Crowding Out Theory: Protecting Shareholders by Balancing Executives’ Incentives in France, the United States, & China

This paper explores the differences between executive compensation regimes in France, the United States, and China. It asks whether there is a link between state regulation of real options as a form of executive compensation and state regulation of shareholder protections. This paper argues that if a country regulates the use of real options as compensation, then that country is also more likely to have strong shareholder protection laws. This argument seems to be true based on a descriptive review of executive compensation law and shareholder protections in France, the United States, and China. If it is true that countries that regulate real options compensation are more likely to enact strong shareholders protections, then it is also likely that these countries are relying on the Crowding Out Theory. Under the Crowding Out Theory, executive compensation is designed to strike a balance between low pay, which motivates executives to work harder , and high pay, which disincentives executives from pursuing alternative forms of compensation that would harm shareholders

Book review: Islamic Shangri-La: inter-Asian relations and Lhasa’s Muslim communities, 1600 to 1960 by David G. Atwill

In Islamic Shangri-La: Inter-Asian Relations and Lhasa’s Muslim Communities, 1600 to 1960, David G. Atwill investigates the neglected history of the Khache from the seventeenth to the twentieth century, with keen attention to the complexities and contradictions surrounding notions of identity, subjecthood and citizenship. This is a pioneering work in the study of Tibetan Muslims and an indispensable contribution to the growing literature and scholarship in Tibetan borderlands studies, writes Palden Gyal

V-Cache: Towards Flexible Resource Provisioning for Multi-tier Applications in IaaS Clouds

Abstract—Although the resource elasticity offered by Infrastructure-as-a-Service (IaaS) clouds opens up opportunities for elastic application performance, it also poses challenges to application management. Cluster applications, such as multi-tier websites, further complicates the management requiring not only accurate capacity planning but also proper partitioning of the resources into a number of virtual machines. Instead of burdening cloud users with complex management, we move the task of determining the optimal resource configuration for cluster applications to cloud providers. We find that a structural reorganization of multi-tier websites, by adding a caching tier which runs on resources debited from the original resource budget, significantly boosts application performance and reduces resource usage. We propose V-Cache, a machine learning based approach to flexible provisioning of resources for multi-tier applications in clouds. V-Cache transparently places a caching proxy in front of the application. It uses a genetic algorithm to identify the incoming requests that benefit most from caching and dynamically resizes the cache space to accommodate these requests. We develop a reinforcement learning algorithm to optimally allocate the remaining capacity to other tiers. We have implemented V-Cache on a VMware-based cloud testbed. Exper-iment results with the RUBiS and WikiBench benchmarks show that V-Cache outperforms a representative capacity management scheme and a cloud-cache based resource provisioning approach by at least 15 % in performance, and achieves at least 11 % and 21 % savings on CPU and memory resources, respectively. I

Autonomic Provisioning with Self-Adaptive Neural Fuzzy Control for End-to-end Delay Guarantee

Abstract—Autonomic server provisioning for performance as-surance is a critical issue in data centers. It is important but challenging to guarantee an important performance metric, percentile-based end-to-end delay of requests flowing through a virtualized multi-tier server cluster. It is mainly due to dynamically varying workload and the lack of an accurate system performance model. In this paper, we propose a novel autonomic server allocation approach based on a model-independent and self-adaptive neural fuzzy control. There are model-independent fuzzy controllers that utilize heuristic knowledge in the form of rule base for performance assurance. Those controllers are designed manually on trial and error basis, often not effective in the face of highly dynamic workloads. We design the neural fuzzy controller as a hybrid of control theoretical and machine learning techniques. It is capable of self-constructing its structure and adapting its parameters through fast online learning. Unlike other supervised machine learning techniques, it does not require off-line training. We further enhance the neural fuzzy controller to compensate for the effect of server switching delays. Extensive simulations demonstrate the effectiveness of our new approach in achieving the percentile-based end-to-end delay guarantees. Com-pared to a rule-based fuzzy controller enabled server allocation approach, the new approach delivers superior performance in the face of highly dynamic workloads. It is robust to workload variation, change in delay target and server switching delays. I

PERFUME: Power and performance guarantee with fuzzy MIMO control in virtualized servers

Abstract—It is important but challenging to assure the per-formance of multi-tier Internet applications with the power consumption cap of virtualized server clusters mainly due to system complexity of shared infrastructure and dynamic and bursty nature of workloads. This paper presents PERFUME, a system that simultaneously guarantees power and performance targets with flexible tradeoffs while assuring control accuracy and system stability. Based on the proposed fuzzy MIMO control technique, it accurately controls both the throughput and percentile-based response time of multi-tier applications due to its novel fuzzy modeling that integrates strengths of fuzzy logic, MIMO control and artificial neural network. It is self-adaptive to highly dynamic and bursty workloads due to online learning of control model parameters using a computationally efficient weighted recursive least-squares method. We implement PERFUME in a testbed of virtualized blade servers hosting two multi-tier RUBiS applications. Experimental results demonstrate its control accuracy, system stability, flexibility in selecting trade-offs between conflicting targets and robustness against highly dynamic variation and burstiness in workloads. It outperforms a representative utility based approach in providing guarantee of the system throughput, percentile-based response time and power budget in the face of highly dynamic and bursty workloads. I

aMOSS: Automated Multi-objective Server Provisioning with Stress-Strain Curving

Abstract—A modern data center built upon virtualized server clusters for hosting Internet applications has multiple correlated and conflicting objectives. Utility-based approaches are often used for optimizing multiple objectives. However, it is difficult to define a local utility function to suitably represent one objective and to apply different weights on multiple local utility functions. Furthermore, choosing weights statically may not be effective in the face of highly dynamic workloads. In this paper, we propose an automated multi-objective server provisioning with stress-strain curving approach (aMOSS). First, we formulate a multi-objective optimization problem that is to minimize the number of physical machines used, the average response time and the total number of virtual servers allocated for multi-tier applications. Second, we propose a novel stress-strain curving method to automatically select the most efficient solution from a Pareto-optimal set that is obtained as the result of a non-dominated sorting based optimization technique. Third, we en-hance the method to reduce server switching cost and improve the utilization of physical machines. Simulation results demonstrate that compared to utility-based approaches, aMOSS automatically achieves the most efficient tradeoff between performance and resource allocation efficiency. We implement aMOSS in a testbed of virtualized blade servers and demonstrate that it outperforms a representative dynamic server provisioning approach in achieving the average response time guarantee and in resource allocation efficiency for a multi-tier Internet service. aMOSS provides a unique perspective to tackle the challenging autonomic server provisioning problem. I