The emerging space-terrestrial integrated networks (STIN) are envisioned to provide seamless connectivity for global coverage. As a promising key component for STIN, the Low Earth Orbiting (LEO) satellite network still faces significant technological challenges in routing for ensuring both reachability and efficiency, due to frequent topology changes and uneven load distribution. Load-balancing routing is a feasible solution to improve efficiency by alleviating regional overload, however, it may encounter either slow convergences or local perceptions. In this paper, we propose a Neighbor Load Rank (NLR) based load-balancing routing for LEO satellite networks, where potential congestion at the queue buffer of a satellite node is characterized by load scores of its neighboring satellites, so as to reduce the perspective limitation of local load-balancing routing. To accelerate routing convergence and reduce computational complexity, we design region delineation, boundary penalty and directional incentive strategies to obtain the approximate minimum hop count path. Meanwhile, we employ the topology-stabilizing model (TSM) to convert the frequent satellite-ground interconnection changes into traffic fluctuations. Simulations demonstrate that NLR can maintain low-latency capacity with lower transmission overhead and effectively balance the overloaded traffic.

Low Earth Orbit (LEO) satellites are gradually developing towards to a large scale, in order to achieve a desired vision of ubiquitous connectivity and broadband access at anytime and anywhere. However, the increasing scale and highly dynamic nature of LEO constellation pose challenges to network management on its flexibility and scalability. Clustering is introduced as an effective approach to manage LEO satellite networks in a flexible manner. Unfortunately, satellite clusters encounter instability and high communication load due to frequent topology changes and traffic growth. In this paper, we design LEO satellites clustering models that jointly optimize cluster reliability and network communication load in the GEO/LEO network architecture. The coalition game framework is introduced to obtain a stable cluster structure by adopting an automated and centralized approach. A coalition formation algorithm based on the optimization of reliability and communication load is developed for the clustering problem. Finally, numerical simulations are carried out to evaluate the superiority and effectiveness of the proposed grouping and clustering scheme.

Load balancing in data center networks is crucial to effectively utilize network resources and enhance Quality of Service (QoS). Especially, the flowlet-level load balancing has been proven efficient in reducing latency and increasing throughput simultaneously. However, most existing work relying on empirical static timeout encounters performance degradation in dynamic network scenarios, due to a mismatch between the static timeout and changing traffic conditions. To address this problem, we propose a Deep Reinforcement Learning-Based Traffic-Aware Load Balancing scheme (DRL-TAL), which uses deep reinforcement learning (DRL) to update the flowlet timeout adaptively. The agent using a deep deterministic policy gradient (DDPG) algorithm continuously senses network throughput and generates the timeout threshold dynamically for the next time slot. The flowlet granularity is deployed for elephant flows to achieve a balance between throughput and disorder, where the timeout value relies on the threshold generated by the agent. Furthermore, the mice flow gets forwarded under packet granularity by selecting the port with the smallest queue length to ensure a shorter flow completion time. The results demonstrate that DRL-TAL performs impressively well in the symmetric topology, with no packet loss and minimal disorder under high load compared to the state-of-the-art schemes. Moreover, it significantly reduces flow completion time by up to 45% compared to Conga in the asymmetric topology.

With the development of edge-cloud computing technologies, distributed data centers (DCs) have been extensively deployed across the global Internet. Since different users/applications have heterogeneous requirements on specific types of ICT resources in distributed DCs, how to optimize such heterogeneous resources under dynamic and even uncertain environments becomes a challenging issue. Traditional approaches are not able to provide effective solutions for multi-dimensional resource allocation that involves the balanced utilization across different resource types in distributed DC environments. This paper presents a reinforcement learning based approach for multi-dimensional resource allocation (termed as NESRL-MRM) that is able to achieve balanced utilization and availability of resources in dynamic environments. To train NESRL-MRM’s agent with sufficiently quick wall-clock time but without the loss of exploration diversity in the search space, a natural evolution strategy (NES) is employed to approximate the gradient of the reward function. To realistically evaluate the performance of NESRL-MRM, our simulation evaluations are based on real-world workload traces from Amazon EC2 and Google datacenters. Our results show that NESRL-MRM is able to achieve significant improvement over the existing approaches in balancing the utilization of multi-dimensional DC resources, which leads to substantially reduced blocking probability of future incoming workload demands.

Deep convolutional neural networks (DCNNs) have been proposed as enhanced developments of neural networks (NNs) in the field of artificial intelligence (AI) and successfully applied in deep learning (DL) scenarios. With the advancement of technology, the number of network layers has continuously increased, resulting in a huge number of calculations and memory accesses required in the training and inference process of DCNNs and thereby hindering their further deployment and application. Using a specific dataflow formed by reusable DCNN data in the network-on-chip (NoC), reducing the memory access pressure and improving DCNN processing efficiency has become a promising acceleration schemes for the current DCNN. In this paper, a novel convolution layer (CONV) acceleration strategy based on logical PE set segmentation for row stationary (RS) dataflow is proposed to solve the problems of low flexibility and inefficient processing array utilization faced by the conventional folding mapping strategy. The simulation results show that the new mapping strategy based on PE set segmentation can achieve better processing element utilization and CONV acceleration improvement at the expense of little increase in the data movement energy consumption compared with the conventional strategy.

Software defined networking (SDN) can bring considerable flexibility to the management of inter-DC elastic optical networks. However, in the large-scale network, unreasonable placement of multiple SDN controllers may cause the unbalanced distribution of controller loads. To address this issue, we propose a two-sided matching method (TSMM) and design its corresponding algorithm to implement the optimal multi-controller placement. We solve the controller placement problem as the two-sided matching problem and consider optimizing multiple metrics which affect the controller placement. Different from previous research ideas, TSMM implements placement choice from the bilateral perspective of switch and controller. Additionally, TSMM algorithm can achieve the optimal controller placement by maximizing mutual satisfaction between controller and switch. The simulation results show that TSMM can achieve the optimal multi-controller placement and the balanced distribution of controller loads when compared with the existing schemes.

The servers in the data center networks have multi-dimensional physical resources, and there is a lot of diversity in resource consumption among tasks. When virtual machines carrying different user requests are deployed on the same server at the same time, it is very likely that there is an imbalanced usage of multi-dimensional resources, resulting in the waste of physical resources. In this paper, we focus on virtual machine placement in data centers aiming to balance multi-dimensional resource usage and maximize the service rate. To solve such a bi-objective optimization problem, we present a joint bin-packing heuristic and genetic algorithm to reduce the time complexity while obtaining an approximate optimal solution.

With the boom of Internet of Things (IoT), an increasing amount of data from IoT applications is moved to geo-distributed data centers (DCs) for data analysis. Massive compute-demanding applications call for a more flexible and efficient resource allocation for uncertain and heterogeneous traffic in geo-distributed multi-DC systems. Virtual network embedding, a major part of network virtualization, facilitates to provide different kinds of businesses or services by resource sharing. Moreover, due to their elasticity, elastic optical networks are viewed as a very promising solution to support inter-DC networks. This paper focuses on the effectiveness and spectrum fragmentation problem for virtual optical network embedding in elastic optical inter-DC networks by employing multidimensional resources and a topological attribute. In the node mapping, betweenness of a physical node is considered together with multidimensional resource carrying capacity (MRCC) to identify proper matching. Specifically, to reduce the influence of a spectrum fragment, the available spectrum continuity degree is coupled with the computing capacity of a physical node as the MRCC. In the link mapping, a tightest-matching factor is employed for the selection of paths to accommodate virtual links. Compared with baseline algorithms except for the integer linear programming (ILP) solution, analytical and numerous experiments show that our solution reduces the blocking probability by 30% on average, balances the load by 15% on average and improves spectral efficiency significantly. Moreover, our proposal has a slightly lower spectral efficiency but a better blocking performance and a much better link load balance than that of the ILP formulation.

Recently, many energy-aware routing algorithms are proposed to decrease the energy consumption of data center network. However, these methods ignore the effect of working time on energy consumption. In this paper, we analyze the energy consumption model and propose an energy-aware routing algorithm by jointly considering power consumption and working time. According to the simulation results, the flow driven energy-aware routing algorithm saves nearly 50 percent of energy compared with flow preemption energy-aware routing algorithm when transmission rate is limited by the available bandwidth, while it saves about 58.3 percent of energy compared with flow aggregation energy-aware routing algorithm when the transmission rate is limited by the forwarding rate of server's NIC.

To increase the performance of chip multiprocessors, optical network-on-chip (ONoC) becomes promising because of its high bandwidth and low energy consumption. In this paper, we propose an architecture called RPNoC (Ring-based Packet-switched NoC), which uses few optical devices. Specifically, Single-waveguide RPNoC employs only one waveguide. Multiwaveguide RPNoC introduces space division multiplexing to make the architecture highly scalable. A novel wavelength assignment method and a deadlock-free deterministic routing algorithm are jointly designed, which make the network diameter quite small. This design also guarantees deadlock freedom, a little resource use, and low complexity at the same time. Evaluation is carried out for the 64-node RPNoC under different synthetic and realistic traffic patterns. The simulation result shows that it yields high throughput and low latency. Comparison with other packet-switched ONoCs shows that RPNoC has the lowest energy consumption.

In the cloud computing era, the goal of data center network is not only to interconnect a large number of servers, but also to provide low latency and high bandwidth. A new 2-layer architecture, C-tree, is proposed for cloud computing in this paper. It is a flat topology, in which the horizontal traffic's latency is obviously lower than in traditional three-layer architectures. Also, C-tree can provide high bisection bandwidth which is important for bandwidth-intensive applications. We analyze C-tree theoretically and compare it with other architectures in scalability, accommodation capability, network diameter, bisection bandwidth, path diversity and regularity. We have developed a load balancing routing mechanism to balance the traffic on the parallel links. A network simulation platform is set up to verify the performance of C-tree.