Application Utility-Based Bandwidth Allocation Scheme for Data Center Networks

Data centers (DCs), owing to the exponential growth of Internet services, have emerged as an irreplaceable and crucial infrastructure to power this ever-growing trend. A DC typically houses a large number of computing and storage nodes, interconnected by a specially designed network, namely, DC network (DCN). The DCN serves as a communication backbone and plays a pivotal role in optimizing DC operations. However, compared to the traditional network, the unique requirements in the DCN, for example, large scale, vast application diversity, high power density, and high reliability, pose significant challenges to its infrastructure and operations. We have observed from the premium publication venues (e.g., journals and system conferences) that increasing research efforts are being devoted to optimize the design and operations of the DCN. In this paper, we aim to present a systematic taxonomy and survey of recent research efforts on the DCN. Specifically, we propose to classify these research efforts into two areas: 1) DCN infrastructure and 2) DCN operations. For the former aspect, we review and compare the list of transmission technologies and network topologies used or proposed in the DCN infrastructure. For the latter aspect, we summarize the existing traffic control techniques in the DCN operations, and survey optimization methods to achieve diverse operational objectives, including high network utilization, fair bandwidth sharing, low service latency, low energy consumption, high resiliency, and etc., for efficient DC operations. We finally conclude this survey by envisioning a few open research opportunities in DCN infrastructure and operations.

Data centre networks are intended to meet the data transmission needs of the data centre’s highly networked hosts. The network architecture and routing technique can have a major impact on performance parameters such as latency. The fat-tree network is now one of the most popular data centre network architectures. In recent times, data centre network traffic has been steadily expanding. Because of the high volume of traffic, it is nearly impossible for a single server to satisfy all the client’s requests. In this paper, a bio-inspired Territorial Predator Scent Marking Algorithm (TPSMA) is proposed for load balancing in a fat-tree software-defined data centre. The bio-inspired algorithm was developed to increase network Quality of Service (QoS). The proposed technique is validated using the network emulator Mininet, with the Open Network Operating System ONOS controller. The performance study conducted showed that the throughput and network utilization were improved, hence improving the QoS.

An energy-efficient virtual machine placement and route scheduling scheme in data center networks

Data centers consume significant amounts of energy, contributing indirectly to environmental pollution through greenhouse gas emissions during electricity generation. According to the Natural Resources Defense Council, information and communication technologies and networks account for roughly 10% of global energy consumption. Reducing power consumption in Data Center Networks (DCNs) is crucial, especially given that many data center components operate at full capacity even under low traffic conditions, resulting in high costs for both service providers and consumers. Current solutions often prioritize power optimization without considering Quality of Service (QoS). Services such as video streaming and Voice over IP (VoIP) are particularly sensitive to loss or delay and require QoS to be maintained below certain thresholds. This paper introduces a novel framework called QoS-Aware Power-Optimized Path Selection (Q-PoPS) for software-defined DCNs. The objective of Q-PoPS is to minimize DCN power consumption while ensuring that an acceptable QoS is provided, meeting the requirements of DCN services. This paper describes the implementation of a prototype for the Q-PoPS framework that leverages the POX Software-Defined Networking (SDN) controller. The performance of the prototype is evaluated using the Mininet emulator. Our findings demonstrate the performance of the proposed Q-PoPS algorithm in three scenarios. Best-case: Enhancing real-time traffic protocol quality without increasing power consumption. midrange-case: Replacing bottleneck links while preserving real-time traffic quality. Worst-case: Identifying new paths that may increase power consumption but maintain real-time traffic quality. This paper underscores the need for a holistic approach to DCN management, optimizing both power consumption and QoS for critical real-time applications. We present the Q-PoPS framework as evidence that such an approach is achievable.

Maintaining high-performance operation under dynamic and nonuniform network traffic has been a technical challenge in current data center networks (DCNs). With the aim to provide better quality of service (QoS) for diverse applications, this work presents a dynamic and adaptive DCN reconfiguration framework based on deep reinforcement learning (DCR2L). The proposed framework is integrated into the SDN control plane of the DCN, implementing real-time and automatic DCN reconfiguration. Performance of the DCR2L framework is experimentally demonstrated in our DCN lab, including 4 racks and 16 servers. Experimental results show a network latency improvement of 6.9% based on the DCR2L at an average network bandwidth of 2.3 Gb/s. Based on measured traffic and physical parameters in the experiment, performance of the DCR2L framework is numerically assessed with the realistic traffic of diverse QoS requirements for both electrical and optical DCNs and for different data center scales. Leaf–spine electrical and OPSquare optical networks are set up in an OMNeT++ simulator. For a data plane network consisting of 16 racks and 320 servers, results indicate that the DCR2L framework improves the network latency of up to 16.4% under leaf–spine and up to 24.6% under OPSquare for the overall traffic with respect to the classical heuristic method. For a DCN scale of 10,240 servers, the DCR2L framework provides up to 12.5% lower latency for the leaf–spine electrical network and up to 17.8% latency improvement for the OPSquare optical network.

Data center networks (DCNs) are at the forefront of a radical transformation, fueled by the rapid rise of artificial intelligence (AI), cloud-native adoption, and enterprise digitalization, which together place unprecedented demands on their design and performance. From healthcare AI and intelligent transportation to business analytics, modern DCNs are expected to support a diverse and evolving mix of bandwidth-demanding applications, each with distinct quality of service (QoS) requirements. Yet, most state-of-the-art DCN solutions rely on oversimplified DCN data traffic models, failing to address the required application-level differentiation in terms of end-to-end (E2E) latency demands. In response, we propose ALAS-DCN, a novel, to our knowledge, application-aware, low-latency, scalable DCN architecture, based on optical packet switching in the intra-rack and inter-rack domains that is both designed and rigorously evaluated using commercial QoS specifications and realistic traffic patterns of different applications coexisting within the same DCN. ALAS-DCN integrates three hierarchical optical sub-networks: intra-rack, inter-rack, and bridge, each governed by a custom wavelength division multiplexing (WDM) medium access control (MAC) protocol that dynamically prioritizes critical traffic packets, according to the specific application E2E latency demands. Unlike prior approaches, our evaluation leverages realistic workload traces generated by established traffic generators, capturing the application diversity within modern data center (DC) environments. Extensive experimental simulations across five representative DC categories, each hosting a different mix of modern applications, demonstrate that ALAS-DCN consistently achieves up to 95% throughput efficiency and sub-µs latency for high priority traffic across a variety of applications, consistently meeting their differentiated latency constraints. Scalable up to 20,000 servers, ALAS-DCN redefines the DCN as not just a transport layer but an intelligent enabler of next-generation application performance.

This paper proposes an adaptive virtual machine (VM) assignment scheme for multi-tenant data center networks. In multi-tenant data centers, tenants submit their resource requirements and the data centers provide VMs which are assigned to physical servers. These VMs communicate with each other to execute distributed processing. The amount of traffic communicated by VMs is very large and this is one of big issues in data center networks. In order to reduce the amount of traffic injected into data center networks, we need an appropriate VM assignment strategy which considers traffic between VMs. Furthermore, VMs are dynamically assigned to and removed from physical servers according to resource requirements of tenants, and thus we should consider such dynamic behavior. The proposed scheme provides an VM assignment strategy which reduces the amount of traffic injected into data center networks, taking into account dynamic behavior of the data center networks. Through simulation experiments, we demonstrate that the proposed scheme reduces the network load efficiently.

By means of virtualization, computing and storage resources are effectively multiplexed by different applications in cloud data centers. However, there lacks useful approaches to share the internal network resource of cloud data centers. Invalid network sharing not only degrade the performance of applications, but also affect the efficiency of data center operation. To guarantee network performance of applications and provide fine-grained service differentiation, in this paper, we propose a differentiated bandwidth guarantee scheme for data center networks. Utility functions are constructed according to the throughput and delay sensitive characteristics of different applications. Aiming to maximize the utility of all applications, the problem is formulated as a multi-objective optimization problem. We solve this problem using a heuristic algorithm: the elitist Non-Dominated Sorted Genetic Algorithm-II(NSGA-II), and we make a multi-attribute decision to refine the solutions. Extensive simulations are conducted to show that our scheme provides minimum band-width guarantees and achieves more fine-grained service differentiation than existing approaches. The simulation also verifies that the proposed mechanism is suitable for arbitrary data center architectures.

Data center networks, using a reliable, high speed communication network, is a core component of cloud computing. Recently, optical networks using Space Division Multiplexing has been proposed as a solution to the speed bottleneck anticipated in data center networks. To our knowledge the new challenges of designing such communication systems have not been addressed yet. In this paper we have proposed an optimal approach to the problem of developing a path-protection scheme to handle communication requests in data center (DC) networks using elastic optical networking and space division multiplexing. We have formulated our problem as an Integer Linear Program (ILP). We have also proposed a heuristic that can handle problems of practical size. Our simulations explore important features of our approach.

The number of data centers has been increased for various reasons such as cloud computing, big-data analysis, multimedia service, etc. With public interests on data center, many novel technologies for data center networks have been proposed and deployed to support data center operations more efficiently and effectively. However, the construction of data center network incurs significant costs. Moreover, various technologies interplay each other to achieve multiple objectives, and it makes difficult to validate and/or verify characteristics of data center network. In addition, it difficult to perform experiments with a number of hosts and switches. Therefore, it is necessary to observe the characteristics of target data center network before building it. A common approach to evaluate data center is to run simulations that should be similar with real-world data center environment. However, generating traffic with the characteristics of data center networks is not matured yet. People still employ a traffic generator based on the characteristics of Internet traffic. We design a traffic generator that shows more accurate characteristics of data center network traffic. Various traffic characteristics exploited explored by several studies are considered. The proposed method generates flow-level network traffic matrix based on Poisson Shot-Noise model. We implemented the traffic generator using Python programming language to create traffic matrix. To evaluate the proposed method, we compare the results with real data center network traffic. Our results show that the generated traffic owns similar characteristics with the real network traffic in terms of flow size, duration, and the mean and variance of total traffic rate.

Given the current expansion of cloud computing, the expected advent of the Internet of Things, and the requirements of future fifth‐generation network infrastructures, significantly larger pools of computational and storage resources will soon be required. This emphasizes the need for more scalable data centers that are capable of providing such an amount of resources in a cost‐effective way. A quick look into today's commercial data centers shows that they tend to rely on variations of well‐defined leaf‐spine/Clos data center network (DCN) topologies, offering low latency, ultrahigh bisectional bandwidth, and enhanced reliability against concurrent failures. However, DCNs are typically restricted by the use of the Transmission Control Protocol/Internet Protocol (TCP/IP) suite, thus suffering limited routing scalability. In this work, we study the benefits that replacing TCP/IP with the recursive internetwork architecture (RINA) can bring into commercial DCNs, focusing on forwarding and routing scalability. We quantitatively evaluate the benefits that RINA solutions can yield against those based on TCP/IP and highlight how, by deploying RINA, topological routing solutions can improve even more the efficiency of the network. To this goal, we propose a rule‐and‐exception forwarding policy tailored to the characteristics of several DCN variants, enabling fast forwarding decisions with merely neighbors' information. Upon failures, few exceptions are necessary, whose computation can also profit from the known topology. Extensive numerical results show that the proposed policy requirements depend mainly on the number of neighbors and concurrent failures in the DCN rather than its size, dramatically reducing the amount of forwarding and routing information stored at DCN nodes.

To solve the bandwidth and latency issues in current hierarchical data center network (DCN) architectures based on electrical switches, we propose a novel hybrid DCN architecture based on distributed flow-controlled fast optical switches (FOS) and modified top-of-the-rack (TOR) switches (HiFOST). The intra-cluster interconnection of HiFOST is built by FOS with wavelength switching in nanoseconds’ time for an efficient statistical multiplexing operation, while the inter-cluster interconnection is connected by the TOR interfaces directly. Due to the lack of practical optical buffers, optical flow control is implemented to retransmit packets in case of contention. We investigate the performance of HiFOST DCN with different TOR buffer sizes, optical link capacities, elastic allocation of transceivers, and network scales under realistic data center (DC) traffic. The results show an average server-to-server latency of less than 2.8 μs, a packet loss <5.6×10−6 at load of 0.5 for a DC size of 94,080 servers with limited 50 KB TOR buffer. In addition, for scaling out the servers’ number and scaling up the data rate of connected servers, the cost and power consumption of the HiFOST DCN have been investigated and compared with the electrical Fat-Tree and Leaf-Spine DCN architectures, as well as with the optical H-LION and OPSquare DCN architectures. Results indicate that, for 94,080 servers operating at 10 Gb/s, HiFOST has a 48.2% and 34.1% savings of the cost and 46.3% and 32.5% savings of the power consumption with respect to the Fat-Tree and Leaf-Spine, respectively. For a HiFOST DCN supporting a 10880 server, scaling up the operating data rate of the server to 100 Gb/s, the HiFOST solution has a cost savings of 35.6% and 34.1% and power consumption of 56.5% and 59.2% as compared to the Fat-Tree and Leaf-Spine, respectively.

Cloud applications have reshaped the model of services and infrastructure of the Internet. Search engines, social networks, content delivery and retail and e-commerce sites belong to this group of applications. An important element in the architecture of data centers where these applications run is the communication infrastructure, commonly known as data center networks (DCNs). A critical challenge DCNs have to address is the processing of the traffic of cloud applications, which due to its properties is essentially different to the traffic of other Internet applications. In order to improve the responsiveness and throughput of applications, DCNs should be able to prioritize short flows (a few kB) over long flows (several MB). However, given the time and space variations the traffic presents, the information about flow sizes is not available in advance in order to plan the flow scheduling. There has been a wealth of solutions developed in this space, and prior work includes flow scheduling mechanisms optimizing for a specific workload but fall short when workloads are not known in advance, or comprise a collection of applications changing dynamically. In this paper, we present an adaptable mechanism called Adaptable Workload-Agnostic Flow Scheduling (AWAFS). It is an adaptable approach that can adjust in an agnostic way the scheduling configuration of DCN forwarding devices. This agnostic adjustment contributes to reduce the Flow Completion Time (FCT) of those short flows, representing around 85% of the traffic handled by cloud applications. AWAFS operates by observing the traffic and detecting statistical properties that provide a hint to adapt the scheduling parameters. Our evaluation results based on simulation show that AWAFS can reduce the average FCT of short flows between 16.9% and 45.2% when compared to the best existing agnostic non-adaptable solution, without inducing starvation on long flows. Indeed, it can provide improvements as high as 39% for long flows. Additionally, AWAFS can improve the FCT for short flows in scenarios with high heterogeneity in the traffic present in the network, with a reduction up to 5% for the average FCT and 15% for the tail FCT.

The information and communication technology nowadays more than ever depends on the Internet and cloud computing, so that the data centers (DCs) have been converted to a constitutive unit of the cloud computing. A DC is composed of two primary parts: servers and Data Center Networks (DCNs). Robustness and scalability are two major challenges of the DCNs that are expanded based on two strategies, scale-out, and scale-up. This paper is distinctive from the related studies in two aspects. The first one is to simultaneously focus on both the scalability and the robustness challenges of the DCNs. For this purpose, we will concentrate on the comparison of robustness in the scalable models of these networks. The second one is, despite the previous work that only evaluated the DCN robustness under topological changes, we evaluated the robustness and fault tolerance against three types of unexpected changes in topology, traffic, and COI (community of interest) in the present work. Hence, we have chosen the network criticality (NC) as a graph-theoretic metric for analyzing DCN robustness. Afterward, we compare some structural and spectral graph metrics with NC among some well-known DCNs, and their scale-out and scale-up. Our results are useful to select the appropriate scaling strategy with the goal of maximizing the robustness of existing DCNs and provide a guideline for designing the new robust and scalable DCN.

CoMap: An efficient virtual network re-mapping strategy based on coalitional matching theory