scholarly journals Efficient resource management for Cloud computing environments

2010 ◽  
Author(s):  
Andrew J. Younge ◽  
Gregor von Laszewski ◽  
Lizhe Wang ◽  
Sonia Lopez-Alarcon ◽  
Warren Carithers
Keyword(s):  
Cloud Computing ◽  
Resource Management ◽  
Efficient Resource ◽  
Computing Environments

A Cloud Trusting Mechanism Based on Resource Ranking

2020 ◽  
pp. 130-155
Author(s):  
Ajai K. Daniel
Keyword(s):  
Cloud Computing ◽  
Resource Management ◽  
Service Selection ◽  
Cloud Service ◽  
Cloud Services ◽  
Storage And Retrieval ◽  
Computing Paradigm ◽  
Efficient Resource ◽  
Software And Hardware ◽  
Cloud User

The cloud-based computing paradigm helps organizations grow exponentially through means of employing an efficient resource management under the budgetary constraints. As an emerging field, cloud computing has a concept of amalgamation of database techniques, programming, network, and internet. The revolutionary advantages over conventional data computing, storage, and retrieval infrastructures result in an increase in the number of organizational services. Cloud services are feasible in all aspects such as cost, operation, infrastructure (software and hardware) and processing. The efficient resource management with cloud computing has great importance of higher scalability, significant energy saving, and cost reduction. Trustworthiness of the provider significantly influences the possible cloud user in his selection of cloud services. This chapter proposes a cloud service selection model (CSSM) for analyzing any cloud service in detail with multidimensional perspectives.

scholarly journals ThermoSim: Deep learning based framework for modeling and simulation of thermal-aware resource management for cloud computing environments

2020 ◽  
Vol 166 ◽  
pp. 110596 ◽  
Author(s):  
Sukhpal Singh Gill ◽  
Shreshth Tuli ◽  
Adel Nadjaran Toosi ◽  
Felix Cuadrado ◽  
Peter Garraghan ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Deep Learning ◽  
Resource Management ◽  
Modeling And Simulation ◽  
Computing Environments

scholarly journals Ganging of Resources via Fuzzy Manhattan Distance Similarity with Priority Tasks Scheduling in Cloud Computing

2018 ◽  
Vol 1 ◽  
pp. 32-41
Author(s):  
S. Sharon Priya ◽  
K. M. Mehata ◽  
W. Aisha Banu
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Manhattan Distance ◽  
Priority Task ◽  
Tasks Scheduling ◽  
Efficient Resource ◽  
Current Task ◽  
Computing Environments ◽  
Resource Characteristics ◽  
The Cost

This paper proposes a fuzzy Manhattan distance-based similarity for gang formation of resources (FMDSGR) method with priority task scheduling in cloud computing. The proposed work decides which processor is to execute the current task in order to achieve efficient resource utilization and effective task scheduling. FMDSGR groups the resources into gangs which rely upon the similarity of resource characteristics in order to use the resources effectively. Then, the tasks are scheduled based on the priority in the gang of processors using gang-based priority scheduling (GPS). This reduces mainly the cost of deciding which processor is to execute the current task. Performance has been evaluated in terms of makespan, scheduling length ratio, speedup, efficiency and load balancing. CloudSim simulator is the toolkit used for simulation and for demonstrating experimental results in cloud computing environments.

Application of Particle Swarm Optimisation-R Factor Optimisation Algorithm for Efficient Resource Allocation and Scheduling in Cloud Environment

2020 ◽  
Vol 17 (4) ◽  
pp. 1990-1998
Author(s):  
R. Valarmathi ◽  
T. Sheela
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Resource Management ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Cost Optimization ◽  
Optimal Solution ◽  
Service Needs ◽  
Cloud Environment ◽  
Efficient Resource

Cloud computing is a powerful technology of computing which renders flexible services anywhere to the user. Resource management and task scheduling are essential perspectives of cloud computing. One of the main problems of cloud computing was task scheduling. Usually task scheduling and resource management in cloud is a tough optimization issue at the time of considering quality of service needs. Huge works under task scheduling focuses only on deadline issues and cost optimization and it avoids the significance of availability, robustness and reliability. The main purpose of this study is to develop an Optimized Algorithm for Efficient Resource Allocation and Scheduling in Cloud Environment. This study uses PSO and R factor algorithm. The main aim of PSO algorithm is that tasks are scheduled to VM (virtual machines) to reduce the time of waiting and throughput of system. PSO is a technique inspired by social and collective behavior of animal swarms in nature and wherein particles search the problem space to predict near optimal or optimal solution. A hybrid algorithm combining PSO and R-factor has been developed with the purpose of reducing the processing time, make span and cost of task execution simultaneously. The test results and simulation reveals that the proposed method offers better efficiency than the previously prevalent approaches.

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