Live migration of Virtual Machines (VMs) has significantly improved the flexibility of modern Data Centers (DCs). Ideally, live migration ought to be seamless which in turn raises challenges on how to minimize service disruption and avoid performance degradation. To address these challenges, a comprehensive support from the underlying network is required. However, legacy DC networks fall short to help as they take a reactive approach to live migration procedure. Moreover, the complexity and inflexibility of legacy DC networks make it difficult to deploy, manage, and improve network technologies that DC providers may need to use for migration. In this thesis, we explore the application of Software Defined Networking (SDN) paradigm for making live VM migration more seamless. Exploiting the characteristics of SDN such as its centralized view on network states, we contribute to the body of knowledge by enhancing the quality of intra- and inter-DC live migration. Firstly, for intra-DC migration, we provide an SDN-based solution which minimizes the service disruption by employing OpenFlow-based resiliency mechanisms to prepare a DC network for migration proactively. Secondly, we improve the inter-DC live migration by accelerating the network convergence through announcing the migration in the control plane using MP-BGP protocol. Further, our proposed framework resolves the sub-optimal routing problem by conducting the gateway functionality at the SDN controller. Finally, with the ultimate goal of improving the inter-DC migration, we develop an SDN-based framework which automates the deployment, improves the management, enhances the performance, and increases the scalability of interconnections among DCs. / Live migration of Virtual Machines (VMs) has significantly improved the flexibility of modern Data Centers (DCs). Ideally, live migration ought to be seamless which requires a comprehensive support from the underlying network. However, legacy DC networks fall short to address the challenges of migration due to their inflexible and decentralized characteristics. In contrast, Software Defined Networking (SDN) is a new networking paradigm, which has the potential to improve the live migration thanks to its comprehensive view over the network, flexible structure, and its close integration with DC management infrastructures. This thesis investigates networking challenges of short and long-haul live VM migration in SDN-based DCs. We propose solutions to make the intra- and inter-DC live migration procedures more seamless. Furthermore, our proposed SDN-based framework for inter-DC migration improves the management, enhances the performance, and increases the scalability of interconnections among DCs. / HITS, 4707

The evolution of cloud technologies which allows the provisioning of IT resources over the Internet promises many benefits for the individuals and enterprises alike. However, this new resource provisioning model comes with the security challenges which did not exist in the traditional resource procurement mechanisms. We focus on the possible security concerns of a cloud user (e.g. an organization, government department, etc.) to lease cloud services such as resources in the form of Virtual Machines (VM) from a public Infrastructure-as-a-Service (IaaS) provider. There are many security critical areas in the cloud systems, such as data confidentiality, resource integrity, service compliance, security audits etc. In this thesis, we focus on the security aspects which result in the trust deficit among the cloud stakeholders and hence hinder a security sensitive user to benefit from the opportunities offered by the cloud computing. Based upon our findings from the security requirements analysis,we propose solutions that enable user trust in the public IaaS clouds. Our solutions mainly deal with the secure life cycle management of the user VM which include mechanisms for VM launch and migration. The VM launch and migration solutions ensure that the user VM is always protected in the cloud by only allowing it to run on the user trusted platforms. This is done by using trusted computing techniques that allow the users to remotely attest and hence rate the cloud platforms trusted or untrusted. We also provide a prototype implementation to prove the implementation feasibility of the proposed trust enabling principles used in the VM launch and migration solutions.

Virtualization is the key technology that has provided the Cloud computing platforms a new way for small and large enterprises to host their applications by renting the available resources. Live VM migration allows a Virtual Machine to be transferred form one host to another while the Virtual Machine is active and running. The main challenge in Live migration over WAN is maintaining the network connectivity during and after the migration. We have carried out live VM migration over the WAN migrating different sizes of VM memory states and presented our solutions based on Open vSwitch/VXLAN and Cisco GRE approaches. VXLAN provides the mobility support needed to maintain the network connectivity between the client and the Virtual machine. We have setup an experimental testbed to calculate the concerned performance metrics and analyzed the performance of live migration in VXLAN and GRE network. Our experimental results present that the network connectivity was maintained throughout the migration process with negligible signaling overhead and minimal downtime. The downtime variation experience with change in the applied network delay was relatively higher when compared to variation experienced when migrating different VM memory states. The total migration time experienced showed a strong relationship with size of the migrating VM memory state. / 0763472814

Live migration is a mechanism that allows a VM to be moved from one host to another while the guest operating system is running. Current live migration implementations are able to maintain network connectivity in a LAN. However, the same techniques cannot be applied for live migration over the Internet. We present a solution based on PMIPv6, a light-weight mobility protocol standardized by IETF. PMIPv6 handles node mobility without requiring any support from the moving nodes. In addition, PMIPv6 works with IPv4, IPv6 and dual-stack nodes. We have setup a testbed to measure the performance of live migration in a PMIPv6 network. Our results show that network connectivity is successfully maintained with little signaling overhead and short VM downtime. As far as we know, this is the first time PMIPv6 is used to enable live migration beyond the scope of a LAN.

Autonomic management is important in storage systems and the space of autonomics in storage systems is vast. Such autonomic management systems can employ a variety of techniques depending upon the specific problem. In this thesis, we first take an algorithmic approach towards reliability enhancement and then we use learning along with a reactive framework to facilitate storage optimization for applications. We study how the reliability of non-repairable systems can be improved through automatic reconfiguration of their XOR-coded structure. To this regard we propose to increase the fault tolerance of non-repairable systems by reorganizing the system, after a failure is detected, to a new XOR-code with a better fault tolerance. As errors can manifest during reorganization due to whole reads of multiple submodules, our framework takes them in to account and models such errors as based on access intensity (ie.BER-biterrorrate). We present and evaluate the reliability of an example storage system with and without reorganization. Motivated by the critical need for automating various aspects of data management in virtualized data centers, we study the specific problem of automatically implementing Virtual Machine (VM) migration in a dynamic environment according to some pre-set policies. This is a problem that requires automated identification of various workloads and their execution environments running inside virtual machines in a non-intrusive manner. To this end we propose AuM (for Autonomous Manager) that has the capability to learn workloads by aggregating variety of information obtained from network traces of storage protocols. We use state of the art Machine Learning tools, namely Multiple Kernel learning ,to aggregate information and show that AuM is indeed very accurate in identifying work loads, their execution environments and is also successful in following user set policies very closely for the VM migration tasks. Storage infrastructure in large-scale cloud data center environments must support applications with diverse, time-varying data access patterns while observing the quality of service. To meet service level requirements in such heterogeneous application phases, storage management needs to be phase-aware and adaptive ,i.e. ,identify specific storage access patterns of applications as they occur and customize their handling accordingly. We build LoadIQ, an online application phase detector for networked (file and block) storage systems. In a live deployment , LoadIQ analyzes traces and emits phase labels learnt online. Such labels could be used to generate alerts or to trigger phase-specific system tuning.