The structure connectivity of Data Center Networks

Last decade, numerous giant data center networks are built to provide increasingly fashionable web applications. For two integers $m\geq 0$ and $n\geq 2$, the $m$-dimensional DCell network with $n$-port switches $D_{m,n}$ and $n$-dimensional BCDC network $B_{n}$ have been proposed. Connectivity is a basic parameter to measure fault-tolerance of networks. As generalizations of connectivity, structure (substructure) connectivity was recently proposed. Let $G$ and $H$ be two connected graphs. Let $\mathcal{F}$ be a set whose elements are subgraphs of $G$, and every member of $\mathcal{F}$ is isomorphic to $H$ (resp. a connected subgraph of $H$). Then $H$-structure connectivity $\kappa(G; H)$ (resp. $H$-substructure connectivity $\kappa^{s}(G; H)$) of $G$ is the size of a smallest set of $\mathcal{F}$ such that the rest of $G$ is disconnected or the singleton when removing $\mathcal{F}$. Then it is meaningful to calculate the structure connectivity of data center networks on some common structures, such as star $K_{1,t}$, path $P_k$, cycle $C_k$, complete graph $K_s$ and so on. In this paper, we obtain that $\kappa (D_{m,n}; K_{1,t})=\kappa^s (D_{m,n}; K_{1,t})=\lceil \frac{n-1}{1+t}\rceil+m$ for $1\leq t\leq m+n-2$ and $\kappa (D_{m,n}; K_s)= \lceil\frac{n-1}{s}\rceil+m$ for $3\leq s\leq n-1$ by analyzing the structural properties of $D_{m,n}$. We also compute $\kappa(B_n; H)$ and $\kappa^s(B_n; H)$ for $H\in \{K_{1,t}, P_{k}, C_{k}|1\leq t\leq 2n-3, 6\leq k\leq 2n-1 \}$ and $n\geq 5$ by using $g$-extra connectivity of $B_n$

Bisection (Band)Width of Product Networks with Application to Data Centers

Abstract. The bisection width of interconnection networks has always been important in parallel computing, since it bounds the amount of information that can be moved from one side of a network to another, i.e., the bisection bandwidth. The problem of finding the exact bisection width of the multidimensional torus was posed by Leighton and has remained open for 20 years. In this paper we provide the exact value of the bisection width of the torus, as well as of several ddimensional classical parallel topologies that can be obtained by the application of the Cartesian product of graphs. To do so, we first provide two general results that allow to obtain upper and lower bounds on the bisection width of a product graph as a function of some properties of its factor graphs. We also apply these results to obtain bounds for the bisection bandwidth of a d-dimensional BCube network, a recently proposed topology for data centers

Bisection (Band)Width of Product Networks with Application to Data Centers

Abstract. The bisection width of interconnection networks has always been important in parallel computing, since it bounds the amount of information that can be moved from one side of a network to another, i.e., the bisection bandwidth. Finding its exact value has proven to be challenging for some network families. For instance, the problem of finding the exact bisection width of the multidimensional torus was posed by Leighton and has remained open for almost 20 years. In this paper we provide the exact value of the bisection width of the torus, as well as of several d-dimensional classical parallel topologies that can be obtained by the application of the Cartesian product of graphs. To do so, we first provide two general results that allow to obtain upper and lower bounds on the bisection width of a product graph as a function of some properties of its factor graphs. We also apply these results to obtain bounds for the bisection bandwidth of a d-dimensional BCube network, a recently proposed topology for data centers

Structural issues and energy efficiency in data centers

Mención Internacional en el título de doctorWith the rise of cloud computing, data centers have been called to play a main role in the Internet scenario nowadays. Despite this relevance, they are probably far from their zenith yet due to the ever increasing demand of contents to be stored in and distributed by the cloud, the need of computing power or the larger and larger amounts of data being analyzed by top companies such as Google, Microsoft or Amazon. However, everything is not always a bed of roses. Having a data center entails two major issues: they are terribly expensive to build, and they consume huge amounts of power being, therefore, terribly expensive to maintain. For this reason, cutting down the cost of building and increasing the energy efficiency (and hence reducing the carbon footprint) of data centers has been one of the hottest research topics during the last years. In this thesis we propose different techniques that can have an impact in both the building and the maintenance costs of data centers of any size, from small scale to large flagship data centers. The first part of the thesis is devoted to structural issues. We start by analyzing the bisection (band)width of a topology, of product graphs in particular, a useful parameter to compare and choose among different data center topologies. In that same part we describe the problem of deploying the servers in a data center as a Multidimensional Arrangement Problem (MAP) and propose a heuristic to reduce the deployment and wiring costs. We target energy efficiency in data centers in the second part of the thesis. We first propose a method to reduce the energy consumption in the data center network: rate adaptation. Rate adaptation is based on the idea of energy proportionality and aims to consume power on network devices proportionally to the load on their links. Our analysis proves that just using rate adaptation we may achieve average energy savings in the order of a 30-40% and up to a 60% depending on the network topology. We continue by characterizing the power requirements of a data center server given that, in order to properly increase the energy efficiency of a data center, we first need to understand how energy is being consumed. We present an exhaustive empirical characterization of the power requirements of multiple components of data center servers, namely, the CPU, the disks, and the network card. To do so, we devise different experiments to stress these components, taking into account the multiple available frequencies as well as the fact that we are working with multicore servers. In these experiments, we measure their energy consumption and identify their optimal operational points. Our study proves that the curve that defines the minimal power consumption of the CPU, as a function of the load in Active Cycles Per Second (ACPS), is neither concave nor purely convex. Moreover, it definitively has a superlinear dependence on the load. We also validate the accuracy of the model derived from our characterization by running different Hadoop applications in diverse scenarios obtaining an error below 4:1% on average. The last topic we study is the Virtual Machine Assignment problem (VMA), i.e., optimizing how virtual machines (VMs) are assigned to physical machines (PMs) in data centers. Our optimization target is to minimize the power consumed by all the PMs when considering that power consumption depends superlinearly on the load. We study four different VMA problems, depending on whether the number of PMs and their capacity are bounded or not. We study their complexity and perform an offline and online analysis of these problems. The online analysis is complemented with simulations that show that the online algorithms we propose consume substantially less power than other state of the art assignment algorithms.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Joerg Widmer.- Secretario: José Manuel Moya Fernández.- Vocal: Shmuel Zak