Dynamic Routing of Multiple QoS-Required Flows in Cloud-Edge Autonomous Multi-Domain Data Center Networks

The 6th generation mobile networks(6G)network is a kind of multi-network interconnection and multi-scenario coexistence network,where multiple network domains break the original fixed boundaries to form connections and convergence.In this paper,with the optimization objective of maximizing network utility while ensuring flows performance-centric weighted fairness,this paper designs a reinforcement learning-based cloud-edge autonomous multi-domain data center network architecture that achieves single-domain autonomy and multi-domain collaboration.Due to the conflict between the utility of different flows,the bandwidth fairness allocation problem for various types of flows is formulated by considering different defined reward functions.Regarding the tradeoff between fairness and utility,this paper deals with the corresponding reward functions for the cases where the flows undergo abrupt changes and smooth changes in the flows.In addition,to accommodate the Quality of Service(QoS)requirements for multiple types of flows,this paper proposes a multi-domain autonomous routing algorithm called LSTM+MADDPG.Introducing a Long Short-Term Memory(LSTM)layer in the actor and critic networks,more information about temporal continuity is added,further enhancing the adaptive ability changes in the dynamic network environment.The LSTM+MADDPG algorithm is compared with the latest reinforcement learning algorithm by conducting experiments on real network topology and traffic traces,and the experimental results show that LSTM+MADDPG improves the delay convergence speed by 14.6%and delays the start moment of packet loss by 18.2%compared with other algorithms.

AMAD:Adaptive Mapping Approach for Datacenter Networks,an Energy-Friend Resource Allocation Framework via Repeated Leader Follower Game

Cloud Datacenter Network(CDN)providers usually have the option to scale their network structures to allow for far more resource capacities,though such scaling options may come with exponential costs that contradict their utility objectives.Yet,besides the cost of the physical assets and network resources,such scaling may also imposemore loads on the electricity power grids to feed the added nodes with the required energy to run and cool,which comes with extra costs too.Thus,those CDNproviders who utilize their resources better can certainly afford their services at lower price-units when compared to others who simply choose the scaling solutions.Resource utilization is a quite challenging process;indeed,clients of CDNs usually tend to exaggerate their true resource requirements when they lease their resources.Service providers are committed to their clients with Service Level Agreements(SLAs).Therefore,any amendment to the resource allocations needs to be approved by the clients first.In this work,we propose deploying a Stackelberg leadership framework to formulate a negotiation game between the cloud service providers and their client tenants.Through this,the providers seek to retrieve those leased unused resources from their clients.Cooperation is not expected from the clients,and they may ask high price units to return their extra resources to the provider’s premises.Hence,to motivate cooperation in such a non-cooperative game,as an extension to theVickery auctions,we developed an incentive-compatible pricingmodel for the returned resources.Moreover,we also proposed building a behavior belief function that shapes the way of negotiation and compensation for each client.Compared to other benchmark models,the assessment results showthat our proposed models provide for timely negotiation schemes,allowing for better resource utilization rates,higher utilities,and grid-friend CDNs.

How Chinese Financial Centers Integrate into Global Financial Center Networks:An Empirical Study Based on Overseas Expansion of Chinese Financial Service Firms

The increasing globalization of the Chinese economy has been enabled by both Chinese financial institutions operating globally as well as international firms operating within China. In geographical terms, this has been organized through a number of strategic cities serving as gateways for the exchange of financial functions, products and practices between China and the global economy. Drawing on location data of financial service firms in China listed on stock exchanges in Shenzhen, Shanghai and Hong Kong, this paper shows that Chinese financial firms are expanding globally and how Chinese financial centers are positioned and connected in the urban networks shaped by these financial service firms. It is found that Hong Kong, China, holds strategic positions in the integration of Chinese cities into global financial center networks, and that establishing a foothold in global financial centers such as New York and London has been a priority for Chinese financial institutions. The increasing capital flows directed by Chinese financial institutionssuggests a shifting global financial geography, with numerous Chinese cities playing increasingly important roles within global financial center networks.

Accurate and Robust Eye Center Localization via Fully Convolutional Networks

Eye center localization is one of the most crucial and basic requirements for some human-computer interaction applications such as eye gaze estimation and eye tracking. There is a large body of works on this topic in recent years, but the accuracy still needs to be improved due to challenges in appearance such as the high variability of shapes, lighting conditions, viewing angles and possible occlusions. To address these problems and limitations, we propose a novel approach in this paper for the eye center localization with a fully convolutional network(FCN),which is an end-to-end and pixels-to-pixels network and can locate the eye center accurately. The key idea is to apply the FCN from the object semantic segmentation task to the eye center localization task since the problem of eye center localization can be regarded as a special semantic segmentation problem. We adapt contemporary FCN into a shallow structure with a large kernel convolutional block and transfer their performance from semantic segmentation to the eye center localization task by fine-tuning.Extensive experiments show that the proposed method outperforms the state-of-the-art methods in both accuracy and reliability of eye center localization. The proposed method has achieved a large performance improvement on the most challenging database and it thus provides a promising solution to some challenging applications.

A novel recurrent neural network forecasting model for power intelligence center

In order to accurately forecast the load of power system and enhance the stability of the power network, a novel unascertained mathematics based recurrent neural network (UMRNN) for power intelligence center (PIC) was created through three steps. First, by combining with the general project uncertain element transmission theory (GPUET), the basic definitions of stochastic, fuzzy, and grey uncertain elements were given based on the principal types of uncertain information. Second, a power dynamic alliance including four sectors: generation sector, transmission sector, distribution sector and customers was established. The key factors were amended according to the four transmission topologies of uncertain elements, thus the new factors entered the power intelligence center as the input elements. Finally, in the intelligence handing background of PIC, by performing uncertain and recursive process to the input values of network, and combining unascertained mathematics, the novel load forecasting model was built. Three different approaches were put forward to forecast an eastern regional power grid load in China. The root mean square error (ERMS) demonstrates that the forecasting accuracy of the proposed model UMRNN is 3% higher than that of BP neural network (BPNN), and 5% higher than that of autoregressive integrated moving average (ARIMA). Besides, an example also shows that the average relative error of the first quarter of 2008 forecasted by UMRNN is only 2.59%, which has high precision.

An Efficient Priority-Driven Congestion Control Algorithm for Data Center Networks

With the emerging diverse applications in data centers,the demands on quality of service in data centers also become diverse,such as high throughput of elephant flows and low latency of deadline-sensitive flows.However,traditional TCPs are ill-suited to such situations and always result in the inefficiency(e.g.missing the flow deadline,inevitable throughput collapse)of data transfers.This further degrades the user-perceived quality of service(QoS)in data centers.To reduce the flow completion time of mice and deadline-sensitive flows along with promoting the throughput of elephant flows,an efficient and deadline-aware priority-driven congestion control(PCC)protocol,which grants mice and deadline-sensitive flows the highest priority,is proposed in this paper.Specifically,PCC computes the priority of different flows according to the size of transmitted data,the remaining data volume,and the flows’deadline.Then PCC adjusts the congestion window according to the flow priority and the degree of network congestion.Furthermore,switches in data centers control the input/output of packets based on the flow priority and the queue length.Different from existing TCPs,to speed up the data transfers of mice and deadline-sensitive flows,PCC provides an effective method to compute and encode the flow priority explicitly.According to the flow priority,switches can manage packets efficiently and ensure the data transfers of high priority flows through a weighted priority scheduling with minor modification.The experimental results prove that PCC can improve the data transfer performance of mice and deadline-sensitive flows while guaranting the throughput of elephant flows.

Data Center Network Architecture

1 Introduction The history of data centers can be traced back to the 1960s. Early data centers were deployed on main- frames that were time-shared by users via remote terminals. The boom in data centers came duringthe internet era. Many companies started building large inter- net-connected facililies,

SDN Orchestration for Dynamic End-to-End Control of Data Center Multi-Domain Optical Networking

New and emerging use cases, such as the interconnection of geographically distributed data centers(DCs), are drawing attention to the requirement for dynamic end-to-end service provisioning, spanning multiple and heterogeneous optical network domains. This heterogeneity is, not only due to the diverse data transmission and switching technologies, but also due to the different options of control plane techniques. In light of this, the problem of heterogeneous control plane interworking needs to be solved, and in particular, the solution must address the specific issues of multi-domain networks, such as limited domain topology visibility, given the scalability and confidentiality constraints. In this article, some of the recent activities regarding the Software-Defined Networking(SDN) orchestration are reviewed to address such a multi-domain control plane interworking problem. Specifically, three different models, including the single SDN controller model, multiple SDN controllers in mesh, and multiple SDN controllers in a hierarchical setting, are presented for the DC interconnection network with multiple SDN/Open Flow domains or multiple Open Flow/Generalized Multi-Protocol Label Switching( GMPLS) heterogeneous domains. I n addition, two concrete implementations of the orchestration architectures are detailed, showing the overall feasibility and procedures of SDN orchestration for the end-to-endservice provisioning in multi-domain data center optical networks.

STUDY ON DEVELOPMENT STRATEGY OF SHIPPING CENTERS AND TRANSPORTATION NETWORKS IN THE YELLOW SEA RIM

It is very important to establish cooperative mechanism to guarantee allmembers to develop their e-conomies in the Yellow Sea Rim. In this paper, the development strategiesof shipping centers and transportation network are discussed based on economic globalizationtendency. The results argue that a united transportation network should be built in order to promotethe economic competition of Northeast Asia in the world. As a key component of the economiccooperation, a hierarchical shipping centers network should be established with Hong Kong, Shanghai,Pusan, Kobe, and Tokyo as cores. The authorities of China, Japan, R. 0. Korea and D. P. R. Koreashould make more efforts to build a set of cooperation institutions based on raising thetransportation efficiency.

A Spatio-temporal Data Model for Road Network in Data Center Based on Incremental Updating in Vehicle Navigation System

The technique of incremental updating,which can better guarantee the real-time situation of navigational map,is the developing orientation of navigational road network updating.The data center of vehicle navigation system is in charge of storing incremental data,and the spatio-temporal data model for storing incremental data does affect the efficiency of the response of the data center to the requirements of incremental data from the vehicle terminal.According to the analysis on the shortcomings of several typical spatio-temporal data models used in the data center and based on the base map with overlay model,the reverse map with overlay model (RMOM) was put forward for the data center to make rapid response to incremental data request.RMOM supports the data center to store not only the current complete road network data,but also the overlays of incremental data from the time when each road network changed to the current moment.Moreover,the storage mechanism and index structure of the incremental data were designed,and the implementation algorithm of RMOM was developed.Taking navigational road network in Guangzhou City as an example,the simulation test was conducted to validate the efficiency of RMOM.Results show that the navigation database in the data center can response to the requirements of incremental data by only one query with RMOM,and costs less time.Compared with the base map with overlay model,the data center does not need to temporarily overlay incremental data with RMOM,so time-consuming of response is significantly reduced.RMOM greatly improves the efficiency of response and provides strong support for the real-time situation of navigational road network.

Workload-aware request routing in cloud data center using software-defined networking

Large latency of applications will bring revenue loss to cloud infrastructure providers in the cloud data center. The existing controllers of software-defined networking architecture can fetch and process traffic information in the network. Therefore, the controllers can only optimize the network latency of applications. However, the serving latency of applications is also an important factor in delivered user-experience for arrival requests. Unintelligent request routing will cause large serving latency if arrival requests are allocated to overloaded virtual machines. To deal with the request routing problem, this paper proposes the workload-aware software-defined networking controller architecture. Then, request routing algorithms are proposed to minimize the total round trip time for every type of request by considering the congestion in the network and the workload in virtual machines（VMs）. This paper finally provides the evaluation of the proposed algorithms in a simulated prototype. The simulation results show that the proposed methodology is efficient compared with the existing approaches.

Research on the Trusted Energy-Saving Transmission of Data Center Network

According to the high operating costs and a large number of energy waste in the current data center network architectures, we propose a kind of trusted flow preemption scheduling combining the energy-saving routing mechanism based on typical data center network architecture. The mechanism can make the network flow in its exclusive network link bandwidth and transmission path, which can improve the link utilization and the use of the network energy efficiency. Meanwhile, we apply trusted computing to guarantee the high security, high performance and high fault-tolerant routing forwarding service, which helps improving the average completion time of network flow.

Modeling TCP Incast Issue in Data Center Networks and an Adaptive Application-Layer Solution

In data centers, the transmission control protocol(TCP) incast causes catastrophic goodput degradation to applications with a many-to-one traffic pattern. In this paper, we intend to tame incast at the receiver-side application. Towards this goal, we first develop an analytical model that formulates the incast probability as a function of connection variables and network environment settings. We combine the model with the optimization theory and derive some insights into minimizing the incast probability through tuning connection variables related to applications. Then,enlightened by the analytical results, we propose an adaptive application-layer solution to the TCP incast.The solution equally allocates advertised windows to concurrent connections, and dynamically adapts the number of concurrent connections to the varying conditions. Simulation results show that our solution consistently eludes incast and achieves high goodput in various scenarios including the ones with multiple bottleneck links and background TCP traffic.

Detection of Multiscale Center Point Objects Based on Parallel Network

Anchor-based detectors are widely used in object detection.To improve the accuracy of object detection,multiple anchor boxes are intensively placed on the input image,yet.Most of which are invalid.Although the anchor-free method can reduce the number of useless anchor boxes,the invalid ones still occupy a high proportion.On this basis,this paper proposes a multiscale center point object detection method based on parallel network to further reduce the number of useless anchor boxes.This study adopts the parallel network architecture of hourglass-104 and darknet-53 of which the first one outputs heatmaps to generate the center point for object feature location on the output attribute feature map of darknet-53.Combining feature pyramid and CIoU loss function,this algorithm is trained and tested on MSCOCO dataset,increasing the detection rate of target location and the accuracy rate of small object detection.Though resembling the state-of-the-art two-stage detectors in overall object detection accuracy,this algorithm is superior in speed.

Load Balancing Fat-Tree on Long-Lived Flows:Avoiding Congestion in a Data Center Network

In a data center network （DCN）, load balancing is required when servers transfer data on the same path. This is necessary to avoid congestion. Load balancing is challenged by the dynamic transferral of demands and complex routing control. Because of the distributed nature of a traditional network, previous research on load balancing has mostly focused on improving the performance of the local network; thus, the load has not been optimally balanced across the entire network. In this paper, we propose a novel dynamic load-balancing algorithm for fat-tree. This algorithm avoids congestions to the great possible extent by searching for non-conflicting paths in a centralized way. We implement the algorithm in the popular software-defined networking architecture and evaluate the algorithm＇ s performance on the Mininet platform. The results show that our algorithm has higher bisection band- width than the traditional equal-cost multi-path load-balancing algorithm and thus more effectively avoids congestion.

SOPA:Source Routing Based Packet-Level Multi-Path Routing in Data Center Networks

Many ＂rich - connected＂ topologies with multiple parallel paths between smwers have been proposed for data center networks recently to provide high bisection bandwidth, but it re mains challenging to fully utilize the high network capacity by appropriate multi- path routing algorithms. As flow-level path splitting may lead to trafl＇ic imbalance between paths due to flow- size difference, packet-level path splitting attracts more attention lately, which spreads packets from flows into multiple available paths and significantly improves link utilizations. However, it may cause packet reordering, confusing the TCP congestion control algorithm and lowering the throughput of flows. In this paper, we design a novel packetlevel multi-path routing scheme called SOPA, which leverag- es OpenFlow to perform packet-level path splitting in a round- robin fashion, and hence significantly mitigates the packet reordering problem and improves the network throughput. Moreover, SOPA leverages the topological feature of data center networks to encode a very small number of switches along the path into the packet header, resulting in very light overhead. Compared with random packet spraying （RPS）, Hedera and equal-cost multi-path routing （ECMP）, our simulations demonstrate that SOPA achieves 29.87%, 50.41% and 77.74% higher network throughput respectively under permutation workload, and reduces average data transfer completion time by 53.65%, 343.31% and 348.25% respectively under production workload.

Congestion Control Using In-Network Telemetry for Lossless Datacenters

In the Ethernet lossless Data Center Networks (DCNs) deployedwith Priority-based Flow Control (PFC), the head-of-line blocking problemis still difficult to prevent due to PFC triggering under burst trafficscenarios even with the existing congestion control solutions. To addressthe head-of-line blocking problem of PFC, we propose a new congestioncontrol mechanism. The key point of Congestion Control Using In-NetworkTelemetry for Lossless Datacenters (ICC) is to use In-Network Telemetry(INT) technology to obtain comprehensive congestion information, which isthen fed back to the sender to adjust the sending rate timely and accurately.It is possible to control congestion in time, converge to the target rate quickly,and maintain a near-zero queue length at the switch when using ICC. Weconducted Network Simulator-3 (NS-3) simulation experiments to test theICC’s performance. When compared to Congestion Control for Large-ScaleRDMA Deployments (DCQCN), TIMELY: RTT-based Congestion Controlfor the Datacenter (TIMELY), and Re-architecting Congestion Managementin Lossless Ethernet (PCN), ICC effectively reduces PFC pause messages andFlow Completion Time (FCT) by 47%, 56%, 34%, and 15.3×, 14.8×, and11.2×, respectively.

Exploring High-Performance Architecture for Data Center Networks

As a critical infrastructure of cloud computing,data center networks(DCNs)directly determine the service performance of data centers,which provide computing services for various applications such as big data processing and artificial intelligence.However,current architectures of data center networks suffer from a long routing path and a low fault tolerance between source and destination servers,which is hard to satisfy the requirements of high-performance data center networks.Based on dual-port servers and Clos network structure,this paper proposed a novel architecture RClos to construct high-performance data center networks.Logically,the proposed architecture is constructed by inserting a dual-port server into each pair of adjacent switches in the fabric of switches,where switches are connected in the form of a ring Clos structure.We describe the structural properties of RClos in terms of network scale,bisection bandwidth,and network diameter.RClos architecture inherits characteristics of its embedded Clos network,which can accommodate a large number of servers with a small average path length.The proposed architecture embraces a high fault tolerance,which adapts to the construction of various data center networks.For example,the average path length between servers is 3.44,and the standardized bisection bandwidth is 0.8 in RClos(32,5).The result of numerical experiments shows that RClos enjoys a small average path length and a high network fault tolerance,which is essential in the construction of high-performance data center networks.

Fast and scalable routing protocols for data center networks

Data center networks may comprise tens or hundreds of thousands of nodes,and,naturally,suffer from frequent software and hardware failures as well as link congestions.Packets are routed along the shortest paths with sufficient resources to facilitate efficient network utilization and minimize delays.In such dynamic networks,links frequently fail or get congested,making the recalculation of the shortest paths a computationally intensive problem.Various routing protocols were proposed to overcome this problem by focusing on network utilization rather than speed.Surprisingly,the design of fast shortest-path algorithms for data centers was largely neglected,though they are universal components of routing protocols.Moreover,parallelization techniques were mostly deployed for random network topologies,and not for regular topologies that are often found in data centers.The aim of this paper is to improve scalability and reduce the time required for the shortest-path calculation in data center networks by parallelization on general-purpose hardware.We propose a novel algorithm that parallelizes edge relaxations as a faster and more scalable solution for popular data center topologies.

CAS COMPUTER NETWORK INFORMATION CENTER

The Computer Network Information Center(CNIC) is a newly-established CAS institute,which is engaged in the research and development of network and scientific databases.At present the Center is in charge of two key projects:a scientific database project and a national computing and networking facility of China (NCFC).