Cloud Task Scheduling Based on Policy Gradient Algorithm in Heterogeneous Cloud Data Center for Energy Consumption Optimization

2020 ◽  
Author(s):  
CuiHua He ◽  
YongQuan Yang ◽  
BoWei Hong
Keyword(s):  
Energy Consumption ◽  
Task Scheduling ◽  
Data Center ◽  
Gradient Algorithm ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Energy Consumption Optimization ◽  
Policy Gradient ◽  
Heterogeneous Cloud

scholarly journals An Energy-Efficient Task Scheduling Mechanism with Switching On/Sleep Mode of Servers in Virtualized Cloud Data Centers

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Chunxia Yin ◽  
Jian Liu ◽  
Shunfu Jin
Keyword(s):  
Energy Consumption ◽  
Task Scheduling ◽  
Data Center ◽  
Energy Efficient ◽  
Data Centers ◽  
Sleep Mode ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Response Performance

In recent years, the energy consumption of cloud data centers has continued to increase. A large number of servers run at a low utilization rate, which results in a great waste of power. To save more energy in a cloud data center, we propose an energy-efficient task-scheduling mechanism with switching on/sleep mode of servers in the virtualized cloud data center. The key idea is that when the number of idle VMs reaches a specified threshold, the server with the most idle VMs will be switched to sleep mode after migrating all the running tasks to other servers. From the perspective of the total number of tasks and the number of servers in sleep mode in the system, we establish a two-dimensional Markov chain to analyse the proposed energy-efficient mechanism. By using the method of the matrix-geometric solution, we mathematically estimate the energy consumption and the response performance. Both numerical and simulated experiments show that our proposed energy-efficient mechanism can effectively reduce the energy consumption and guarantee the response performance. Finally, by constructing a cost function, the number of VMs hosted on each server is optimized.

Novel Resource Allocation Algorithm for Energy-Efficient Cloud Computing in Heterogeneous Environment

2015 ◽  
pp. 2225-2239
Author(s):  
Wei-Wei Lin ◽  
Liang Tan ◽  
James Z. Wang
Keyword(s):  
Resource Allocation ◽  
Energy Consumption ◽  
Constraint Satisfaction ◽  
Data Center ◽  
Data Centers ◽  
Bin Packing ◽  
Constraint Satisfaction Problem ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Heterogeneous Cloud

Energy efficiency is one of the most important design considerations for a cloud data center. Recent approaches to the energy-efficient resource management for data centers usually model the problem as a bin packing problem with the goal of minimizing the number of physical machines (PMs) employed. However, minimizing the number of PMs may not necessarily minimize the energy consumption in a heterogeneous cloud environment. To address the problem, this paper models the resource allocation problem in a heterogeneous cloud data center as a constraint satisfaction problem (CSP). By solving this constraint satisfaction problem, an optimal resource allocation scheme, which includes a virtual machine provision algorithm and a virtual machine packing algorithm, is designed to minimize the energy consumption in a virtualized heterogeneous cloud data center. Performance studies show that this proposed new scheme outperforms the existing bin-packing based approaches in terms of energy consumption in heterogeneous cloud data centers.

Novel Resource Allocation Algorithm for Energy-Efficient Cloud Computing in Heterogeneous Environment

2014 ◽  
Vol 6 (1) ◽  
pp. 63-76 ◽  
Author(s):  
Wei-Wei Lin ◽  
Liang Tan ◽  
James Z. Wang
Keyword(s):  
Resource Allocation ◽  
Energy Consumption ◽  
Data Center ◽  
Energy Efficient ◽  
Data Centers ◽  
Bin Packing ◽  
Constraint Satisfaction Problem ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Heterogeneous Cloud

Energy efficiency is one of the most important design considerations for a cloud data center. Recent approaches to the energy-efficient resource management for data centers usually model the problem as a bin packing problem with the goal of minimizing the number of physical machines (PMs) employed. However, minimizing the number of PMs may not necessarily minimize the energy consumption in a heterogeneous cloud environment. To address the problem, this paper models the resource allocation problem in a heterogeneous cloud data center as a constraint satisfaction problem (CSP). By solving this constraint satisfaction problem, an optimal resource allocation scheme, which includes a virtual machine provision algorithm and a virtual machine packing algorithm, is designed to minimize the energy consumption in a virtualized heterogeneous cloud data center. Performance studies show that this proposed new scheme outperforms the existing bin-packing based approaches in terms of energy consumption in heterogeneous cloud data centers.

Task Scheduling Mechanism Based on Multi-QoS Genetic Algorithm in Cloud Data Center

2013 ◽  
Vol 846-847 ◽  
pp. 1468-1471
Author(s):  
De Wen Wang ◽  
Yang Liu
Keyword(s):  
Genetic Algorithm ◽  
Electric Power ◽  
Task Scheduling ◽  
Data Center ◽  
Evaluation Model ◽  
Operating Efficiency ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Computing Platform ◽  
Qos Evaluation

A multi-QoS evaluation model for electric power users is defined, combined with the characteristics of data center in electric power corporation, based on the research of cloud computing platform of data center in electric power corporation and task scheduling strategies of cloud data center. And a genetic algorithm based on multi-QoS, which fitness functions are QoS utility value and completion time, is put forward. Tests in Cloudsim platform and the result shows that the genetic algorithm based on multi-QoS can satisfy the requirements of multi-QoS of electric power users and improve the operating efficiency of data center in electric power corporation.

Minimization of VM Placement Change in Energy-Aware Resource Provisioning for Cloud Data Center

2013 ◽  
Vol 325-326 ◽  
pp. 1730-1733 ◽  
Author(s):  
Si Yuan Jing ◽  
Shahzad Ali ◽  
Kun She
Keyword(s):  
Energy Consumption ◽  
Data Center ◽  
Network Flow ◽  
Resource Provisioning ◽  
Approximate Algorithm ◽  
Vm Placement ◽  
Energy Aware ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Placement Change

Numerous part of the energy-aware resource provision research for cloud data center just considers how to maximize the resource utilization, i.e. minimize the required servers, without considering the overhead of a virtual machine (abbreviated as a VM) placement change. In this work, we propose a new method to minimize the energy consumption and VM placement change at the same time, moreover we also design a network-flow-theory based approximate algorithm to solve it. The simulation results show that, compared to existing work, the proposed method can slightly decrease the energy consumption but greatly decrease the number of VM placement change

scholarly journals FHCS: Hybridised optimisation for virtual machine migration and task scheduling in cloud data center

2020 ◽  
Vol 14 (12) ◽  
pp. 1942-1948
Author(s):  
Banavath Balaji Naik ◽  
Dhananjay Singh ◽  
Arun B. Samaddar
Keyword(s):  
Virtual Machine ◽  
Task Scheduling ◽  
Data Center ◽  
Virtual Machine Migration ◽  
Cloud Data Center ◽  
Cloud Data ◽  
And Task

scholarly journals WARM: Workload-Aware Multi-Application Task Scheduling for Revenue Maximization in SDN-Based Cloud Data Center

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 645-657 ◽  
Author(s):  
Haitao Yuan ◽  
Jing Bi ◽  
Mengchu Zhou ◽  
Khaled Sedraoui
Keyword(s):  
Task Scheduling ◽  
Data Center ◽  
Revenue Maximization ◽  
Cloud Data Center ◽  
Cloud Data
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