scholarly journals SLAMMP Framework for Cloud Resource Management and Its Impact on Healthcare Computational Techniques

2021 ◽  
Vol 12 (2) ◽  
pp. 1-31
Author(s):  
Vivek Kumar Prasad ◽  
Madhuri D. Bhavsar
Keyword(s):  
Short Term Memory ◽  
Service Level Agreement ◽  
Cost Effective ◽  
Service Level ◽  
Healthcare Sector ◽  
Computational Techniques ◽  
Healthcare Organizations ◽  
Cloud Resource Management ◽  
Cloud Resource ◽  
Cloud Users

Technology such as cloud computing(CC) is constantly evolving and being adopted by the industries to manage their data and tasks. CC provides the resources for managing the tasks of the cloud users. The acceptance of the CC in healthcare industries is proven to be more cost-effective and convenient. CC manager has to manage the resources to provide services to the end-users of the healthcare sector. The SLAMMP framework discussed here shows how the resources are managed by using the concept of reinforcement learning (RL) and LSTM (long short-term memory) for monitoring and prediction of the cloud resources for healthcare organizations. The task(s) pattern and anti-pattern scenarios have been observed using HMM (hidden Markov model). These patterns will tune the SLA parameters (service level agreement) using blockchain-based smart contracts (SC). The result discussed here indicates that the variations in the cloud resource demand will be handled carefully using the SLAMMP framework. From the result obtained, it is identified that SLAMMP performs well with the parameter used here.

scholarly journals QRAS: efficient resource allocation for task scheduling in cloud computing

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Harvinder Singh ◽  
Anshu Bhasin ◽  
Parag Ravikant Kaveri
Keyword(s):  
Resource Allocation ◽  
Task Scheduling ◽  
Service Level Agreement ◽  
Service Level ◽  
Cloud Resource ◽  
Time Problem ◽  
Efficient Resource ◽  
Np Hard Problem ◽  
Quality Of Service Parameters

AbstractCloud resource allocation, a real-time problem can be dealt with efficaciously to reduce execution cost and improve resource utilization. Resource usability can fulfill customers’ expectations if the allocation has performed according to demand constraint. Task Scheduling is NP-hard problem where unsuitable matching leads to performance degradation and violation of service level agreement (SLA). In this research paper, the workflow scheduling problem has been conducted with objective of higher exploitation of resources. To overcome scheduling optimization problem, the proposed QoS based resource allocation and scheduling has used swarm-based ant colony optimization provide more predictable results. The experimentation of proposed algorithms has been done in a simulated cloud environment. Further, the results of the proposed algorithm have been compared with other policies, it performed better in terms of Quality of Service parameters.

scholarly journals Cloud Resource Optimization System Based on Time and Cost

2020 ◽  
Vol 5 (4) ◽  
pp. 758-768
Author(s):  
Bhupesh Kumar Dewangan ◽  
Amit Agarwal ◽  
Tanupriya Tanupriya ◽  
Ashutosh Pasricha
Keyword(s):  
Resource Management ◽  
Evaluation Method ◽  
Cost Effective ◽  
Management Technique ◽  
Cloud Resource Management ◽  
Effective Activity ◽  
Cloud Resource ◽  
Management Techniques ◽  
Performance Evaluation Method ◽  
Antlion Optimization

Resource management in cloud could be a time and cost-effective activity if it is managed property. These resources are accessible and computable which is totally dependent upon the management techniques applied in cloud. In a cloud setting, heterogeneous, vulnerability, and scattering of resources creates many issues of distribution among the workloads which need to be compute. Specialists still face inconveniences to pick the prudent, material and expend less time to execution of resource portion to the cloud. This investigation delineates an expansive composed writing examination of asset administration inside the space of cloud typically and cloud asset administration based on SLA with multi-objective functions like cost and time. In this paper, an autonomic cloud resource-management technique is proposed to resolve identified issues by adopting the self-characteristics mechanism and improved Antlion optimization algorithm and tested in cloudsim toolkit and Aws Ec2 environment. The implementation results of proposed work are the evidence that it is better performing as compared with the existing frameworks, however, the performance evaluation method depends upon the different cloud environment and it may vary.

scholarly journals Elastic Technique for Load Balancing in Cloud Computing

2020 ◽  
Vol 8 (6) ◽  
pp. 1123-1127
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Service Level ◽  
Advanced Technology ◽  
Service Provisioning ◽  
Web Traffic ◽  
Cloud Environments ◽  
Cloud Users

The cloud computing is the architecture that is decentralized in nature due to which various issues in the network get raised which reduces its efficiency. The exchange of data over the network is also continuously increasing. New advanced technology, cloud computing is becoming popular because of providing the above services beneficially. Other vital technologies like virtualization and scalability by designing virtual machines in cloud computing. In cloud computing, web traffic and service provisioning are increasing day by day, so load balancing is becoming a big research issue in cloud computing. Cloud Computing is a new propensity emerging in the IT environment within huge requirements of infrastructure and resources. The load Balancing technique for cloud computing is a vital aspect of the cloud computing environment. Peerless Load balancing scheme ensures splendid resource utilization by provisioning resources to cloud users on-demand services basis in a pay-as-you-use manner. The technique of Load Balancing may further support prioritizing requests of users/clients by applying appropriate scheduling criteria. This paper presents various load balancing schemes in different cloud environments based on requirements specified in the Service Level Agreement (SLA).

Time and Cost Based Cloud Resource Optimization System

2021 ◽  
Vol 23 (09) ◽  
pp. 1167-1177
Author(s):  
Dr. Ashish Kumar Tamrakar ◽  
◽  
Dr. Abhishek Verma ◽  
Dr. Vishnu Kumar Mishra ◽  
Dr. Megha Mishra ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Resource Management ◽  
Evaluation Method ◽  
Scheduling Algorithm ◽  
Resource Scheduling ◽  
Cost Effective ◽  
Management Technique ◽  
Cloud Resource Management ◽  
Cloud Resource ◽  
Efficient Resource

Cloud computing is an emerging technology through which resources can be shared over the internet with different users either free or on a rent basis. Resource scheduling in cloud computing is a challenging area for researchers as is maximum utilization can opt through efficient resource scheduling algorithm. Other than this, virtual machine provisioning, packaging, and availability guarantee decrease the performance. Resource management in cloud could be a time and cost effective activity if it is managed property. These resources are accessible and computable which is totally dependent upon the management techniques applied in cloud.In a cloud setting, heterogeneous, vulnerability, and scattering of resources creates many issues of distribution among the workloads which need to be compute. Specialists still face inconveniences to pick the prudent, material and expend less time to execution of resource portion to the cloud. This investigation delineates an expansive composed writing examination of asset administration inside the space of cloud typically and cloud asset administration based on SLA with multi-objective functions like cost and time. In this paper, an autonomic cloud resource management technique is proposed to resolve identified issues by adopting the self-characteristics mechanism and improved Antlion optimization algorithm and tested in cloudsim toolkit and Aws Ec2 environment. The implementation results of proposed work are the evidence that it is better performing as compared with the existing frameworks, however, the performance evaluation method depends upon the different cloud environment and it may vary.

Carrier-Grade Distributed Cloud Computing

2013 ◽  
pp. 264-281
Author(s):  
Dapeng Wang ◽  
Jinsong Wu
Keyword(s):  
Cloud Computing ◽  
Service Level Agreement ◽  
Cost Effective ◽  
Cloud Service ◽  
Service Level ◽  
Cloud Services ◽  
Cloud Infrastructure ◽  
Key Issues ◽  
Security Network ◽  
Distributed Cloud

This chapter discusses and surveys the concepts, demands, requirements, solutions, opportunities, challenges, and future perspectives and potential of Carrier Grade Cloud Computing (CGCC). This chapter also introduces a carrier grade distributed cloud computing architecture and discusses the benefits and advantages of carrier grade distributed cloud computing. Unlike independent cloud service providers, telecommunication operators may integrate their conventional communications networking capabilities with the new cloud infrastructure services to provide inexpensive and high quality cloud services together with their deep understandings of, and strong relationships with, individual and enterprise customers. The relevant design requirements and challenges may include the performance, scalability, service-level agreement management, security, network optimization, and unified management. The relevant key issues in CGCC designs may include cost effective hardware and software configurations, distributed infrastructure deployment models, and operation processes.

scholarly journals Efficient Cloud Resource Scaling based on Prediction Approaches

2018 ◽  
Vol 7 (4.10) ◽  
pp. 413
Author(s):  
K Dinesh Kumar ◽  
E Umamaheswari
Keyword(s):  
Virtual Machines ◽  
Service Level Agreement ◽  
Service Level ◽  
Machine Learning Algorithms ◽  
Fuzzy Time Series ◽  
Cloud Environment ◽  
Cloud Resource ◽  
Efficient Resource ◽  
The Right

Resource Scaling is one of the important job in cloud environment while adapting resource configurations due to elasticity mechanism. In the view of cloud computing, resource scaling mechanism hold the assurance of QoS (Quality of Service), So, one of the key challenging task in cloud environment is, resource scaling. Effective scaling mechanism gives an optimal solutions for computational problems while achieving QoS and avoiding SLA (Service Level Agreement) violations. To enhance resource scaling mechanism in cloud environment, predicting future workload to the each application in different manners like number of physical machines, number of virtual machines, number of requests and resource utilization etc., is an essential step. According to the prediction results, resource scaling can be done in the right time, while preventing QoS dropping and SLA violations. To achieve efficient resource scaling, proposed approach lease advantages of fuzzy time series and machine learning algorithms. The proposed approach is able to reach effective resource scaling mechanism with better results.  

scholarly journals VM Consolidation Plan for Improving the Energy Efficiency of Cloud

2021 ◽  
Vol 21 (3) ◽  
pp. 145-159
Author(s):  
Satveer ◽  
Mahendra Singh Aswal
Keyword(s):  
Energy Efficiency ◽  
Power Consumption ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Service Level ◽  
Sla Violation ◽  
Dynamic Consolidation ◽  
Cloud Datacenter ◽  
Cloud Users ◽  
Vm Consolidation

Abstract Achieving energy-efficiency with minimal Service Level Agreement (SLA) violation constraint is a major challenge in cloud datacenters owing to financial and environmental concerns. The static consolidation of Virtual Machines (VMs) is not much significant in recent time and has become outdated because of the unpredicted workload of cloud users. In this paper, a dynamic consolidation plan is proposed to optimize the energy consumption of the cloud datacenter. The proposed plan encompasses algorithms for VM selection and VM placement. The VM selection algorithm estimates power consumption of each VM to select the required VMs for migration from the overloaded Physical Machine (PM). The proposed VM allocation algorithm estimates the net increase in Imbalance Utilization Value (IUV) and power consumption of a PM, in advance before allocating the VM. The analysis of simulation results suggests that the proposed dynamic consolidation plan outperforms other state of arts.

scholarly journals Efficiency aware scheduling techniques in cloud computing: a descriptive literature review

2021 ◽  
Vol 7 ◽  
pp. e509
Author(s):  
Muhammad Usman Sana ◽  
Zhanli Li
Keyword(s):  
Cloud Computing ◽  
State Of The Art ◽  
Scheduling Algorithm ◽  
Service Level Agreement ◽  
Cost Effective ◽  
Service Level ◽  
Allocation Of Resources ◽  
Cloud Environment ◽  
Set Up ◽  
Insight Into

In the last decade, cloud computing becomes the most demanding platform to resolve issues and manage requests across the Internet. Cloud computing takes along terrific opportunities to run cost-effective scientific workflows without the requirement of possessing any set-up for customers. It makes available virtually unlimited resources that can be attained, organized, and used as required. Resource scheduling plays a fundamental role in the well-organized allocation of resources to every task in the cloud environment. However along with these gains many challenges are required to be considered to propose an efficient scheduling algorithm. An efficient Scheduling algorithm must enhance the implementation of goals like scheduling cost, load balancing, makespan time, security awareness, energy consumption, reliability, service level agreement maintenance, etc. To achieve the aforementioned goals many state-of-the-art scheduling techniques have been proposed based upon hybrid, heuristic, and meta-heuristic approaches. This work reviewed existing algorithms from the perspective of the scheduling objective and strategies. We conduct a comparative analysis of existing strategies along with the outcomes they provide. We highlight the drawbacks for insight into further research and open challenges. The findings aid researchers by providing a roadmap to propose efficient scheduling algorithms.

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