You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
This paper studies the design of self-adjusting networks whose topology dynamically adapts to the workload, in an online and demand-aware manner. This problem is motivated by emerging optical technologies which allow to reconfigure the datacenter topology at runtime. Our main contribution is ReNet, a self-adjusting network which maintains a balance between the benefits and costs of reconfigurations. In particular, we show that ReNets are statically optimal for arbitrary sparse communication demands, i.e., perform at least as good as any fixed demand-aware network designed with a perfect knowledge of the future demand. Furthermore, ReNets provide compact and local routing, by leveraging ideas from self-adjusting datastructures.

Cited Abstract(s):
[1]: To be agile and cost effective, data centers should allow dynamic resource allocation across large server pools. In particular, the data center network should enable any server to be assigned to any service. To meet these goals, we present VL2, a practical network architecture that scales to support huge data centers with uniform high capacity between servers, performance isolation between services, and Ethernet layer-2 semantics. VL2 uses (1) flat addressing to allow service instances to be placed anywhere in the network, (2) Valiant Load Balancing to spread traffic uniformly across network paths, and (3) end-system based address resolution to scale to large server pools, without introducing complexity to the network control plane. VL2's design is driven by detailed measurements of traffic and fault data from a large operational cloud service provider. VL2's implementation leverages proven network technologies, already available at low cost in high-speed hardware implementations, to build a scalable and reliable network architecture. As a result, VL2 networks can be deployed today, and we have built a working prototype. We evaluate the merits of the VL2 design using measurement, analysis, and experiments. Our VL2 prototype shuffles 2.7 TB of data among 75 servers in 395 seconds - sustaining a rate that is 94 of the maximum possible.

[2]: 

[3]: Datacenters provide cost-effective and flexible access to scalable compute and storage resources necessary for today’s cloud computing needs. A typical datacenter is made up of thousands of servers connected with a large network and usually managed by one operator. To provide quality access to the variety of applications and services hosted on datacenters and maximize performance, it deems necessary to use datacenter networks effectively and efficiently. Datacenter traffic is often a mix of several classes with different priorities and requirements. This includes user-generated interactive traffic, traffic with deadlines, and long-running traffic. To this end, custom transport protocols and traffic management techniques have been developed to improve datacenter network performance. In this tutorial paper, we review the general architecture of datacenter networks, various topologies proposed for them, their traffic properties, general traffic control challenges in datacenters and general traffic control objectives. The purpose of this paper is to bring out the important characteristics of traffic control in datacenters and not to survey all existing solutions (as it is virtually impossible due to massive body of existing research). We hope to provide readers with a wide range of options and factors while considering a variety of traffic control mechanisms. We discuss various characteristics of datacenter traffic control, including management schemes, transmission control, traffic shaping, prioritization, load balancing, multipathing, and traffic scheduling. Next, we point to several open challenges as well as new and interesting networking paradigms. At the end of this paper, we briefly review inter-datacenter networks that connect geographically dispersed datacenters, which have been receiving increasing attention recently and pose interesting and novel research problems. To measure the performance of datacenter networks, different performance metrics have been used, such as flow completion times, deadline miss rate, throughput, and fairness. Depending on the application and user requirements, some metrics may need more attention. While investigating different traffic control techniques, we point out the tradeoffs involved in terms of costs, complexity, and performance. We find that a combination of different traffic control techniques may be necessary at particular entities and layers in the network to improve the variety of performance metrics. We also find that despite significant research efforts, there are still open problems that demand further attention from the research community.

[4]: Recent studies have observed that large data center networks often have a few hotspots while most of the network is underutilized. Consequently, numerous data center network designs have explored the approach of identifying these communication hotspots in real-time and eliminating them by leveraging flexible optical or wireless connections to dynamically alter the network topology. These proposals are based on the premise that statically wired network topologies, which lack the opportunity for such online optimization, are fundamentally inefficient, and must be built at uniform full capacity to handle unpredictably skewed traffic. We show this assumption to be false. Our results establish that state-of-the-art static networks can also achieve the performance benefits claimed by dynamic, reconfigurable designs of the same cost: for the skewed traffic workloads used to make the case for dynamic networks, the evaluated static networks can achieve performance matching full-bandwidth fat-trees at two-thirds of the cost. Surprisingly, this can be accomplished even without relying on any form of online optimization, including the optimization of routing configuration in response to the traffic demands. Our results substantially lower the barriers for improving upon today's data centers by showing that a static, cabling-friendly topology built using commodity equipment yields superior performance when combined with well-understood routing methods.

[5]: We introduce a high-performance cost-effective network topology called Slim Fly that approaches the theoretically optimal network diameter. Slim Fly is based on graphs that approximate the solution to the degree-diameter problem. We analyze Slim Fly and compare it to both traditional and state-of the-art networks. Our analysis shows that Slim Fly has significant advantages over other topologies in latency, bandwidth, resiliency, cost, and power consumption. Finally, we propose deadlock-free routing schemes and physical layouts for large computing centres as well as a detailed cost and power model. Slim Fly enables constructing cost effective and highly resilient data enter and HPC networks that offer low latency and high bandwidth under different HPC workloads such as stencil or graph computations.

[6]: The data center network is increasingly a cost, reliability and performance bottleneck for cloud computing. Although multi-tree topologies can provide scalable bandwidth and traditional routing algorithms can provide eventual fault tolerance, we argue that recovery speed can be dramatically improved through the co-design of the network topology, routing algorithm and failure detector. We create an engineered network and routing protocol that directly address the failure characteristics observed in data centers. At the core of our proposal is a novel network topology that has many of the same desirable properties as FatTrees, but with much better fault recovery properties. We then create a series of failover protocols that benefit from this topology and are designed to cascade and complement each other. The resulting system, F10, can almost instantaneously reestablish connectivity and load balance, even in the presence of multiple failures. Our results show that following network link and switch failures, F10 has less than 1 7th the packet loss of current schemes. A trace-driven evaluation of MapReduce performance shows that F10's lower packet loss yields a median application-level 30 speedup.

[7]: Shipping-container-based data centers have been introduced as building blocks for constructing mega-data centers. However, it is a challenge on how to interconnect those containers together with reasonable cost and cabling complexity, due to the fact that a mega-data center can have hundreds or even thousands of containers and the aggregate bandwidth among containers can easily reach tera-bit per second. As a new inner-container server-centric network architecture, BCube [9] interconnects thousands of servers inside a container and provides high bandwidth support for typical traffic patterns. It naturally serves as a building block for mega-data center. In this paper, we propose MDCube, a high performance interconnection structure to scale BCube-based containers to mega-data centers. MDCube uses the high-speed uplink interfaces of the commodity switches in BCube containers to build the inter-container structure, reducing the cabling complexity greatly. MDCube puts its inter- and inner-container routing intelligences solely into servers to handle load-balance and fault-tolerance, thus directly leverages commodity instead of high-end switches to scale. Through analysis, we prove that MDCube has low diameter and high capacity. Both simulations and experiments in our testbed demonstrate the fault-tolerance and high network capacity of MDCube.

[8]: 

[9]: Today's data centers may contain tens of thousands of computers with significant aggregate bandwidth requirements. The network architecture typically consists of a tree of routing and switching elements with progressively more specialized and expensive equipment moving up the network hierarchy. Unfortunately, even when deploying the highest-end IP switches routers, resulting topologies may only support 50 of the aggregate bandwidth available at the edge of the network, while still incurring tremendous cost. Non-uniform bandwidth among data center nodes complicates application design and limits overall system performance. In this paper, we show how to leverage largely commodity Ethernet switches to support the full aggregate bandwidth of clusters consisting of tens of thousands of elements. Similar to how clusters of commodity computers have largely replaced more specialized SMPs and MPPs, we argue that appropriately architected and interconnected commodity switches may deliver more performance at less cost than available from today's higher-end solutions. Our approach requires no modifications to the end host network interface, operating system, or applications; critically, it is fully backward compatible with Ethernet, IP, and TCP.

[10]: This paper presents BCube, a new network architecture specifically designed for shipping-container based, modular data centers. At the core of the BCube architecture is its server-centric network structure, where servers with multiple network ports connect to multiple layers of COTS (commodity off-the-shelf) mini-switches. Servers act as not only end hosts, but also relay nodes for each other. BCube supports various bandwidth-intensive applications by speeding-up one-to-one, one-to-several, and one-to-all traffic patterns, and by providing high network capacity for all-to-all traffic. BCube exhibits graceful performance degradation as the server and or switch failure rate increases. This property is of special importance for shipping-container data centers, since once the container is sealed and operational, it becomes very difficult to repair or replace its components. Our implementation experiences show that BCube can be seamlessly integrated with the TCP IP protocol stack and BCube packet forwarding can be efficiently implemented in both hardware and software. Experiments in our testbed demonstrate that BCube is fault tolerant and load balancing and it significantly accelerates representative bandwidth-intensive applications.

[11]: This paper proposes an approach to the design of large-scale general-purpose data center networks based on the notions of volume and area universality introduced by Leiserson in the 1980's in the context of VLSI design. In particular, we suggest that the principle goal of the network designer should be to build a single network that is provably competitive, for any application, with any network that can be built for the same amount of money. After describing our approach, we survey the technology choices available to network designers today, and examine several existing commercial data center networks. In the most recent of these networks resources are allocated roughly as we suggest in this paper.

[12]: 

[13]: Industry experience indicates that the ability to incrementally expand data centers is essential. However, existing high-bandwidth network designs have rigid structure that interferes with incremental expansion. We present Jellyfish, a high-capacity network interconnect which, by adopting a random graph topology, yields itself naturally to incremental expansion. Somewhat surprisingly, Jellyfish is more cost-efficient than a fat-tree, supporting as many as 25 more servers at full capacity using the same equipment at the scale of a few thousand nodes, and this advantage improves with scale. Jellyfish also allows great flexibility in building networks with different degrees of oversubscription. However, Jellyfish's unstructured design brings new challenges in routing, physical layout, and wiring. We describe approaches to resolve these challenges, and our evaluation suggests that Jellyfish could be deployed in today's data centers.

[14]: We present our approach for overcoming the cost, operational complexity, and limited scale endemic to datacenter networks a decade ago. Three themes unify the five generations of datacenter networks detailed in this paper. First, multi-stage Clos topologies built from commodity switch silicon can support cost-effective deployment of building-scale networks. Second, much of the general, but complex, decentralized network routing and management protocols supporting arbitrary deployment scenarios were overkill for single-operator, pre-planned datacenter networks. We built a centralized control mechanism based on a global configuration pushed to all datacenter switches. Third, modular hardware design coupled with simple, robust software allowed our design to also support inter-cluster and wide-area networks. Our datacenter networks run at dozens of sites across the planet, scaling in capacity by 100x over ten years to more than 1Pbps of bisection bandwidth.

[15]: With high throughput networks acquiring a crucial role in supporting data-intensive applications, a variety of data center network topologies have been proposed to achieve high capacity at low cost. While this work explores a large number of design points, even in the limited case of a network of identical switches, no proposal has been able to claim any notion of optimality. The case of heterogeneous networks, incorporating multiple line-speeds and port-counts as data centers grow over time, introduces even greater complexity. In this paper, we present the first non-trivial upper-bound on network throughput under uniform traffic patterns for any topology with identical switches. We then show that random graphs achieve throughput surprisingly close to this bound, within a few percent at the scale of a few thousand servers. Apart from demonstrating that homogeneous topology design may be reaching its limits, this result also motivates our use of random graphs as building blocks for design of heterogeneous networks. Given a heterogeneous pool of network switches, we explore through experiments and analysis, how the distribution of servers across switches and the interconnection of switches affect network throughput. We apply these insights to a real-world heterogeneous data center topology, VL2, demonstrating as much as 43 higher throughput with the same equipment.
Datacenter networks have become a critical infrastructure and especially the popularity of online services [3] (e.g., web search, social networks, storage, financial services, multimedia, etc.) has led to a fast increase of datacenter traffic [4]7 [2] . Many applications (such as scatter-gather and batch computing applications) generate much datacenter traffic, and consequently, the traffic staying inside the datacenter is often much larger than the traffic entering or leaving the datacenter [3] . It is hence not surprising that the design of effective and efficient (also in terms of cost and cabling) datacenter networks has received much interest over the last years [9] [4]9 [4] [10] [13] [7] [1] [4]8 [8] [5] [15] . The situation has recently been compared to the early 1980s, when many new interconnection network designs were proposed [11] , not for datacenters, but for parallel computers.