Survivable Virtual Infrastructure Mapping in Virtualized Data Centers

In a virtualized data center, survivability can be enhanced by creating redundant VMs as backup for VMs such that after VM or server failures, affected services can be quickly switched over to backup VMs. To enable flexible and efficient resource management, we propose to use a service-aware approach in which multiple correlated Virtual Machines (VMs) and their backups are grouped together to form a Survivable Virtual Infrastructure (SVI) for a service or a tenant. A fundamental problem in such a system is to determine how to map each SVI to a physical data center network such that operational costs are minimized subject to the constraints that each VM’s resource requirements are met and bandwidth demands between VMs can be guaranteed before and after failures. This problem can be naturally divided into two sub-problems: VM Placement (VMP) and Virtual Link Mapping (VLM). We present a general optimization framework for this mapping problem. Then we present an efficient algorithm for the VMP subproblem as well as a polynomial-time algorithm that optimally solves the VLM subproblem, which can be used as subroutines in the framework. We also present an effective heuristic algorithm that jointly solves the two subproblems. It has been shown by extensive simulation results based on the real VM data traces collected from the green data center at Syracuse University that compared with the First Fit Descending (FFD) and single shortest path based baseline algorithm, both our VMP+VLM algorithm and joint algorithm significantly reduce the reserved bandwidth, and yield comparable results in terms of the number of active servers

Resource Management in Virtualized Data Center

As businesses are increasingly relying on the cloud to host their services, cloud providers are striving to offer guaranteed and highly-available resources. To achieve this goal, recent proposals have advocated to offer both computing and networking resources in the form of Virtual Data Centers (VDCs). However, to offer VDCs, cloud providers have to overcome several technical challenges. In this thesis, we focus on two key challenges: (1) the VDC embedding problem: how to efficiently allocate resources to VDCs such that energy costs and bandwidth consumption are minimized, and (2) the availability-aware VDC embedding and backup provisioning problem which aims at allocating resources to VDCs with hard guarantees on their availability. The first part of this thesis is primarily concerned with the first challenge. The goal of the VDC embedding problem is to allocate resources to VDCs while minimizing the bandwidth usage in the data center and maximizing the cloud provider's revenue. Existing proposals have focused only on the placement of VMs and ignored mapping of other types of resources like switches. Hence, we propose a new VDC embedding solution that explicitly considers the embedding of virtual switches in addition to virtual machines and communication links. Simulations show that our solution results in high acceptance rate of VDC requests, less bandwidth consumption in the data center network, and increased revenue for the cloud provider. In the second part of this thesis, we study the availability-aware VDC embedding and backup provisioning problem. The goal is to provision virtual backup nodes and links in order to achieve the desired availability for each VDC. Existing solutions addressing this challenge have overlooked the heterogeneity of the data center equipment in terms of failure rates and availability. To address this limitation, we propose a High-availability Virtual Infrastructure (Hi-VI) management framework that jointly allocates resources for VDCs and their backups while minimizing total energy costs. Hi-VI uses a novel technique to compute the availability of a VDC that considers both (1) the heterogeneity of the data center networking and computing equipment, and (2) the number of redundant virtual nodes and links provisioned as backups. Simulations demonstrate the effectiveness of our framework compared to heterogeneity-oblivious solutions in terms of revenue and the number of physical servers used to embed VDCs

Assuring virtual network reliability and resilience

A framework developed that uses reliability block diagrams and continuous-time Markov chains to model and analyse the reliability and availability of a Virtual Network Environment (VNE). In addition, to minimize the unpredicted failures and reduce the impact of failure on a virtual network, a dynamic solution proposed for detecting a failure before it occurs in the VNE. Moreover, to predict failure and establish a tolerable maintenance plan before failure occurs in the VNE, a failure prediction method for VNE can be used to minimise the unpredicted failures, reduce backup redundancy and maximise system performance

Survivable Virtual Network Redesign and Embedding in Cloud Data Center Networks

Today, the cloud computing paradigm enables multiple virtualized services to co- exist on the same physical machine and share the same physical resources, hard- ware, as well as energy consumption expenses. To allow cloud customers migrate their services on to the cloud side, the Infrastructure Provider (InP) or cloud data centre operator provisions to its tenants virtual networks (VNs) to host their services. Virtual Networks can be thought of as segmenting the physical net- work and its resources, and such VN requests (or tenants) need to be mapped onto the substrate network and provisioned with sufficient physical resources as per the users’ requirements. With this emerging computing paradigm, cloud cus- tomers may demand to have highly reliable services for the hosted applications; however, failures often happen unexpectedly in data-centers, interrupting critical cloud services. Consequently, VN or cloud services are provisioned with redun- dant resources to achieve the demanded level of service reliability. To maintain a profitable operation of their network and resources, and thus achieve increased long term revenues, cloud network operators often rely on optimizing the map- ping of reliable cloud services. Such problem is referred to as in the literature as “Survivable Virtual Network Embedding (SVNE) ” problem. In this thesis, the survivable VN embedding problem is studied and a novel cost-efficient Survivable Virtual Network Redesign algorithm is carefully designed, presented, and evalu- ated. Subsequently, we distinguish between the communication services provided by the cloud provider and study the problem of survivable embedding of multicast services; we formally model the problem, and present two algorithms to reactively maintain multicast trees in cloud data centers upon failures

Software Defined Applications in Cellular and Optical Networks

abstract: Small wireless cells have the potential to overcome bottlenecks in wireless access through the sharing of spectrum resources. A novel access backhaul network architecture based on a Smart Gateway (Sm-GW) between the small cell base stations, e.g., LTE eNBs, and the conventional backhaul gateways, e.g., LTE Servicing/Packet Gateways (S/P-GWs) has been introduced to address the bottleneck. The Sm-GW flexibly schedules uplink transmissions for the eNBs. Based on software defined networking (SDN) a management mechanism that allows multiple operator to flexibly inter-operate via multiple Sm-GWs with a multitude of small cells has been proposed. This dissertation also comprehensively survey the studies that examine the SDN paradigm in optical networks. Along with the PHY functional split improvements, the performance of Distributed Converged Cable Access Platform (DCCAP) in the cable architectures especially for the Remote-PHY and Remote-MACPHY nodes has been evaluated. In the PHY functional split, in addition to the re-use of infrastructure with a common FFT module for multiple technologies, a novel cross functional split interaction to cache the repetitive QAM symbols across time at the remote node to reduce the transmission rate requirement of the fronthaul link has been proposed.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Traffic and Resource Management in Robust Cloud Data Center Networks

Cloud Computing is becoming the mainstream paradigm, as organizations, both large and small, begin to harness its benefits. Cloud computing gained its success for giving IT exactly what it needed: The ability to grow and shrink computing resources, on the go, in a cost-effective manner, without the anguish of infrastructure design and setup. The ability to adapt computing demands to market fluctuations is just one of the many benefits that cloud computing has to offer, this is why this new paradigm is rising rapidly. According to a Gartner report, the total sales of the various cloud services will be worth 204 billion dollars worldwide in 2016. With this massive growth, the performance of the underlying infrastructure is crucial to its success and sustainability. Currently, cloud computing heavily depends on data centers for its daily business needs. In fact, it is through the virtualization of data centers that the concept of "computing as a utility" emerged. However, data center virtualization is still in its infancy; and there exists a plethora of open research issues and challenges related to data center virtualization, including but not limited to, optimized topologies and protocols, embedding design methods and online algorithms, resource provisioning and allocation, data center energy efficiency, fault tolerance issues and fault tolerant design, improving service availability under failure conditions, enabling network programmability, etc. This dissertation will attempt to elaborate and address key research challenges and problems related to the design and operation of efficient virtualized data centers and data center infrastructure for cloud services. In particular, we investigate the problem of scalable traffic management and traffic engineering methods in data center networks and present a decomposition method to exactly solve the problem with considerable runtime improvement over mathematical-based formulations. To maximize the network's admissibility and increase its revenue, cloud providers must make efficient use of their's network resources. This goal is highly correlated with the employed resource allocation/placement schemes; formally known as the virtual network embedding problem. This thesis looks at multi-facets of this latter problem; in particular, we study the embedding problem for services with one-to-many communication mode; or what we denote as the multicast virtual network embedding problem. Then, we tackle the survivable virtual network embedding problem by proposing a fault-tolerance design that provides guaranteed service continuity in the event of server failure. Furthermore, we consider the embedding problem for elastic services in the event of heterogeneous node failures. Finally, in the effort to enable and support data center network programmability, we study the placement problem of softwarized network functions (e.g., load balancers, firewalls, etc.), formally known as the virtual network function assignment problem. Owing to its combinatorial complexity, we propose a novel decomposition method, and we numerically show that it is hundred times faster than mathematical formulations from recent existing literature