Soft power as the new norm: How the Chinese-Russian strategic partnership (soft) balances American hegemony in an era of unipolarity

This study explores how great powers not allied with the United States formulate their grand strategies in a unipolar international system. Specifically, it analyzes the strategies China and Russia have developed to deal with U.S. hegemony by examining how Moscow and Beijing have responded to American intervention in Central Asia. The study argues that China and Russia have adopted a soft balancing strategy of to indirectly balance the United States at the regional level. This strategy uses normative capabilities such as soft power, alternative institutions and regionalization to offset the overwhelming material hardware of the hegemon. The theoretical and methodological approach of this dissertation is neoclassical realism. Chinese and Russian balancing efforts against the United States are based on their domestic dynamics as well as systemic constraints. Neoclassical realism provides a bridge between the internal characteristics of states and the environment which those states are situated. Because China and Russia do not have the hardware (military or economic power) to directly challenge the United States, they must resort to their software (soft power and norms) to indirectly counter American preferences and set the agenda to obtain their own interests. Neoclassical realism maintains that soft power is an extension of hard power and a reflection of the internal makeup of states. The dissertation uses the heuristic case study method to demonstrate the efficacy of soft balancing. Such case studies help to facilitate theory construction and are not necessarily the demonstrable final say on how states behave under given contexts. Nevertheless, it finds that China and Russia have increased their soft power to counterbalance the United States in certain regions of the world, Central Asia in particular. The conclusion explains how soft balancing can be integrated into the overall balance-of-power framework to explain Chinese and Russian responses to U.S. hegemony. It also suggests that an analysis of norms and soft power should be integrated into the study of grand strategy, including both foreign policy and military doctrine.

Leakage temperature dependency aware real-time scheduling for power and thermal optimization

Catering to society's demand for high performance computing, billions of transistors are now integrated on IC chips to deliver unprecedented performances. With increasing transistor density, the power consumption/density is growing exponentially. The increasing power consumption directly translates to the high chip temperature, which not only raises the packaging/cooling costs, but also degrades the performance/reliability and life span of the computing systems. Moreover, high chip temperature also greatly increases the leakage power consumption, which is becoming more and more significant with the continuous scaling of the transistor size. As the semiconductor industry continues to evolve, power and thermal challenges have become the most critical challenges in the design of new generations of computing systems. ^ In this dissertation, we addressed the power/thermal issues from the system-level perspective. Specifically, we sought to employ real-time scheduling methods to optimize the power/thermal efficiency of the real-time computing systems, with leakage/ temperature dependency taken into consideration. In our research, we first explored the fundamental principles on how to employ dynamic voltage scaling (DVS) techniques to reduce the peak operating temperature when running a real-time application on a single core platform. We further proposed a novel real-time scheduling method, “M-Oscillations” to reduce the peak temperature when scheduling a hard real-time periodic task set. We also developed three checking methods to guarantee the feasibility of a periodic real-time schedule under peak temperature constraint. We further extended our research from single core platform to multi-core platform. We investigated the energy estimation problem on the multi-core platforms and developed a light weight and accurate method to calculate the energy consumption for a given voltage schedule on a multi-core platform. Finally, we concluded the dissertation with elaborated discussions of future extensions of our research. ^

Leakage Temperature Dependency Aware Real-Time Scheduling for Power and Thermal Optimization

Catering to society’s demand for high performance computing, billions of transistors are now integrated on IC chips to deliver unprecedented performances. With increasing transistor density, the power consumption/density is growing exponentially. The increasing power consumption directly translates to the high chip temperature, which not only raises the packaging/cooling costs, but also degrades the performance/reliability and life span of the computing systems. Moreover, high chip temperature also greatly increases the leakage power consumption, which is becoming more and more significant with the continuous scaling of the transistor size. As the semiconductor industry continues to evolve, power and thermal challenges have become the most critical challenges in the design of new generations of computing systems. In this dissertation, we addressed the power/thermal issues from the system-level perspective. Specifically, we sought to employ real-time scheduling methods to optimize the power/thermal efficiency of the real-time computing systems, with leakage/ temperature dependency taken into consideration. In our research, we first explored the fundamental principles on how to employ dynamic voltage scaling (DVS) techniques to reduce the peak operating temperature when running a real-time application on a single core platform. We further proposed a novel real-time scheduling method, “M-Oscillations” to reduce the peak temperature when scheduling a hard real-time periodic task set. We also developed three checking methods to guarantee the feasibility of a periodic real-time schedule under peak temperature constraint. We further extended our research from single core platform to multi-core platform. We investigated the energy estimation problem on the multi-core platforms and developed a light weight and accurate method to calculate the energy consumption for a given voltage schedule on a multi-core platform. Finally, we concluded the dissertation with elaborated discussions of future extensions of our research.