scholarly journals Sharing with Live Migration Energy Optimization Scheduler for Cloud Computing Data Centers

2018 ◽  
Vol 10 (9) ◽  
pp. 86 ◽  
Author(s):  
Samah Alshathri ◽  
Bogdan Ghita ◽  
Nathan Clarke
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Data Storage ◽  
Resource Utilization ◽  
Data Centers ◽  
Virtual Machines ◽  
Energy Optimization ◽  
Optimization Method ◽  
Live Migration ◽  
Novel Algorithm

The cloud-computing concept has emerged as a powerful mechanism for data storage by providing a suitable platform for data centers. Recent studies show that the energy consumption of cloud computing systems is a key issue. Therefore, we should reduce the energy consumption to satisfy performance requirements, minimize power consumption, and maximize resource utilization. This paper introduces a novel algorithm that could allocate resources in a cloud-computing environment based on an energy optimization method called Sharing with Live Migration (SLM). In this scheduler, we used the Cloud-Sim toolkit to manage the usage of virtual machines (VMs) based on a novel algorithm that learns and predicts the similarity between the tasks, and then allocates each of them to a suitable VM. On the other hand, SLM satisfies the Quality of Services (QoS) constraints of the hosted applications by adopting a migration process. The experimental results show that the algorithm exhibits better performance, while saving power and minimizing the processing time. Therefore, the SLM algorithm demonstrates improved virtual machine efficiency and resource utilization compared to an adapted state-of-the-art algorithm for a similar problem.

scholarly journals Energy-Efficient Load Balancing Algorithm for Workflow Scheduling in Cloud Data Centers Using Queuing and Thresholds

2021 ◽  
Vol 11 (13) ◽  
pp. 5849
Author(s):  
Nimra Malik ◽  
Muhammad Sardaraz ◽  
Muhammad Tahir ◽  
Babar Shah ◽  
Gohar Ali ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Load Balancing ◽  
Resource Utilization ◽  
Data Centers ◽  
Virtual Machines ◽  
Cloud Data ◽  
Tasks Scheduling ◽  
Benchmark Datasets ◽  
Cloud Data Centers

Cloud computing is a rapidly growing technology that has been implemented in various fields in recent years, such as business, research, industry, and computing. Cloud computing provides different services over the internet, thus eliminating the need for personalized hardware and other resources. Cloud computing environments face some challenges in terms of resource utilization, energy efficiency, heterogeneous resources, etc. Tasks scheduling and virtual machines (VMs) are used as consolidation techniques in order to tackle these issues. Tasks scheduling has been extensively studied in the literature. The problem has been studied with different parameters and objectives. In this article, we address the problem of energy consumption and efficient resource utilization in virtualized cloud data centers. The proposed algorithm is based on task classification and thresholds for efficient scheduling and better resource utilization. In the first phase, workflow tasks are pre-processed to avoid bottlenecks by placing tasks with more dependencies and long execution times in separate queues. In the next step, tasks are classified based on the intensities of the required resources. Finally, Particle Swarm Optimization (PSO) is used to select the best schedules. Experiments were performed to validate the proposed technique. Comparative results obtained on benchmark datasets are presented. The results show the effectiveness of the proposed algorithm over that of the other algorithms to which it was compared in terms of energy consumption, makespan, and load balancing.

scholarly journals ALGORITHM FOR TASK CONSOLIDATION IN CLOUD COMPUTING: A COMPARATIVE SURVEY

2018 ◽  
Vol 6 (5) ◽  
pp. 340-345
Author(s):  
Rajat Pugaliya ◽  
Madhu B R
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Resource Utilization ◽  
Service Provider ◽  
Virtual Machines ◽  
Cloud Service ◽  
Cloud Service Provider ◽  
Dynamic Task ◽  
Main Challenge ◽  
High Level

Cloud Computing is an emerging field in the IT industry. Cloud computing provides computing services over the Internet. Cloud Computing demand increasing drastically, which has enforced cloud service provider to ensure proper resource utilization with less cost and less energy consumption. In recent time various consolidation problems found in cloud computing like the task, VM, and server consolidation. These consolidation problems become challenging for resource utilization in cloud computing. We found in the literature review that there is a high level of coupling in resource utilization, cost, and energy consumption. The main challenge for cloud service provider is to maximize the resource utilization, reduce the cost and minimize the energy consumption. The dynamic task consolidation of virtual machines can be a way to solve the problem. This paper presents the comparative study of various task consolidation algorithms.

scholarly journals Energy-Aware Autonomic Resource Scheduling Framework for Cloud

2019 ◽  
Vol 4 (1) ◽  
pp. 41-55 ◽  
Author(s):  
Bhupesh Kumar Dewangan ◽  
Amit Agarwal ◽  
Venkatadri M. ◽  
Ashutosh Pasricha
Keyword(s):  
Energy Consumption ◽  
Resource Utilization ◽  
Data Centers ◽  
Virtual Machines ◽  
Service Providers ◽  
Optimal Solution ◽  
Cloud Service ◽  
Cloud Services ◽  
Energy Aware ◽  
Increasing Demand

Cloud computing is a platform where services are provided through the internet either free of cost or rent basis. Many cloud service providers (CSP) offer cloud services on the rental basis. Due to increasing demand for cloud services, the existing infrastructure needs to be scale. However, the scaling comes at the cost of heavy energy consumption due to the inclusion of a number of data centers, and servers. The extraneous power consumption affects the operating costs, which in turn, affects its users. In addition, CO2 emissions affect the environment as well. Moreover, inadequate allocation of resources like servers, data centers, and virtual machines increases operational costs. This may ultimately lead to customer distraction from the cloud service. In all, an optimal usage of the resources is required. This paper proposes to calculate different multi-objective functions to find the optimal solution for resource utilization and their allocation through an improved Antlion (ALO) algorithm. The proposed method simulated in cloudsim environments, and compute energy consumption for different workloads quantity and it increases the performance of different multi-objectives functions to maximize the resource utilization. It compared with existing frameworks and experiment results shows that the proposed framework performs utmost.

Data Center Enhancement by Server Resources Utilization

2021 ◽  
Vol 39 (1B) ◽  
pp. 203-208
Author(s):  
Haider A. Ghanem ◽  
Rana F. Ghani ◽  
Maha J. Abbas
Keyword(s):  
Cloud Computing ◽  
Power Consumption ◽  
Resource Utilization ◽  
Virtual Machine ◽  
Data Center ◽  
Data Centers ◽  
Virtual Machines ◽  
The Internet ◽  
Memory Resources

Data centers are the main nerve of the Internet because of its hosting, storage, cloud computing and other services. All these services require a lot of work and resources, such as energy and cooling. The main problem is how to improve the work of data centers through increased resource utilization by using virtual host simulations and exploiting all server resources. In this paper, we have considered memory resources, where Virtual machines were distributed to hosts after comparing the virtual machines with the host from where the memory and putting the virtual machine on the appropriate host, this will reduce the host machines in the data centers and this will improve the performance of the data centers, in terms of power consumption and the number of servers used and cost.

scholarly journals Minimizing Energy Consumption by Task Consolidation in Cloud Centers with Optimized Resource Utilization

2016 ◽  
Vol 6 (6) ◽  
pp. 3283 ◽  
Author(s):  
Mahendra Kumar Gourisaria ◽  
S. S. Patra ◽  
P. M. Khilar
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Power Consumption ◽  
Resource Utilization ◽  
Data Centers ◽  
Carbon Dioxide Emission ◽  
Computing Environment ◽  
Energy Aware ◽  
Cloud Computing Environment ◽  
Cpu Utilization

<p>Cloud computing is an emerging field of computation. As the data centers consume large amount of power, it increases the system overheads as well as the carbon dioxide emission increases drastically. The main aim is to maximize the resource utilization by minimizing the power consumption. However, the greatest usages of resources does not mean that there has been a right use of energy.  Various resources which are idle, also consumes a significant amount of energy. So we have to keep minimum resources idle. Current studies have shown that the power consumption due to unused computing resources is nearly 1 to 20%. So, the unused resources have been assigned with some of the tasks to utilize the unused period. In the present paper, it has been suggested that the energy saving with task consolidation which has been saved the energy by minimizing the number of idle resources in a cloud computing environment. It has been achieved far-reaching experiments to quantify the performance of the proposed algorithm. The same has also been compared with the FCFSMaxUtil and Energy aware Task Consolidation (ETC) algorithm. The outcomes have shown that the suggested algorithm surpass the FCFSMaxUtil and ETC algorithm in terms of the CPU utilization and energy consumption.</p>

scholarly journals MeReg: Managing Energy-SLA Tradeoff for Green Mobile Cloud Computing

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Rahul Yadav ◽  
Weizhe Zhang
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Large Scale ◽  
Data Centers ◽  
Mobile Cloud Computing ◽  
Virtual Machines ◽  
Mobile Cloud ◽  
Energy Aware ◽  
Cpu Utilization ◽  
Selection Algorithms

Mobile cloud computing (MCC) provides various cloud computing services to mobile users. The rapid growth of MCC users requires large-scale MCC data centers to provide them with data processing and storage services. The growth of these data centers directly impacts electrical energy consumption, which affects businesses as well as the environment through carbon dioxide (CO2) emissions. Moreover, large amount of energy is wasted to maintain the servers running during low workload. To reduce the energy consumption of mobile cloud data centers, energy-aware host overload detection algorithm and virtual machines (VMs) selection algorithms for VM consolidation are required during detected host underload and overload. After allocating resources to all VMs, underloaded hosts are required to assume energy-saving mode in order to minimize power consumption. To address this issue, we proposed an adaptive heuristics energy-aware algorithm, which creates an upper CPU utilization threshold using recent CPU utilization history to detect overloaded hosts and dynamic VM selection algorithms to consolidate the VMs from overloaded or underloaded host. The goal is to minimize total energy consumption and maximize Quality of Service, including the reduction of service level agreement (SLA) violations. CloudSim simulator is used to validate the algorithm and simulations are conducted on real workload traces in 10 different days, as provided by PlanetLab.

scholarly journals Power Management as a Service (Pmaas) – An Energy Saving Service by ―Auto-Fit VM Placement‖ Algorithm in Cloud Computing for Maximum Resource Utilization at Minimum Energy Consumption Without Live-Migrating the Virtual Machines by using the Concept of Virtual-Servers

2019 ◽  
Vol 9 (2) ◽  
pp. 1437-1446
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Power Consumption ◽  
Power Management ◽  
Resource Utilization ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Memory Storage ◽  
Vm Placement ◽  
Placement Algorithm

Now a day Energy Consumption is one of the most promising fields amongst several computing services of cloud computing. A maximum amount of Power resources are absorbed by the data centre because of huge amount of data processing which is increased abnormally. So it’s the time to think about the energy consumption in cloud environment. Existing Energy Consumption systems are limited in terms of virtualization because improper virtualization leads to loads imbalance and excessive power consumption and inefficiency in terms of computational power. Billing[1,2 ] is another exciting feature that is closely related to energy consumption, because higher or lesser billing depends on energy consumption somehow-as we know that cloud providers allow cloud users to access resources as pay-per-use, so these resources need to be optimally selected to process the user request to maximize user satisfaction in the distributed virtualized environment. There may be an inequity between the actual power consumption by the users and the provided billing records by the providers, So any false accusation that may claimed by each other to get illegal compensations. To avoid such accusation, we propose a work to consolidate the VMs using the Power Management as a Service (PMaaS) model in such a way, to reduce power consumption by maximum resource utilization without live-migration of the virtual machines by using the concept of Virtual Servers. The proposed PMaaS model uses a new “Auto-fit VM placement algorithm”, which computes tasks resource demands, models a Virtual Machine that fits those demands, and places the Virtual Machines on a Virtual server made by the collective resources (CPU, Memory, Storage and Bandwidth) from the respective schedulers directly connected to the actual physical servers and that has the minimum remaining resources which is large enough to accommodate such a Virtual Machine.

Dynamic Virtual Machine Placement in Cloud Computing

2017 ◽  
pp. 136-167 ◽  
Author(s):  
Arnab Kumar Paul ◽  
Bibhudatta Sahoo
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Data Centers ◽  
Virtual Machines ◽  
Virtual Machine Placement ◽  
Optimal Placement ◽  
On Demand ◽  
Energy Consumption Model ◽  
Consumption Model

The aim of cloud computing is to enable users to access resources on demand. The number of users is continuously increasing. In order to fulfil their needs, we need more number of physical machines and data centers. The increase in the number of physical machines is directly proportional to the consumption of energy. This gives us one of the major challenges; minimization of energy consumption. One of the most effective ways to minimize the consumption of energy is the optimal virtual machine placement on physical machines. This chapter focuses on finding the solution to the problem of dynamic virtual machine placement for the optimized consumption of energy. An energy consumption model is built which takes into account the states of physical machines and live migration of virtual machines. On top of this, the cloud computing model is built. Unlike centralized approaches towards virtual machine placement which result in many unreachable solutions, a decentralized approach is used in this chapter which provides a list of virtual machine migrations for their optimal placement.

scholarly journals Energy-Efficient Scheduling for Tasks with Deadline in Virtualized Environments

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Guangyu Du ◽  
Hong He ◽  
Qinggang Meng
Keyword(s):  
Energy Consumption ◽  
Resource Utilization ◽  
Energy Efficient ◽  
Data Centers ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Large Numbers ◽  
Task Scheduling Algorithm ◽  
Tremendous Amount ◽  
The Many

Data centers, as resource providers, take advantage of virtualization technology to achieve excellent resource utilization, scalability, and high availability. However, large numbers of computing servers containing virtual machines of data centers consume a tremendous amount of energy. Thus, it is necessary to significantly improve resource utilization. Among the many issues associated with energy, scheduling plays a very important role in successful task execution and energy consumption in virtualized environments. This paper seeks to implement an energy-efficient task scheduling algorithm for virtual machines with changeless speed comprised of two main steps: assigning as many tasks as possible to virtual machines with lower energy consumption and keeping the makespan of each virtual machine within a deadline. We propose a novel scheduling algorithm for heterogeneous virtual machines in virtualized environments to effectively reduce energy consumption and finish all tasks before a deadline. The new scheduling strategy is simulated using the CloudSim toolkit package. Experimental results show that our approach outperforms previous scheduling methods by a significant margin in terms of energy consumption.

Sign in / Sign up

Export Citation Format

Share Document