Budget and SLA Aware Dynamic Workflow Scheduling in Cloud Computing with 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

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Dynamic Workflow Scheduling Approach for Minimizing the Response Time Using An Efficient Workflow Scheduler in Cloud Computing

2019 International Conference on Smart Systems and Inventive Technology (ICSSIT) ◽

10.1109/icssit46314.2019.8987804 ◽

2019 ◽

Author(s):

G. Justy Mirobi ◽

L. Arockiam

Keyword(s):

Cloud Computing ◽

Response Time ◽

Workflow Scheduling ◽

Dynamic Workflow

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Multiple DAGs Dynamic Workflow Scheduling Based on the Primary Backup Algorithm in Cloud Computing System

2014 Ninth International Conference on Broadband and Wireless Computing, Communication and Applications ◽

10.1109/bwcca.2014.62 ◽

2014 ◽

Cited By ~ 2

Author(s):

Yu Wang ◽

Changyun Jia ◽

Yang Xu

Keyword(s):

Cloud Computing ◽

Computing System ◽

Workflow Scheduling ◽

Cloud Computing System ◽

Dynamic Workflow

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Workflow Scheduling for Scientific Application in Homogeneous Cloud Environment

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i7.114 ◽

2017 ◽

Vol 7 (7) ◽

pp. 137

Author(s):

. Monika ◽

Pardeep Kumar ◽

Sanjay Tyagi

Keyword(s):

Cloud Computing ◽

Big Data ◽

Large Data ◽

Workflow Scheduling ◽

Cloud Environment ◽

Computing Environment ◽

Scientific Application ◽

Cloud Computing Environment ◽

Flow Of Information

In Cloud computing environment QoS i.e. Quality-of-Service and cost is the key element that to be take care of. As, today in the era of big data, the data must be handled properly while satisfying the request. In such case, while handling request of large data or for scientific applications request, flow of information must be sustained. In this paper, a brief introduction of workflow scheduling is given and also a detailed survey of various scheduling algorithms is performed using various parameter.

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Energy Efficient and Optimized Makespan Workflow Scheduling Algorithm for Heterogeneous Resources in Fog-Cloud-Edge Collaboration

2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE) ◽

10.1109/wiecon-ece52138.2020.9398042 ◽

2020 ◽

Author(s):

Jyoti Bisht ◽

V. V. Subrahmanyam

Keyword(s):

Energy Efficient ◽

Scheduling Algorithm ◽

Workflow Scheduling ◽

Heterogeneous Resources

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Minimizing Resource Waste in Heterogeneous Resource Allocation for Data Stream Processing on Clouds

Applied Sciences ◽

10.3390/app11010149 ◽

2020 ◽

Vol 11 (1) ◽

pp. 149

Author(s):

Wu-Chun Chung ◽

Tsung-Lin Wu ◽

Yi-Hsuan Lee ◽

Kuo-Chan Huang ◽

Hung-Chang Hsiao ◽

...

Keyword(s):

Resource Allocation ◽

Cloud Computing ◽

Data Stream ◽

Limited Attention ◽

Heterogeneous Servers ◽

Resource Allocation Algorithm ◽

System Utilization ◽

Allocation Algorithms ◽

Considerable Resource ◽

Heterogeneous Resources

Resource allocation is vital for improving system performance in big data processing. The resource demand for various applications can be heterogeneous in cloud computing. Therefore, a resource gap occurs while some resource capacities are exhausted and other resource capacities on the same server are still available. This phenomenon is more apparent when the computing resources are more heterogeneous. Previous resource-allocation algorithms paid limited attention to this situation. When such an algorithm is applied to a server with heterogeneous resources, resource allocation may result in considerable resource wastage for the available but unused resources. To reduce resource wastage, a resource-allocation algorithm, called the minimizing resource gap (MRG) algorithm, for heterogeneous resources is proposed in this study. In MRG, the gap between resource usages for each server in cloud computing and the resource demands among various applications are considered. When an application is launched, MRG calculates resource usage and allocates resources to the server with the minimized usage gap to reduce the amount of available but unused resources. To demonstrate MRG performance, the MRG algorithm was implemented in Apache Spark. CPU- and memory-intensive applications were applied as benchmarks with different resource demands. Experimental results proved the superiority of the proposed MRG approach for improving the system utilization to reduce the overall completion time by up to 24.7% for heterogeneous servers in cloud computing.

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