Energy Consumption Analysis and Proposed Power-Aware Scheduling Algorithm in Cloud Computing

2020 ◽  
pp. 193-201
Author(s):  
Juhi Singh
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Scheduling Algorithm ◽  
Energy Consumption Analysis ◽  
Power Aware Scheduling

Virtual Machines Scheduling Algorithm Based on Multi-Objective Optimization in Cloud Computing

2014 ◽  
Vol 1046 ◽  
pp. 508-511
Author(s):  
Jian Rong Zhu ◽  
Yi Zhuang ◽  
Jing Li ◽  
Wei Zhu
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Machine Scheduling ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Virtual Machine Scheduling ◽  
Set Up

How to reduce energy consumption while improving utility of datacenter is one of the key technologies in the cloud computing environment. In this paper, we use energy consumption and utility of data center as objective functions to set up a virtual machine scheduling model based on multi-objective optimization VMSA-MOP, and design a virtual machine scheduling algorithm based on NSGA-2 to solve the model. Experimental results show that compared with other virtual machine scheduling algorithms, our algorithm can obtain relatively optimal scheduling results.

Load and Cost Aware Min-Min Workflow Scheduling Algorithm for Heterogeneous Resources in Fog, Cloud and Edge Scenarios

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Load Balancing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Fog Computing ◽  
Workflow Scheduling ◽  
Heterogeneous Environment ◽  
Cloud Servers ◽  
Heterogeneous Resources

Fog computing and Edge computing are few of the latest technologies which are offered as solution to challenges faced in Cloud Computing. Instead of offloading of all the tasks to centralized cloud servers, some of the tasks can be scheduled at intermediate Fog servers or Edge devices. Though this solves most of the problems faced in cloud but also encounter other traditional problems due to resource-related constraints like load balancing, scheduling, etc. In order to address task scheduling and load balancing in Cloud-fog-edge collaboration among servers, we have proposed an improved version of min-min algorithm for workflow scheduling which considers cost, makespan, energy and load balancing in heterogeneous environment. This algorithm is implemented and tested in different offloading scenarios- Cloud only, Fog only, Cloud-fog and Cloud-Fog-Edge collaboration. This approach performed better and the result gives minimum makespan, less energy consumption along with load balancing and marginally less cost when compared to min-min and ELBMM algorithms

Research on Communication Scheduling Algorithm for Smart Home in Internet of Things Under Cloud Computing

2019 ◽  
Vol 23 (1) ◽  
pp. 124-128 ◽  
Author(s):  
Jie Zhang ◽  
◽  
Mantao Wang
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Internet Of Things ◽  
Objective Function ◽  
Smart Home ◽  
Data Flow ◽  
Scheduling Algorithm ◽  
Optimal Solution ◽  
Low Latency ◽  
Communication Scheduling

The current communication scheduling algorithm for smart home cannot realize low latency in scheduling effect with unreasonable control of communication throughput and large energy consumption. In this paper, a communication scheduling algorithm for smart home in Internet of Things under cloud computing based on particle swarm is proposed. According to the fact that the transmission bandwidth of any data flow is limited by the bandwidth of network card of sending end and receiving end, the bandwidth limits of network card of smart home communication server are used to predict the maximum practicable bandwidth of data flow. Firstly, the initial value of communication scheduling objective function of smart home and particle swarm is set, and the objective function is taken as the fitness function of particle. Then the current optimal solution of objective function is calculated through predicted value and objective function, current position and flight speed of particle should be updated until the iteration conditions are met. Finally, the optimal solution is output, the communication scheduling of smart home is thus realized. Experiments show that this algorithm can realize low latency with small energy consumption, and the throughput is relatively reasonable.

scholarly journals Applying Dynamic Priority Scheduling Scheme to Static Systems of Pinwheel Task Model in Power-Aware Scheduling

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Ye-In Seol ◽  
Young-Kuk Kim
Keyword(s):  
Energy Consumption ◽  
Scheduling Algorithm ◽  
Priority Scheduling ◽  
Dynamic Priority ◽  
Task Model ◽  
Dynamic Voltage ◽  
Priority Method ◽  
Hard Real Time ◽  
Static Systems ◽  
Power Aware Scheduling

Power-aware scheduling reduces CPU energy consumption in hard real-time systems through dynamic voltage scaling (DVS). In this paper, we deal with pinwheel task model which is known as static and predictable task model and could be applied to various embedded or ubiquitous systems. In pinwheel task model, each task’s priority is static and its execution sequence could be predetermined. There have been many static approaches to power-aware scheduling in pinwheel task model. But, in this paper, we will show that the dynamic priority scheduling results in power-aware scheduling could be applied to pinwheel task model. This method is more effective than adopting the previous static priority scheduling methods in saving energy consumption and, for the system being still static, it is more tractable and applicable to small sized embedded or ubiquitous computing. Also, we introduce a novel power-aware scheduling algorithm which exploits all slacks under preemptive earliest-deadline first scheduling which is optimal in uniprocessor system. The dynamic priority method presented in this paper could be applied directly to static systems of pinwheel task model. The simulation results show that the proposed algorithm with the algorithmic complexity ofO(n) reduces the energy consumption by 10–80% over the existing algorithms.

scholarly journals Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

2021 ◽  
Author(s):  
Poria Pirozmand ◽  
Ali Asghar Rahmani Hosseinabadi ◽  
Maedeh Farrokhzad ◽  
Mehdi Sadeghilalimi ◽  
Seyedsaeid Mirkamali ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Scheduling Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Computing System ◽  
Computing Systems ◽  
Cloud Computing System ◽  
Scheduling Heuristic ◽  
The Cost

AbstractThe cloud computing systems are sorts of shared collateral structure which has been in demand from its inception. In these systems, clients are able to access existing services based on their needs and without knowing where the service is located and how it is delivered, and only pay for the service used. Like other systems, there are challenges in the cloud computing system. Because of a wide array of clients and the variety of services available in this system, it can be said that the issue of scheduling and, of course, energy consumption is essential challenge of this system. Therefore, it should be properly provided to users, which minimizes both the cost of the provider and consumer and the energy consumption, and this requires the use of an optimal scheduling algorithm. In this paper, we present a two-step hybrid method for scheduling tasks aware of energy and time called Genetic Algorithm and Energy-Conscious Scheduling Heuristic based on the Genetic Algorithm. The first step involves prioritizing tasks, and the second step consists of assigning tasks to the processor. We prioritized tasks and generated primary chromosomes, and used the Energy-Conscious Scheduling Heuristic model, which is an energy-conscious model, to assign tasks to the processor. As the simulation results show, these results demonstrate that the proposed algorithm has been able to outperform other methods.

scholarly journals Task Scheduling Algorithm Based on Bacterial Foraging Optimization (BFO) in Cloud Computing

2018 ◽  
Vol 7 (1) ◽  
pp. 16-19
Author(s):  
Anupama Gupta ◽  
Kulveer Kaur ◽  
Rajvir Kaur
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Execution Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Bacterial Foraging Optimization ◽  
Task Scheduling Algorithm ◽  
Steady Rate ◽  
Time And Energy

Cloud computing is the architecture in which cloudlets are executed by the virtual machines. The most applicable virtual machines are selected on the basis of execution time and failure rate. Due to virtual machine overloading, the execution time and energy consumption is increased at steady rate. In this paper, BFO technique is applied in which weight of each virtual machine is calculated and the virtual machine which has the maximum weight is selected on which cloudlet will be migrated. The performance of proposed algorithm is tested by implementing it in CloudSim and analyzing it in terms of execution time, energy consumption.

scholarly journals Wireless-Uplinks-Based Energy-Efficient Scheduling in Mobile Cloud Computing

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Xing Liu ◽  
Chaowei Yuan ◽  
Zhen Yang ◽  
Enda Peng
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Energy Efficient ◽  
Mobile Cloud Computing ◽  
Scheduling Algorithm ◽  
Average Energy ◽  
Mobile Internet ◽  
Mobile Cloud ◽  
Average Energy Consumption ◽  
Energy Efficient Scheduling

Mobile cloud computing (MCC) combines cloud computing and mobile internet to improve the computational capabilities of resource-constrained mobile devices (MDs). In MCC, mobile users could not only improve the computational capability of MDs but also save operation consumption by offloading the mobile applications to the cloud. However, MCC faces the problem of energy efficiency because of time-varying channels when the offloading is being executed. In this paper, we address the issue of energy-efficient scheduling for wireless uplink in MCC. By introducing Lyapunov optimization, we first propose a scheduling algorithm that can dynamically choose channel to transmit data based on queue backlog and channel statistics. Then, we show that the proposed scheduling algorithm can make a tradeoff between queue backlog and energy consumption in a channel-aware MCC system. Simulation results show that the proposed scheduling algorithm can reduce the time average energy consumption for offloading compared to the existing algorithm.

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