# Trusted Electronic Contract for Enabling Peer-to-Peer HPC Resource Sharing
## 2 Literature Review  
Current research studies in HPC areas have been focusing on enhancing the efficiency of resource management. To increase HPC utilization and improve response time, various implementations of tasks scheduling algorithms are proposed [1]. To improve the performance of the scheduler, a study [2] proposes a method for predicting job waiting time, allowing the scheduler to make use of the information in decision making. In addition, a  
Figure 1: Statistics of Job Submission to e-Science Service Grouped by Research Topic. resource reservation scheme [3] is proposed to enable advance schedule planning. Despite the aid from task management schemes, inefficient resource usage remains an issue as many HPC providers let users define the specification and amount of resources needed to carry out their tasks in which, most of the time, the reserve amount is imprecise compared to the actual usage at runtime. To mitigate the problem, an automated monitoring system [4] for detecting jobs with inefficient resource consumption is introduced by analyzing the relationship between the parent process and its spawning child processes [5]. Apart from job and queue management, computing performance and processing time can be improved through modifying operational mechanisms of underlying HPC components, such as kernel modification on a single compute node's Linux scheduler [6; 7]. Recent studies have also focused on introducing machine learning and AI techniques to enable system administrators to better understand the behavior of HPC through analyzing the relationship between operational elements, e.g., determining an influential factor between application and resources usage at runtime [8]. Another research topic in HPC is related to improving security. Through the presence of application layer access control, anomaly user behavior can be detected through log data collection and analysis [9]. Looking at the demand in HPC areas in Thailand, up to present, most of the services are supported by government agencies and mainly provide to non-commercial research innovations from education and research sectors. Nevertheless, with the recent launch of open government data in Thailand [10], over 6000 datasets (e.g., agricultural, logistic, and transportation data) were made publicly available, encouraging business transformation into a data-driven model. As a result, a greater number of individuals and companies began to pay more attention to using HPC as a crucial means to extract insightful value from data. This change can obviously be noticed from the usage statistics of the e-Science service, which shows that the demand for HPC in AI and Big Data areas has been growing significantly in recent years. Unfortunately, the current process for requesting HPC resources requires some paperwork and a pre-screening process. The goals are to ensure that the users are from the trusted institute and to allow the service provider to anticipate an incoming workload. Such processes are not attractive to companies or individuals, who do not have creditability and/or intend to use HPC for computing just a single or a few jobs, to fill out an intention form. Currently, there are few studies on creating a foundational structure for sharing computational resources. In the widely known 'Grid System', jobs are scheduled to the selected HPC frontend and the results are returned after execution is done. The HPC systems under grid are required to have a homogenous environment (i.e., scheduler brand and versions, installed applications). The grid system, therefore, is suitable to run predictable tasks in a specific research area. In contrast, our objectives aim to support access from users from diverse fields; therefore, not all HPC systems possess all the needed requirements (e.g., software license, versions, dependencies). Therefore, the creation of a resource sharing system can enable tasks from different application areas to schedule for running on HPC systems with desirable environments.