Supplying the electronic equipment by exploiting ambient energy sources is a hot spot. In order to achieve the match between power supply and demands under the variance of environments at real time, a reconfigurable t...Supplying the electronic equipment by exploiting ambient energy sources is a hot spot. In order to achieve the match between power supply and demands under the variance of environments at real time, a reconfigurable technique is taken. In this paper, a dynamic power consumption model by using a lookup table as a unit is proposed. Then, we establish a system-level task scheduling model according to the task type. Based on single instruction multiple data （SIMD） architecture which contains a processing system and a control system with a Nios II processor, a practical dynamic reconfigurable system is built. The approach is evaluated on a hardware platform. The test results show that the system can automatically adjust the power consumption in case of external energy input changing. The utilization of the system dynamic power of their portion is from 80.05% to 91.75% during the first task assignment. During the entire processing cycle, the total energy efficiency is 97.67%.展开更多
With the increasing energy consumption of computing systems and the growing advocacy for green computing, energy efficiency has become one of the critical challenges in high-performance heterogeneous computing systems...With the increasing energy consumption of computing systems and the growing advocacy for green computing, energy efficiency has become one of the critical challenges in high-performance heterogeneous computing systems. Energy consumption can be reduced by not only hardware design but also software design. In this paper, we propose an energy-aware scheduling algorithm with equalized frequency, called EASEF, for parallel applications on heterogeneous computing systems. The EASEF approach aims to minimize the finish time and overall energy consumption. First, EASEF extracts the set of paths from an application. Then, it reconstructs the application based on the extracted set of paths to achieve a reasonable schedule. Finally, it adopts a progressive way to equalize the frequency of tasks to reduce the total energy consumption of systems. Randomly generated applications and two real-world applications are examined in our experiments. Experimental results show that the EASEF algorithm outperforms two existing algorithms in terms of makespan and energy consumption.展开更多
Cloud computing infrastructures have intended to provide computing services to end-users through the internet in a pay-per-use model.The extensive deployment of the Cloud and continuous increment in the capacity and u...Cloud computing infrastructures have intended to provide computing services to end-users through the internet in a pay-per-use model.The extensive deployment of the Cloud and continuous increment in the capacity and utilization of data centers(DC)leads to massive power consumption.This intensifying scale of DCs has made energy consumption a critical concern.This paper emphasizes the task scheduling algorithm by formulating the system model to minimize the makespan and energy consumption incurred in a data center.Also,an energy-aware task scheduling in the Blockchain-based data center was proposed to offer an optimal solution that minimizes makespan and energy consumption.The estab-lished model was analyzed with a target-time responsive precedence scheduling algorithm.The observations were analyzed and compared with the traditional scheduling algorithms.The outcomes exhibited that the developed solution incurs better performance with a response to resource utilization and decreasing energy consumption.The investigation revealed that the applied strategy considerably enhanced the effectiveness of the designed schedule.展开更多
基金supported by the National Natural Science Foundation of China under Grant No. 61176025 and No. 61006027the Fundamental Research Funds for the Central Universities under Grant No.ZYGX2012J003+1 种基金National Laboratory of Analogue Integrated Circuit Grants under Grants No. 9140C0901101002 and No. 9140C0901101003New Century Excellent Talents Program under Grant No.NCET-10-0297
文摘Supplying the electronic equipment by exploiting ambient energy sources is a hot spot. In order to achieve the match between power supply and demands under the variance of environments at real time, a reconfigurable technique is taken. In this paper, a dynamic power consumption model by using a lookup table as a unit is proposed. Then, we establish a system-level task scheduling model according to the task type. Based on single instruction multiple data （SIMD） architecture which contains a processing system and a control system with a Nios II processor, a practical dynamic reconfigurable system is built. The approach is evaluated on a hardware platform. The test results show that the system can automatically adjust the power consumption in case of external energy input changing. The utilization of the system dynamic power of their portion is from 80.05% to 91.75% during the first task assignment. During the entire processing cycle, the total energy efficiency is 97.67%.
基金Project supported by the National Natural Science Foundation of China （Nos. 61133005, 61432005, 61370095, 61472124, and 61402400）
文摘With the increasing energy consumption of computing systems and the growing advocacy for green computing, energy efficiency has become one of the critical challenges in high-performance heterogeneous computing systems. Energy consumption can be reduced by not only hardware design but also software design. In this paper, we propose an energy-aware scheduling algorithm with equalized frequency, called EASEF, for parallel applications on heterogeneous computing systems. The EASEF approach aims to minimize the finish time and overall energy consumption. First, EASEF extracts the set of paths from an application. Then, it reconstructs the application based on the extracted set of paths to achieve a reasonable schedule. Finally, it adopts a progressive way to equalize the frequency of tasks to reduce the total energy consumption of systems. Randomly generated applications and two real-world applications are examined in our experiments. Experimental results show that the EASEF algorithm outperforms two existing algorithms in terms of makespan and energy consumption.
文摘Cloud computing infrastructures have intended to provide computing services to end-users through the internet in a pay-per-use model.The extensive deployment of the Cloud and continuous increment in the capacity and utilization of data centers(DC)leads to massive power consumption.This intensifying scale of DCs has made energy consumption a critical concern.This paper emphasizes the task scheduling algorithm by formulating the system model to minimize the makespan and energy consumption incurred in a data center.Also,an energy-aware task scheduling in the Blockchain-based data center was proposed to offer an optimal solution that minimizes makespan and energy consumption.The estab-lished model was analyzed with a target-time responsive precedence scheduling algorithm.The observations were analyzed and compared with the traditional scheduling algorithms.The outcomes exhibited that the developed solution incurs better performance with a response to resource utilization and decreasing energy consumption.The investigation revealed that the applied strategy considerably enhanced the effectiveness of the designed schedule.
