Software-defined networking (SDN) decouples the control and forwarding of network devices, providing benefits such as simplified control. However, due to cost constraints and other factors, SDN is difficult to fully deploy. It has been proposed that SDN devices can be incrementally deployed in a traditional IP network, i.e., hybrid SDN, to provide partial SDN benefits. Studies have shown that better traffic engineering performance can be achieved by modifying the coverage and placement of SDN devices in hybrid SDN, because they can influence the behavior of legacy switches through certain strategies. However, it is difficult to develop and execute a traffic engineering strategy in hybrid SDN. This article proposes a routing algorithm to achieve approximate load balancing, which minimizes the maximum link utilization by using the optimal solution of linear programming and merging the minimum split traffic flows. A multipath forwarding mechanism under the same problem is designed to optimize transmission time. Experiments show that our algorithm has certain advantages in link utilization and transmission time compared to traditional distributed routing algorithms like OSPF and some hybrid SDN routing mechanisms. Furthermore, our algorithm can approximate the control effect of full SDN when the deployment rate of SDN devices is 40%.

This letter proposes a load balance and power transfer scheme among small cell base stations (SBSs) to maximize the sum rate of small cell network. In the proposed scheme, small cell users (SUEs) are firstly associated with their nearest SBSs, then the overloaded SBSs can be determined. Further, the methods, i.e., Case 1: SUEs of overloaded SBSs are offloaded to their neighbor underloaded SBSs or Case 2: SUEs of overloaded SBSs are served by their original associated SBSs through obtaining power from their nearby SBSs that can provide higher data rate is selected. Finally, numerical simulations demonstrate that the proposed scheme has better performance.

The periodic disaster backup activity among geographically distributed multiple datacenters consumes huge network resources and therefore imposes a heavy burden on datacenters and transmission links. Previous work aims at least completion time, maximum utility or minimal cost, without consideration of load balance for limited network resources, likely to result in unfair distribution of backup load or significant impact on daily network services. In this paper, we propose a new progressive forwarding disaster backup strategy in the Software Defined Network scenarios to mitigate forwarding burdens on source datacenters and balance backup loads on backup datacenters and transmission links. We construct a new redundancy-aware time-expanded network model to divide time slots according to redundancy requirement, and propose role-switching method over time to utilize forwarding capability of backup datacenters. In every time slot, we leverage two-step optimization algorithm to realize capacity-constrained backup datacenter selection and fair backup load distribution. Simulations results prove that our strategy achieves good performance in load balance under the condition of guaranteeing transmission completion and backup redundancy.

Linux container technology and clusters of the containers are expected to make web services consisting of multiple web servers and a load balancer portable, and thus realize easy migration of web services across the different cloud providers and on-premise datacenters. This prevents service to be locked-in a single cloud provider or a single location and enables users to meet their business needs, e.g., preparing for a natural disaster. However existing container management systems lack the generic implementation to route the traffic from the internet into the web service consisting of container clusters. For example, Kubernetes, which is one of the most popular container management systems, is heavily dependent on cloud load balancers. If users use unsupported cloud providers or on-premise datacenters, it is up to users to route the traffic into their cluster while keeping the redundancy and scalability. This means that users could easily be locked-in the major cloud providers including GCP, AWS, and Azure. In this paper, we propose an architecture for a group of containerized load balancers with ECMP redundancy. We containerize Linux ipvs and exabgp, and then implement an experimental system using standard Linux boxes and open source software. We also reveal that our proposed system properly route the traffics with redundancy. Our proposed load balancers are usable even if the infrastructure does not have supported load balancers by Kubernetes and thus free users from lock-ins.

With shortest path bridging MAC (SPBM), shortest path trees are computed based on link metrics from each node to all other participating nodes. When an edge bridge receives a frame, it selects a path along which to forward the frame to its destination node from multiple shortest paths. Blocking ports are eliminated to allow full use of the network links. This approach is expected to use network resources efficiently and to simplify the operating procedure. However, there is only one multipath distribution point in the SPBM network. This type of network can be defined as an end-to-end multipath network. Edge bridges need to split flows to achieve the load balancing of the entire network. This paper proposes a rate-based path selection scheme that can be employed for end-to-end multipath networks including SPBM. The proposed scheme assumes that a path with a low average rate will be congested because the TCP flow rates decrease on a congested path. When a new flow arrives at an edge bridge, it selects the path with the highest average rate since this should provide the new flow with the highest rate. The performance of the proposed scheme is confirmed by computer simulations. The appropriate timeout value is estimated from the expected round trip time (RTT). If an appropriate timeout value is used, the proposed scheme can realize good load balancing. The proposed scheme improves the efficiency of link utilization and throughput fairness. The performance is not affected by differences in the RTT or traffic congestion outside the SPBM network.

Most existing deterministic multithreading systems are costly on pipeline parallel programs due to load imbalance. In this letter, we propose a Load-Balanced Deterministic Runtime (LBDR) for pipeline parallelism. LBDR deterministically takes some tokens from non-synchronization-intensive threads to synchronization-intensive threads. Experimental results show that LBDR outperforms the state-of-the-art design by an average of 22.5%.

By exploiting data-level parallelism, Graphics Processing Units (GPUs) have become a high-throughput, general purpose computing platform. Many real-world applications especially those following a stream processing pattern, however, feature interleaved task-pipelined and data parallelism. Current GPUs are ill equipped for such applications due to the insufficient usage of computing resources and/or the excessive off-chip memory traffic. In this paper, we focus on microarchitectural enhancements to enable task-pipelined execution of data-parallel kernels on GPUs. We propose an efficient adaptive dynamic scheduling mechanism and a moderately modified L2 design. With minor hardware overhead, our techniques orchestrate both task-pipeline and data parallelisms in a unified manner. Simulation results derived by a cycle-accurate simulator on real-world applications prove that the proposed GPU microarchitecture improves the computing throughput by 18% and reduces the overall accesses to off-chip GPU memory by 13%.

MapReduce is a parallel processing framework for large scale data. In the reduce phase, MapReduce employs the hash scheme in order to distribute data sharing the same key across cluster nodes. However, this approach is not robust for the skewed data distribution. In this paper, we propose a skew-tolerant key distribution method for MapReduce. The proposed method assigns keys to cluster nodes balancing their workloads. We implemented our proposed method on Hadoop. Through experiments, we evaluate the performance of the proposed method in comparison with the conventional method.

The Medium Access Control (MAC) protocol that uses non-overlapping multiple channels, called the multi-channel MAC protocol, was proposed in order to increase the capacity of ad hoc networks. Since the number of packet interfaces on each node is less than the number of channels in ad hoc networks in general, the node needs to select a suitable channel for data transmission. This means that the multi-channel MAC protocol must be provided with a good channel selection algorithm. In this paper, we design a channel selection algorithm called Conditionally Randomized Channel Selection (CRCS) based on Extended Receiver Directed Transmission (xRDT) protocol that only uses one packet interface. Briefly, CRCS uses the acitve channel for data transmission until the amount of data packets reaches a threshold, at which point it selects one of the available channels other than the active channel. Although CRCS is a very simple channel selection algorithm, by using network simulator we find that CRCS is effective to increase the capacity of ad hoc networks and to keep the load balance of all channels compared to the other channel selection algorithms.

Wireless Sensor and Actor Networks (WSAN) are commonly used to monitor physical parameters and execute opportune actions in response to specific events. In order to achieve this goal it is necessary to implement efficient coordination and cooperation among the network nodes (i.e. sensors and actors) with the aim of reducing the energy consumption and improving the response time of the system. This work propose a clustering mechanism that organizes the sensor nodes to form clusters where the mobile actors nodes in the WSAN perform the cluster head role. The proposal considers the mobility aspect of the actor nodes and implements a mechanism to dynamically change the geographical location of the actors while trying to reduce the load in terms of the number of sensors within each cluster, all this with the aim of extending the network lifetime.

Wireless mesh networks have been attracting many users in recent years. By connecting base stations (mesh nodes) with wireless connections, these network can achieve a wide-area wireless environment with flexible configuration and low cost at the risk of radio interference between wireless links. When we utilize wireless mesh networks as infrastructures for Internet access, all network traffic from mobile nodes goes through a gateway node that is directly connected to the wired network. Therefore, it is necessary to distribute the traffic load by deploying multiple gateway nodes. In this paper, we propose a spanning tree construction algorithm for TDMA-based wireless mesh networks with multiple gateway nodes so as to maximize the traffic volume transferred between the mesh network and the Internet (system throughput) by taking account of the traffic load on the gateway nodes, the access link capacity and radio interference. Through a performance evaluation, we show that the proposed algorithm increases the system throughput regardless of the bottleneck position and achieves up to 3.1 times higher system throughput than a conventional algorithm.

A novel mobile assignment method based on transmit power and cell load is proposed for WCDMA base station location planning. Experimental results show that, compared with the currently widely used mobile assignment method based on link attenuation, the proposed mobile assignment method is more reasonable and unnecessary base stations are reduced in the planning results.

In order to benefit from the advantages of soft handoff (SHO), it is important that the SHO parameters (the SHO thresholds; T_ADD and T_DROP are well assigned. T_ADD is the threshold used for triggering a pilot with high strength to be added to the Active Set (AS) list. The AS means the pilots associated with the forward traffic channels assigned to mobile station. In contrast, T_DROP is the threshold used for triggering a pilot with low strength to be dropped from the AS list. This paper analyzes the effects of varying SHO thresholds in a cellular code division multiple access (CDMA) system on the blocking probability based on traffic load and geometrical distances in hexagonal layout of base stations (BSs). In addition, the previously proposed traffic load equation is applied to the proposed SHO model for balancing the numbers of new and handoff calls on the forward link capacity in case of uniform traffic load. The results show that the blocking probability is more sensitive to T_DROP than to T_ADD variations.

This paper first examines the issues related to scheduling loop applications on a software distributed shared memory (DSM) system. Then, a dynamic scheduling scheme is developed based on the examined issues to enhance the performance of loop applications on DSM. Compared with previous works, the proposed scheme has several specialties. The first is that the workload of processors can be effectively balanced even when the computational capabilities of processors and the computational needs of threads are not identical. The second is it divides thread mapping into two phases, each with one consideration, i.e., load balance or communication cost, and adopts thread migration and exchange in the two phases, respectively. The third is the exploitation of data sharing among threads to reduce data-consistency communication, and the last is to attack the negative effect of the unnecessary inter-node sharing caused by thread re-mapping. The proposed scheme has been implemented on a page-based DSM system called Cohesion. Our experiments show that the proposed scheme is more effective to improve the performance of the test programs than related schemes.