Virtualization environments have become basic building blocks in consolidated data centers and cloud computing infrastructures. By running multiple virtual machines (VMs) in a shared physical machine, virtualization achieves high utilization of hardware resources and provides strong isolation between virtual machines. This dissertation discusses the implementation and the evaluation of an approach, called kernel service outsourcing, which improves the performance and the reliability of guest systems in the virtualized, multi-kernel environments. Kernel service outsourcing allows applications to exploit OS services from an external kernel existing in the shared system, not limiting application OS service requests to the local kernel. Because external kernels may provide more efficient services than the local kernel does, kernel service outsourcing provides new opportunities with applications in the guest OS for better performance. In addition, we apply the kernel service outsourcing technique to implement natural diversity, improving the reliability of the virtualized systems. We present two major benefits of kernel service outsourcing. First, we show that I/O service outsourcing can significantly improve the I/O performance of guest OSes by up to several times. In some important cases, the performance of network applications in the guest OS using network outsourcing was comparable to that of native OS (Linux). We also apply kernel service outsourcing between Windows and Linux, and show that kernel service outsourcing is viable even with two heterogeneous OS kernels. In addition, we study further performance optimization techniques that can be achieved in the external kernel when certain OS services are outsourced to the external kernel. The second benefit of kernel service outsourcing is to improve system reliability through natural diversity created by the combination of different kinds of the OS kernel implementations. Because OS services can be outsourced to different versions or even heterogeneous types of OS kernel for equivalent functions, malicious attacks that aim to exploit certain vulnerabilities in specific versions of OS kernels would not succeed in the outsourced kernels. Our case studies with Windows and Linux show that kernel service outsourcing was able to prevent the malicious attacks designed to exploit implementation-dependent vulnerabilities in the OSes from becoming successful in the outsourced systems.

Thesis (M.S.)--Mississippi State University. Department of Computer Science and Engineering. / Title from title screen. Includes bibliographical references.

Thesis (M. Phil.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 63-65).

This dissertation presents techniques for detecting and tolerating faults in distributed systems. Detecting faults in distributed or parallel systems is often very difficult. We look at the problem of determining if a property or assertion was true in the computation. We formally define a logic called BTL that can be used to define such properties. Our logic takes temporal properties in consideration as these are often necessary for expressing conditions like safety violations and deadlocks. We introduce the idea of a basis of a computation with respect to a property. A basis is a compact and exact representation of the states of the computation where the property was true. We exploit the lattice structure of the computation and the structure of different types of properties and avoid brute force approaches. We have shown that it is possible to efficiently detect all properties that can be expressed by using nested negations, disjunctions, conjunctions and the temporal operators possibly and always. Our algorithm is polynomial in the number of processes and events in the system, though it is exponential in the size of the property. After faults are detected, it is necessary to act on them and, whenever possible, continue operation with minimal impact. This dissertation also deals with designing systems that can recover from faults. We look at techniques for tolerating faults in data and the state of the program. Particularly, we look at the problem where multiple servers have different data and program state and all of these need to be backed up to tolerate failures. Most current approaches to this problem involve some sort of replication. Other approaches based on erasure coding have high computational and communication overheads. We introduce the idea of fusible data structures to back up data. This approach relies on the inherent structure of the data to determine techniques for combining multiple such structures on different servers into a single backup data structure. We show that most commonly used data structures like arrays, lists, stacks, queues, and so on are fusible and present algorithms for this. This approach requires less space than replication without increasing the time complexities for any updates. In case of failures, data from the back up and other non-failed servers is required to recover. To maintain program state in case of failures, we assume that programs can be represented by deterministic finite state machines. Though this approach may not yet be practical for large programs it is very useful for small concurrent programs like sensor networks or finite state machines in hardware designs. We present the theory of fusion of state machines. Given a set of such machines, we present a polynomial time algorithm to compute another set of machines which can tolerate the required number of faults in the system. / text

published_or_final_version / Computer Science and Information Systems / Doctoral / Doctor of Philosophy

published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / Computer Science and Information Systems / Master / Master of Philosophy

<p>Self-tuning has been an elusive goal for operating systems and is becoming a pressing issue for modern operating systems. Well-trained system administrators are able to tune an operating system to achieve better system performance for a specific system class. Unfortunately, the system class can change when the running applications change. The model for self-tuning operating system is based on a monitor-classify-adjust loop. The idea of this loop is to continuously monitor certain performance metrics, and whenever these change, the system determines the new system class and dynamically adjusts tuning parameters for this new class. This thesis described KernTune, a prototype tool that identifies the system class and improves system performance automatically. A key aspect of KernTune is the notion of Artificial Intelligence oriented performance tuning. Its uses a support vector machine to identify the system class, and tunes the operating system for that specific system class. This thesis presented design and implementation details for KernTune. It showed how KernTune identifies a system class and tunes the operating system for improved performance.</p>

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