Jun Okitsu

Concept and evaluation of X-NAS: a highly scalable NAS system

X-NAS (expandable network attached storage), a highly scalable, distributed file system designed for entry-level NAS, has been developed. It virtualizes multiple NAS systems into a single-file-system view for different kinds of clients. The core of X-NAS is a multi-protocol virtualized file system (MVFS), and its key features - a smart-code wrapper daemon, file-group mapping, and a file-handle cache - improve X-NAS scalability. X-NAS has other key features for improving the manageability on many NAS systems; namely, on-line reconfiguration, autonomous rebalancing, and automatic migration, in which files are migrated automatically and dynamically independently of file-sharing services for clients. To validate the X-NAS concept, an X-NAS prototype was designed and tested according to the NFSv2 implementation. These tests indicate that X-NAS attains a quicker response time and higher throughput than a conventional single NAS, so its cost-performance scalability is also higher.

Performance comparison of least slack time based heuristics for job scheduling on computational grid

In recent years, increasing demand for computing has led to the development of computational grid. Typically scheduling challenges tend to be NP-hard problems where there is no optimal solution. The research reported here therefore is focused on the development of hybrids scheduling algorithms based on deadline and slack time parameters and its variations, using the concept of optimization techniques. An extensive performance comparison has been presented using real workload traces as benchmark on a grid computational environment. The results were compared with some baseline scheduling approaches in extant literature. The results have shown that the performances of grid scheduling algorithms developed and reported in this paper give good results in most of the cases and also support true scalability, when in the scenario of increasing workload and number of processors on a computational grid environment.

Cooling efficiency aware workload placement using historical sensor data on IT-facility collaborative control

A priority metric for IT equipment is proposed, and a method for extracting it from historical sensor data is devised. The proposed method consists of classification of cooling efficiency from sampled sensor data and calculation of the priority metric from statistics on these cooling-efficiency classes. A proof-of-concept experiment using the priority metric was conducted in a server room with a thermal environment including IT equipment units and cooling facilities. The results of the experiment indicate that IT-workload consolidation based on the proposed metric equalizes variation in server-room temperatures.

Performance comparison of thermal aware job scheduling based on multi priorities on computational grid

Electricity consumption for cooling purpose is known to be the most expensive operational cost factor in data centers. Inefficient cooling leads to high temperature and this in turn leads to hardware failure. This paper proposes a Thermal Aware Modified Least Slack Time Round Robin Based (MLST-RR) scheduling algorithm that can avoid high thermal stress circumstances such as large hotspots, thermal violations as well as reduce electricity consumption for cooling in data center labs. The experimental results show that, the thermal aware MLST-RR is able to significantly decrease the electricity consumption while maintaining competitive performance. Specifically, the thermal aware scheduling algorithm saves electricity consumption as much as 15000KW compared to the benchmark job scheduling algorithms such as Round Robin (RR) and First Come First Serve (FCFS); this is an electrical saving of 8.4%.

Towards greening a campus grid: Free cooling during unsociable hours

Free cooling has become widely used in the area of computer room thermal control, especially where the temperature constraint can be relaxed. However, in tropical regions, such as Malaysia, free cooling cannot be applied all of the time. This paper presents a strategy for grids that allows jobs to be executed on resources that are free cooled. The paper describes how a recommended period of time for using free cooled resources is predicted. The period is predicted from a historical analysis by using machine learning. Experiments on a classroom used for campus grid computing showed that, typically, free cooled resources can be used for 5 hours per day, when the temperature is less than 28 degrees Celsius. The result is of use to those developing campus grids in tropical countries.

Towards an architecture for integrated Gas District Cooling with Data Center control to reduce CO2 emission

Gas District Cooling (GDC) provides electricity and chilled water to facilities with relatively low running cost and has the potential to reduce CO2 emission as it can make effective use of wasted energy. However, the present CO2 emission tends to be higher than expected due to the chilled water supply-demand gap. To efficiently manage the gap, this paper introduces a novel chilled water supply-demand gap model and proposes an integrated GDC and Data Center (DC) control based on the model. The gap model, defined by GDC plant and DC controllable parameters, estimates the required additional chilled water supply. Then, DC and chillers in the plant are controlled based on the model to minimize the required additional supply. The analysis using GDC operational data in Universiti Teknologi PETRONAS shows that the accuracy of the models depends on temperature differences between rooms and outdoor, Steam Absorption Chillers (SAC) operations and the target period of the models. Thus, the analysis suggests that GDC with room and outdoor temperature sensors, SAC operation scheduling, and thermal storage tank (TES) can improve accuracy of the model and optimize the GDC operations more accurately to reduce CO2 emission.