Cloud based multicasting using fat tree data confidential recurrent neural network

With the progress of cloud computing, more users are attracted by its strong and cost-effective computation potentiality. Nevertheless, whether Cloud Service Providers can efficiently protect Cloud Users data confidentiality (DC) remains a demanding issue. The CU may execute several applications with multicast needs. In Cloud different techniques were used to provide DC with multicast necessities. In this work, we aim at ensuring DC in the cloud. This is achieved using a two-step technique, called Fat Tree Data Confidential Recurrent Neural Network (FT-DCRNN) in a cloud environment. The first step performs the construction of Fat Tree based on Multicast model. The aim to use Fat Tree with Multicast model is that the multicast model propagates traffic on multiple links. With the Degree Restrict Multicast Fat Tree construction algorithm using a reference function, the minimum average between two links is measured. With these measured links, multicast is said to be performed that in turn improves the throughput and efficiency of cloud service. Then, with the objective of providing DC for the multi-casted data or messages, DCRNN model is applied. With the Non-linear Recurrent Neural Network using Logistic Activation Function, by handling complex non-linear relationships, average response time is said to be reduced

Survivable Virtual Network Embedding in Transport Networks

Network Virtualization (NV) is perceived as an enabling technology for the future Internet and the 5th Generation (5G) of mobile networks. It is becoming increasingly difficult to keep up with emerging applications’ Quality of Service (QoS) requirements in an ossified Internet. NV addresses the current Internet’s ossification problem by allowing the co-existence of multiple Virtual Networks (VNs), each customized to a specific purpose on the shared Internet. NV also facilitates a new business model, namely, Network-as-a-Service (NaaS), which provides a separation between applications and services, and the networks supporting them. 5G mobile network operators have adopted the NaaS model to partition their physical network resources into multiple VNs (also called network slices) and lease them to service providers. Service providers use the leased VNs to offer customized services satisfying specific QoS requirements without any investment in deploying and managing a physical network infrastructure. The benefits of NV come at additional resource management challenges. A fundamental problem in NV is to efficiently map the virtual nodes and virtual links of a VN to physical nodes and paths, respectively, known as the Virtual Network Embedding (VNE) problem. A VNE that can survive physical resource failures is known as the survivable VNE (SVNE) problem, and has received significant attention recently. In this thesis, we address variants of the SVNE problem with different bandwidth and reliability requirements for transport networks. Specifically, the thesis includes four main contributions. First, a connectivity-aware VNE approach that ensures VN connectivity without bandwidth guarantee in the face of multiple link failures. Second, a joint spare capacity allocation and VNE scheme that provides bandwidth guarantee against link failures by augmenting VNs with necessary spare capacity. Third, a generalized recovery mechanism to re-embed the VNs that are impacted by a physical node failure. Fourth, a reliable VNE scheme with dedicated protection that allows tuning of available bandwidth of a VN during a physical link failure. We show the effectiveness of the proposed SVNE schemes through extensive simulations. We believe that the thesis can set the stage for further research specially in the area of automated failure management for next generation networks

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

Restoration methods for cloud multicast virtual networks

Providing reliability guarantees for services hosted in cloud data center networks is a well studied problem that received much attention from the literature in recent years. Yet, the existing literature failed to account for the mode of communication that these hosted services may exhibit. Rather, it is assumed that the constituent Virtual Machines (VMs) are always exchanging traffic in a one-to-one communication mode. This paper is concerned with the problem of restoring failed services with a one-to-many communication mode, the mode of communication for a multitude of multicast applications and services hosted today in the cloud. Through motivational examples we show that the problem of restoring multicast Virtual Networks (VNs) greatly differs from that of unicast services, thereby demanding separate attention. To this extent, we formally define the problem of restoring multicast services in event of facility node failure, and we mathematically formulate it using an Integer Linear Programming (ILP) model. Further, we propose REAL: a multicast virtual network re storation al gorithm. REAL distinguishes the failure of multicast source node from that of multicast terminal nodes, and adopts a hop-to-hop search to handle receiver or terminal node failures, and a path convergence approach to recover from source node failures. We evaluate our proposed schemes against a Greedy and a Steiner-based restoration schemes, and we show that our suggested method outperforms its peers in terms of restoration ratio and total achievable revenue.This work was made possible by the NPRP 5 - 137 - 2 - 045 grant from the Qatar National Research Fund (a member of Qatar Foundation). This work has also been partially supported by NSERC Discovery Grant. The statements made herein are solely the responsibility of the authors.Scopu