This study examines the role of corporate governance in predicting firm’s CSR performance. In particular, we measure related-party transactions (RPTs), which can provide benefits as well as detrimental practices such as "tunneling", that infringe minority shareholder value. By applying machine learning techniques, the research investigates how corporate governance influence predicting market performance, as measured by the Price-to-Book Ratio (PBR), and Corporate Social Responsibility (CSR) performance. The results suggest that model including related-party transactions variables improve prediction performance on max average 10% compared to those using only financial variables, particularly with a noticeable improvement in lower 20% of PBR value companies. This emphasizes the importance of considering related-party transactions variables in corporate valuation and the value of taking a comprehensive approach to these variables in related research. The study offers practical implications for improving corporate governance and CSR, ultimately supporting investor decision-making.

In a heterogeneous cluster, virtual machine (VM) placement for a distributed parallel application is challenging due to numerous possible ways of placing the application and complexity of estimating the performance of the application. This study investigates a holistic VM placement technique for distributed parallel applications in a heterogeneous cluster, aiming to maximize the efficiency of the cluster and consequently reduce the costs for service providers and users. The proposed technique accommodates various factors that have an impact on performance in a combined manner. First, we analyze the effects of the heterogeneity of resources, different VM configurations, and interference between VMs on the performance of distributed parallel applications with a wide diversity of characteristics, including scientific and big data analytics applications. We then propose a placement technique that uses a machine learning algorithm to estimate the runtime of a distributed parallel application. To train a performance estimation model, a distributed parallel application is profiled against synthetic workloads that mostly utilize the dominant resource of the application, which strongly affects the application performance, reducing the profiling space dramatically. Through experimental and simulation studies, we show that the proposed placement technique can find good VM placement configurations for various workloads.

In a virtualized heterogeneous cluster, for a distributed parallel application which runs in multiple virtual machines (VMs) concurrently, there are a huge number of possible ways to place its VMs. This paper investigates a performance estimation technique for distributed parallel applications in virtualized heterogeneous clusters. We first analyze the effects of different VM configurations on the performance of various distributed parallel applications. We then present a machine-learning based performance model for a distributed parallel application. Using a heterogeneous cluster with two different types of nodes, we show that our machine-learning based models can estimate the runtimes of distributed parallel applications with modest error rates.

Recent emerging applications from a wide range of scientific domains often require a very large number of loosely coupled tasks to be efficiently processed. To support such applications effectively, all the available resources from different types of computing platforms such as supercomputers, grids, and clouds need to be utilized. However, exploiting heterogeneous resources from the platforms for multiple loosely coupled many-task applications is challenging, since the performance of an application can vary significantly depending on which platform is used to run it, and which applications co-run in the same node with it. In this paper, we analyze the platform and co-runner affinities of many-task applications in distributed computing platforms. We perform a comprehensive experimental study using four different platforms, and five many-task applications. We then present a two-level scheduling algorithm, which distributes the resources of different platforms to each application based on the platform affinity in the first level, and maps tasks of the applications to computing nodes based on the co-runner affinity for each platform in the second level. Finally, we evaluate the performance of our scheduling algorithm, using a trace-based simulator. Our simulation results demonstrate that our scheduling algorithm can improve the performance up to 30.0%, compared to a baseline scheduling algorithm.

The IETF's new protocols for Low-power and Lossy Networks are delegating their several functions to an IP border router that has more resources than other constrained nodes in the network. It makes the complexity of the IP border router higher than other conventional routers. For this reason, a well-designed platform for routers is important for fast development and easy debugging. In this paper, we check the feasibility of Click Modular Router platform to develop the IP border router for LLNs. Then, we present a design and an implementation of the IP border router on top of Click.