global strategy is employed first for preliminary global traffic allocation based on long-range baselines, and a local strategy is then employed for route adjustments based on short-range fluctuations. With the combination of global planning and local real-time adjustments, network traffic can eventually obtain a near-optimal allocation. Numerical simulations indicate that in contrast to single-strategy schemes, hybrid global-local load balancing routing scheme can more thoroughly eliminate congestion, and it performs better in measures such as packet loss rate, average queuing delay, traffic distribution, route oscillation, and communication overhead. [ABSTRACT FROM AUTHOR]