Network Optimization Through Traffic Patterns and Efficient Bandwidth Consumption in Large DCN

Today, there is a generalized standard usage of internet for all.The devices via multiple technologies that facilitates to provide few communication methods to scholars to work with. By forming multiple paths in the data center network, latest generation data centers offer maximum bandwidth with robustness. To utilize this bandwidth, it is necessary that different data flows take separate paths. In brief, a single-path transport seems inappropriate for such networks. By using Multipath TCP, we must reconsider data center networks, with a diverse approach as to the association between topology, transport protocols, routing. Multipath TCP allows certain topologies that single path TCP cannot use. In newer generation data centers, Multipath TCP is already deployable using extensively deployed technologies such as Equal-cost multipath routing. But, major benefits will come when data centers are specifically designed for multipath transports. Due to manifold of technologies like Cloud computing, social networking, and information networks there is a need to deploy the number of large data centers. While Transport Control Protocol is the leading Layer-3 transport protocol in data center networks, the operating conditions like high bandwidth, small-buffered switches, and traffic patterns causes poor performance of TCP. Data Center TCP algorithm has newly been anticipated as a TCP option for data centers which address these limitations. It is worth noting that traditional TCP protocol.

High-speed routers and high-performance data centers share a common system-level architecture in which multiple processing nodes are connected by an interconnection network for high-speed communications. Load balancing is an important technique for maximizing throughput and minimizing delay of the interconnection network. In this thesis, efficient load balancing schemes are designed and analyzed for next-generation routers and data centers.
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\nIn high-speed router design, two preferred switch architectures are input-queued switch and load-balanced switch. In an input-queued switch, time-domain load balancing can be carried out by an iterative algorithm that schedules packets for sending in different time slots. The complexity of an iterative algorithm increases rapidly with the number of scheduling iterations. To address this problem, a single-iteration scheduling algorithm called D-LQF is designed, in which exhaustive service policy is adopted for reusing the matched input-output pairs in the previous time slots to grow the match size. 
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\nUnlike an input-queued switch, a load-balanced switch consists of two stages of crossbar switch fabrics, where load balancing is carried out in both time and space domains. Among various load-balanced switches, the feedback-based switch gives the best delay-throughput performance. In this thesis, the feedback-based switch is enhanced in three aspects. Firstly, we focus on reducing its switch fabric complexity. Instead of using crossbars, a dual-banyan network is proposed. The complexity of dual-banyan can be further reduced by merging the two banyans to form a Clos network, resulting in a Clos-banyan network. Secondly, we target at improving the delay performance of the feedback-based switch. A Clos-feedback switch architecture is devised where each switch module in the Clos network is a small feedback-based switch. With application-flow based load balancing, packet order is ensured and the average packet delay is reduced from O(N) to O(n), where N and n are the switch and switch module sizes, respectively. Thirdly, we extend the feedback-based switch to support multicast traffic. Based on the notion of pointer-based multicast VOQ, an efficient multicast scheduling algorithm with packet replication at the middle-stage ports only is proposed. In order to provide close-to-100% throughput for any admissible multicast traffic patterns, a three-stage implementation of feedback-based switch is also designed.
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\nIn designing load balancing schemes for data centers, we focus on the most popular fat-tree based data centers. Notably, packet-based load balancing is widely considered infeasible for data centers. This is because the associated packet out-of-order problem will cause unnecessary TCP fast retransmits, and as a result, severely undermine TCP performance. In this thesis, we show that if packet-based load balancing is performed properly, the packet out-of-order problem can be easily addressed by slightly increasing the number of duplicate ACKs required for triggering fast retransmit. Admittedly, in case of a real packet loss, the loss recovery time will be increased. But our simulation results show that such an increase is far less than the reduction in the network queueing delay (due to a better load-balanced network). As compared to a flow-based load balancing scheme, our packet-based scheme consistently provides significantly higher goodput and noticeably smaller delay.

The Internet offers a large number of applications and services that we use on a daily basis. These widely used applications are hosted on large-scale, high-performance computing systems called data centers. The performance of TCP is inefficient in many-to-one communication, which is a common traffic pattern in data center networks. This many-to-one communication causes significant packet losses followed by timeouts, which consequently results in throughput collapse in data center networks; this problem is known as TCP Incast. In this paper, we present a queue length feedback-based solution to mitigate TCP Incast. The scheme has two parts: i) a novel queue length-based congestion parameter, which accurately measures congestion along the path from source to destination, and ii) a congestion control scheme that effectively uses the new congestion parameter to prevent throughput collapse due to Incast traffic patterns. Results are compared with TCP and DCTCP, the two most common transport protocols deployed in data center networks. The results show that the proposed scheme minimizes packet drops and achieves high utilization and burst tolerance.

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.

The rapid increasing traffic in data centers (DCs) puts tremendous pressure to the present multi-tier network architectures and electrical switching techniques. Switching traffic in the optical domain featuring ultra-high bandwidth, therefore, has been intensively investigated to build the high capacity data center networks (DCNs). To handle the variable traffic pattern in DCs, the network reconfigurability with adaptable optical bandwidth allocation is of key importance to flexibly assign the optical bandwidth. To this end, we propose and experimentally evaluate a software-defined networking enabled reconfigurable optical DCN with dynamic bandwidth allocation in this work, based on novel optical top of racks exploiting a wavelength selective switch. Experimental assessments show that the proposed solution can automatically reallocate the optical bandwidth in real-time to adapt the dynamic traffic pattern. Compared with the conventional optical DCN with static bandwidth provision, the end-to-end latency performance of the reconfigurable scheme with adaptable bandwidth allocation improves of 58.3% and the average packet loss decreases one order of magnitude. Moreover, the reconfigurable optical DCN features deterministic latency performance, with much lower time variations of packets delivery completion. Based on the experimental parameters, the simulation platform is also built to validate the good scalability of the proposed reconfigurable DCN. Numerical results illustrate the negligible performance degradation (11%) as the network scales from 2560 to 40 960 servers.

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 centers are integral part of cloud computing that support Web services, online social networking, data analysis, computation intensive applications and scientific computing. They require high performance components for their inter-process communication, storage and sub-communication systems. The performance bottleneck that used to be the processing power has now been shifted to communication speed within data centers. The performance of a data center, in terms of throughput and delay, is directly related to the performance of the underlying internal communication network. In this paper, we introduce an analytical model that can be used to evaluate the underlying network architecture in data centers. The model can further be used to develop simulation tools that extend the scope of performance evaluation beyond what it can be achieved by the theoretical model in terms of various network topologies, different traffic distributions, scalability, and load balancing. While the model is generic, we focus on its implementation for fat-tree networks that are widely used in data centers. The theoretical results are compared and validated with the simulation results for several network configurations. The results of this analysis provide a basis for data center network design and optimization.

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.

We highlight the advantages of dynamic wavelength allocation and path rerouting in hybrid optical flow-switched data center networks compared with static electronic packet-switched data center networks. Current electronic packet-switched networks perform routing based on lookup tables and shortest path routing over high speed optical connections. However, load balancing and path rerouting are not designed in coordination with the optical data plane. In this work, we design and analyze the performance of load balancing and resource allocation in hybrid flow data center architectures. In the hybrid flow architecture, the transport layer and control plane are responsible for load balancing, congestion control and reliable data delivery in wavelength division multiplexed networks. The transport layer abstracts the underlying network and data link layers from user applications. Applications are unaware of whether flows are transmitted via the electronic data plane, optical data plane, or both, where the relevant metric for applications is transaction delay. We show that the data center architecture that minimizes delay in the presence of elephant flows and dynamic loads is the hybrid flow architecture with dynamic resource allocation.

As the scale of data centers and cloud computing applications increases, data center networks play a critical role in meeting the huge communication bandwidth requirement of such applications. The scalability of conventional electronic data center networks is limited by wiring complexity and reaching distance of links under fixed power budget. To overcome this problem, in this paper we propose a nonblocking hybrid switch architecture, called NEO (Nonblocking Electronic and Optical), which is able to provide nonblocking interconnections for as many as 1,000,000 servers in a data center. NEO maintains electronic interconnections for intra-pod networks, while providing interpod interconnections by optical core switches, which not only increases the scalability of the switch architecture, but also lowers the switch cost and power consumption compared to other existing optical switch architectures. We also design a packet scheduler for NEO, which adopts a credit flow control mechanism and a parallel scheduling algorithm to avoid packet loss, and provide low communication latency. Our simulation results demonstrate that NEO achieves very low average packet delay compared to other existing optical switching architectures under various traffic patterns.

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.

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

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.

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.

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.