scholarly journals A Two-Tier Energy-Aware Resource Management for Virtualized Cloud Computing System

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Wei Huang ◽  
Zhen Wang ◽  
Mianxiong Dong ◽  
Zhuzhong Qian
Keyword(s):  
Cloud Computing ◽  
Energy Saving ◽  
Virtual Machines ◽  
Dynamic Environment ◽  
Computing System ◽  
Energy Aware ◽  
Energy Prediction ◽  
Cloud Computing System ◽  
Save Energy

The economic costs caused by electric power take the most significant part in total cost of data center; thus energy conservation is an important issue in cloud computing system. One well-known technique to reduce the energy consumption is the consolidation of Virtual Machines (VMs). However, it may lose some performance points on energy saving and the Quality of Service (QoS) for dynamic workloads. Fortunately, Dynamic Frequency and Voltage Scaling (DVFS) is an efficient technique to save energy in dynamic environment. In this paper, combined with the DVFS technology, we propose a cooperative two-tier energy-aware management method including local DVFS control and global VM deployment. The DVFS controller adjusts the frequencies of homogenous processors in each server at run-time based on the practical energy prediction. On the other hand, Global Scheduler assigns VMs onto the designate servers based on the cooperation with the local DVFS controller. The final evaluation results demonstrate the effectiveness of our two-tier method in energy saving.

scholarly journals Load Balancing Using SJF-MMBF and SJF-ELM Approach

2021 ◽  
pp. 74-86
Author(s):  
S. Rekha ◽  
C. Kalaiselvi
Keyword(s):  
Cloud Computing ◽  
Scheduling Algorithm ◽  
Dynamic Environment ◽  
Scheduling Algorithms ◽  
Computing System ◽  
Priority Queue ◽  
Lower Priority ◽  
Cloud Computing System ◽  
Queue Scheduling ◽  
Multi Level

This paper studies the delay-optimal virtual machine (VM) scheduling problem in cloud computing systems, which have a constant amount of infrastructure resources such as CPU, memory and storage in the resource pool. The cloud computing system provides VMs as services to users. Cloud users request various types of VMs randomly over time and the requested VM-hosting durations vary vastly. A multi-level queue scheduling algorithm partitions the ready queue into several separate queues. The processes are permanently assigned to one queue, generally based on some property of the process, such as memory size, process priority or process type. Each queue has its own scheduling algorithm. Similarly, a process that waits too long in a lower-priority queue may be moved to a higher-priority queue. Multi-level queue scheduling is performed via the use of the Particle Swarm Optimization algorithm (MQPSO). It checks both Shortest-Job-First (SJF) buffering and Min-Min Best Fit (MMBF) scheduling algorithms, i.e., SJF-MMBF, is proposed to determine the solutions. Another scheme that combines the SJF buffering and Extreme Learning Machine (ELM)-based scheduling algorithms, i.e., SJF- ELM, is further proposed to avoid the potential of job starva¬tion in SJF-MMBF. In addition, there must be scheduling among the queues, which is commonly implemented as fixed-priority preemptive scheduling. The simulation results also illustrate that SJF- ELM is optimal in a heavy-loaded and highly dynamic environment and it is efficient in provisioning the average job hosting rate.

scholarly journals EVALUATION OF CLOUD COMPUTING CLUSTER PERFORMANCE

T-Comm ◽  
2020 ◽  
Vol 14 (12) ◽  
pp. 72-79
Author(s):  
Aleksandr O. Volkov ◽  
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Service Providers ◽  
Poisson Model ◽  
Cloud Service ◽  
Computing System ◽  
Processor Sharing ◽  
Cloud Systems ◽  
Cloud Computing System

For cloud service providers, one of the most relevant tasks is to maintain the required quality of service (QoS) at an acceptable level for customers. This condition complicates the work of providers, since now they need to not only manage their resources, but also provide the expected level of QoS for customers. All these factors require an accurate and well-adapted mechanism for analyzing the performance of the service provided. For the reasons stated above, the development of a model and algorithms for estimation the required resource is an urgent task that plays a significant role in cloud systems performance evaluation. In cloud systems, there is a serious variance in the requirements for the provided resource, as well as there is a need to quickly process incoming requests and maintain the proper level of quality of service – all of these factors cause difficulties for cloud providers. The proposed analytical model for processing requests for a cloud computing system in the Processor Sharing (PS) service mode allows us to solve emerging problems. In this work, the flow of service requests is described by the Poisson model, which is a special case of the Engset model. The proposed model and the results of its analysis can be used to evaluate the main characteristics of the performance of cloud systems.

Fuzzy Decoupling Energy Efficiency Optimization Algorithm in Cloud Computing Environment

2021 ◽  
Vol 14 (2) ◽  
pp. 52-69
Author(s):  
Xiaohong Wang
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing ◽  
Optimization Algorithm ◽  
Computing System ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Efficiency Optimization ◽  
Cloud Computing System ◽  
Computing Performance

With the vigorous development of information technology, cloud computing, as a distributed computing technology, has become a research hotspot in the industry. The cloud computing system has a huge resource pool. In order to meet user-specific quality of service requests, it needs to perform reasonable scheduling of various tasks. Under the premise of ensuring high computing performance and better service quality in the cloud computing environment, system energy efficiency optimization has become a key issue to be promoted in the promotion of cloud computing. The research purpose of this paper is to study the fuzzy decoupling energy efficiency optimization algorithm in cloud computing environment. This paper designs a fuzzy decoupling energy efficiency optimization scheme.

Task Scheduling and Resource Allocation of Cloud Computing Based on QoS

2014 ◽  
Vol 915-916 ◽  
pp. 1382-1385 ◽  
Author(s):  
Bai Lin Pan ◽  
Yan Ping Wang ◽  
Han Xi Li ◽  
Jie Qian
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Computing System ◽  
Computing Power ◽  
Power Scheduling ◽  
Cloud Computing System ◽  
User Demand

With the enlargement of the scope of cloud computing application, the number of users and types also increases accordingly, the special demand for cloud computing resources has also improved. Cloud computing task scheduling and resource allocation are key technologies, mainly responsible for assigning user jobs to the appropriate resources to perform. But the existing scheduling algorithm is not fully consider the user demand for resources is different, and not well provided for different users to meet the requirements of its resources. As the demand for quality of service based on cloud computing and cloud computing original scheduling algorithm, the computing power scheduling algorithm is proposed based on the QoS constraints to research the cloud computing task scheduling and resource allocation problems, improving the overall efficiency of cloud computing system.

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