Data Center Networks Research Articles

Application study of ant colony algorithm for network data transmission path scheduling optimization

Abstract With the rapid development of the information age, the traditional data center network management can no longer meet the rapid expansion of network data traffic needs. Therefore, the research uses the biological ant colony foraging behavior to find the optimal path of network traffic scheduling, and introduces pheromone and heuristic functions to improve the convergence and stability of the algorithm. In order to find the light load path more accurately, the strategy redefines the heuristic function according to the number of large streams on the link and the real-time load. At the same time, in order to reduce the delay, the strategy defines the optimal path determination rule according to the path delay and real-time load. The experiments show that under the link load balancing strategy based on ant colony algorithm, the link utilization ratio is 4.6% higher than that of ECMP, while the traffic delay is reduced, and the delay deviation fluctuates within ±2 ms. The proposed network data transmission scheduling strategy can better solve the problems in traffic scheduling, and effectively improve network throughput and traffic transmission quality.

Efficient data persistence and data division for distributed computing in cloud data center networks

Efficient data persistence and data division for distributed computing in cloud data center networks

Leveraging Software-Defined Networking for a QoS-Aware Mobility Architecture for Named Data Networking

The internet’s future architecture, known as Named Data Networking (NDN), is a creative way to offer content-based services. NDN is more appropriate for content distribution because of its special characteristics, such as naming conventions for packets and methods for in-network caching. Mobility is one of the main study areas for this innovative internet architecture. The software-defined networking (SDN) method, which is employed to provide mobility management in NDN, is one of the feasible strategies. Decoupling the network control plane from the data plane creates an improved programmable platform and makes it possible for outside applications to specify how a network behaves. The SDN is a straightforward and scalable network due to its key characteristics, including programmability, flexibility, and decentralized control. To address the problem of consumer mobility, we proposed an efficient SDPCACM (software-defined proactive caching architecture for consumer mobility) in NDN that extends the SDN model to allow mobility control for the NDN architecture (NDNA), through which the MC (mobile consumer) receives the data proactively after handover while the MC is moving. When an MC is watching a real-time video in a state of mobility and changing their position from one attachment point to another, the controllers in the SDN preserve the network layout and topology as well as link metrics to transfer updated routes with the occurrence of the handoff or handover scenario, and through the proactive caching mechanism, the previous access router proactively sends the desired packets to the new connected routers. Furthermore, the intra-domain and inter-domain handover processing situations in the SDPCACM for NDNA are described here in detail. Moreover, we conduct a simulation of the proposed SDPCACM for NDN that offers an illustrative methodology and parameter configuration for virtual machines (VMs), OpenFlow switches, and an ODL controller. The simulation result demonstrates that the proposed scheme has significant improvements in terms of CPU usage, reduced delay time, jitter, throughput, and packet loss ratio.

PFDCT: An Enhanced Transport Layer Protocol for Precise Flow Control in Data Centers

This paper proposes an enhanced transport layer protocol for precise flow control in data centers, referred to as PFDCT, combining explicit rate control (priority scheduling) and implicit rate control (marking algorithm with dynamic threshold) innovatively, while satisfying the transmission requirements of each data flow. It enables multiple data flows to share the bandwidth fairly, which is rarely achieved by previous data transmission protocols. Our design stems from the observation that different packets in a data center network need to be processed differentially and that the accuracy of the feedback of the indication information to the link state will directly affect the effectiveness of the congestion control mechanism. Our tests show that PFDCT has lower network jitter, better fairness, and significant performance gains over existing proposals (DCTCP, ICTCP, and L2DCT) in terms of Flow Completion Time (FCT) and throughput on average. Furthermore, we discussed the impact of the marking algorithm with dynamic thresholds on the performance of PFDCT, and the results of the experiments show that the addition of the marking algorithm will further improve the performance of PFDCT in terms of packet loss rate, latency, and FCT.

Data-centric Networking with Parallel and Distributed Architecture Performs Performance Analysis to Protect Critical Infrastructure in the Future Power System Network Based on IoT

Modern information and communication technologies are being incorporated into traditional power grid systems to create the smart grid of the real world. The newly provided information flow and intrinsic creation, transport, storage, and the use of electricity are all facilitated by the energy transfer, especially as the complete deployment of the Internet of Things in the power grid, also known as the power Internet of Things (PIoT). These new 5G technologies and the value generated by novel services and market processes can all be used to maximize the value of scarce resources like energy. This paper develops a framework for a cyber-physical power system based on IoT (CPPS-IoT). Automobiles, aircraft, defense, factory equipment, wellness equipment, industrial control, connected cars, and other sectors and industries are all benefiting from the fast growth of CPS technology. A smart electric grid is created when dispersed sources of energy and electrical infrastructure are linked together to provide global exchange of information, sensible decision, including true flexible control using the CPPS. Cyber-physical systems have great benefits because they combine IoT with physical processes and mediate how humans interact with the natural environment. Cyber-physical systems use sensor networks and embedded computers to keep tabs on and manipulate the physical world around them. They include built-in feedback loops that enable the environment to trigger their communication, control, or processing. The proposed method makes systems safer and more efficient, decreasing the cost of developing and running these systems. The accuracy is 89%, and the proposed method’s error rate is 48%.

Structure connectivity of data center networks

Last decade, numerous giant data center networks are built to provide increasingly fashionable web applications. For two integers m≥0 and n≥2, the m-dimensional DCell network with n-port switches Dm,n and n-dimensional BCDC network Bn have been proposed. As a generalization of connectivity, structure (substructure) connectivity was recently proposed. Let G and H be two connected graphs. Let F be a set whose elements are subgraphs of G, and every member of F is isomorphic to H (resp. a connected subgraph of H). Then H-structure connectivity κ(G;H) (resp. H-substructure connectivity κs(G;H)) of G is the size of a smallest set of F such that G−F is disconnected or the singleton. Then it is meaningful to calculate the structure connectivity of data center networks on some common structures, such as star K1,t, path Pk, cycle Ck, complete graph Ks and so on. In this paper, we obtain that κ(Dm,n;K1,t)=κs(Dm,n;K1,t)=⌈n−11+t⌉+m for 1≤t≤m+n−2 and κ(Dm,n;Ks)=⌈n−1s⌉+m for 3≤s≤n−1 by analyzing the structural properties of Dm,n. We also compute κ(Bn;H) and κs(Bn;H) for H∈{K1,t,Pk,Ck|1≤t≤2n−3,6≤k≤2n−1} and n≥5 by using g-extra connectivity of Bn.

Fault tolerability analysis of folded crossed cubes based on g-component and g-good neighbor fault pattern

As data center networks and network-on-chip are developing rapidly, many graph invariants have been proposed to measure the reliability and fault tolerance of the network so that the multiprocessor systems can be ensured to operate efficiently and smoothly. The g-component connectivity of a graph G, denoted by cκg(G), is the minimum size of vertex cut such that G has at least g components. The g-good neighbor connectivity of a graph G, represented by κg(G), is the minimum size of vertex cut such that each vertex has at least g fault-free neighbors. Motivated by the conditional connectivities above, the g-component diagnosability (ctg(G)) and g-good neighbor diagnosability (tg(G)) of graph G have been suggested to evaluate the fault diagnostic capability of multiprocessor systems successively. In this paper, we determine the (g+1)-component connectivity of folded crossed cubes FCQn, i.e., cκg+1(FCQn)=ng−g22+g2+1 for 1≤g≤⌊n2⌋, and (g+1)-component diagnosability of FCQn under the PMC model and MM⁎ model, i.e., ctg+1(FCQn)=ng+n−g22−g2+1. Moreover, we establish the g-good neighbor connectivity of FCQn, i.e., κg(FCQn)=(n+1−g)2g for 0≤g≤n−5, and g-good neighbor diagnosability of FCQn under the PMC model and MM⁎ model, i.e., tg(FCQn)=(n+2−g)2g−1.

R2: A Distributed Remote Function Execution Mechanism With Built-In Metadata

Named data networking (NDN) constructs a network by names, providing a flexible and decentralized way to manage resources within the edge computing continuum. This paper aims to solve the question, “Given a function with its parameters and metadata, how to select the executor in a distributed manner and obtain the result in NDN?” To answer it, we design R2 that involves the following stages. First, we design a name structure including data, function names, and other function parameters. Second, we develop a 2-phase mechanism, where in the first phase, the function request from a client-first reaches the data source and retrieves the metadata. Then the best node is selected while the metadata responds to the client. In the second phase, the chosen node directly retrieves the data, executes the function, and provides the result to the client. Furthermore, we propose a stop condition to intelligently reduce the processing time of the first phase and provide a simple proof and range analysis. Simulations confirm that R2 outperforms the current solutions in terms of resource allocation, especially when the data volume and the function complexity are high. In the experiments, when the data size is 100 KiB and the function complexity is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(n^{2})$ </tex-math></inline-formula> , the speedup ratio is 4.61. To further evaluate R2, we also implement a general intermediate data processing logic named “Bolt” implemented on an app-level in ndnSIM. We believe that R2 shall help the researchers and developers to verify their ideas smoothly.

Node Essentiality Assessment and Distributed Collaborative Virtual Network Embedding in Datacenters

Network virtualization (NV) has extensive and significant applications in cloud computing and parallel and distributed systems. Virtual network embedding (VNE) is a key issue in NV, which is an effective means to advance systems' performance. While existing VNE research lacks resource allocation coordination between mappings of different virtual network requests, resulting in insufficient resource utilization and high overhead. In this paper, we propose a novel node essentiality evaluation model for data center networks (DCNs), and design an efficient distributed collaborative virtual network embedding. Firstly, we propose a node essentiality evaluation scheme based on dynamic model, which combines the characteristics of network topology and nodes to make the evaluation results more comprehensive. Secondly, we establish the two-stage node importance evaluation criteria for the deviation mean of the data center dynamic model and the variance based on the deviation mean. Furthermore, we investigate a nodal importance assessment method based on the data center dynamic model for perturbation testing. Finally, we design a distributed coordinated VNE algorithm ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CNI-VNE</b> ) which calculates the importance index of physical nodes through topology awareness. The proposed algorithm can increase the coordination between different request mappings, thereby reducing the mapping cost of physical node resources and minimizing the cost of VNE. We use the real Fat-tree DCN of 128 servers and 80 switches as testbed, and evaluate them from indicators such as average reliability, average bandwidth consumption, average energy consumption, and average mapping time. Massive simulation results in different scenarios show that our algorithm achieves the best performance on most indicators compared with the existing state-of-the-art proposals, mapping acceptance and average revenue increased by 19.4% and 21.3%, respectively, and DCN reduced bandwidth consumption by about 30%.

Cost-effective anonymous data sharing with forward security using improved authentication

Cloud computing gives customers instant access to a network of remote servers, networks, and data centres. Cloud computing makes data analysis helpful to society and individuals. Sharing data with many people causes efficiency, integrity, and privacy issues. Ring signatures may enable secure and anonymous data transfer. It anonymizes data verification for cloud-based analytics. Identity-based (ID) ring signatures are becoming popular alternatives to public key infrastructure (PKI)-based public-key encryption. PKI bottlenecks are certificate verification time and cost. ID-based ring signatures speed up certificate verification. We observed that encrypting ID-based ring signatures with a variation of SHA-384 and adding forward security considerably improves their security. Padding divides the input text into 512-byte blocks and adds the length as a 48-bit value to the hash in newer SHA versions. Signatures made before a user's secret key was compromised are legitimate. If a user's secret key is compromised, it's impossible to compel all data owners to re-verify their data, hence a large-scale data sharing system must contain this feature. We implement, secure, and prove our method's use. SHA-384 is safer for cloud-based anonymous data sharing.

NACDA: Naming-Based Access Control and Decentralized Authorization for Secure Many-to-Many Data Sharing

The rapid development of wearable technology has facilitated the collection and sharing of health data, allowing patients to benefit from caretakers and medical research. However, these personal health data often contain sensitive information and it is typically not known in advance with whom the information will be shared. Therefore, messages must be encrypted and shared while adhering to the decoupled communication model. This paper presents NACDA, a secure many-to-many data-sharing service on the Named Data Network (NDN). NACDA uses Identity-Based Encryption with Wildcard Key Derivation (WKD-IBE) to allow naming-based access control, enabling data subjects to share data securely and flexibly regardless of the data processor. In addition, NACDA supplements a decentralized authorization mechanism with blockchain to ensure data subjects’ data ownership and enforce access policies. We developed an NDN-based prototype and performed a security analysis to demonstrate NACDA’s feasibility.

Load balancing inside programmable data planes based on network modeling prediction using a GNN with network behaviors

In data center networks, existing control plane- and end host-based load-balancing methods are encumbered by excessively large decision delays during rapid reactions to microbursts. However, existing programmable data plane-based load balancing methods require large overheads involved in active probing. Accurate network modeling can optimize load balancing. However, existing modeling methods suffer from low generalization and high overhead. In this study, we propose a network modeling method based on a graph neural network (GNN) with basic network behavior (hereinafter called GNN-Behavior). This is derived from two inherent correlations observed: the correlation between global network behavior and basic network behavior and the correlation among basic network behaviors. We employed the GNN with an improved message-passing neural network to learn such two inherent correlations. Particularly, we considered modeling end-to-end delay as a use case to validate GNN-Behavior. Furthermore, we propose a packet-level load-balancing scheme inside programmable data planes (PDPs) based on the accurate prediction of end-to-end delay from the GNN-Behavior model(LBPP). LBPP is a control plane-PDP collaborative method that integrates a global view from a controller and quick response from switches. Experimental results demonstrate the feasibility and effectiveness of the GNN-Behavior and LBPP. Compared with queuing theory (QT), RouteNet, and GNN-based scheme, GNN-Behavior increases goodness of fit (R2) by 73.1%, 11.1%, and 3.74%, respectively. Under an unknown traffic control strategy, the generalization ability of GNN-Behavior is considerably better than that of QT and RouteNet. Compared with flow-level ECMP, flowlet-level LetFlow, and packet-level DRILL, LBPP can reduce average flow completion time by up to 43.9%, 37.4%, and 17.2%, respectively.

Transmission characteristics of all-optically converted QPSK and 16QAM signals

Various types of all-optical modulation format conversions have been proposed for realizing low latency and efficient connection of networks that have different modulation formats. All-optical PAM4 to QPSK and 16QAM modulation format conversions using an HNLF and NOLM have been demonstrated for connecting data center networks. However, there has been no study on the transmission characteristics of the converted signal over a long distance. In this study, we numerically investigate the spectral efficiency and transmission characteristics of the QPSK and 16QAM signals generated by the all-optical modulation format converters. The all-optically converted 50 Gbps class QPSK and 100 Gbps class 16QAM signals require a bandwidth of 55 GHz to maintain the EVM below 10%. We demonstrate that the converted QPSK and 16QAM signals can be transmitted up to 19,500 km and 4,000 km, respectively, in both single and 8 channels transmission by preserving the sufficient channel spacing of 62.5 GHz. Because the transmission distance of the typical inter-data-center network is shorter than 200 km, the converted signals are applicable to inter-datacenter networks. Moreover, the QPSK and 16QAM signals converted from PAM4 signals after 40 km transmission in an intra-data center network are valid for transmission in inter-data-center networks.

PF-EdgeCache: Popularity and freshness aware edge caching scheme for NDN/IoT networks

Named Data Networking (NDN) is considered an appropriate architecture for IoT as it naturally supports consumer mobility and provides in-network caching capabilities as leverage to meet IoT requirements. Some caching techniques have been introduced to meet IoT application requirements and enforce the caching at the network edge. However, it remains challenging to design a popularity and freshness aware caching technique that places cached contents at the edge of the network as close to consumers as possible in a natural and simple manner without resorting to cumbersome networking mechanisms and hard-to-insure assumptions. In this paper, we propose PF-EdgeCache, an efficient popularity and freshness aware caching technique that naturally brings requested popular contents to the edge of the network in a manner fully compliant with the NDN standard. Simulations performed using the ndnSIM simulator and a large transit stub topology clearly show the competitiveness of PF-EdgeCache in terms of server hit reduction, eviction rate, and retrieval time compared to some representative work proposed in the literature.

Reinforced practical Byzantine fault tolerance consensus protocol for cyber physical systems

Cyber–physical systems (CPS) are becoming an essential component of modern life. CPS services enable information to be exchanged between physical devices and virtual systems. The increasing malicious activities causing confidential data leakage and incorrect performance of devices are posing challenges for protection against cyber threats. Therefore, development of effective solutions that can protect both CPS data and data exchange networks are extremely urgent. This study makes some improvements on Practical Byzantine Fault Tolerance (PBFT), and provides a critical analysis of the feasibility of using blockchain technology to protect constrained CPS data. It also justifies the choice of the improved PBFT for implementation on such devices and simulates the main distributed ledger scenarios. The improved PBFT works as follows: first, the client broadcasts the signed transaction data to the entire network, rather than just the master node, followed by a hash value comparison verification process; second, select the master node based on the longest chain principle, and punish the malicious node via the “blacklist” mechanism; third, add the data synchronization and verification process, as well as dynamically entering and leaving nodes via state transitions. Ultimately, the PBFT commit phase is eliminated, resulting in a two-stage process. In comparison to the original PBFT protocol, the Reinforced Practical Byzantine Fault Tolerance (RPBFT) protocol may substantially enhance system throughput and minimize consensus communication time, thereby improving overall system efficiency while guaranteeing security.

Reclaiming Light Dropped During Optical Modulation to Bias Avalanche Photodetectors

The introduction of avalanche photodetectors in optical interconnects can extend the unallocated optical budget while unleashing further opportunities to migrate to novel datacenter network architectures. The required high-voltage rail that biases the photodiodes would involve a specialized electronic circuitry but will be instead harvested directly at the optical layer, taking advantage of the dropped power during optical modulation at local transmitters. Rather than dumping light resulting from extinct space bits, we will collect this contribution and convert it to a high-voltage bias by means of a photovoltaic power conversion circuit that is shared among and powered from all constituent data lanes of the optical interconnect. We will experimentally demonstrate that this energy reclamation circuit can sustain the sourced current during avalanche photodetection whilst maintaining a bias rail at ∼25 V with continuity, as will be proven for up to 64 data lanes. We demonstrate an optical budget of ∼30 dB for 10 Gb/s/lane transmission, with a reception penalty as small as 0.2 dB with respect to an electrically biased photoreceiver. We will further elaborate on the limitations linked to the proposed concept, such as in terms of dynamic range.

An hybrid cluster-based data centric routing protocol assisted by mobile sink for IoT system

Nowadays, using mobile computing devices and the Internet of Things (IoT) in networks have posed several challenges to match up the forthcoming technological requirements. Wireless Sensors Network (WSN) is considered as an important component of the IoT which produces a massive amount of data (big data). However, dealing with limited capacities of the elementary components of a network in an IoT enabled WSN, is a key challenge. The existing approaches in the literature are inadequate for large networks and cannot be applied to IoT platform without adaptation. Data Centric Network (DCN) is an important notion for the future Internet architecture to resolve the problems related to big data manipulation. In fact, using a DCN strategy for the resource limited capacities WSN enabled IoT networks is beneficial to manage densely deployed and mobile components to enhance the data gathering mechanism. In this context, this paper proposes an IoT cluster based routing protocol for data centric mobile wireless sensors networks named ICMWSN. The proposed algorithm fits with a WSN belonging a large number of mobile sensors as well as a mobile sink. It is based on a clustering technique to form multi-hops clusters around fixed pre-elected CHs. Besides, an effective tour construction method is involved for the mobile sink to collect data from the eventual cluster heads. The extensive simulation results proved that ICMWSN outperformed the compared methods in terms of energy consumption, network lifetime and data delivery rate.

Multicore fiber interconnects for multi-terabit spine-leaf datacenter network topologies

This work demonstrates the use of spatial division multiplexing (SDM) to implement spine-leaf datacenter network topologies. We consider two possible scenarios, with the first consisting of the direct use of high-capacity multicore fiber interconnects. This was implemented with an 8-core multicore fiber to support 64×200Gb/s SDM wavelength division multiplexing (WDM) lanes and combined throughput of 12.8 Tb/s. We also demonstrate a scenario in which each multicore fiber interconnect is used to establish multiple connections between pairs of spin-leaf switches. An optical circuit switch is used between the spine and leaf layers to establish core-selective connections, which significantly reduces the number of cables required to implement the network. We demonstrate this approach with a commercial optical circuit switch and 8-core and 4-core multicore fiber interconnects.

Performance Evaluation of Data-Centric Networks Based on Parallel and Distributed Architectures for Machine Intelligence Research

Maintaining computer network security has long been an essential component of computer administration. Network security has become essential to companies’ safety and steady development in real-time implementation for diverse physical domains employing computers. Establishing a specific physical domain has consistently included a priority job of improving the safety of computer systems management. Study results on the practical implementation of machine intelligence in network security maintenance. Specifically, this paper examines and offers security management techniques for computer network data protection to establish an all-encompassing security shield for networked computers, with the ultimate goal of improving the safety and reliability of the net-worked computers used in commercial settings. Furthermore, the comprehensive research analysis determines an architectural model for machine intelligence-based network security maintenance (MI-NSM), implying an intrusion detection scheme with a novel neural network system. Labelling important properties of objects or data points and searching for commonalities allows the auto-mobile’s AI to distinguish between a human, the street, another car, and the sky. The simulation evaluation is performed using the NS2 simulator and observes the security maintenance efficiency over the current security solutions.

Solid-state 360° optical beamforming for reconfigurable multicast optical wireless communications.

Optical wireless communication is an attractive technique for data center interconnects due to its low latency line-of-sight connectivity. Multicast, on the other hand, is an important data center network function that can improve traffic throughput, reduce latency, and make efficient use of network resources. To enable reconfigurable multicast in data center optical wireless networks, we propose a novel 360° optical beamforming scheme based on the principle of superposition of orbital angular momentum modes, emitting beams from the source rack pointing towards any combination of other racks so that connections are established between the source and multiple destination racks. We experimentally demonstrate the scheme using solid state devices for a scenario where racks are arranged in a hexagonal formation in which a source rack can connect with any number of adjacent racks simultaneously, with each link transmitting 70 Gb/s on-off-keying modulations at bit error rates of <10-6 at 1.5-m and 2.0-m link distances.