Incremental Scalability Research Articles

An Improved Fault Diagnosis Algorithm for Highly Scalable Data Center Networks

Fault detection and localization are vital for ensuring the stability of data center networks (DCNs). Specifically, adaptive fault diagnosis is deemed a fundamental technology in achieving the fault tolerance of systems. The highly scalable data center network (HSDC) is a promising structure of server-centric DCNs, as it exhibits the capacity for incremental scalability, coupled with the assurance of low cost and energy consumption, low diameter, and high bisection width. In this paper, we first determine that both the connectivity and diagnosability of the m-dimensional complete HSDC, denoted by HSDCm(m), are m. Further, we propose an efficient adaptive fault diagnosis algorithm to diagnose an HSDCm(m) within three test rounds, and at most N+4m(m−2) tests with m≥3 (resp. at most nine tests with m=2), where N=m·2m is the total number of nodes in HSDCm(m). Our experimental outcomes demonstrate that this diagnosis scheme of HSDC can achieve complete diagnosis and significantly reduce the number of required tests.

The disjoint path cover in the data center network HSDC with prescribed vertices in each path

The n-dimensional HSDC with the logic graph Hn is one of the most attractive server-centric data center networks for high incremental scalability. The routing design in network topology is very important for information transmission. In particular, the application of disjoint path covers can solve various problems such as program code optimization and mapping parallel programs to parallel structures. Hn consists of 2n cliques and each two vertices in a clique are adjacent. For any vertex sets {x,u} and {y,v} in W and B, respectively, and any two disjoint vertex sets A1,A2 in some clique, where (W,B) is a bipartition of Hn, in this paper, we prove that, when |Ai|≠1 and Ai∩{x,y,u,v}=∅ for i=1,2, if |A1∪A2|≤n−3, then there exists an (x,y)-path P1 and a (u,v)-path P2 in Hn passing through A1 and A2, respectively, such that P1 and P2 have no common vertices and P1∪P2 contains all vertices of Hn. Then P1∪P2 is called the 2-disjoint path cover in Hn and we propose the efficient algorithms for the path design. Furthermore, we prove that our main result is optimal.

RibsNet: A Scalable, High-Performance, and Cost-Effective Two-Layer-Based Cloud Data Center Network Architecture

Today, we witness an unprecedented surge of data traffic due to the explosive growth of smart devices and intelligent applications. Hence, supporting the Data Center Networks (DCNs) infrastructure by accommodating myriads of servers becomes natural and inevitable. This vision can be realized by providing an interconnecting architecture with higher scalability and remarkable capacity while maintaining fault tolerance, minimum latency, and cost and power efficiency. Motivated by this trend, this paper proposes a new network based on dual-centric dubbed RibsNet, which is a symmetric network composed of two layers of switches and dual-port servers. The proposed network enables the servers to establish all kinds of connections (server-switch and server-server), thus enhancing network fault tolerance with high bisection width and maximizing its performance and reliability. RibsNet allows the gradual addition of servers while preserving all its topological properties; it has a stable and low diameter, which does not exceed six regardless of the DCN’s size. Furthermore, an efficient and fault tolerant routing scheme, exclusively designed for this network, is proposed to enhance the routing efficiency and address different types of failures. Theoretical analysis proves that RibsNet strikes a good balance among several connections within the network and brings outstanding gains in terms of incremental scalability, bisection width, and cost and power savings against various cutting-edge DCNs architectures. Simulation results demonstrate RibsNet as a promising structure that outperforms BCube, DCell, HSDC, and FiConn in terms of latency and throughput by approximately 8%, 6%, 7%, and 26%, respectively.

GHDC: a dual-centric data center network architecture by using multi-port servers with greater incremental scalability

As the volume of data keeps growing rapidly, more and more storage devices, servers, and network devices are continuously added into data center networks (DCNs) to store, manage, and analyze the data. The industry experience indicates that, instead of adding a huge number of servers into the DCNs at a time, the DCN can also be expanded gradually by adding a small number of servers from time to time. This paper proposes a new type of dual-centric data center network structure, called GHDC (Generalized Hypercube Data Center network architecture), which is constructed by using commodity switches and multi-port servers. After analyzing the shortest distance between any two vertices, a routing algorithm for GHDC is developed. To achieve incremental scalability, two incomplete GHDC structures are proposed. A small number of servers can be added into the incomplete GHDC structures while their topological properties are maintained. The analysis and experiment results show that GHDC significantly outperform the other DCN structures, such as FatTree, BCube, Platonica, FCell, FSquare, and FRectangle, in terms of the incremental scalability and robustness. The average throughput of GHDC is approximately comparable to that of FatTree, BCube, and FSquare, and is higher than that of Platonica, FCell, and FRectangle by about 130.2%, 17.45% and 25.5%. Compared with the FatTree, BCube, FCell, FRectangle, and FSquare, GHDC reduces the cost by about 68.49%, 78.04%, 10.84%, 22.85%, and 29.55%, and reduces the max energy consumption by about 69.45%, 34.48%, 11.55%, 24.31%, and 29.58%, respectively. The actual energy consumption of GHDC is much lower than that of FatTree, BCube, FCell, FRectangle, and FSquare, and is little higher than that of Platonia.

GHB: a cost-effective and energy-efficient data center network structure with greater incremental scalability

GHB: a cost-effective and energy-efficient data center network structure with greater incremental scalability

Flexible, highly scalable and cost-effective network structures for data centers

The ever-increasing number of online services and tremendous data volume make the data center networks expand rapidly. The data center network must be flexible in scalability and cost-effective in order to respond quickly to the ever changing service requirements. However, the costs of switch-centric topologies are high, while the scalabilities of server-centric topologies are not good. How to build a flexible, highly scalable and cost-effective data center network structure is becoming a very challenging problem. Based on the Cartesian product graph, this paper proposes a new type of data center network structure named SDCCP (Scalable Data Center network structure based on Cartesian Product graphs). It is constructed by using commodity m-port switches and 2-port servers. Based on the m-degree Cartesian product graphs, we can construct different kinds of SDCCP structures which can be expanded in different scales by using the same type of m-port switches. In order to achieve incremental scalability, we further propose an incomplete SDCCP structure. Servers can be gradually added into the incomplete SDCCP structure while maintaining their topological properties unchanged. Compared with Fat-Tree and BCube, the SDCCP reduces the cost by about 30% and 60%, respectively; and reduces the energy consumption by about 27% and 35%, respectively. The performance analyzes and experimental results demonstrate that SDCCP strikes a good balance in terms of flexibility, scalability, cost-effectiveness, and low energy consumption in contrast to the typical data center network structures.

Hamiltonian Properties of the Data Center Network HSDC with Faulty Elements

Abstract The data center network HSDC is a superior candidate for building large-scale data centers, and strikes a good balance among diameter, bisection width, incremental scalability and other important characteristics in contrast to the state-of-the-art data center network architectures. The Hamiltonian property is an important indicator to measure the reliability of a network. In this paper, we study the Hamiltonian properties of HSDC’s logic graph $H_n$. Firstly, we prove that $H_n$ is Hamiltonian-connected for $n\geq 3$. Secondly, we propose an $O(NlogN)$ algorithm for finding a Hamiltonian path between any two distinct nodes in $H_n$, where $N$ is the number of nodes in $H_n$. Furthermore, we consider the Hamiltonian properties of $H_n$ with faulty elements, and prove that $H_n$ is $(n-3)$-fault-tolerant Hamiltonian-connected and $(n-2)$-fault-tolerant Hamiltonian for $n\geq 3$.

Fault-Tolerant and Unicast Performances of the Data Center Network HSDC

In order to satisfy the rapidly increasing demand for data volume, large data center networks (DCNs) have been proposed. In 2019, Zhang et al. proposed a new highly scalable DCN architecture named HSDC (Highly Scalable Data Center Network), which can achieve greater incremental scalability. In this paper, we give the definition of the logical graph of HSDC, named $$H_n$$ , which can be treated as a compound graph of hypercube and complete graph of the same dimension. First, we prove that the connectivity and tightly super connectivity of $$H_n$$ are both n. Then, we give an O(n) shortest unicast algorithm to find a shortest path between any two distinct nodes in $$H_n$$ , and prove the correctness of this algorithm. In fact, we also prove that the length of the shortest path constructed by this algorithm is no more than $$2d+1$$ if $$d<n$$ and at most 2d if $$d=n$$ , where d is the Hamming distance between the start and end nodes, and the diameter of $$H_n$$ is 2n.

HSDC: A Highly Scalable Data Center Network Architecture for Greater Incremental Scalability

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.

Scale-Out Networking in the Data Center

Scale-out architectures supporting flexible, incremental scalability are common for computing and storage. However, the network remains the last bastion of the traditional scale-up approach, making it the data center's weak link. Through the UCSD Triton network architecture, the authors explore issues in managing the network as a single plug-and-play virtualizable fabric scalable to hundreds of thousands of ports and petabits per second of aggregate bandwidth.

Clustering support and replication management for scalable network services

The ubiquity of the Internet and various intranets has brought about widespread availability of online services and applications accessible through the network. Cluster-based network services have been rapidly emerging due to their cost-effectiveness in achieving high availability and incremental scalability. We present the design and implementation of the Neptune middleware system that provides clustering support and replication management for scalable network services. Neptune employs a loosely connected and functionally symmetric clustering architecture to achieve high scalability and robustness. It shields the clustering complexities from application developers through simple programming interfaces. In addition, Neptune provides replication management with flexible replication consistency support at the clustering middleware level. Such support can be easily applied to a large number of applications with different underlying data management mechanisms or service semantics. The system has been implemented on Linux and Solaris clusters, where a number of applications have been successfully deployed. Our evaluations demonstrate the system performance and smooth failure recovery achieved by proposed techniques.

Optimizing data aggregation for cluster-based internet services

Large-scale cluster-based Internet services often host partitioned datasets to provide incremental scalability. The aggregation of results produced from multiple partitions is a fundamental building block for the delivery of these services. This paper presents the design and implementation of a programming primitive -- Data Aggregation Call (DAC) -- to exploit partition parallelism for cluster-based Internet services. A DAC request specifies a local processing operator and a global reduction operator, and it aggregates the local processing results from participating nodes through the global reduction operator. Applications may allow a DAC request to return partial aggregation results as a tradeoff between quality and availability. Our architecture design aims at improving interactive responses with sustained throughput for typical cluster environments where platform heterogeneity and software/hardware failures are common. At the cluster level, our load-adaptive reduction tree construction algorithm balances processing and aggregation load across servers while exploiting partition parallelism. Inside each node, we employ an event-driven thread pool design that prevents slow nodes from adversely affecting system throughput under highly concurrent workload. We further devise a staged timeout scheme that eagerly prunes slow or unresponsive servers from the reduction tree to meet soft deadlines. We have used the DAC primitive to implement several applications: a search engine document retriever, a parallel protein sequence matcher, and an online parallel facial recognizer. Our experimental and simulation results validate the effectiveness of the proposed optimization techniques for reducing response time, improving throughput, and gracefully handling server unresponsiveness. We also demonstrate the ease-of use of the DAC primitive and the scalability of our architecture design.

Performance analysis of a pull-based parallel video server

In conventional video-on-demand systems, video data are stored in a video server for delivery to multiple receivers over a communications network. The video server's hardware limits the maximum storage capacity as well as the maximum number of video sessions that can concurrently be delivered. Clearly, these limits will eventually be exceeded by the growing need for better video quality and larger user population. This paper studies a parallel video server architecture that exploits server parallelism to achieve incremental scalability. First, unlike data partition and replication, the architecture employs data striping at the server level to achieve fine-grain load balancing across multiple servers. Second, a client-pull service model is employed to eliminate the need for interserver synchronization. Third, an admission-scheduling algorithm is proposed to further control the instantaneous load at each server so that linear scalability can be achieved. This paper analyzes the performance of the architecture by deriving bounds for server service delay, client buffer requirement, prefetch delay, and scheduling delay. These performance metrics and design tradeoffs are further evaluated using numerical examples. Our results show that the proposed parallel video server architecture can be linearly scaled up to more concurrent users simply by adding more servers and redistributing the video data among the servers.

A spanning multichannel linked hypercube: a gradually scalable optical interconnection network for massively parallel computing

A new, scalable interconnection topology called the Spanning Multichannel Linked Hypercube (SMLH) is proposed. This proposed network is very suitable to massively parallel systems and is highly amenable to optical implementation. The SMLH uses the hypercube topology as a basic building block and connects such building blocks using two-dimensional multichannel links (similar to spanning buses). In doing so, the SMLH combines positive features of both the hypercube (small diameter, high connectivity, symmetry, simple routing, and fault tolerance) and the spanning bus hypercube (SBH) (constant node degree, scalability, and ease of physical implementation), while at the same time circumventing their disadvantages. The SMLH topology supports many communication patterns found in different classes of computation, such as bus-based, mesh-based, and tree-based problems, as well as hypercube-based problems. A very attractive feature of the SMLH network is its ability to support a large number of processors with the possibility of maintaining a constant degree and a constant diameter. Other positive features include symmetry, incremental scalability, and fault tolerance. It is shown that the SMLH network provides better average message distance, average traffic density, and queuing delay than many similar networks, including the binary hypercube, the SBH, etc. Additionally, the SMLH has comparable performance to other high-performance hypercubic networks, including the Generalized Hypercube and the Hypermesh. An optical implementation methodology is proposed for SMLH. The implementation methodology combines both the advantages of free space optics with those of wavelength division multiplexing techniques. A detailed analysis of the feasibility of the proposed network is also presented.

Incrementally scalable optical interconnection network with a constant degree and constant diameter for parallel computing

A new scalable interconnection topology called the spanning-bus connected hypercube (SBCH) that is suitable for massively parallel systems is proposed. The SBCH uses the hypercube topology as a basic building block and connects such building blocks by use of multidimensional spanning buses. In doing so, the SBCH combines positive features of both the hypercube (small diameter, high connectivity, symmetry, simple routing, and fault tolerance) and the spanning-bus hypercube (SBH) (constant node degree, scalability, and ease of physical implementation), while at the same time circumventing their disadvantages. The SBCH topology permits the efficient support of many communication patterns found in different classes of computation, such as bus-based, mesh-based, and tree-based problems, as well as hypercube-based problems. A very attractive feature of the SBCH network is its ability to support a large number of processors while maintaining a constant degree and a constant diameter. Other positive features include symmetry, incremental scalability, and fault tolerance. An optical implementation methodology is proposed for the SBCH. The implementation methodology combines the advantages of free-space optics with those of wavelength-division multiplexing techniques. An analysis of the feasibility of the proposed network is also presented.

A spanning bus connected hypercube: a new scalable optical interconnection network for multiprocessors and massively parallel systems

A new scalable interconnection topology suitable for massively parallel systems called the spanning bus connected hypercube (SBCH) is proposed. The SBCH uses the hypercube topology as a basic building block and connects such building blocks using multidimensional spanning buses. In doing so, the SBCH combines positive features of both the hypercube (small diameter, high connectivity, symmetry, simple routing, and fault tolerance) and the spanning bus hypercube (SBH) (constant node degree, scalability, and ease of physical implementation), while at the same time circumventing their disadvantages. The SBCH topology permits the efficient support of many communication patterns found in different classes of computation such as bus-based, mesh-based, tree-based problems as well as hypercube-based problems. A very attractive feature of the SBCH network is its ability to support a large number of processors while maintaining a constant degree and constant diameter. Other positive features include symmetry, incremental scalability, and fault-tolerance. An optical implementation methodology is proposed for SBCH. The implementation methodology combines both the advantages of free space optics with those of wavelength division multiplexing techniques. A detailed analysis of the feasibility of the proposed network is also presented.