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

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

Nature-inspired survivability: Prey-inspired survivability countermeasures for cloud computing security challenges

As cloud computing environments become complex, adversaries have become highly sophisticated and unpredictable. Moreover, they can easily increase attack power and persist longer before detection. Uncertain malicious actions, latent risks, Unobserved or Unobservable risks (UUURs) characterise this new threat domain. This thesis proposes prey-inspired survivability to address unpredictable security challenges borne out of UUURs. While survivability is a well-addressed phenomenon in non-extinct prey animals, applying prey survivability to cloud computing directly is challenging due to contradicting end goals. How to manage evolving survivability goals and requirements under contradicting environmental conditions adds to the challenges. To address these challenges, this thesis proposes a holistic taxonomy which integrate multiple and disparate perspectives of cloud security challenges. In addition, it proposes the TRIZ (Teorija Rezbenija Izobretatelskib Zadach) to derive prey-inspired solutions through resolving contradiction. First, it develops a 3-step process to facilitate interdomain transfer of concepts from nature to cloud. Moreover, TRIZ’s generic approach suggests specific solutions for cloud computing survivability. Then, the thesis presents the conceptual prey-inspired cloud computing survivability framework (Pi-CCSF), built upon TRIZ derived solutions. The framework run-time is pushed to the user-space to support evolving survivability design goals. Furthermore, a target-based decision-making technique (TBDM) is proposed to manage survivability decisions. To evaluate the prey-inspired survivability concept, Pi-CCSF simulator is developed and implemented. Evaluation results shows that escalating survivability actions improve the vitality of vulnerable and compromised virtual machines (VMs) by 5% and dramatically improve their overall survivability. Hypothesis testing conclusively supports the hypothesis that the escalation mechanisms can be applied to enhance the survivability of cloud computing systems. Numeric analysis of TBDM shows that by considering survivability preferences and attitudes (these directly impacts survivability actions), the TBDM method brings unpredictable survivability information closer to decision processes. This enables efficient execution of variable escalating survivability actions, which enables the Pi-CCSF’s decision system (DS) to focus upon decisions that achieve survivability outcomes under unpredictability imposed by UUUR

Data Driven Network Design for Cloud Services Based on Historic Utilization

In recent years we have seen a shift from traditional networking in enterprises with Data Center centric architectures moving to cloud services. Companies are moving away from private networking technologies like MPLS as they migrate their application workloads to the cloud. With these migrations, network architects must struggle with how to design and build new network infrastructure to support the cloud for all their end users including office workers, remote workers, and home office workers. The main goal for network design is to maximize availability and performance and minimize cost. However, network architects and network engineers tend to over provision networks by sizing the bandwidth for worst case scenarios wasting millions of dollars per year. This thesis will analyze traditional network utilization data from twenty-five of the Fortune 500 companies in the United States and determine the most efficient bandwidth to support cloud services from providers like Amazon, Microsoft, Google, and others. The analysis of real-world data and the resulting proposed scaling factor is an original contribution from this study