High availability, cost effectiveness and ease of application deployment have accelerated the adoption rate of cloud computing. This fast proliferation of cloud computing promotes the rapid development of large-scale infrastructures. However, large cloud datacenters (DCs) require infrastructure, design, deployment, scalability and reliability and need better management techniques to achieve sustainable design benefits. Resources inside cloud infrastructures often operate at low utilization, rarely exceeding 20-30%, which increases the operational cost significantly, especially due to energy consumption. To reduce operational cost without affecting quality of service (QoS) requirements, cloud applications should be allocated just enough resources to minimize their completion time or to maximize utilization. 

The focus of this thesis is to enable resource-efficient and performance-aware cloud infrastructures by addressing above mentioned cost and performance related challenges. In particular, we propose algorithms, techniques, and deployment strategies for improving the dynamic allocation of virtual machines (VMs) into physical machines (PMs). 

For minimizing the operational cost, we mainly focus on optimizing energy consumption of PMs by applying dynamic VM consolidation methods. To make VM consolidation techniques more efficient, we propose to utilize multiple paths to spread traffic and deploy recent queue management schemes which can maximize network resource utilization and reduce both downtime and migration time for live migration techniques. In addition, a dynamic resource allocation scheme is presented to distribute workloads among geographically dispersed DCs considering their location based time varying costs due to e.g. carbon emission or bandwidth provision. For optimizing performance level objectives, we focus on interference among applications contending in shared resources and propose a novel VM consolidation scheme considering sensitivity of the VMs to their demanded resources. Further, to investigate the impact of uncertain parameters on cloud resource allocation and applications’ QoS such as unpredictable variations in demand, we develop an optimization model based on the theory of robust optimization. Furthermore, in order to handle the scalability issues in the context of large scale infrastructures, a robust and fast Tabu Search algorithm is designed and evaluated.

High availability, cost effectiveness and ease of application deployment have accelerated the adoption rate of cloud computing. This fast proliferation of cloud computing promotes the rapid development of large-scale infrastructures. However, large cloud datacenters (DCs) require infrastructure, design, deployment, scalability and reliability and need better management techniques to achieve sustainable design benefits. Resources inside cloud infrastructures often operate at low utilization, rarely exceeding 20-30%, which increases the operational cost significantly, especially due to energy consumption. To reduce operational cost without affecting quality of service (QoS) requirements, cloud applications should be allocated just enough resources to minimize their completion time or to maximize utilization. 

The focus of this thesis is to enable resource-efficient and performance-aware cloud infrastructures by addressing above mentioned cost and performance related challenges. In particular, we propose algorithms, techniques, and deployment strategies for improving the dynamic allocation of virtual machines (VMs) into physical machines (PMs).

Modern virtualized Data Centers (DCs) require efficient management techniques to guarantee high quality services while reducing their economical cost. The ability to live migrate virtual instances, e.g., Virtual Machines (VMs), both inside and among DCs is a key operation for the majority of DC management tasks that brings significant flexibility into the DC infrastructure. However, live migration introduces new challenges as it ought to be fast and seamless while at the same time imposing a minimum overhead on the network. In this thesis, we study the networking problems of live service migration in modern DCs when services are deployed in virtualized environments, e.g., VMs and containers. In particular, this thesis has the following main objectives: (1) improving the live VM migration in Software-Defined Network (SDN) enabled DCs by addressing networking challenges of live VM migration, and (2) investigating the trade-off between the reconfiguration cost and optimality of the Service Function Chains (SFCs) placement after the reconfiguration has been applied when SFCs are composed of stateful Virtual Network Functions (VNFs).

To achieve the first objective, in this thesis, we use distinctive characteristics of SDN architectures such as their centralized control over the network to accelerate the network convergence time and address suboptimal routing problem. Consequently, we enhance the quality of intra- and inter-DC live migrations. Furthermore, we develop an SDN-based framework to improve the inter-DC live VM migration by automating the deployment, improving the management, enhancing the performance, and increasing the scalability of interconnections among DCs.

To accomplish the second objective, we investigate the overhead of dynamic reconfiguration of stateful VNFs. Dynamic reconfiguration of VNFs is frequently required in various circumstances, and live migration of VNFs is an integral part of this operation. By mathematically formulating the reconfiguration costs of stateful VNFs and developing a multi-objective heuristic solution, we explore the trade-off between the reconfiguration cost required to improve a given placement and the degree of optimality achieved after the reconfiguration is performed. Results show that the cost of performing the reconfiguration operations required to realize an optimal VNF placement might hamper the gain that could be achieved.

Modern virtualized Data Centers (DCs) require efficient management techniques to guarantee high quality services while reducing their economical cost. The ability to live migrate virtual instances, e.g., Virtual Machines (VMs), both inside and among DCs, is a key operation for the majority of DC management tasks that brings significant flexibility into the DC infrastructure. However, live migration introduces new challenges as it ought to be fast and seamless while at the same time imposing a minimum overhead on the network.

This thesis investigates the networking challenges of short and long-haul live VM migration in Software Defined Networking (SDN) enabled DCs. We propose solutions to make the intra- and inter-DC live VM migration more seamless. Our proposed SDN-based framework for inter-DC migration improves the management, enhances the performance, and increases the scalability of interconnections among DCs.

Moreover, by considering the overhead of VM migration over the network, servers, and quality of service the VM provides, we explore the trade-off between the costs required to change the placement of VMs and the optimality degree of the placement in the DC. Results show that the cost of improving the placement might hamper the gain that could be achieved.

Traffic management in data centers is paramount to improving network and application performance, thereby improving the quality of service by reducing network congestion, packet loss, and latency. However, the deployment and configuration of traffic management techniques are challenging due to diverse data-center traffic characteristics, large data center topologies, and the interplay of different protocols at the routing, transport, and link layer. Software-Defined Networking (SDN) emerges as a new paradigm towards a centralized network configuration and traffic management by decoupling the control plane from forwarding devices. Despite its holistic view of the network, data centers are commonly interconnected by traditional networks that use standard routing protocols. It is therefore essential to achieve interoperability with legacy systems, end-to-end traffic management, and to avoid the cumbersome, time-consuming, and error-prone configuration process of data-center edge network devices.

In this thesis, we aim to improve traffic management and its configuration for software-defined data center networks. To achieve this objective, we provide novel approaches that enhance the control plane as well as leverage novel concepts of data plane programmability.

At the control plane, we first propose different mechanisms that enable the fast restoration of network connectivity after a virtual machine migration. Second, we suggest a network management automation framework that extends layer 2 connectivity to the tenants' services hosted across geo-distributed data centers. Moreover, we provide high-level policy-based mechanisms that make network configuration and traffic management simpler for data-center operators. At the data plane, we develop MP-HULA that load-balances multipath connections across least-congested paths. MP-HULA leverages advanced data plane mechanisms to rank multiple paths according to congestion metrics and uses that information for fine-grained load-balancing decisions considering transport layer information. To improve flowlet-based load-balancing decisions, we propose FlowDyn, which efficiently estimates round-trip time using programmable telemetry data. Finally, we present pCoflow, an in-network support mechanism that uses advanced programmable scheduling primitives to effectively avoid reordering for data-parallel applications even when there are flow priority changes due to global coflow scheduling updates.

Traffic management in data centers is paramount to improving network and application performance, thereby improving the quality of service by reducing network congestion, packet loss, and latency. However, the deployment and configuration of traffic management techniques are challenging due to diverse data center traffic characteristics, large data center topologies, and the interplay of different protocols at the routing, transport, and link layer. Software-Defined Networking (SDN) emerges as a new paradigm towards a centralized network configuration and traffic management by decoupling the control plane from forwarding devices.

In this thesis, we aim to improve traffic management and its configuration for software-defined data center networks by providing novel approaches that enhance the control plane as well as leveraging novel concepts of data plane programmability. At the control plane, we provide solutions such as high-level based policy framework, automation of layer 2 services, and fast restoration of network connectivity for virtual machine migration, that make network configuration and traffic management simpler for data-center operators. At the data plane, we propose approaches to improve load balancing in data centers and an in-network support mechanism to avoid reordering for data-parallel applications even when there are flow priority changes due to global coflow scheduling updates.