Fat-tree Data Center Networks Research Articles

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%.

P4TE: PISA switch based traffic engineering in fat-tree data center networks

This work presents P4TE, an in-band traffic monitoring, load-aware packet forwarding, and flow rate controlling mechanism for traffic engineering in fat-tree topology-based data center networks using PISA switches. It achieves sub-RTT reaction time to change in network conditions, improved flow completion time, and balanced link utilization. Unlike the classical probe-based monitoring approach, P4TE uses an in-band monitoring approach to identify traffic events in the data plane. Based on these events, it re-adjusts the priorities of the paths. It uses a heuristic-based load-aware forwarding path selection mechanism to respond to changing network conditions and control the flow rate by sending feedback to the end hosts. It is implementable on emerging v1model.p4 architecture-based programmable switches and capable of maintaining the line-rate performance. Our evaluation shows that P4TE uses a small amount of resources in the PISA pipeline and achieves an improved flow completion time than ECMP and HULA.

Dynamic Flow Scheduling Technique for Load Balancing in Fat-Tree Data Center Networks

Dynamic Flow Scheduling Technique for Load Balancing in Fat-Tree Data Center Networks

Network Performance Comparison of Fat-Tree and BCube Data Center Architecture: Case Study on Government Office Network

The internet now plays crucial roles such as streaming videos, social networking, managing large scientific data. There is a need for high-speed Internet. The volume of data handled by the data centers is increasing very rapidly. Hence, the need to handle data center traffic effectively becomes inevitable. At present, the government office network uses a data center network design based on Fat-Tree that employs high-end IP switches. Unfortunately, the resulting network performance has delays of more than 0.06 ms, throughput of about 500 kbps, and packet loss rate is 2. This research introduces BCube to re-design and improve network performance (i.e., delay, throughput, and packet loss). Through this research, we propose a new data center design in the government office that improves the network performance significantly, with delays less than 0.004 ms, throughput more than 515 kbps, and packet loss rate 0, two–three percent better compared to the Fat-Tree data center network design that contributes significantly to the data center operations in the government office and its overall business performance through the data center network design.

Failure Aware Semi-Centralized Virtual Network Embedding in Cloud Computing Fat-Tree Data Center Networks

In Cloud Computing, the tenants opting for the Infrastructure as a Service (IaaS) send the resource requirements to the Cloud Service Provider (CSP) in the form of Virtual Network (VN) consisting of a set of inter-connected Virtual Machines (VM). Embedding the VN onto the existing physical network is known as Virtual Network Embedding (VNE) problem. One of the major research challenges is to allocate the physical resources such that the failure of the physical resources would bring less impact onto the users' service. Additionally, the major challenge is to handle the embedding process of growing number of incoming users' VNs from the algorithm design point-of-view. Considering both of the above-mentioned research issues, a novel Failure aware Semi-Centralized VNE (FSC-VNE) algorithm is proposed for the Fat-Tree data center network with the goal to reduce the impact of the resource failure onto the existing users. The impact of failure of the Physical Machines (PMs), physical links and network devices are taken into account while allocating the resources to the users. The beauty of the proposed algorithm is that the VMs are assigned to different PMs in a semi-centralized manner. In other words, the embedding algorithm is executed by multiple physical servers in order to concurrently embed the VMs of a VN and reduces the embedding time. Extensive simulation results show that the proposed algorithm can outperform over other VNE algorithms.

Dynamic Big-Data Broadcast in Fat-Tree Data Center Networks With Mobile IoT Devices

In this paper, we study the problem of throughput and delay-optimal dynamic big-data broadcast in fat-tree data center networks (DCNs) in the presence of mobile Internet-of-Things (IoT) devices, where one of the IoT devices acts as a source node. In existing literature, researchers studied that a balanced traffic distribution in DCNs is a NP-hard problem. With the integration of heterogeneous IoT devices in DCNs, the difficulty in achieving a balanced traffic distribution increases significantly. Hence, there is a need to design a throughput and delay-optimal big-data broadcast scheme in DCNs in the presence of IoT devices. In this paper, we propose a dynamic big-data broadcasting scheme, named D2B, using a single-leader-multiple-follower Stackelberg game for solving the aforementioned problem. Here, each switch acts as the leader, and the IoT devices act as the followers. We consider that the source node broadcasts the generated data in real time. We represent bandwidth distribution as a pseudo-Cournot competition, where each follower decides the optimal downloading bandwidth. The existence of the generalized Nash–Stackelberg equilibrium for D2B is evaluated theoretically. We observed that using D2B, the network throughput increases by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{55.32}\%$</tex-math></inline-formula> , while ensuring at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{33}\%$</tex-math></inline-formula> increase in the average bandwidth allocation per IoT device, and the overall delay in broadcasting is reduced.

On Maximum Elastic Scheduling in Cloud-Based Data Center Networks for Virtual Machines with the Hose Model

With the growing popularity of cloud-based data center networks (DCNs), task resource allocation has become more and more important to the efficient use of resource in DCNs. This paper considers provisioning the maximum admissible load (MAL) of virtual machines (VMs) in physical machines (PMs) with underlying tree-structured DCNs using the hose model for communication. The limitation of static load distribution is that it assigns tasks to nodes in a once-and-for-all manner, and thus requires a priori knowledge of program behavior. To avoid load redistribution during runtime when the load grows, we introduce maximum elasticity scheduling, which has the maximum growth potential subject to the node and link capacities. This paper aims to find the schedule with the maximum elasticity across nodes and links. We first propose a distributed linear solution based on message passing, and we discuss several properties and extensions of the model. Based on the assumptions and conclusions, we extend it to the multiple paths case with a fat tree DCN, and discuss the optimal solution for computing the MAL with both computation and communication constraints. After that, we present the provision scheme with the maximum elasticity for the VMs, which comes with provable optimality guarantee for a fixed flow scheduling strategy in a fat tree DCN. We conduct the evaluations on our testbed and present various simulation results by comparing the proposed maximum elastic scheduling schemes with other methods. Extensive simulations validate the effectiveness of the proposed policies, and the results are shown from different perspectives to provide solutions based on our research.

Methods for Predicting Behavior of Elephant Flows in Data Center Networks

Several Traffic Engineering (TE) techniques based on SDN (Software-defined networking) proposed to resolve flow competitions for network resources. However, there is no comprehensive study on the probability distribution of their throughput. Moreover, there is no study on predicting the future of elephant flows. To address these issues, we propose a new stochastic performance evaluation model to estimate the loss rate of two state-of-art flow scheduling algorithms including Equalcost multi-path routing (ECMP), Hedera besides a flow congestion control algorithm which is Data Center TCP (DCTCP). Although these algorithms have theoretical and practical benefits, their effectiveness has not been statistically investigated and analyzed in conserving the elephant flows. Therefore, we conducted extensive experiments on the fat-tree data center network to examine the efficiency of the algorithms under different network circumstances based on Monte Carlo risk analysis. The results show that Hedera is still risky to be used to handle the elephant flows due to its unstable throughput achieved under stochastic network congestion. On the other hand, DCTCP found suffering under high load scenarios. These outcomes might apply to all data center applications, in particular, the applications that demand high stability and productivity.

An Efficient Route Management Framework for Load Balance and Overhead Reduction in SDN-Based Data Center Networks

A data center network (DCN) is composed of many servers interconnected by well-organized switches, which involves massive amount of data transmissions to provide cloud services. It is critical to manage packet routes in DCNs to avoid congestion on some links. The software-defined networking (SDN) technique supports auto-configuration of switches by a central controller, and gives an easy way to manage traffic flows. In the paper, we exploit SDN to improve performance of fat-tree DCNs, and propose a low-cost, load-balanced route management (L2RM) framework. L2RM keeps monitoring network traffics and computes a load-deviation parameter to check if some links of switches are burden with heavy loads. Then, an adaptive route modification mechanism is triggered if necessary, which considers the size limitation of flow tables and uses group tables in OpenFlow to distribute flows among different links to balance their loads. Besides, L2RM uses a dynamic information polling mechanism to query switches about their statuses, so as to reduce the message overhead of the controller. Through Mininet simulations, we show that our L2RM framework can better increase link utilization, alleviate table overflow, and reduce message cost, as comparing with other SDN-based approaches for fat-tree DCNs.

Global Round Robin: Efficient Routing With Cut-Through Switching in Fat-Tree Data Center Networks

Fat tree is a scalable and widely deployed data center network topology. In this paper, a novel framework for designing per-packet load-balanced routing algorithms in fat tree called global round robin (GRR) is proposed. Routing in fat tree consists of uprouting and downrouting. In uprouting, a packet is sent to a switch that is a common ancestor (CA) of the source (server) and the destination. In downrouting, the packet is sent from the CA switch to the destination. Assume that time is slotted and each slot can accommodate one packet. With GRR, in each slot, a connection configuration is formed by establishing an uprouting path from each server to a spine switch port such that no paths will cross each other. Packets are sent from sources to respective spine switches with cut-through switching. The connection configuration is updated in a round robin fashion such that in every $m$ slots, where $m$ is the number of spine switches, each server is connected to each spine switch exactly once. Since a CA does not need to be a spine switch, an improved GRR (IGRR) is then proposed to allow the nearest CA to intercept packets for downrouting. We prove that both GRR and IGRR can guarantee 100% throughput under a wide class of traffic. An analytical model is also constructed for studying their delay performance under uniform traffic. Finally, simulation results show that IGRR provides the best delay-throughput performance among all the existing per-packet load-balanced routing algorithms.

Application and Analysis of Multicast Blocking Modelling in Fat-Tree Data Center Networks

Multicast can improve network performance by eliminating unnecessary duplicated flows in the data center networks (DCNs). Thus it can significantly save network bandwidth. However, the network multicast blocking may cause the retransmission of a large number of data packets and seriously influence the traffic efficiency in data center networks, especially in the fat-tree DCNs with multirooted tree structure. In this paper, we build a multicast blocking model and apply it to solve the problem of network blocking in the fat-tree DCNs. Furthermore, we propose a novel multicast scheduling strategy. In the scheduling strategy, we select the uplink connecting to available core switch whose remaining bandwidth is close to and greater than the three times of bandwidth multicast requests so as to reduce the operation time of the proposed algorithm. Then the blocking probability of downlink in the next time-slot is calculated in multicast subnetwork by using Markov chains theory. With the obtained probability, we select the optimal downlink based on the available core switch. In addition, theoretical analysis shows that the multicast scheduling algorithm has close to zero network blocking probability as well as lower time complexity. Simulation results verify the effectiveness of our proposed multicast scheduling algorithm.

OPSquare: A Flat DCN Architecture Based on Flow-Controlled Optical Packet Switches

Aiming at solving the scaling issues of bandwidth and latency in current hierarchical data center network (DCN) architectures, we propose and investigate a novel optical flat DCN architecture in which the number of interconnected ToRs scales as the square of the optical packet switches&#x2019; (OPS) port count (OPSquare). The proposed flat DCN architecture consists of two parallel inter- and intra-cluster networks that are built on a single-hop OPS with nanosecond time and wavelength switching for efficient statistical multiplexing operations. Fast optical flow control is implemented for solving packet contentions that may occur at the buffer-less optical switches. The performance of OPSquare DCN in terms of scalability, packet loss, latency, and throughput is assessed by a numerical simulation employing OMNeT++ under realistic data center (DC) traffic. The results report a server-to-server latency of less than 2 &#x03BC;s (including packet retransmission), a packet loss &lt;10&#x2212;5 at a load of 0.4, and a DC size of 10,240 servers with a ToR buffer size equal to 50 KB for all traffic patterns. Moreover, the cost and power consumption of the OPSquare DCN have been studied and compared with fat-tree DCN based on electrical switches and H-LION connected by an arrayed wave guide grating router (AWGR). The results indicate 23.8% and 39% cost and power savings, respectively, for the OPSquare DCN supporting 160,000 servers with respect to the fat-tree DCN. The OPSquare has a cost saving of 56% compared with H-LION for a 160,000-server DCN.

BEEP: Balancing Energy, Redundancy, and Performance in Fat-Tree Data Center Networks

BEEP is an energy-efficient strategy for data center networks (DCNs) based on a fat-tree topology. This strategy combines multipaths and the global overview offered by software-defined networks (SDNs) to balance energy efficiency, equipment redundancy levels, and DCN performance. BEEP is implemented in an OpenFlow network with multipaths enabled by using Multipath TCP (MPTCP). Experimental results in different variations of the fat-tree topology and redundancy levels show gains in energy savings of 30-45 percent. Additionally, BEEP achieves better available bandwidth usage, because more alternative paths are available.

Placement and Performance Analysis of Virtual Multicast Networks in Fat-Tree Data Center Networks

Virtualization of servers and networks is a key technique to resolve the conflict between the increasing demands on computing power and the high cost of hardware in data centers. In order to map virtual networks to physical infrastructure efficiently, designers have to make careful decisions on the allocation of limited resources, which makes placement of virtual networks in data centers a critical issue. In this paper, we study the placement of virtual networks in fat-tree data center networks. In order to meet the requirements of instant parallel data transfer between multiple computing units, we propose a model of multicast-capable virtual networks (MVNs). We then design four virtual machine (VM) placement schemes to embed MVNs into fat-tree data center networks, named Most-Vacant-Fit (MVF), Most-Compact-First (MCF), Mixed-Bidirectional-Fill (MBF), and Malleable-Shallow-Fill (MSF). All these VM placement schemes guarantee the nonblocking multicast capability of each MVN while simultaneously achieving significant saving in the cost of network hardware. In addition, each VM placement scheme has its unique features. The MVF scheme has zero interference to existing computing tasks in data centers; the MCF scheme leads to the greatest cost saving; the MBF scheme simultaneously possesses the merits of MVF and MCF, and it provides an adjustable parameter allowing cloud providers to achieve preferred balance between the cost and the overhead; the MSF scheme performs at least as well as MBF, and possesses some additional predictable features. Finally, we compare the performance and overhead of these VM placement schemes, and present simulation results to validate the theoretical results.

On implementation of DCTCP on three-tier and fat-tree data center network topologies.

A data center is a facility for housing computational and storage systems interconnected through a communication network called data center network (DCN). Due to a tremendous growth in the computational power, storage capacity and the number of inter-connected servers, the DCN faces challenges concerning efficiency, reliability and scalability. Although transmission control protocol (TCP) is a time-tested transport protocol in the Internet, DCN challenges such as inadequate buffer space in switches and bandwidth limitations have prompted the researchers to propose techniques to improve TCP performance or design new transport protocols for DCN. Data center TCP (DCTCP) emerge as one of the most promising solutions in this domain which employs the explicit congestion notification feature of TCP to enhance the TCP congestion control algorithm. While DCTCP has been analyzed for two-tier tree-based DCN topology for traffic between servers in the same rack which is common in cloud applications, it remains oblivious to the traffic patterns common in university and private enterprise networks which traverse the complete network interconnect spanning upper tier layers. We also recognize that DCTCP performance cannot remain unaffected by the underlying DCN architecture hence there is a need to test and compare DCTCP performance when implemented over diverse DCN architectures. Some of the most notable DCN architectures are the legacy three-tier, fat-tree, BCube, DCell, VL2, and CamCube. In this research, we simulate the two switch-centric DCN architectures; the widely deployed legacy three-tier architecture and the promising fat-tree architecture using network simulator and analyze the performance of DCTCP in terms of throughput and delay for realistic traffic patterns. We also examine how DCTCP prevents incast and outcast congestion when realistic DCN traffic patterns are employed in above mentioned topologies. Our results show that the underlying DCN architecture significantly impacts DCTCP performance. We find that DCTCP gives optimal performance in fat-tree topology and is most suitable for large networks.

DiFS: Distributed Flow Scheduling for adaptive switching in FatTree data center networks

Data center networks leverage multiple parallel paths connecting end host pairs to offer high bisection bandwidth for cluster computing applications. However, the state-of-the-art routing protocols such as Equal Cost Multipath (ECMP) is load-oblivious due to the static flow-to-link assignment. They may cause bandwidth loss due to flow collisions. Recently proposed centralized scheduling algorithm or host based adaptive routing that requires network-wide state information may suffer from scalability problems. In this paper, we present Distributed Flow Scheduling (DiFS), a new adaptive switching method, for FatTree data center networks, which is a localized and switch-only solution. DiFS allows switches to cooperate to avoid over-utilized links and find available paths without centralized control. DiFS is scalable and can react quickly to dynamic traffic because it is independently executed on switches and requires no synchronization. DiFS provides global bounds of flow balance based on local optimization. Extensive simulations show that the aggregate throughput of DiFS using various traffic patterns is much better than that of ECMP, and is similar to or higher than those of two representative protocols that use network-wide optimization.

Minimizing Energy Consumption of FatTree Data Center Networks

Many data centers are built using a fat-tree network topology because of its high bisection bandwidth. Therefore thereis a need to develop analytical models for the energy behavior of fat-tree networks and examine strategies to reduce energy consumption. The most effective strategy is to power off entire switches, if possible. In this paper, we derive formulas for the minimum number of active switches needed in a fat-tree data center network for arbitrary types of loading. We also derive expressions for the expected traffic loss when these networks are overloaded with external (Internet) traffic. Results of detailed simulations conducted using well-known traffic models for data center networks [4] closely match our derived formulas. We show that a fat-tree network incurs significant energy cost even when very lightly loaded. In order to further reduce energy consumption, we consolidate traffic into fewer switches and derive expressions for energy cost versus load assuming traffic consolidation and show linear scaling. Finally, we observe that traffic patterns have a significant impact on energy consumption and this fact is evident in the analytical formulas.

On-Line Multicast Scheduling with Bounded Congestion in Fat-Tree Data Center Networks

Multicast benefits numerous data center applications that require group communication by eliminating sending unnecessary duplicated packets in the network, thus significantly reduces network traffic and improves application throughput. Meanwhile, most data center networks (DCNs) today adopt a multi-rooted tree structure called fat-tree, which utilizes rich path multiplicity to deliver high bisection bandwidth. However, without an efficient flow scheduling algorithm that appropriately routes multicast flows to achieve traffic load balance, heavy congestion may occur throughout the network, which prevents full utilization of such high degree of link parallelism and causes unpredictable network performance. Hence, in this paper we study multicast flow scheduling in fat-tree DCNs, where multicast flow requests arrive one by one without a priori knowledge of future traffic. To address the drastic traffic fluctuation in data centers, we consider a very general traffic model called hose traffic model, where the only assumption is that the total bandwidth demand of traffic that enters (leaves) an ingress (egress) link of each server at any time is bounded by the capacity of its network interface card. We present a low-complexity on-line multicast flow scheduling algorithm for fat-tree DCNs. The algorithm can achieve bounded congestion and efficient bandwidth utilization under any arbitrary sequence of multicast flow requests that satisfy the hose model. We also derive the bound on congestion that the algorithm can achieve in a fat-tree DCN. Finally, we evaluate the algorithm by an event-driven DCN simulator under various types of traffic patterns, and show that the algorithm achieves superior performance in terms of network throughput and evenness of traffic load distribution.

Guest Editors’ Introduction to Special Issue on Advances in DSP System Design

The constant advances in IC technologies have introduced new challenges for implementations and design methodologies; higher integration level allows more complex systems to be implemented but on the other hand implementations often have strict constraints on power consumption. These challenges are present in signal processing systems implying the need to improve design methods and find more efficient algorithm-architecture optimizations. This special issue contains a selection of recent papers on design and implementation of signal processing systems ranging from circuit level architectures to scheduling methods and from application-specific architectures to implementations on many-core systems. In Data Center Switch for Load Balanced Fat-Trees, Lai, and Chiu demonstrate a fault tolerant switch IC operating at the maximum rate of 5.8 Gbps per channel. This work employs a load-balanced fat-tree architecture that does not consume all of its bandwidth even under heavy traffic. When there are broken links or faulty switches in the network even in heavy traffic load situations, available bandwidth remains in every connection pattern and alternative paths are provided to re-route the traffic. Fault tolerance capability evaluations of link or switch faults in the fattree network are given to support the presented idea, and a 4×4 Banyan type switch IC is developed as the commodity switch for building the fault tolerant fat-tree data center networks. Lee and Sung propose a cell-to-cell interference (CCI) cancellation technique for multi-level NAND flash memory in their paper Least Squares Based Coupling Cancellation for MLC NAND Flash Memory with a Small Number of Voltage Sensing Operations. Their two-step algorithm consists of training and then interference removal performed during the page read operation. A least-squares adaptive CCI canceller is developed and optimal quantization schemes are studied. Experimental results show a significant BER improvement despite a low number of voltage sensing operations. In A Fast Recursive Algorithm and Architecture for Pruned Bit-Reversal Interleavers, Mansour describes an algorithm and architecture for implementing interleavers used in communications applications such as errorcorrecting codes (turbo codes) and bit-interleaved coded modulation. A mathematical formulation for developing flexible-length interleavers is developed along with a study of permutation statistics. Practical examples of implementations of parallel interleavers are provided. In Highly Parallelable Bidimensional Median Filter for Modern Parallel Programming Models, Sanchez and Rodriguez present efficient parallel implementation methods for median filtering. The authors implement their previous work on the parallel ccdf-based median filter (PCMF) on a GPU (Graphics Processing Unit), and show that the proposed median filtering algorithm is efficient and can outperform other generic median filters for the GPU. The proposed algorithm is implemented in three parallel programming models: SIMD Intel, multi-core Intel with SIMD, and SIMT (CUDA). Additionally they make use of the salt & pepper noise model to improve the image reconstruction quality with a small performance impact. J. Takala (*) Department of Pervasive Computing, Tampere University of Technology, Tampere, Finland e-mail: jarmo.takala@tut.fi

Data Center Switch for Load Balanced Fat-Trees

With the growing of cloud computing, the need of computing power no longer can be satisfied with a few powerful servers or small scale parallel computer systems. More and more servers are connected together as a data center network. Then, fault tolerance becomes an import issue when building a massive data center network. Currently, many researches focus on building fat-tree data center networks. In this paper, we propose a load balanced fat-tree architecture with uniform mapping connection patterns to provide higher fault tolerance capability for heavy traffic load networks. Two fault tolerated 4 ? 4 banyan type switch designs are introduced to improve the fault tolerance capability of fat-tree networks. Finally, fault tolerance capability evaluations of link or switch faults in fat-tree network are given to support our idea, and a 4 ? 4 banyan type switch IC is demonstrated as the commodity switch for building the fault tolerant fat-tree data center networks. The 4 ? 4 banyan type switch IC is fabricated in 90 nm CMOS technology, and the maximum operation rate of the IC is 5.8 Gbps per channel or 23.2 Gbps total data rate with only 23 ps peak-to-peak jitter.