Cloud computing has developed as an important information technology paradigm which can provide on-demand services. Meanwhile,its energy consumption problem has attracted a grow-ing attention both from academic and in...Cloud computing has developed as an important information technology paradigm which can provide on-demand services. Meanwhile,its energy consumption problem has attracted a grow-ing attention both from academic and industrial communities. In this paper,from the perspective of cloud tasks,the relationship between cloud tasks and cloud platform energy consumption is established and analyzed on the basis of the multidimensional attributes of cloud tasks. Furthermore,a three-way clustering algorithm of cloud tasks is proposed for saving energy. In the algorithm,f irst,t he cloud tasks are classified into three categories according to the content properties of the cloud tasks and resources respectively. Next,cloud tasks and cloud resources are clustered according to their computation characteristics（ e. g. computation-intensive,data-intensive）. Subsequently,greedy scheduling is performed. The simulation results showthat the proposed algorithm can significantly reduce the energy cost and improve resources utilization,compared with the general greedy scheduling algorithm.展开更多
The performance of task scheduling algorithm in cloud computing determines the performance of the cloud system.This study mainly analyzed the application of the artificial bee colony(ABC)algorithm in the cloud task sc...The performance of task scheduling algorithm in cloud computing determines the performance of the cloud system.This study mainly analyzed the application of the artificial bee colony(ABC)algorithm in the cloud task scheduling.In order to solve the problem of cloud task scheduling,the ABC algorithm was discretized to get the discrete artificial bee colony(DABC)algorithm.Then the mathematical model of cloud task scheduling was established and solved by the DABC algorithm.Finally,the simulation experiment was carried out,and the performance of first-come-first-served(FCFS),MIN–MIN,ABC and DABC algorithms under different cloud tasks was compared to verify the performance of the proposed algorithm.The results showed that the user waiting time of the DABC algorithm was 1210s,the load balance degree was 0.01,and the user payment fee was 1688 yuan when the number of cloud tasks was 500;compared with other algorithms,the user waiting time of the DABC algorithm was shorter,the resource load balance degree was higher,and the overall performance was better.The research results verify the effectiveness of the DABC algorithm in solving the problem of cloud task optimal scheduling,and it can be further extended and applied in practice.展开更多
High energy consumption is one of the key issues of cloud computing systems. Incoming jobs in cloud computing environments have the nature of randomness, and compute nodes have to be powered on all the time to await i...High energy consumption is one of the key issues of cloud computing systems. Incoming jobs in cloud computing environments have the nature of randomness, and compute nodes have to be powered on all the time to await incoming tasks. This results in a great waste of energy. An energy-saving task scheduling algorithm based on the vacation queuing model for cloud computing systems is proposed in this paper. First, we use the vacation queuing model with exhaustive service to model the task schedule of a heterogeneous cloud computing system.Next, based on the busy period and busy cycle under steady state, we analyze the expectations of task sojourn time and energy consumption of compute nodes in the heterogeneous cloud computing system. Subsequently, we propose a task scheduling algorithm based on similar tasks to reduce the energy consumption. Simulation results show that the proposed algorithm can reduce the energy consumption of the cloud computing system effectively while meeting the task performance.展开更多
Mobile devices are resource-limited, and task migration has become an important and attractive feature of mobile clouds. To validate task migration, we propose a novel approach to the simulation of task migration in a...Mobile devices are resource-limited, and task migration has become an important and attractive feature of mobile clouds. To validate task migration, we propose a novel approach to the simulation of task migration in a pervasive cloud environment. Our approach is based on Colored Petri Net（CPN）. In this research, we expanded the semantics of a CPN and created two task migration models with different task migration policies： one that took account of context information and one that did not. We evaluated the two models using CPN-based simulation and analyzed their task migration accessibility, integrity during the migration process, reliability, and the stability of the pervasive cloud system after task migration. The energy consumption and costs of the two models were also investigated. Our results suggest that CPN with context sensing task migration can minimize energy consumption while preserving good overall performance.展开更多
基金Supported by the Harbin Technology Bureau Youth Talented Project(2014RFQXJ073)China Postdoctoral Fund Projects(2014M561330)
文摘Cloud computing has developed as an important information technology paradigm which can provide on-demand services. Meanwhile,its energy consumption problem has attracted a grow-ing attention both from academic and industrial communities. In this paper,from the perspective of cloud tasks,the relationship between cloud tasks and cloud platform energy consumption is established and analyzed on the basis of the multidimensional attributes of cloud tasks. Furthermore,a three-way clustering algorithm of cloud tasks is proposed for saving energy. In the algorithm,f irst,t he cloud tasks are classified into three categories according to the content properties of the cloud tasks and resources respectively. Next,cloud tasks and cloud resources are clustered according to their computation characteristics（ e. g. computation-intensive,data-intensive）. Subsequently,greedy scheduling is performed. The simulation results showthat the proposed algorithm can significantly reduce the energy cost and improve resources utilization,compared with the general greedy scheduling algorithm.
文摘The performance of task scheduling algorithm in cloud computing determines the performance of the cloud system.This study mainly analyzed the application of the artificial bee colony(ABC)algorithm in the cloud task scheduling.In order to solve the problem of cloud task scheduling,the ABC algorithm was discretized to get the discrete artificial bee colony(DABC)algorithm.Then the mathematical model of cloud task scheduling was established and solved by the DABC algorithm.Finally,the simulation experiment was carried out,and the performance of first-come-first-served(FCFS),MIN–MIN,ABC and DABC algorithms under different cloud tasks was compared to verify the performance of the proposed algorithm.The results showed that the user waiting time of the DABC algorithm was 1210s,the load balance degree was 0.01,and the user payment fee was 1688 yuan when the number of cloud tasks was 500;compared with other algorithms,the user waiting time of the DABC algorithm was shorter,the resource load balance degree was higher,and the overall performance was better.The research results verify the effectiveness of the DABC algorithm in solving the problem of cloud task optimal scheduling,and it can be further extended and applied in practice.
基金supported by Research and Innovation Projects for Graduates of Jiangsu Graduates of Jiangsu Province (No. CXZZ12 0483)the Science and Technology Support Program of Jiangsu Province (No. BE2012849)
文摘High energy consumption is one of the key issues of cloud computing systems. Incoming jobs in cloud computing environments have the nature of randomness, and compute nodes have to be powered on all the time to await incoming tasks. This results in a great waste of energy. An energy-saving task scheduling algorithm based on the vacation queuing model for cloud computing systems is proposed in this paper. First, we use the vacation queuing model with exhaustive service to model the task schedule of a heterogeneous cloud computing system.Next, based on the busy period and busy cycle under steady state, we analyze the expectations of task sojourn time and energy consumption of compute nodes in the heterogeneous cloud computing system. Subsequently, we propose a task scheduling algorithm based on similar tasks to reduce the energy consumption. Simulation results show that the proposed algorithm can reduce the energy consumption of the cloud computing system effectively while meeting the task performance.
文摘Mobile devices are resource-limited, and task migration has become an important and attractive feature of mobile clouds. To validate task migration, we propose a novel approach to the simulation of task migration in a pervasive cloud environment. Our approach is based on Colored Petri Net（CPN）. In this research, we expanded the semantics of a CPN and created two task migration models with different task migration policies： one that took account of context information and one that did not. We evaluated the two models using CPN-based simulation and analyzed their task migration accessibility, integrity during the migration process, reliability, and the stability of the pervasive cloud system after task migration. The energy consumption and costs of the two models were also investigated. Our results suggest that CPN with context sensing task migration can minimize energy consumption while preserving good overall performance.
