Scaled Out Optically Switched (SOOS) network architecture for Web Scale Data Centers

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.

With the development of cloud computing, data center network (DCN) architectures as the core of the cloud platform received a surge of interesting from both the industry and academia. However, assessments of those new DCN architectures are mainly concentrated in load balancing, improvement of architectures and as well as some research of performance analysis in visualized environment. Moreover, none of them focus on the security in DCN architectures. In this paper, we propose contribution matrix method based on information entropy theory from the point of view of node importance which is the basic of the topology vulnerability. In addition, we conduct an experimental evaluation of the state-of-the-art Multi-rooted Tree and FiConn architectures, each respectively as a representative of the switch-centric network architecture and server-centric network architecture. Firstly, we use an undirected graph theory to describe the architecture. Secondly, we obtain the associated matrix of betweenness and degree, and calculate their weights by information entropy theory. And then, according to the definition 3 (see Section III-B); we obtain the contribution matrix for each one. Last but not least, we get the value of node importance for each node of normalized. Compared with other methods, our proposed method is effective and has a much higher accuracy. Furthermore, our method is generic and can be widely used for the new DCN architectures.

The network of traditional data center has been usually designed and constructed for the provision of user's equal access of data centre's resource or data. Therefore, network administrators have a strong tendency to manage user traffic from the viewpoint that the traffic has a similar size and characteristics. But, the emersion of big data begins to make data centers have to deal with 1015 byte-data transfer at once. Such a big data transfer can cause problems in network traffic management in the existed data center. And, the tiered network architecture of the legacy data center magnifies the magnitude of the problems. One of the well-known big data in science is from large hadron collider such as LHC in Swiss CERN. CERN LHC generates multi-peta byte data per year. From our experience of CERN data service, this paper showed the impact of network traffic affected by large-scale data stream using NS2 simulation, and then, suggested the evolution direction based on separating of large-scale data stream for the big data center's network architecture.

We investigate the performance of a novel flat data center network (DCN) architecture based on distributed buffer-less optical switches and fast flow control. Scalability of the novel DCN is assessed by numerical simulation of medium and large size data centers reporting an end-to-end latency of less than 8μs (including retransmission), a packet loss 9Tb/s average network throughput under load of 0.4.

In recent years, data center network (DCN) architectures (e.g., DCell [5], FiConn [6], BCube [4], FatTree [1], and VL2 [2]) received a surge of interest from both the industry and academia. However, none of existing efforts provide an in-depth understanding of the impact of these architectures on application performance in practical multi-tier systems under realistic workload. Moreover, it is also unclear how these architectures are affected in virtualized environments. In this paper, we fill this void by conducting an experimental evaluation of FiConn and FatTree, each respectively as a representative of hierarchical and flat architectures, in a three-tier transaction system using virtual machine (VM) based implementation. We observe several fundamental characteristics that are embedded in both classes of network topologies and cast a new light on the implication of virtualization in DCN architectures. Issues observed in this paper are generic and should be properly addressed by any DCN architectures before being considered for actual deployment, especially in mission-critical real-time transaction systems.

Understanding data center network architectures in virtualized environments: A view from multi-tier applications

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.

As the volume of data keeps growing rapidly, more and more storage devices, servers and network devices are continuously added into data centers to store, manage and analyze the data. The industry experience indicates that, instead of a huge number of servers added at a time, the data center network also expands gradually by adding a small number of servers from time to time. As a result, how to achieve an incremental scalability is becoming a very important challenge in designing modern data center network architectures in order to maintain the topological properties unchanged when the size of data centers grows. In this paper, we propose a new type of data center network architecture named HSDC (High Scalability Data Center Network Architecture) based on the hypercube network. The HSDC is constructed by using $m$m-port switches and 2-port servers. The fault-tolerant routing algorithm designed in this paper for HSDC can be executed on any vertex and is able to construct a path between any pair of vertices. In order to achieve an incremental scalability, we further propose three types of incomplete HSDC structures that allow gradually adding servers into the structures, while maintaining all the topological properties. The simulation experiments and performance results demonstrate that the throughput of HSDC is comparable to that of Fat-Tree, BCube and DCell. Furthermore, the analysis results indicate that HSDC strikes a good balance among diameter, bisection width, incremental scalability, cost and energy consumption in contrast to the state-of-the-art data center network architectures.

Advances in data intensive computing and high performance computing facilitate rapid scaling of data center networks, resulting in a growing body of research exploring new network architectures that enhance scalability, cost effectiveness and performance. Understanding the tradeoffs between these different network architectures could not only help data center operators improve deployments, but also assist system designers to optimize applications running on top of them. In this paper, we present a comparative analysis of several well known data center network architectures using important metrics, and present our results on different network topologies. We show the tradeoffs between these topologies and present implications on practical data center implementations.

Virtualization and energy management optimization of high speed computer network data centers based on optical switching and network technology

Optical switching based small-world data center network

Data Centers (DCs) are experiencing a tremendous growth in the number of hosted servers. Aggregate bandwidth requirement is a major bottleneck to data center performance. New Data Center Network (DCN) architectures are proposed to handle different challenges faced by current DCN architecture. In this paper we have implemented and simulated two promising DCN architectural models, namely switch-based and hybrid models, and compared their effectiveness by monitoring the network throughputs and average packet latencies. The presented analysis may be a background for the further studies on the simulation and implementation of the DCN customized topologies, and customized addressing protocols in the large-scale data centers.

This paper presents the design, analysis, and implementation of a novel data center network architecture, named NovaCube. Based on regular Torus topology, NovaCube is constructed by adding a number of most beneficial jump-over links, which offers many distinct advantages and practical benefits. Moreover, in order to enable NovaCube to achieve its maximum theoretical performance, a probabilistic oblivious routing algorithm PORA is carefully designed. PORA is a both deadlock and livelock free routing algorithm, which achieves near-optimal performance in terms of average routing path length with better load balancing thus leading to higher throughput. Theoretical derivation and mathematical analysis further prove the good performance of NovaCube and PORA.

All optical or hybrid electrical/optical switching data center network (DCN) architectures are capable of adapting the optical-layer topology to various network traffic demands based on the flexibility of optical switching technology. The topology reconfiguration strategies in previous research simply attempt to reconfigure optical circuit switching (OCS) links to offload heavier rack-to-rack traffic. However, due to the complex multi-layer interconnections and the various network traffic patterns in DCNs, the OCS links will not only offload the network traffic between its source and destination racks, but also forward the network traffic that belongs to the adjacent racks of the source and destinations racks. Previous strategies ignore the forwarding traffic and result in sub-optimal solutions. In order to overcome the shortcomings of previous methods and make full use of the OCS link resources in DCNs, an effective topology reconfiguration strategy named optical circuit bridge (OCBridge) has been proposed in our recent work. The main idea of our OCBridge is to take into account the traffic demands between the source and destination regions of each OCS link. In this paper, we first review our OCBridge, and then in simulation, we evaluate the performance of our OCBridge by testing it in two typical OCS-based DCNs, including OpenScale and Helios. Under our OCBridge, we analyze the outstanding network throughput performance of the DCNs and investigate the communication distance of traffic demands. Finally, in experiment, we deploy our OCBridge to a small prototype of OpenScale and verify that our OCBridge is capable of accelerating the distributed computing applications compared with previous topology reconfiguration strategies.

Network interconnection critically impacts the performance of data centers (DCs) and high-performance computing (HPC) systems, with scalability becoming vital as computing demands grow. This necessitates interconnection architectures that meet stringent latency, bandwidth, cost, and power consumption requirements. Optical interconnections provide cost-efficiency, reduced power consumption, and scalability to fulfill bandwidth needs. However, optical switches lack optical buffers, complicating the operation of all optical networks. To this end, we propose HiveNet , a novel hybrid interconnect architecture based on dual-port nodes and arrayed waveguide grating routers (AWGRs). HiveNet integrates low-radix electrical switches at lower layers to reduce cable complexity and construction costs, while AWGR-based optical connections at upper layers ensure fast switching and high bandwidth. The dual-port capability enables robust fault tolerance and supports all communication types (node-switch and node-node). Furthermore, a customized routing algorithm significantly enhances performance. Simulations conducted under various traffic patterns demonstrate that HiveNet achieves controlled delay and superior aggregate throughput. For large-scale networks (with 104,976 nodes at 10 Gb/s), HiveNet reduces construction costs by 49.3%, 26.4%, 32.7%, 54.1%, and 59.3% compared to Fat - Tree , H - LION , Leaf - Spine , BCube , and Lotus , respectively. Additionally, HiveNet decreases power consumption by 34.8%, 48.2%, 29.8%, and 23.1% compared to Fat - Tree , BCube , Leaf - Spine , and Lotus , respectively.