Cloud Data Center Networks Research Articles

Maximum elastic scheduling of virtual machines in general graph cloud data center networks

ABSTRACT In this research, we pioneer a novel method to evaluate the maximum admissible load (MAL) for virtual machines (VMs) in physical machines (PMs) in data centre networks (DCNs), without restricting DCN topologies. This unique approach simplifies the issue into a single-source, multiple-sink maximum flow problem. It also resolves the maximum elastic scheduling problem by determining the optimal load for consistent growth without reassigning tasks. An effective strategy for these challenges is introduced and validated through extensive simulations.

A Weighted Optimal Scheduling Scheme for Congestion Control in Cloud Data Center Networks

Cloud services are experiencing a remarkable increase in the number of users and the resource required over the past few years. Thus, it has become a great challenge for the internet vendors to make a robust framework to serve the customers with low cost and delay. Congestion control is one of the essential topics of routing algorithms in cloud data center networks. In this paper, we propose a weighted optimal scheduling scheme WSPR for congestion control in cloud data center networks which prevents the congestion in advance with the global view so that it can make good use of vacant network resources. We choose BCube as our network model and modify the network topology to fit software-defined networks so as to have a full view of the topology. First, we design the SP graph which contains all shortest paths between a source server and a destination server. Second, we propose WSPR to allocate the most appropriate path to each flow for congestion control. We implement a system to simulate a data center, and evaluate our proposed scheme WSPR by comparing WSPR with other classical methods. The experimental results demonstrate that our proposed scheme WSPR has the best performance in terms of the maximum delay, average delay, and throughput among all compared methods.

RibsNet: A Scalable, High-Performance, and Cost-Effective Two-Layer-Based Cloud Data Center Network Architecture

Today, we witness an unprecedented surge of data traffic due to the explosive growth of smart devices and intelligent applications. Hence, supporting the Data Center Networks (DCNs) infrastructure by accommodating myriads of servers becomes natural and inevitable. This vision can be realized by providing an interconnecting architecture with higher scalability and remarkable capacity while maintaining fault tolerance, minimum latency, and cost and power efficiency. Motivated by this trend, this paper proposes a new network based on dual-centric dubbed RibsNet, which is a symmetric network composed of two layers of switches and dual-port servers. The proposed network enables the servers to establish all kinds of connections (server-switch and server-server), thus enhancing network fault tolerance with high bisection width and maximizing its performance and reliability. RibsNet allows the gradual addition of servers while preserving all its topological properties; it has a stable and low diameter, which does not exceed six regardless of the DCN’s size. Furthermore, an efficient and fault tolerant routing scheme, exclusively designed for this network, is proposed to enhance the routing efficiency and address different types of failures. Theoretical analysis proves that RibsNet strikes a good balance among several connections within the network and brings outstanding gains in terms of incremental scalability, bisection width, and cost and power savings against various cutting-edge DCNs architectures. Simulation results demonstrate RibsNet as a promising structure that outperforms BCube, DCell, HSDC, and FiConn in terms of latency and throughput by approximately 8%, 6%, 7%, and 26%, respectively.

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

Dynamic Parallel Flow Algorithms With Centralized Scheduling for Load Balancing in Cloud Data Center Networks

BCube is a well-known recursively defined network structure. It provides multiple low-diameter paths and good fault-tolerance for data center networks (DCNs). Its distributed routing algorithm, BCube Source Routing (BSR), can be deployed rapidly and conveniently to build multiple parallel path sets. But in the worst case, BSR may suffer from flow collisions and waste $50\%$ of the capacity of each BCube link. In this paper, to fully use those redundant links and to improve BCube load balancing, we supplement the BCube topology with a central master computer and we design two centralized dynamic parallel flow scheduling algorithms: CDPFS and CDPFSMP, for single-path and multi-path respectively. We focus on finding the least congested path for each flow by analyzing the information about the state of the global network. Furthermore, we allocate those paths to each flow in parallel. The simulation result shows that our proposed algorithms take advantage of BCube structure and deliver high-performance solutions for load balancing problems.

Reliability Assurance Dynamic SSC Placement Using Reinforcement Learning

Software-defined networking (SDN) and network function virtualization (NFV) make a network programmable, resulting in a more flexible and agile network. An important and promising application for these two technologies is network security, where they can dynamically chain virtual security functions (VSFs), such as firewalls, intrusion detection systems, and intrusion prevention systems, and thus inspect, monitor, or filter traffic flows in cloud data center networks. In view of the strict delay constraints of security services and the high failure probability of VSFs, we propose the use of a security service chain (SSC) orchestration algorithm that is latency aware with reliability assurance (LARA). This algorithm includes an SSC orchestration module and VSF backup module. We first use a reinforcement learning (RL) based Q-learning algorithm to achieve efficient SSC orchestration and try to reduce the end-to-end delay of services. Then, we measure the importance of the physical nodes carrying the VSF instance and backup VSF according to the node importance of VSF. Extensive simulation results indicate that the LARA algorithm is more effective in reducing delay and ensuring reliability compared with other algorithms.

Performance evaluation of transport protocols in cloud data center networks

Performance evaluation of transport protocols in cloud data center networks

Moving Target Defense for SDN-Based Cloud Datacenter Network Protection

The significant advance of software Defined Networking (SDN) technology has enabled several complex system operations to be highly dynamic, flexible and robust; particularly in terms of programmability and controllability with the help of SDN controllers. Accordingly, many security operations have utilized this capability to be optimally deployed in a complex network using the SDN functionalities. Moving target defense (MTD) has emerged as an adaptive and proactive defense mechanism aiming to thwart a potential attacker. The key underlying idea of MTD is to increase uncertainty and confusion for attackers by changing attack surface (i.e., system or network configurations) that can invalidate the intelligence collected by the attackers and interrupt attack execution; ultimately leading to attack failure. In this research, by leveraging the advanced SDN technology, the model of MTD using SDN-based system framework design is proposed. The model uses a runtime model that allows the proposed framework to infer the current state of the system. Based on the obtained information, the MTD mechanism using SDN can provide proactive, adaptive and affordable defense services for the exploitable aspects of the cloud datacenter network to increase uncertainty and complexityto the attackers and reduce the likelihood of an attack and minimize cloud security risk. The research also validates the outperformance of the proposed MTD technique in attack success rate via simulation on SDN-based cloud datacenter network experiments in a virtualized environment

Doughnutie: An efficient and low‐latency cloud data center network architecture

SummaryData center network provides the supporting infrastructure for cloud computing. Torus interconnection network is widely employed in the cloud data center networks. However, its long network diameter and average path length (APL) are prohibitive factors for supporting the delay‐sensitive applications. With the aim of meeting latency, network capacity, fault‐tolerance, scalability, and energy‐efficiency requirements of cloud data center networks, we propose a new data center network architecture, named Doughnutie. Doughnutie is built by adding a number of flyway connections to the base Torus network to ameliorate its performance. Doughnutie does not follow a single principle for establishing all of its flyways and consequently, each part of the network can profit from all local, faraway, and neither‐local‐nor‐faraway types of connection. Doughnutie decreases both network diameter and APL, which in turn leads to higher communication efficiency and lower network latency. Moreover, an exclusively designed loop‐free load‐balanced adaptive routing algorithm, named Doughnuter, is proposed. Employing the Doughnuter routing algorithm results in more efficient utilization of all the Doughnutie network resources. Theoretical analysis and simulation results also prove the superiorities of Doughnutie in throughput, load balancing, fault tolerance, and energy‐efficiency.

Virtual Network Recognition and Optimization in SDN-Enabled Cloud Environment

Cloud computing is a scalable and efficient technology for providing different services. For better reconfigurability and other purposes, users build virtual networks in cloud environments. Since some applications bring heavy pressure to cloud datacenter networks, it is necessary to recognize and optimize virtual networks with different applications. In some cloud environments, cloud providers are not allowed to monitor user private information in cloud instances. Therefore, in this paper, we present a virtual network recognition and optimization method to improve quality-of-service (QoS) of cloud services. We first introduce a community detection method to recognize virtual networks from the cloud datacenter network. Then, we design a scheduling strategy by combining SDN-based network management and instance placement to improve the service-level agreements (SLA) fulfillment. Our experimental result shows that we can achieve a recognition accuracy as high as 80 percent to find out the virtual networks, and the scheduling strategy increases the number of SLA fulfilled virtual networks.

A port-based forwarding load-balancing scheduling approach for cloud datacenter networks

Today’s datacenter networks (DCNs) scale is rapidly increasing because of the wide deployment of cloud services and the rapid rise of edge computing. The bandwidth consumption and cost of a DCN are growing sharply with the extensions of network size. Thus, how to keep the traffic balanced is a key and challenging issue. However, the traditional load balancing algorithms such as Equal-Cost Multi-Path routing (ECMP) are not suitable for high dynamic traffic in cloud DCNs. In this paper, we propose a port-based forwarding load balancing scheduling (PFLBS) approach for Fat-tree based DCNs with some new features which can overcome the disadvantages of the existing load balancing methods in the following aspects. Firstly, we define a port-based source-routing addressing scheme, which decreases the switch complexity and makes the table-lookup operation unnecessary. Secondly, based on this addressing scheme, we proposed an effective routing mechanism which can obtain multiple available paths for flow scheduling based in Fat-tree. All the path information is saved in servers and each server only needs to maintain its own path information. Thirdly, we propose an efficient algorithm to implement large flows scheduling dynamically in terms of current link utilization ratio. This method is suitable for cloud DCNs and edge computing, which can reduce the complexity of the switches and the power consumption of the whole network. The experiment results indicate that the PFLBS approach has better performance compared with the ECMP, Hedera and MPTCP approaches, which decreases the flow completion time and improves the average throughput significantly. PFLBS is simple and can be implemented with a few signaling overheads.

Online Virtual Machine Evacuation for Disaster Resilience in Inter-Data Center Networks

With the risk of natural disaster occurrence rising globally, the interest in innovative disaster resilience techniques is greatly increasing. In particular, Data Center (DC) operators are investigating techniques to avoid data-loss and service downtime in case of disaster occurrence. In cloud DC networks, DCs host Virtual Machines (VM) that support cloud services. A VM can be migrated, i.e., transferred, across DCs without service disruption, using a technique known as “online VM migration”. In this article, we investigate how to schedule online VMs migrations in an alerted disaster scenario (i.e., for those disasters, such as tsunami and hurricanes, that grant an alert time to DC operators) where VMs are migrated from a risky DC, i.e., a DC at risk to be affected by a disaster, to a DC in safe locations, within a deadline set by the alert time of the incoming disaster. We propose a multi-objective Integer Linear Programming (ILP) model and heuristic algorithms for efficient online VMs migration to maximize number of VMs migrated, minimize service downtime and minimize network resource occupation. The proposed approaches perform scheduling, destination DC selection and assign route and bandwidth to VM migrations. Compared to baseline approaches, our proposed algorithms eliminate service downtime in exchange of an acceptable additional network resource occupation. Results also give insights on how to calculate the minimum amount of time required to evacuate all VMs with no service downtime. Moreover, since the proposed approaches exhibit different execution times, we design an ‘alert-aware VM evacuation’ tool to intelligently select the most suitable approach based on the number and size of VMs, alert time and available network capacity.

Cloud Data Center Network Construction Based on Virtualization Technology

With the rapid development of information technology, the IT infrastructure in traditional data centers has high investment costs, low utilization rate, long application deployment period, and the difficulty of operation and maintenance increases with the increase of system and energy consumption. In order to solve the above problems, the article analyzes the current network virtualization technology and systematically studies the application scenarios of network virtualization technology, and proposes a cloud computing data center network construction scheme based on network virtualization technology.

A Novel Method for Resource Efficient Security Service Chain Embedding Oriented to Cloud Datacenter Networks

An important and promising application for the network function virtualization (NFV) technology is in network security, where can dynamically and flexibly accomplish the chaining virtualized security network functions (VSNFs), e.g., network address translation, antispam and packet filter firewall, etc., and thus inspect, monitor or filter traffic flows in the cloud datacenter networks. However, the traffic flows addressed by the VSNFs mainly depend on the security service requirements from mobile users, such as the network security level, end-to-end latency, and security resource, etc. Considering the dynamic nature of cloud datacenter networks, determining the embedding of VSNFs and routing security service paths that optimizes the security resource utilization is a challenging problem, particularly without violating the end-to-end delay constraints and security service requirements. This can also be called security service chain dynamic embedding problem (SSC-DMP). In this paper, we present an NFV-enabled framework for a system that achieves the SSC dynamic embedding in the cloud datacenter networks. We first formulate an integer linear programming (ILP) model to solve the SSC-DMP exactly in small-scale network topology. Then, in order to reduce the time complexity when applying the large-scale network topology, we propose an efficient SSC dynamic embedding solution that is based on the particle swarm optimization. Extensive simulation results show that the proposed algorithm could significantly outperform the current benchmarks at least 35.2% and 23.1% in terms of resource consumption and end-to-end delay, respectively.

Clustered Multicast Source Routing for Large-Scale Cloud Data Centers

The multi-tenancy concept in cloud data center (DC) networks paves the way towards advancements and innovation in the underlying infrastructure such as network virtualization. Multicast routing is essential in leveraging multi-tenancy to its full potential. However, traditional IP multicast routing is not suitable for DC networks due to the need to support a massive amount of multicast groups and hosts. State-of-the-art DC multicast routing approaches aim to overcome these scalability issues by, for instance, taking advantage of the symmetry of DC topologies and the programmability of DC switches to compactly encode multicast group information inside packets, thereby reducing the overhead resulting from the need to store the states of flows at the network switches. Although these approaches scale well with the number of multicast groups, they do not perform well with group sizes and, as a result, yield substantial traffic control overhead and network congestion. In this article, we present Bert, a scalable source-initiated DC multicast routing approach that scales well with both the number and size of multicast groups through the clustering of multicast group members where each cluster employs its own forwarding rules. Compared to the state-of-the-art approach, Bert yields much less traffic control overhead by significantly reducing packet header sizes and eliminating switch memory usage across the switches.

A Novel Approach for Service Function Chain Embedding in Cloud Datacenter Networks

Network function virtualization (NFV) and software defined networking (SDN) have recently introduced together, which show great potential in openness, innovation, flexibility and scalability of the networks. Embedding SDN/NFV-enabled service function chain (SFC) has been considered as an efficient way to achieve the end-to-end communication. Considering the multiple users’ delay diversities oriented to future networks, from the perspective of network operators, determining the placement of virtual network functions (VNFs) and routing the service paths is a challenging problem. However, the traditional solutions (e.g., exact, heuristic, meta-heuristic, game, etc.) based on the SFC dynamic embedding problem (SFC-DMP) follow the strong assumptions as usual, which might limit their applications in dynamic and complex network scenarios. To address this narrow, we are motivated to present a novel approach for handling SFC-DMP in the cloud datacenter networks that simultaneously combines the quantum genetic algorithm (QGA) and fuzzy logic. Simulation results show the efficient performance of our proposal.

SDN-RA: An Optimized Reschedule Algorithm of SDN Load Balancer for Data Center Networks Based on QoS

With the development of cloud computing during the latest years, data center networks have become a great topic in both industrial and academic societies. Nevertheless, traditional methods based on manual and hardware devices are burdensome, expensive, and cannot completely utilize the ability of physical network infrastructure. Thus, Software-Defined Networking (SDN) has been hyped as one of the best encouraging solutions for future Internet performance. SDN notable by two features; the separation of control plane from the data plane, and providing the network development by programmable capabilities instead of hardware solutions. Current paper introduces an SDN-based optimized Reschedule Algorithm (called SDN-RA) for cloud data center networks. The SDN-RA performance is validated and compared as results to other two corresponding SDN; ECMP and Hedera methods. The simulation environment of current work implemented using Fat-Tree topology over Mininet emulator which is connected to the Ryu-SDN controller. The performance evaluation of SDN-RA shows an increase in the network in terms of throughput and link utilization besides a reduction of RTT delay and loss rate.

Progressive disaster evacuation in cloud datacenter network

SummaryIn cloud datacenter network, deadline‐aware disaster evacuation transfers the endangered data out of disaster zone using limited residual network resources. Previous work has not jointly considered the selection of safe datacenter and reasonable allocation of bandwidth proportion in time‐varying postdisaster network environment. Therefore, they cannot make full use of network transmission capability. Based on our earlier work, we propose a new time‐varying disaster evacuation strategy with flexible traffic scheduling. We aim to maximize disaster evacuation capability in the disaster spread scenario. We construct a new disaster‐aware time‐expanded network model to divide time slots according to progressive disaster spread, and optimize the utilization of evacuation capability in the current disaster stage. In each time slot, we carry out two‐step optimization including safe datacenter selection and proportional bandwidth allocation. Especially, we select store‐and‐forward node to ensure the safety of evacuated data in the next time slot, and use marked evacuation routing search based on transmission requirement to improve the utilization of evacuation capability. Through extensive simulations we demonstrate that our strategy achieves better performance with higher evacuation transmission efficiency in the disaster spread scenario.

Joint Customer/Provider Evolutionary Multi-objective Utility Maximization in Cloud Data Center Networks

Cloud computing has found an important role in cost-effective and elastic network resource allocation community. Most researchers and practitioners have focused on cloud provider side for efficient resource allocation to end-users and to warrant their service-level agreements (SLAs). Due to heterogeneous user demands which request services from cloud data center networks, simultaneous satisfaction of all users to their pre-defined SLA level while making the cloud provider and all of the data centers to be cost-effective in terms of the financial income and energy-efficiency is a challenging issue. To address this problem, in the current work, the efficient joint provider/customer efficient resource allocation has been first formulated as a multi-objective optimization problem with convex constraint set in terms of virtual machine (VM) parameters (e.g., storage, CPU core, memory, etc.). Then, an evolutionary solution based on cooperative co-evolution algorithm has been proposed that can conduct a divide-and-conquer methodology for solving the complex optimization problem. Numerical results show the prominent feature of the method in terms of VM usage effectiveness and overall joint network cost/user SLA satisfaction.

Platonica: an efficient and high-performance dual-centric data center network architecture

The unprecedented growth in data volume results in an urgent need for a dramatic increase in the size of data center networks. Accommodating millions to even billions of servers in the structure of data center networks, while providing highly-efficient interconnecting architectures, is of prominent importance nowadays. Generally, high scalability, noticeable fault-tolerance, great network capacity, limited delay, reasonable power consumption, and justifiable construction cost have to be listed as the principal design goals of well-organized cloud data center networks. In this paper, Platonica, a novel dual-centric and recursively defined architecture, is proposed. Platonica is constructed by employing fault-tolerant building blocks inspired by the edge connection pattern of five varieties of Platonic solids that turns the proposed architecture to a flexible scheme. It also escapes the adoption of any leveling policy for the network equipment, which leads to the efficient usage of all the employed resources. Moreover, to ameliorate the routing efficiency and the network fault-tolerance, two exclusively designed routing algorithms are presented. Both theoretical analysis and simulation results demonstrate that Platonica is highly fault-tolerant and can provide notable network capacity, good average path length, and low latency to support delay-sensitive and data-intensive applications. It can also be considered as a cost-effective and power-efficient network architecture. Altogether, Platonica achieves a good balance among all the major goals of designing cloud data center networks.