Conceptualizing American power and security in a post-9/11 security context : conflict, resistance, and global security, 2001-present

In a post-Cold War, post-9/11 world, the advent of US global supremacy resulted in the installation, perpetuation, and dissemination of an Absolutist Security Agenda (hereinafter, ASA). The US ASA explicitly and aggressively articulates and equates US national security interests with the security of all states in the international system, and replaced the bipolar, Cold War framework that defined international affairs from 1945-1992. Since the collapse of the USSR and the 11 September 2001 terrorist attacks, the US has unilaterally defined, implemented, and managed systemic security policy. The US ASA is indicative of a systemic category of knowledge (security) anchored in variegated conceptual and material components, such as morality, philosophy, and political rubrics. The US ASA is based on a logic that involves the following security components: 1., hyper militarization, 2., intimidation, 3., coercion, 4., criminalization, 5., panoptic surveillance, 6., plenary security measures, and 7., unabashed US interference in the domestic affairs of select states. Such interference has produced destabilizing tensions and conflicts that have, in turn, produced resistance, revolutions, proliferation, cults of personality, and militarization. This is the case because the US ASA rests on the notion that the international system of states is an extension, instrument of US power, rather than a system and/or society of states comprised of functionally sovereign entities. To analyze the US ASA, this study utilizes: 1., official government statements, legal doctrines, treaties, and policies pertaining to US foreign policy; 2., militarization rationales, budgets, and expenditures; and 3., case studies of rogue states. The data used in this study are drawn from information that is publicly available (academic journals, think-tank publications, government publications, and information provided by international organizations). The data supports the contention that global security is effectuated via a discrete set of hegemonic/imperialistic US values and interests, finding empirical expression in legal acts (USA Patriot ACT 2001) and the concept of rogue states. Rogue states, therefore, provide test cases to clarify the breadth, depth, and consequentialness of the US ASA in world affairs vis-a-vis the relationship between US security and global security.

Improving Resource Management in Virtualized Data Centers using Application Performance Models

The rapid growth of virtualized data centers and cloud hosting services is making the management of physical resources such as CPU, memory, and I/O bandwidth in data center servers increasingly important. Server management now involves dealing with multiple dissimilar applications with varying Service-Level-Agreements (SLAs) and multiple resource dimensions. The multiplicity and diversity of resources and applications are rendering administrative tasks more complex and challenging. This thesis aimed to develop a framework and techniques that would help substantially reduce data center management complexity. We specifically addressed two crucial data center operations. First, we precisely estimated capacity requirements of client virtual machines (VMs) while renting server space in cloud environment. Second, we proposed a systematic process to efficiently allocate physical resources to hosted VMs in a data center. To realize these dual objectives, accurately capturing the effects of resource allocations on application performance is vital. The benefits of accurate application performance modeling are multifold. Cloud users can size their VMs appropriately and pay only for the resources that they need; service providers can also offer a new charging model based on the VMs performance instead of their configured sizes. As a result, clients will pay exactly for the performance they are actually experiencing; on the other hand, administrators will be able to maximize their total revenue by utilizing application performance models and SLAs. This thesis made the following contributions. First, we identified resource control parameters crucial for distributing physical resources and characterizing contention for virtualized applications in a shared hosting environment. Second, we explored several modeling techniques and confirmed the suitability of two machine learning tools, Artificial Neural Network and Support Vector Machine, to accurately model the performance of virtualized applications. Moreover, we suggested and evaluated modeling optimizations necessary to improve prediction accuracy when using these modeling tools. Third, we presented an approach to optimal VM sizing by employing the performance models we created. Finally, we proposed a revenue-driven resource allocation algorithm which maximizes the SLA-generated revenue for a data center.