scholarly journals Balancing Load of Cloud Data Center using Efficient Task Scheduling Algorithm

2017 ◽  
Vol 159 (5) ◽  
pp. 1-5
Author(s):  
Subhadra Bose
Keyword(s):  
Task Scheduling ◽  
Data Center ◽  
Scheduling Algorithm ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Task Scheduling Algorithm

scholarly journals Server consolidation: A technique to enhance cloud data center power efficiency and overall cost of ownership

2021 ◽  
Vol 17 (3) ◽  
pp. 155014772199721
Author(s):  
Mueen Uddin ◽  
Mohammed Hamdi ◽  
Abdullah Alghamdi ◽  
Mesfer Alrizq ◽  
Mohammad Sulleman Memon ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Center ◽  
Data Centers ◽  
Scheduling Algorithm ◽  
Cloud Data Center ◽  
Server Consolidation ◽  
Cloud Data ◽  
Task Scheduling Algorithm ◽  
Cloud Data Centers

Cloud computing is a well-known technology that provides flexible, efficient, and cost-effective information technology solutions for multinationals to offer improved and enhanced quality of business services to end-users. The cloud computing paradigm is instigated from grid and parallel computing models as it uses virtualization, server consolidation, utility computing, and other computing technologies and models for providing better information technology solutions for large-scale computational data centers. The recent intensifying computational demands from multinationals enterprises have motivated the magnification for large complicated cloud data centers to handle business, monetary, Internet, and commercial applications of different enterprises. A cloud data center encompasses thousands of millions of physical server machines arranged in racks along with network, storage, and other equipment that entails an extensive amount of power to process different processes and amenities required by business firms to run their business applications. This data center infrastructure leads to different challenges like enormous power consumption, underutilization of installed equipment especially physical server machines, CO2 emission causing global warming, and so on. In this article, we highlight the data center issues in the context of Pakistan where the data center industry is facing huge power deficits and shortcomings to fulfill the power demands to provide data and operational services to business enterprises. The research investigates these challenges and provides solutions to reduce the number of installed physical server machines and their related device equipment. In this article, we proposed server consolidation technique to increase the utilization of already existing server machines and their workloads by migrating them to virtual server machines to implement green energy-efficient cloud data centers. To achieve this objective, we also introduced a novel Virtualized Task Scheduling Algorithm to manage and properly distribute the physical server machine workloads onto virtual server machines. The results are generated from a case study performed in Pakistan where the proposed server consolidation technique and virtualized task scheduling algorithm are applied on a tier-level data center. The results obtained from the case study demonstrate that there are annual power savings of 23,600 W and overall cost savings of US$78,362. The results also highlight that the utilization ratio of already existing physical server machines has increased to 30% compared to 10%, whereas the number of server machines has reduced to 50% contributing enormously toward huge power savings.

A Novel PageRank Based Fault Handling Strategy for Workflow Scheduling in Cloud Data Centers

2021 ◽  
Vol 18 (4) ◽  
pp. 0-0
Keyword(s):  
Task Scheduling ◽  
Data Center ◽  
Scheduling Algorithm ◽  
Priority Assignment ◽  
Holistic View ◽  
Cloud Data ◽  
Pagerank Algorithm ◽  
Fault Handling ◽  
Task Scheduling Algorithm ◽  
Workflow Tasks

Unexpected faults result in unscheduled cloud outage, which negatively affects the completion of workflow tasks in the cloud. This paper presents a novel PageRank based fault handling strategy to rescue workflow tasks at the faulty data center. The proposed approach uses a holistic view and considers the task attributes, the timeline scenario, and the overall cloud performance. A priority assignment system is developed based on the modified PageRank algorithm to prioritise workflow tasks. A Min-Max normalization method is applied to select the target data center and match the timeline at this data center. Additionally, a dynamic PageRank-constrained task scheduling algorithm is proposed to generate the task scheduling solution. The simulation results show that the proposed approach can achieve better fault handling performance, measured by task resilience ratio, workflow resilience ratio and workflow continuity ratio, in both the traditional 3-replica and the image backup cloud environment.

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.

QoS-aware simulation job scheduling algorithm in virtualized cloud environment

2020 ◽  
Vol 11 (05) ◽  
pp. 2050048
Author(s):  
Zhen Li ◽  
Bin Chen ◽  
Xiaocheng Liu ◽  
Dandan Ning ◽  
Xiaogang Qiu
Keyword(s):  
Data Center ◽  
Job Scheduling ◽  
Scheduling Algorithm ◽  
Cloud Data Center ◽  
Cloud Data ◽  
High Productivity ◽  
Complex Simulation ◽  
Size Adjustment ◽  
Computational Service ◽  
Job Scheduling Algorithm

Cloud computing is attracting an increasing number of simulation applications running in the virtualized cloud data center. These applications are submitted to the cloud in the form of simulation jobs. Meanwhile, the management and scheduling of simulation jobs are playing an essential role to offer efficient and high productivity computational service. In this paper, we design a management and scheduling service framework for simulation jobs in two-tier virtualization-based private cloud data center, named simulation execution as a service (SimEaaS). It aims at releasing users from complex simulation running settings, while guaranteeing the QoS requirements adaptively. Furthermore, a novel job scheduling algorithm named adaptive deadline-aware job size adjustment (ADaSA) algorithm is designed to realize high job responsiveness under QoS requirement for SimEaaS. ADaSA tries to make full use of the idle fragmentation resources by tuning the number of requested processes of submitted jobs in the queue adaptively, while guaranteeing that jobs’ deadline requirements are not violated. Extensive experiments with trace-driven simulation are conducted to evaluate the performance of our ADaSA. The results show that ADaSA outperforms both cloud-based job scheduling algorithm KCEASY and traditional EASY in terms of response time (up to 90%) and bounded slow down (up to 95%), while obtains approximately equivalent deadline-missed rate. ADaSA also outperforms two representative moldable scheduling algorithms in terms of deadline-missed rate (up to 60%).

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

Classification-based and Energy-Efficient Dynamic Task Scheduling Scheme for Virtualized Cloud Data Center

2019 ◽  
pp. 1-1 ◽  
Author(s):  
Avinab Marahatta ◽  
Sandeep Pirbhulal ◽  
Fa Zhang ◽  
Reza M. Parizi ◽  
Kim-Kwang Raymond Choo ◽  
...  
Keyword(s):  
Task Scheduling ◽  
Data Center ◽  
Energy Efficient ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Dynamic Task ◽  
Scheduling Scheme ◽  
Dynamic Task Scheduling

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.

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