scholarly journals An Adaptive Task Scheduling Method for Networked UAV Combat Cloud System Based on Virtual Machine and Task Migration

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Bo Li ◽  
Shiyang Liang ◽  
Linyu Tian ◽  
Daqing Chen ◽  
Ming Zhang
Keyword(s):  
Virtual Machine ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Experimental Simulation ◽  
Cloud System ◽  
Task Migration ◽  
Scheduling Method ◽  
Task Processing ◽  
And Task

This paper presents a systematic work aiming to improve the efficiency of task processing in a networked UAV combat cloud system. The work consists of three major aspects: (1) an architecture of UAV combat cloud systems—such a system provides the necessary resource pool for powerful computing and storage facilities and defines the attributes of the entities in the resource pool in detail; (2) an online adaptive task redistribution and scheduling algorithm—the algorithm involves task migration being performed on virtual machines on the cloud system and aims to address the problems caused by static task scheduling approaches; and (3) an online virtual machine and task migration algorithm—the algorithm considers collectively the priority type and quantity of the tasks to be migrated on virtual machines along with time constraints to determine the migration of virtual machine or task and optimize resource usages. Experimental simulation results have demonstrated that the proposed system and the relevant algorithms can significantly improve the efficiency of task schedule.

Energy Efficiency Oriented Scheduling for Heterogeneous Cloud Systems

2014 ◽  
Vol 6 (4) ◽  
pp. 1-14 ◽  
Author(s):  
Weiwei Lin ◽  
Chao Yang ◽  
Chaoyue Zhu ◽  
James Z. Wang ◽  
Zhiping Peng
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Memory Load ◽  
Scheduling Algorithm ◽  
System Level ◽  
Cloud System ◽  
Improve Energy Efficiency ◽  
Heterogeneous Cloud

Nowadays, how to improve energy efficiency has become a challenging problem in cloud computing. However, most existing efforts in improving the energy efficiency of a cloud system only focus on resource allocation at the system level like managing physical nodes or virtual machines. This paper tries to address the energy efficiency problem of a heterogeneous cloud system at the task scheduling level. A novel task scheduling algorithm is proposed to reduce the energy consumption of the system while maintaining its performance, without closing or consolidating any system resources such as virtual machines or storage systems. The algorithm dynamically monitors CPU and memory load information of participating nodes on a heterogeneous Hadoop platform with Ganglia, then selects and submits an appropriate task to the node with relatively low workload to avoid excessive energy consumption on some nodes. Experimental results show that this novel scheduling algorithm can effectively improve the energy-saving ratio of a heterogeneous cloud platform while maintaining a high system performance.

Task Scheduling Algorithm based on Resources Segregation in Cloud Environment

2018 ◽  
pp. 96-101
Author(s):  
Shailendra Raghuvanshi ◽  
Priyanka Dubey
Keyword(s):  
Load Balancing ◽  
Task Scheduling ◽  
Waiting Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Cloud Environment ◽  
Task Scheduling Algorithm ◽  
Machine Utilization ◽  
Independent Tasks ◽  
Optimal Machine

Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing, which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue using workflowsim simulator in JAVA.

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.

scholarly journals Using a vision cognitive algorithm to schedule virtual machines

2014 ◽  
Vol 24 (3) ◽  
pp. 535-550 ◽  
Author(s):  
Jiaqi Zhao ◽  
Yousri Mhedheb ◽  
Jie Tao ◽  
Foued Jrad ◽  
Qinghuai Liu ◽  
...  
Keyword(s):  
Global Optimization ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Optimization Problem ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Primary Concern ◽  
Global Optimization Problem ◽  
Vm Scheduling ◽  
Operation Cost

Abstract Scheduling virtual machines is a major research topic for cloud computing, because it directly influences the performance, the operation cost and the quality of services. A large cloud center is normally equipped with several hundred thousand physical machines. The mission of the scheduler is to select the best one to host a virtual machine. This is an NPhard global optimization problem with grand challenges for researchers. This work studies the Virtual Machine (VM) scheduling problem on the cloud. Our primary concern with VM scheduling is the energy consumption, because the largest part of a cloud center operation cost goes to the kilowatts used. We designed a scheduling algorithm that allocates an incoming virtual machine instance on the host machine, which results in the lowest energy consumption of the entire system. More specifically, we developed a new algorithm, called vision cognition, to solve the global optimization problem. This algorithm is inspired by the observation of how human eyes see directly the smallest/largest item without comparing them pairwisely. We theoretically proved that the algorithm works correctly and converges fast. Practically, we validated the novel algorithm, together with the scheduling concept, using a simulation approach. The adopted cloud simulator models different cloud infrastructures with various properties and detailed runtime information that can usually not be acquired from real clouds. The experimental results demonstrate the benefit of our approach in terms of reducing the cloud center energy consumption

Workload-driven coordination between virtual machine allocation and task scheduling

2019 ◽  
Vol 32 (10) ◽  
pp. 5535-5551
Author(s):  
Zheng Xiao ◽  
Bangyong Wang ◽  
Xing Li ◽  
Jiayi Du
Keyword(s):  
Virtual Machine ◽  
Task Scheduling ◽  
Machine Allocation ◽  
Virtual Machine Allocation ◽  
And Task

Efficient task scheduling on virtual machine in cloud computing environment

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mahfooz Alam ◽  
Mahak ◽  
Raza Abbas Haidri ◽  
Dileep Kumar Yadav
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Response Time ◽  
Virtual Machine ◽  
Task Allocation ◽  
Completion Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Content Type ◽  
Minimum Completion Time

Purpose Cloud users can access services at anytime from anywhere in the world. On average, Google now processes more than 40,000 searches every second, which is approximately 3.5 billion searches per day. The diverse and vast amounts of data are generated with the development of next-generation information technologies such as cryptocurrency, internet of things and big data. To execute such applications, it is needed to design an efficient scheduling algorithm that considers the quality of service parameters like utilization, makespan and response time. Therefore, this paper aims to propose a novel Efficient Static Task Allocation (ESTA) algorithm, which optimizes average utilization. Design/methodology/approach Cloud computing provides resources such as virtual machine, network, storage, etc. over the internet. Cloud computing follows the pay-per-use billing model. To achieve efficient task allocation, scheduling algorithm problems should be interacted and tackled through efficient task distribution on the resources. The methodology of ESTA algorithm is based on minimum completion time approach. ESTA intelligently maps the batch of independent tasks (cloudlets) on heterogeneous virtual machines and optimizes their utilization in infrastructure as a service cloud computing. Findings To evaluate the performance of ESTA, the simulation study is compared with Min-Min, load balancing strategy with migration cost, Longest job in the fastest resource-shortest job in the fastest resource, sufferage, minimum completion time (MCT), minimum execution time and opportunistic load balancing on account of makespan, utilization and response time. Originality/value The simulation result reveals that the ESTA algorithm consistently superior performs under varying of batch independent of cloudlets and the number of virtual machines’ test conditions.

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 Fair energy-efficient virtual machine scheduling for Internet of Things applications in cloud environment

2017 ◽  
Vol 13 (2) ◽  
pp. 155014771769489 ◽  
Author(s):  
Guowen Xing ◽  
Xiaolong Xu ◽  
Haolong Xiang ◽  
Shengjun Xue ◽  
Sai Ji ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Virtual Machine ◽  
Energy Efficient ◽  
Energy Savings ◽  
Virtual Machines ◽  
Machine Scheduling ◽  
Cloud Services ◽  
Efficient Manner ◽  
Scheduling Method ◽  
Virtual Machine Scheduling

With the rapid resource requirements of Internet of Things applications, cloud computing technology is regarded as a promising paradigm for resource provision. To improve the efficiency and effectiveness of cloud services, it is essential to improve the resource fairness and achieve energy savings. However, it is still a challenge to schedule the virtual machines in an energy-efficient manner while taking into consideration the resource fairness. In view of this challenge, a fair energy-efficient virtual machine scheduling method for Internet of Things applications is designed in this article. Specifically, energy and fairness are analyzed in a formal way. Then, a virtual machine scheduling method is proposed to achieve the energy efficiency and further improve the resource fairness during the executions of Internet of Things applications. Finally, experimental evaluation demonstrates the validity of our proposed method.

Sign in / Sign up

Export Citation Format

Share Document