In many instances of virtual machine deployments today, virtual machine instances are created to support a single application. Traditional operating systems provide an extensive framework for protecting one process from another. In such deployments, this protection layer becomes an additional source of overhead as isolation between services is provided at an operating system level and each instance of an operating system supports only one service. This makes the operating system the equivalent of a process from the traditional operating system perspective. Isolation between these operating systems and indirectly the services they support, is ensured by the virtual machine monitor in these deployments. In these scenarios the process protection provided by the operating system becomes redundant and a source of additional overhead. We propose a new model for these scenarios with operating systems that bypass this redundant protection offered by the traditional operating systems. We prototyped such an operating system by executing parts of the operating system in the same protection ring as user applications. This gives processes more power and access to kernel memory bypassing the need to copy data from user to kernel and vice versa as is required when the traditional ring protection layer is enforced. This allows us to save the system call trap overhead and allows application program mers to directly call kernel functions exposing the rich kernel library. This does not compromise security on the other virtual machines running on the same physical machine, as they are protected by the VMM. We illustrate the design and implementation of such a system with the Xen hypervisor and the XenoLinux kernel.

In many instances of virtual machine deployments today, virtual machine instances are created to support a single application. Traditional operating systems provide an extensive framework for protecting one process from another. In such deployments, this protection layer becomes an additional source of overhead as isolation between services is provided at an operating system level and each instance of an operating system supports only one service. This makes the operating system the equivalent of a process from the traditional operating system perspective. Isolation between these operating systems and indirectly the services they support, is ensured by the virtual machine monitor in these deployments. In these scenarios the process protection provided by the operating system becomes redundant and a source of additional overhead. We propose a new model for these scenarios with operating systems that bypass this redundant protection offered by the traditional operating systems. We prototyped such an operating system by executing parts of the operating system in the same protection ring as user applications. This gives processes more power and access to kernel memory bypassing the need to copy data from user to kernel and vice versa as is required when the traditional ring protection layer is enforced. This allows us to save the system call trap overhead and allows application program mers to directly call kernel functions exposing the rich kernel library. This does not compromise security on the other virtual machines running on the same physical machine, as they are protected by the VMM. We illustrate the design and implementation of such a system with the Xen hypervisor and the XenoLinux kernel.

In many instances of virtual machine deployments today, virtual machine instances are created to support a single application. Traditional operating systems provide an extensive framework for protecting one process from another. In such deployments, this protection layer becomes an additional source of overhead as isolation between services is provided at an operating system level and each instance of an operating system supports only one service. This makes the operating system the equivalent of a process from the traditional operating system perspective. Isolation between these operating systems and indirectly the services they support, is ensured by the virtual machine monitor in these deployments. In these scenarios the process protection provided by the operating system becomes redundant and a source of additional overhead. We propose a new model for these scenarios with operating systems that bypass this redundant protection offered by the traditional operating systems. We prototyped such an operating system by executing parts of the operating system in the same protection ring as user applications. This gives processes more power and access to kernel memory bypassing the need to copy data from user to kernel and vice versa as is required when the traditional ring protection layer is enforced. This allows us to save the system call trap overhead and allows application program mers to directly call kernel functions exposing the rich kernel library. This does not compromise security on the other virtual machines running on the same physical machine, as they are protected by the VMM. We illustrate the design and implementation of such a system with the Xen hypervisor and the XenoLinux kernel. / Science, Faculty of / Computer Science, Department of / Graduate

The current infrastructure as a service (IaaS) cloud systems, allow users to load their own virtual machines. However, most of these systems do not provide users with an automatic mechanism to load a network topology of virtual machines. In order to specify and implement the network topology, we use software switches and routers as network elements. Before running a group of virtual machines, the user needs to set up the system once to specify a network topology of virtual machines. Then, given the user’s request for running a specific topology, our system loads the appropriate virtual machines (VMs) and also runs separated VMs as software switches and routers. Furthermore, we have developed a manager that handles physical hardware failure situations. This system has been designed in order to allow users to use the system without knowing all the internal technical details.

Parallax is a distributed storage system that uses virtualization to provide storage facilities specifically for virtual environments. The system employs a novel archi-tecture in which storage features that have traditionally been implemented directly on high-end storage arrays and switches are relocated into a federation of storage VMs, sharing the same physical hosts as the VMs that they serve. This architecture retains the single administrative domain and OS agnosticism achieved by array- and switch-based approaches, while lowering the bar on hardware requirements and facilitating the development of new features. Parallax offers a comprehensive set of storage features including frequent, low-overhead snapshot of virtual disks, the “gold-mastering” of template images, and the ability to use local disks as a persistent cache to dampen burst demand on networked storage.

Parallax is a distributed storage system that uses virtualization to provide storage facilities specifically for virtual environments. The system employs a novel archi-tecture in which storage features that have traditionally been implemented directly on high-end storage arrays and switches are relocated into a federation of storage VMs, sharing the same physical hosts as the VMs that they serve. This architecture retains the single administrative domain and OS agnosticism achieved by array- and switch-based approaches, while lowering the bar on hardware requirements and facilitating the development of new features. Parallax offers a comprehensive set of storage features including frequent, low-overhead snapshot of virtual disks, the “gold-mastering” of template images, and the ability to use local disks as a persistent cache to dampen burst demand on networked storage.

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Virtual machines (VMs) are useful mechanisms for better resource utilization, support for special software configurations, and the movement of packaged software across systems. Exploiting VM advantages in a production setting, however, often requires computer systems with the smallest possible disk-size footprint. Administrators and programmers who create VMs, however, may need a robust set of tools for development. This introduces an important conflict: Minimalism demands that packaged software be as small as possible, while completeness demands that nothing required is missing. We present a system called Lilliputia, which combines resource usage monitoring (through a Linux FUSE filesystem we created called StatFS), with a filtered cloning system, which copies an existing physical or virtual machine into a smaller clone. Finally, we show how Lilliputia can reduce the size of the Trellis Network-Attached-Storage (NAS) Bridge Appliance and the Chemical Shift to 3D Structure protein structure predictor to 10-30% of their original size.

Parallax is a distributed storage system that uses virtualization to provide storage facilities specifically for virtual environments. The system employs a novel archi-tecture in which storage features that have traditionally been implemented directly on high-end storage arrays and switches are relocated into a federation of storage VMs, sharing the same physical hosts as the VMs that they serve. This architecture retains the single administrative domain and OS agnosticism achieved by array- and switch-based approaches, while lowering the bar on hardware requirements and facilitating the development of new features. Parallax offers a comprehensive set of storage features including frequent, low-overhead snapshot of virtual disks, the “gold-mastering” of template images, and the ability to use local disks as a persistent cache to dampen burst demand on networked storage. / Science, Faculty of / Computer Science, Department of / Graduate

Context: Recent trends show that there is a tremendous shift from IT companies following traditional methods by hosting their applications/systems in self-managed on premise data centers to using the so-called cloud data centers. Cloud computing has received immense popularity due to its architecture and the ease of usage. Due to this increase in demand and shift in practices, there has been a tremendous increase in number of data centers over a period, resulting in increase of energy consumption. In this thesis work, a research is carried out on optimizing the energy consumption of a typical cloud data center. OpenStack cloud computing software is chosen as the platform in this research. We have used live migration as a key aspect in this research. Objectives: In this research, our objectives are as follows: Design an OpenStack testbed to implement the migration of virtual machines. To estimate the energy consumption of the data center. To design a heuristic algorithm to evaluate the performance metrics and to optimize the overall energy consumption. Methods: We have used PowerAPI, a software tool to estimate the energy consumption of hosts as well as virtual machines. A heuristic algorithm is designed and implemented in an instrumental OpenStack testbed to optimize the energy consumption. Server consolidation and load balancing of virtual machines methodologies are used in the heuristic algorithm design. Our research is carried out against the functionality of Nova scheduler of OpenStack. Results: Results section describes the values of performance metrics yielded by carrying out the experiment. The obtained results showed that energy can be optimized significantly by modifying the way OpenStack nova scheduler can work. The experiment is carried out on vanilla OpenStack and OpenStack with the heuristic algorithm in place, In the second case, the nova scheduler algorithms are not used but the heuristic algorithm is used instead. The CPU utilization and CPU load were noticed to be higher than the metrics observed in case of OpenStack with nova scheduler. Energy consumption is observed to be lesser than the consumption in OpenStack design with nova scheduler. Conclusions: The research tells that energy consumption can be optimized significantly using desired algorithms without compromising the service quality it offers. However, the design impacts on CPU slightly as the metrics are observed to be higher when compared to that in case of OpenStack with nova scheduler. Although it won’t have noticeable impact on the system.