scholarly journals Context-aware resource allocation and scheduling of hybrid mobile cloud

2021 ◽  
Author(s):  
Hager M. Ghouma
Keyword(s):  
Resource Allocation ◽  
Resource Constraints ◽  
Cloud Service ◽  
Mobile User ◽  
Cloud Services ◽  
User Mobility ◽  
Context Aware ◽  
Resource Allocation Algorithm ◽  
User Context ◽  
Earliest Deadline

Cloud services can compensate for the resource constraints of mobile devices. However, challenges of utilizing the cloud service by mobile users arise from inherent characteristics such as user mobility and device energy. In this paper, we propose a scheme to monitor the energy level and communication quality as a part of a mobile user context information, and develop a resource allocation and scheduling scheme to adapt to the context changes by exploiting the slack time. The objective is to reduce the execution cost of the jobs while meeting the jobs deadlines set by the users. We developed Simulated Annealing based resource allocation algorithm and Earliest Deadline First scheduling. Simulation of our scheme using CloudSim and synthetic workload based on Google Cluster Traces shows benefits in reducing execution cost and improving resource utilization.

scholarly journals Context-aware resource allocation and scheduling of hybrid mobile cloud

2021 ◽  
Author(s):  
Hager M. Ghouma
Keyword(s):  
Resource Allocation ◽  
Resource Constraints ◽  
Cloud Service ◽  
Mobile User ◽  
Cloud Services ◽  
User Mobility ◽  
Context Aware ◽  
Resource Allocation Algorithm ◽  
User Context ◽  
Earliest Deadline

Cloud services can compensate for the resource constraints of mobile devices. However, challenges of utilizing the cloud service by mobile users arise from inherent characteristics such as user mobility and device energy. In this paper, we propose a scheme to monitor the energy level and communication quality as a part of a mobile user context information, and develop a resource allocation and scheduling scheme to adapt to the context changes by exploiting the slack time. The objective is to reduce the execution cost of the jobs while meeting the jobs deadlines set by the users. We developed Simulated Annealing based resource allocation algorithm and Earliest Deadline First scheduling. Simulation of our scheme using CloudSim and synthetic workload based on Google Cluster Traces shows benefits in reducing execution cost and improving resource utilization.

ENPP: Extended Non-preemptive PP-aware Scheduling for Real-time Cloud Services

2016 ◽  
Vol 6 (5) ◽  
pp. 2291
Author(s):  
Fereshteh Hoseini ◽  
Mostafa Ghobaei Arani ◽  
Alireza Taghizadeh
Keyword(s):  
Resource Allocation ◽  
Real Time ◽  
Processing Time ◽  
Resource Constraints ◽  
Cloud Services ◽  
Scheduling Problems ◽  
Hard Problems ◽  
Real Time Applications ◽  
Simulation Results ◽  
Np Hard Problems

<p class="Abstract">By increasing the use of cloud services and the number of requests to processing tasks with minimum time and costs, the resource allocation and scheduling, especially in real-time applications become more challenging. The problem of resource scheduling, is one of the most important scheduling problems in the area of NP-hard problems. In this paper, we propose an efficient algorithm is proposed to schedule real-time cloud services by considering the resource constraints. The simulation results show that the proposed algorithm shorten the processing time of tasks and decrease the number of canceled tasks.</p>

scholarly journals CAI method in Cloud services for user workload using Cloudsim framework

2019 ◽  
Vol 8 (4) ◽  
pp. 12622-12626
Keyword(s):  
Resource Allocation ◽  
Data Center ◽  
Cloud Service ◽  
Cloud Services ◽  
Time Data ◽  
Real Time Data ◽  
The Impact ◽  
System Designs ◽  
New System

In sighting the distinct patterns of processing capability in a cloud service is pedantic to enhance the resource management and operable conditions of the servers without compromising the Quality of Service is important. Simulations and models based on practicable parameters are required to understand the impact of the load on new system designs and policies. The proposed scheme and analysis provides a requirement for designing new systems which will be lessaffected by process loads. Classifying, analysis and improving (CAI) is done using real-time data center logs and simulations are done based on user requests and data center configurations. Simulations are created using cloudsim framework. Various simulations are done to provide a comprehensive result to improve the resource allocation for the system.

scholarly journals Minimizing the Cost for Resource Allocation from Multiple Cloud Providers

2021 ◽  
Vol 9 (11) ◽  
pp. 1611-1618
Author(s):  
Manasa Jonnagadla
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Service Providers ◽  
Low Cost ◽  
Hybrid Approach ◽  
Cloud Service ◽  
Cloud Services ◽  
Business Efficiency ◽  
Demand Variability ◽  
Cloud Resources

Abstract: Cloud computing provides streamlined tools for exceptional business efficiency. Cloud service providers typically offer two types of plans: reserved and on-demand. Restricted policies provide low-cost long-term contracting, while order contracts are expensive and ready for short periods. Cloud resources must be delivered wisely to meet current customer demands. Many current works rely on low-cost resource-reserved strategies, which may be under- or over-provisioning. Resource allocation has become a difficult issue due to unfairness causing high availability costs and cloud demand variability. That article suggests a hybrid approach to allocating cloud services to complex customer orders. The strategy was built in two stages: accommodation stages and a flexible structure. By treating each step as an optimization problem, we can reduce the overall implementation cost while maintaining service quality. Due to the uncertain nature of cloud requests, we set up a stochastic Optimization-based approach. Our technique is used to assign individual cloud resources and the results show its effectiveness. Keywords: Cloud computing, Resource allocation, Demand

scholarly journals An Empirical Study on Peer-to-Peer Sharing of Resources in Mobile Cloud Environment

2018 ◽  
Vol 8 (3) ◽  
pp. 1933
Author(s):  
R. K. Nadesh ◽  
M. Aramudhan
Keyword(s):  
Real World ◽  
Large Scale ◽  
Cloud Model ◽  
Cloud Service ◽  
Mobile User ◽  
Peer To Peer ◽  
Cloud Services ◽  
Mobile Cloud ◽  
Real World Application ◽  
File Formats

The increase usage of mobile users with internet and interoperability among the cloud services intensifies the role of distributed environemtnt in today’s real world application. Modern technologies are important for building rich, scalable and interoperable applications. To meet the requirements of client,the cloud service provider should offer adequate infrastructure especially under heavy multi-client load.To provide solution for large scale requirements and to statisfy the mobile client from the critical situation like lacking with bandwidth,connectivity issues,service completion ratio, we present adhoc virtual cloud model  for different scenarios that include single and multiple client configurations with various file sizes of various file formats for retrieving files in the mobile cloud environement.We evaluate the strategies with the socket and RMI implemented using java and identify the best model for real world applications. Performance evaluation is done with the results obtained and recommends that when sockets and RMI can be appropriately used in peer-to-peer environment when the mobile user cannot connect directly to the cloud services.

A Novel Multi-Dimension Resource Recycling Mechanism for Cloud Data Centers

2015 ◽  
Vol 7 (2) ◽  
pp. 50-64 ◽  
Author(s):  
Hong-Yi Chang ◽  
Tu-Liang Lin ◽  
Cheng-Kai Huang
Keyword(s):  
Resource Allocation ◽  
Cloud Service ◽  
Allocation Mechanism ◽  
Cloud Data Center ◽  
Resource Allocation Algorithm ◽  
Cloud Data ◽  
Resource Recycling ◽  
Efficient Resource ◽  
Cloud Data Centers ◽  
Cloud Servers

Cloud servers can be started with ineffective resource arrangement, and extra costs are produced if unnecessary servers are started. This is a substantial fee for the cloud service provider. Therefore, each cloud data center needs an efficient resource allocation mechanism to prevent unnecessary cloud servers from being started. In general, resource allocation problems can be classified into either one-dimension or multi-dimension aspects. In 2013, the authors have proposed a multi-dimension resource allocation algorithm that can improve the utilization of cloud servers by as much as 97%. However, most of previous studies are more concerned with solving the resource allocation problem. In fact, after a period of time, the applications will finish their jobs, and resources been occupied on the servers will be released, thus decreasing the utilization of cloud servers. Therefore, this paper proposes a novel multi-dimension resource recycling algorithm to optimize the server resource again, to recycle the excess cloud resources and to reduce the unnecessary operating cloud servers.

Resource-Aware Allocation and Load-Balancing Algorithms for Cloud Computing

2019 ◽  
pp. 90-106
Author(s):  
N. Malarvizhi ◽  
J. Aswini ◽  
E. A. Neeba
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Service Providers ◽  
Cloud Service ◽  
Cloud Services ◽  
Storage Space ◽  
Computing Technique ◽  
Technical Management ◽  
Resource Aware ◽  
Computational Platform

Dynamic cloud computing technique enables resources to be assigned to different clients based on the current demand of each client turning the cloud to a limitless computational platform with limitless storage space which improves the performance of cloud services. To achieve best resource allocation in dynamic hosting frameworks, cloud service providers should provision resources intelligently to all clients. Cloud computing empowers consumers to access online resources using the internet, from anywhere at any time without considering the underlying hardware, technical management, and maintenance problems of the original resources. In this chapter, the authors present a detail study of various resource allocation and other scheduling challenges as well as cloud simulation frameworks tools like CloudSim and ICanCloud.

Cloud service recommendation for small and medium-sized enterprises: A context-aware group decision making approach

2021 ◽  
pp. 1-21
Author(s):  
Hua Ma ◽  
Zhuoxuan Huang ◽  
Xin Zhang ◽  
Hongyu Zhang ◽  
Jianqiang Wang
Keyword(s):  
Decision Making ◽  
Cloud Service ◽  
User Preferences ◽  
Cloud Services ◽  
Multi Criteria Decision Making ◽  
Context Aware ◽  
Service Recommendation ◽  
Independent Evaluation ◽  
Aware Group ◽  
Trustworthy Service

Recently, the enormous advantages of cloud services make them increasingly appealing to the small and medium-sized enterprises. The growing number of available services makes it challenging to select trustworthy services. Existing approaches focus on user preferences to guide personalized services recommendation for individual users, but lack of the research on trustworthy service recommendation for the small and medium-sized enterprises that represents a group user consisting of multiple individual users. For this type of enterprise, the cloud services recommendation must address the challenges from the diverse client context of individual users, the imprecise quality of experience in an uncertain cloud environment and the invalid or unsatisfactory recommendations. A client context-aware approach is proposed to recommend trustworthy cloud services for the small and medium-sized enterprises based on non-compensatory multi-criteria decision-making. In it, a type of client context is viewed as an independent evaluation criterion, and the interval neutrosophic numbers are employed to measure the fuzzy trustworthiness of cloud services. Based on the investigated outranking relations of interval neutrosophic numbers, a non-compensatory multi-criteria decision-making procedure via an improved ELECTRE III method is developed to rank candidate services. Experimental results demonstrate that this approach could efficiently produces the accurate ranking results of cloud services and effectively recommend the trustworthy service for small and medium-sized enterprises.

scholarly journals ENPP: Extended Non-preemptive PP-aware Scheduling for Real-time Cloud Services

2016 ◽  
Vol 6 (5) ◽  
pp. 2291
Author(s):  
Fereshteh Hoseini ◽  
Mostafa Ghobaei Arani ◽  
Alireza Taghizadeh
Keyword(s):  
Resource Allocation ◽  
Real Time ◽  
Processing Time ◽  
Resource Constraints ◽  
Cloud Services ◽  
Scheduling Problems ◽  
Hard Problems ◽  
Real Time Applications ◽  
Simulation Results ◽  
Np Hard Problems

<p class="Abstract">By increasing the use of cloud services and the number of requests to processing tasks with minimum time and costs, the resource allocation and scheduling, especially in real-time applications become more challenging. The problem of resource scheduling, is one of the most important scheduling problems in the area of NP-hard problems. In this paper, we propose an efficient algorithm is proposed to schedule real-time cloud services by considering the resource constraints. The simulation results show that the proposed algorithm shorten the processing time of tasks and decrease the number of canceled tasks.</p>

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