With the popularization of software defined networks, switch migration as an important network management strategy has attracted increasing attention. Most existing switch migration strategies only consider local conditions and simple load thresholds, without fully considering the overall optimization and dynamics of the network. Therefore, this article proposes a switch migration algorithm based on global optimization. This algorithm adds a load prediction module to the migration model, determines the migration controller, and uses an improved whale optimization algorithm to determine the target controller and its surrounding controller set. Based on the load status of the controller and the traffic priority of the switch to be migrated, the optimal migration switch set is determined. The experimental results show that compared to existing schemes, the algorithm proposed in this paper improves the average flow processing efficiency by 15% to 40%, reduces switch migration times, and enhances the security of the controller.

Sharding is a solution to the blockchain scalability problem. A sharded blockchain divides consensus nodes (validators) into groups called shards and processes transactions separately to improve throughput and latency. In this paper, we analyze the rational behavior of users in account/balance model-based sharded blockchains and identify a phenomenon in which accounts (users' wallets and smart contracts) eventually get concentrated in a few shards, making shard loads unfair. This phenomenon leads to bad user experiences, such as delays in transaction inclusions and increased transaction fees. To solve this problem, we propose two load balancing methods in account/balance model-based sharded blockchains. Both methods perform load balancing by periodically reassigning accounts: in the first method, the blockchain protocol itself performs load balancing and in the second method, wallets perform load balancing. We discuss the pros and cons of the two protocols, and apply the protocols to the execution sharding in Ethereum 2.0, an existing sharding design. Further, we analyze by simulation how the protocols behave to confirm that we can observe smaller transaction delays and fees. As a result, we released the simulation program as “Shargri-La,” a simulator designed for general-purpose user behavior analysis on the execution sharding in Ethereum 2.0.

Network Slicing (NS) is recognized as a key technology for the 5G network in providing tailored network services towards various types of verticals over a shared physical infrastructure. It offers the flexibility of on-demand provisioning of diverse services based on tenants' requirements in a dynamic environment. In this work, we focus on two important issues related to 5G Core slices: the deployment and the reconfiguration of 5G Core NSs. Firstly, for slice deployment, balancing the workloads of the underlying network is beneficial in mitigating resource fragmentation for accommodating the future unknown network slice requests. In this vein, we formulate a load-balancing oriented 5G Core NS deployment problem through an Integer Linear Program (ILP) formulation. Further, for slice reconfiguration, we propose a reactive strategy to accommodate a rejected NS request by reorganizing the already-deployed NSs. Typically, the NS deployment algorithm is reutilized with slacked physical resources to find out the congested part of the network, due to which the NS is rejected. Then, these congested physical nodes and links are reconfigured by migrating virtual network functions and virtual links, to re-balance the utilization of the whole physical network. To evaluate the performance of deployment and reconfiguration algorithms we proposed, extensive simulations have been conducted. The results show that our deployment algorithm performs better in resource balancing, hence achieves higher acceptance ratio by comparing to existing works. Moreover, our reconfiguration algorithm improves resource utilization by accommodating more NSs in a dynamic environment.

This letter presents an oblivious and load-balanced routing (OLBR) method without virtual channels for 2D mesh Network-on-chip (NoC). To balance the traffic load of network and avoid deadlock, OLBR divides network nodes into two regions, one region contains the nodes of east and west sides of NoC, in which packets are routed by odd-even turn rule with Y direction preference (OE-YX), and the remaining nodes are divided to the other region, in which packets are routed by odd-even turn rule with alterable priority arbitration (OE-APA). Simulation results show that OLBR's saturation throughput can be improved than related works by 11.73% and OLBR balances the traffic load over entire network.

With the development of cloud computing, the Mobile Edge Computing has emerged and attracted widespread attentions. In this paper, we focus on the load balancing in MEC with energy harvesting. We first introduce the load balancing in MEC as a problem of minimizing both the energy consumption and queue redundancy. Thereafter, we adapt such a optimization problem to the Lyapunov algorithm and solve this optimization problem. Finally, extensive simulation results validate that the obtained strategy improves the capabilities of MEC systems.

End-to-end delay, aiming to realize how much time it will take for a traffic load generated by a Mobile Node (MN) to reach Sink Node (SN), is a principal objective of most new trends in a Wireless Sensor Network (WSN). It has a direct link towards understanding the minimum time delay expected where the packet sent by MN can take to be received by SN. Most importantly, knowing the average minimum transmission time limit is a crucial piece of information in determining the future output of the network and the kind of technologies implemented. In this paper, we take network load and transmission delay issues into account in estimating the Average Minimum Time Limit (AMTL) needed for a health operating cognitive WSN. To further estimate the AMTL based on network load, an end-to-end delay analysis mechanism is presented and considers the total delay (service, queue, ACK, and MAC). This work is proposed to answer the AMTL needed before implementing any cognitive based WSN algorithms. Various time intervals and cogitative channel usage with different application payload are used for the result analysis. Through extensive simulations, our mechanism is able to identify the average time intervals needed depending on the load and MN broadcast interval in any cognitive WSN.

We propose a novel compositing pipeline and a dynamic load balancing technique for volume rendering which utilizes a two-layered group structure to achieve effective and scalable load balancing. The technique enables each process to render data from non-contiguous regions of the volume with minimal impact on the total render time. We demonstrate the effectiveness of the proposed technique by performing a set of experiments on a modern GPU cluster. The experiments show that using the technique results in up to a 35.7% lower worst-case memory usage as compared to a dynamic k-d tree load balancing technique, whilst simultaneously achieving similar or higher render performance. The proposed technique was also able to lower the amount of transferred data during the load balancing stage by up to 72.2%. The technique has the potential to be used in many scenarios where other dynamic load balancing techniques have proved to be inadequate, such as during large-scale visualization.

In this paper, we propose a new dynamic load balancing scheme according to load threshold adjustment and incentives mechanism. The proposed scheme adjusts the load threshold of a node by comparing it with a mean threshold of adjacent nodes, thereby increasing the threshold evenly. We also assign the incentives and penalties to each node through a comparison of the mean threshold of all the nodes in order to increase autonomous load balancing participation.

DHT routing algorithms can provide efficient mechanisms for resource placement and lookup for distributed file sharing systems. However, we must still deal with irregular and frequent join/leave of nodes and the problem of load unbalancing between nodes in DHT-based file sharing systems. This paper presents an efficient file backup scheme based on dynamic DHT key space clustering in order to guarantee data availability and support load balancing. The main idea of our method is to dynamically divide the DHT network into a number of clusters, each of which locally stores and maintains data chunks of data files to guarantee the data availability of user data files even when node churn occurs. Further, high-capacity nodes in clusters are selected as backup nodes to achieve adequate load balancing. Simulation results demonstrate the superior effectiveness of the proposed scheme over other file replication schemes.

In this paper, we present a hybrid message logging protocol consisting of three modules for two-level hierarchical and distributed architectures to address the drawbacks of sender-based message logging. The first module reduces the number of in-group control messages and, the rest, the number of inter-group control messages while localizing recovery. In addition, it can distribute the load of logging and keeping inter-group messages to group members as evenly as possible. The simulation results show the proposed protocol considerably outperforms the traditional protocol in terms of message logging overhead and scalability.

Distributed control plane architecture has been employed in software-defined data center networks to improve the scalability of control plane. However, since the flow space is partitioned by assigning switches to different controllers, the network topology is also partitioned and the rule setup process has to invoke multiple controllers. Besides, the control load balancing based on switch migration is heavyweight. In this paper, we propose a lightweight load partition method which decouples the flow space from the network topology. The flow space is partitioned with hosts rather than switches as carriers, which supports fine-grained and lightweight load balancing. Moreover, the switches are no longer needed to be assigned to different controllers and we keep all of them controlled by each controller, thus each flow request can be processed by exactly one controller in a centralized style. Evaluations show that our scheme reduces rule setup costs and achieves lightweight load balancing.

In cellular network environments, where users are not evenly distributed across cells, overloaded base stations handling many users have difficulties in providing effective and fair services with their limited resources. Additionally, users at the cell edge may suffer from the potential problems resulting from low signal-to-interference ratio owing to the incessant interference from adjacent cells. In this paper, we propose a relay-assisted load balancing scheme to resolve these traffic imbalance. The proposed scheme can improve the performance of the overall network by utilizing relay stations to divert heavy traffic to other cells, and by adopting a partial frequency-reuse scheme to mitigate inter-cell interference. Each user and relay station calculates its own utility influence in the neighboring candidates for reassociation and decides whether to stay or move to another cell presenting the maximum total network utility increment. Simulation results show that the proposed scheme improves the overall network fairness to users by improving the performance of cell boundary users without degrading the total network throughput. We achieve a system performance gain of 16 ∼ 35% when compared with conventional schemes, while ensuring fairness among users.

Many-core architecture is becoming an attractive design choice in high-end embedded systems design. There are, however, many important design issues, and load balancing is one of them. In this work, we take the approach of diffusive load balancing which enables autonomic load distribution in many-core systems. We improve the existing schemes by adding the concept of simulated annealing for more effective load distribution. The modified scheme is also capable of managing a situation of non-uniform granularity of task loading, which the existing ones cannot. In addition, the suggested scheme is extended to be able to handle dependencies existing in task graphs where tasks have communications between each other. As experiments, we tried various existing schemes as well as the proposed one to map synthetic applications and real world applications on a many-core architecture with 21 cores and 4 memory tiles. For the applications without communications, the experiments show that the proposed scheme gives the best results in terms of peak load and standard deviation. For real applications such as mp3 decoder and h.263 encoder which have communications between tasks, we show the effectiveness of our communication-aware scheme for load balancing in terms of throughput.

By installing the various types of cells, imbalance in traffic load and excessive handover among cells in a heterogenous network can be prevalent. To deal with this problem, we propose a mobility-based cell association algorithm for load balancing in a heterogenous network. By defining a dynamic system load as a function of the mobility of mobile stations (MSs) and the transmit powers of cells, the proposed algorithm is designed such that it can optimize a utility function based on the fairness of the dynamic system load. Simulation results verify that the proposed algorithm improves the user perceived rate of MSs located at cell edges with slight increase in the number of handovers compared to a conventional cell association based on received signal strength.

This article describes an approximate model of a group of cells in the wireless 4G network with implemented load balancing mechanism. An appropriately modified model of Erlang's Ideal Grading is used to model this group of cells. The model makes it possible to take into account limited availability of resources of individual cells to multi-rate elastic and adaptive traffic streams generated by Erlang and Engset sources. The developed solution allows the basic traffic characteristics in the considered system to be determined, i.e. the occupancy distribution and the blocking probability. Because of the approximate nature of the proposed model, the results obtained based on the model were compared with the results of a digital simulation. The present study validates the adopted assumptions of the proposed model.

The hop-limited adaptive routing (HLAR) mechanism and its enhancement (EHLAR), both tailored for the packet-switched non-geostationary (NGEO) satellite networks, are proposed and evaluated. The proposed routing mechanisms exploit both the predictable topology and inherent multi-path property of the NGEO satellite networks to adaptively distribute the traffic via all feasible neighboring satellites. Specifically, both mechanisms assume that a satellite can send the packets to their destinations via any feasible neighboring satellites, thus the link via the neighboring satellite to the destination satellite is assigned a probability that is proportional to the effective transmission to the destination satellites of the link. The satellite adjusts the link probability based on the packet sending information observed locally for the HLAR mechanism or exchanged between neighboring satellites for the EHLAR mechanism. Besides, the path of the packets are bounded by the maximum hop number, thus avoiding the unnecessary over-detoured packets in the satellite networks. The simulation results corroborate the improved performance of the proposed mechanisms compared with the existing in the literature.

We demonstrated that load balancing using actual subscriber extension numbers was practical and effective against traffic congestion after a disaster based on actual data. We investigated the ratios of the same subscriber extension numbers in each prefecture and found that most of them were located almost evenly all over the country without being concentrated in a particular area. The ratio of every number except for the fourth-last digit in the last group of four numbers in a telephone number was used almost equally and located almost evenly all over the country. Tolerance against overload in the last, second-, and third-last single digits stays close to that in the ideal situation if we assume that each session initiation protocol server has a capacity in accordance with the ratio of each number on every single digit in the last group of four numbers in Japan. Although tolerance against overload in double-, triple-, and quadruple-digit numbers does not stay close to that in the ideal situation, it still remains sufficiently high in the case of double- and triple-digit numbers. Although tolerance against overload in the quadruple-digit numbers becomes low, disaster congestion is still not likely to occur in almost half of the area of Japan (23 out of 47 prefectures).

Recently, Hadoop has attracted much attention from engineers and researchers as an emerging and effective framework for Big Data. HDFS (Hadoop Distributed File System) can manage a huge amount of data with high performance and reliability using only commodity hardware. However, HDFS requires a single master node, called a NameNode, to manage the entire namespace (or all the i-nodes) of a file system. This causes the SPOF (Single Point Of Failure) problem because the file system becomes inaccessible when the NameNode fails. This also causes a bottleneck of efficiency since all the access requests to the file system have to contact the NameNode. Hadoop 2.0 resolves the SPOF problem by introducing manual failover based on two NameNodes, Active and Standby. However, it still has the efficiency bottleneck problem since all the access requests have to contact the Active in ordinary executions. It may also lose the advantage of using commodity hardware since the two NameNodes have to share a highly reliable sophisticated storage. In this paper, we propose a new HDFS architecture to resolve all the problems mentioned above.

At present, vast numbers of information resources are available on the Internet. However, one emerging issue is how to search and exploit these information resources in an efficient and flexible manner with high scalability. In this study, we focused our attention on the design of a distributed hash table (DHT)-based search system that supports efficient scalable multi-attribute queries of information resources in a distributed manner. Our proposed system, named D-AVTree, is built on top of a ring-based DHT, which partitions a one-dimensional key space across nodes in the system. It utilizes a descriptive naming scheme, which names each resource using an attribute-value (AV) tree, and the resource names are mapped to d-bit keys in order to distribute the resource information to responsible nodes based on a DHT routing algorithm. Our mapping scheme maps each AV branch of a resource name to a d-bit key where AV branches that share a subsequence of AV pairs are mapped to a continuous portion of the key space. Therefore, our mapping scheme ensures that the number of resources distributed to a node is small and it facilitates efficient multi-attribute queries by querying only a small number of nodes. Further, the scheme has good compatibility with key-based load balancing algorithms for DHT-based networks. Our system can achieve both efficiency and a good degree of load balancing even when the distribution of AV pairs in the resource names is skewed. Our simulation results demonstrated the efficiency of our solution in terms of the distribution cost, query hit ratio, and the degree of load balancing compared with conventional approaches.

Mobility load balancing (MLB) is a key technology for self-organization networks (SONs). In this paper, we explore the mobility load balancing problem and propose a unified cell specific offset adjusting algorithm (UCSOA) which more accurately adjusts the largely uneven load between neighboring cells and is easily implemented in practice with low computing complexity and signal overhead. Moreover, we evaluate the UCSOA algorithm in two different traffic conditions and prove that the UCSOA algorithm can get the lower call blocking rates and handover failure rates. Furthermore, the interdependency of the proposed UCSOA algorithm's performance and that of the inter-cell interference coordination (ICIC) algorithm is explored. A self-organization soft frequency reuse scheme is proposed. It demonstrates UCSOA algorithm and ICIC algorithm can obtain a positive effect for each other and improve the network performance in LTE system.