Ting Wang

Bio: Ting Wang is an academic researcher from Concordia University. The author has contributed to research in topics: Anycast & Column generation. The author has an hindex of 2, co-authored 5 publications receiving 21 citations.

Time-varying resilient virtual network mapping for multi-location cloud data centers

Abstract: Optical networks constitute a fundamental building block that has enabled the success of cloud computing. Virtualization, a cornerstone of cloud computing, today is applied in the networking field: physical network infrastructure is logically partitioned into separate virtual networks, thus providing isolation between distinct virtual network operators (VNOs). Hence, the problem of virtual network mapping has arisen: how to decide which physical resources to allocate for a particular virtual network? In a cloud context, not just network connectivity is required, but also data center (DC) resources located at multiple locations, for computation and/or storage. Given the underlying anycast routing principle, the network operator has some freedom to which specific DC to allocate these resources. In this paper, we solve a resilient virtual network mapping problem that optimally decides on the mapping of both network and multi-location data center resources resiliently using anycast routing, considering time-varying traffic conditions. In terms of resilience, we consider the so-called VNO-resilience scheme, where resilience is provided in the virtual network layer. To minimize physical resource capacity requirements, we allow reuse of both network and DC resources. The failures we protect against include both network and DC resource failures: we hence allocate backup DC resources, and also account for synchronization between primary and backup DC. As optimization criteria, we not only consider resource usage minimization, but also aim to limit virtual network reconfigurations from one time period to the next. We propose a scalable column generation approach to solve the dynamic resilient virtual network mapping problem, and demonstrate it in a case study on a nationwide US backbone network.

A scalable model for multi-period virtual network mapping for resilient multi-site data centers

Abstract: In the currently dominant cloud computing paradigm, applications are being served in data centers (DCs) which are connected to high capacity optical networks. For cost efficiency reasons, in both DC and optical network domains, virtualization of the physical hardware is exploited. In a DC, it means that multiple so-called virtual machines (VMs) are being hosted on the same physical server. Similarly, the network is partitioned into separate virtual networks, thus providing isolation between distinct virtual network operators (VNOs). Thus, the problem of virtual network mapping arises: how to decide which physical resources to allocate for a particular virtual network? In this paper, we study that problem in the context of cloud computing with multiple DC sites. This introduces additional flexibility, due to the anycast routing principle: we have the freedom to decide at what particular DC location to serve a particular application. We can exploit this choice to minimize the required resources when solving the virtual network mapping problem. This paper builds on our earlier work and solves a resilient virtual network mapping problem that optimally decides on the mapping of both network and data center resources, considering time-varying traffic conditions and protecting against possible failures of both network and DC resources. We consider the so-called VNO resilience scheme: rerouting under failure conditions is provided in the virtual network layer. To minimize physical resource capacity requirements, we allow reuse of both network and DC resources: we can reuse the same resources for the rerouting under failure scenarios that are assumed not to occur simultaneously. Since we also protect against DC failures, we allocate backup DC resources, and account for synchronization between primary and backup DCs. To deal with the time variations in the volume and geographical pattern of the application traffic, we investigate the potential benefits (in terms of overall resource capacity requirements) of reconfiguring the virtual network mapping from one period to the next. Previously, we developed a model to solve the multi-period traffic case one step at a time: given the virtual network mapping fore time t, we determine the (possibly changed) mapping for t + 1. Compared to that previous work, we now (i) define a truly multi-period model path formulation exploiting column generation, and (ii) demonstrate its scalability on a nation-wide network with hourly varying traffic.

Anycast (re)routing of multi-period traffic in dimensioning resilient backbone networks for multi-site data centers

Abstract: We consider the problem of dimensioning resilient backbone networks for cloud-like scenarios where demand is to be served at one among several candidate data centers (DCs), and where that demand varies over time, which we assume to be slotted. We thus consider multi-period traffic, for which we need to find routes to both a primary DC and a backup DC (in case the primary, or the network connection towards it, fails) and account also for synchronization traffic (following its own routes) between the two chosen DCs. We propose a path formulation and adopt a column generation approach: the (restricted) master problem (RMP) selects “configurations” to use for each demand in each of the time periods, while pricing problems (PPs) construct new, potentially cost-reducing configurations for a given demand. Our model allows for several PPs to be solved in parallel, and we demonstrate the time savings achieved by doing so. We compare several anycast (re)routing strategies, where we allow traffic that spans multiple periods to either (i) not be rerouted in different periods, (ii) only change the backup DC and routes, or (iii) freely change both primary and backup DC choices and routes towards them.

Resilient Backbone Networks for Multi-site Data Centers: Exploiting Anycast (Re)routing for Multi-period Traffic

Abstract: Using column generation, we dimension networks interconnecting multi-site data centers (DCs), for multi-period traffic with anycast routing and resilience against single link/DC failures. We quantify (backup) resource savings from rerouting demands lasting multiple periods.

Exploring the benefit of rerouting multi-period traffic to multi-site data centers [Invited]

Abstract: In cloud-like scenarios, demand is served at one of multiple possible data center (DC) destinations. Usually, the exact DC that is used can be freely chosen, which leads to an anycast routing problem. Furthermore, the demand volume is expected to change over time, e.g., following a diurnal pattern. Given that virtually all application domains today rely heavily on cloud-like services, it is important that the backbone networks connecting users to the DCs are resilient against failures. In this paper, we consider the problem of resiliently routing multi-period traffic: we need to find routes to both a primary DC and a backup DC (to be used in the case of failure of the primary one, or of the network connection to it), and also account for synchronization traffic between the primary and backup DCs. We formulate this as an optimization problem and adopt column generation, using a path formulation in two sub-problems: the (restricted) master problem selects "configurations" to use for each demand in each of the time epochs it lasts, while the pricing problem (PP) constructs a new "configuration" that can lead to lower overall costs (which we express as the number of network resources, i.e., bandwidth, required to serve the demand). Here, a "configuration" is defined by the network paths followed from the demand source to each of the two selected DCs, as well as that of the synchronization traffic in between the DCs. Our decomposition allows for PPs to be solved in parallel, for which we quantitatively explore the reduction in the time required to solve the overall routing problem. The key question that we address with our model is an exploration of the potential benefits of rerouting traffic from one time epoch to the next: we compare several (re)routing strategies, allowing traffic that spans multiple time periods to i) not be rerouted in different periods, ii) only change the backup DC and routes, or iii) freely change both primary and backup DC choices and the routes toward them.

Optimized provisioning of SDN-enabled virtual networks in geo-distributed cloud computing datacenters

Abstract: Cloud computing provides on-demand IT services via large distributed datacenters over high-speed networks. Virtualization, a key cloud computing technology, allows service providers to offer computing services in cloud environments without platform compatibility discrepancies. The recent proliferation of cloud computing has rekindled interest in network virtualization. Thus, network virtualization is emerging as a polymorphic approach for the future Internet that will facilitate the use of shared resources. Virtual network provisioning is considered to be a main resource allocation challenge in any virtualized network environment. Software-defined networking (SDN) imparts flexibility to a network by removing the control layer from the data transfer layer of the network and moving it to the control plane. Network virtualization is further employed to share physical infrastructure to enable multiple service providers to access the network. Flexible access requires efficient management of network resources; the SDN control plane can be used for efficient management of virtual networks. In this study, we formulate virtual network provisioning in SDN-enabled, geographically distributed cloud computing datacenters as a mixed integer linear programming (MILP) problem. The formulation of the proposed optimized virtual network provisioning (OVNP) model is studied by means of simulations. The performance of the proposed approach is measured against enhanced network cloud provisioning (ENCP), our previous research, and other recognized research focused on the ratio of successfully provisioned requests and the efficiency of resource utilization The results verify the effectiveness of the proposed approach.

On Resilience in Cloud Computing: A Survey of Techniques across the Cloud Domain

Abstract: Cloud infrastructures are highly favoured as a computing delivery model worldwide, creating a strong societal dependence. It is therefore vital to enhance their resilience, providing persistent service delivery under a variety of conditions. Cloud environments are highly complex and continuously evolving. Additionally, the plethora of use-cases ensures requirements for persistent service delivery vary. As a contribution to knowledge, this work surveys resilience techniques for cloud environments. We apply a novel perspective using a layered model of traditional and emerging cloud paradigms. Works are then classified according to the Resilinets model. For each layer, the most common techniques with limitations are derived including an actor’s strength in influencing resilience in the cloud with each technique. We conclude with some future challenges to the field of resilient cloud computing.

Fast and Dynamic Resource Provisioning for Quality Critical Cloud Applications

Abstract: As many quality critical applications are migrating to clouds, Quality of Service (QoS) and Quality of Experience (QoE) have become vital properties for cloud applications. Therefore, the provisioning mechanism, which aims to make the virtual infrastructure recover from sudden failures quickly or adapt dynamic properties of applications, is essential. However, most current provisioning mechanisms focus on the cloud provider and are developed for specific hardware. This paper proposes a mechanism to partition a customer's cloud resource requests efficiently across multiple domains or clouds, while ensuring that the partitions are still connected with each other. This mechanism exploits networked infrastructure to make dynamic cloud resource provisioning as fast as possible. It works using a broker-based model that is transparent both to the customer and to the cloud provider. It is easy for customers to use and does not force providers to make any changes to their services. Moreover, the dynamic property makes the provisioned infrastructure better able to recover from failures quickly. We implement the mechanism and carry out experiments on ExoGENI, a networked infrastructure-as-a-service (NIaaS) platform. Comprehensive experimental results and theoretical analysis demonstrate that the mechanism we propose is feasible and can dramatically improve the speed of resource provisioning.

Ant colony based graph theory (ACGT) and resource virtual network mapping (RVNM) algorithm for home healthcare system in cloud environment

Abstract: In recent decades, new efficient heuristic algorithms are introduced which helps in alleviating both large virtual and physical networks in the case when there are a host of healthcare service providers that are part of the evaluation and analysis. Demarcating virtual resources as separate physical nodes as well as co-localization of prerequisites inherent in physical nodes is relatively time consuming. To solve this problem in this work Ant Colony based Graph Theory (ACGT) is proposed for selection of resources which eliminates infeasible mappings between fundamental and material resources available. The major aim of the study here is to provide better resource allocation mapping. This ACGT additionally in conjunction maps jointly nodes as well as links and offers the most probable optimized solutions. Algorithm here breaks-down the graph as topological sequences that are followed by ACGT to resolve mapping related issues. Any possible mapping occurs only when the virtual node capacity that is requested less in comparison than the remainder candidate physical node capacity and also when virtual link latency is comparatively greater than candidate physical path latency or that of the link. ACGT performance and its precise, heuristic and two-stage algorithms have been analyzed and studied in this cloud environment. All the methods are implemented via the use of JAVA environment and applied to google cloud.