scholarly journals A Novel Energy Efficient Resource Management System in Cloud Computing Environment.

2018 ◽  
Vol 7 (4.19) ◽  
pp. 1030
Author(s):  
S. K. Sonkar ◽  
M. U.Kharat
Keyword(s):  
Resource Allocation ◽  
Energy Consumption ◽  
Resource Management ◽  
Virtual Machine ◽  
Energy Efficient ◽  
Service Providers ◽  
Optimization Techniques ◽  
Cloud Provider ◽  
Efficient Resource ◽  
Machine Allocation

Primary target of cloud provider is to provide the maximum resource utilization and increase the revenue by reducing energy consumption and operative cost. In the service providers point of view, resource allocation, resource sharing, migration of resources on demand, memory management, storage management, load balancing, energy efficient resource usage, computational complexity handling in virtualization are some of the major tasks that has to be dealt with. The major issue focused in this paper is to reduce the energy consumption problem and management of computation capacity utilization.  For the same, an energy efficient resource management method is proposed to grip the resource scheduling and to minimize the energy utilized by the cloud datacenters for the computational work. Here a novel resource allocation mechanism is proposed, based on the optimization techniques. Also a novel dynamic virtual machine (VM) allocation method is suggested to help dynamic virtual machine allocation and job rescheduling to improve the consolidation of resources to execute the jobs. Experimental results indicated that proposed strategy outperforms as compared to the existing systems.  

An Energy-Efficient Resource Scheduling Algorithm for Cloud Computing based on Resource Equivalence Optimization

2016 ◽  
Vol 8 (2) ◽  
pp. 43-57 ◽  
Author(s):  
Li Mao ◽  
De Yu Qi ◽  
Wei Wei Lin ◽  
Bo Liu ◽  
Ye Da Li
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Data Center ◽  
Energy Efficient ◽  
Resource Scheduling ◽  
Constraint Satisfaction Problems ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Efficient Resource

With the rapid growth of energy consumption in global data centers and IT systems, energy optimization has become an important issue to be solved in cloud data center. By introducing heterogeneous energy constraints of heterogeneous physical servers in cloud computing, an energy-efficient resource scheduling model for heterogeneous physical servers based on constraint satisfaction problems is presented. The method of model solving based on resource equivalence optimization is proposed, in which the resources in the same class are pruning treatment when allocating resource so as to reduce the solution space of the resource allocation model and speed up the model solution. Experimental results show that, compared with DynamicPower and MinPM, the proposed algorithm (EqPower) not only improves the performance of resource allocation, but also reduces energy consumption of cloud data center.

scholarly journals Thermal-Aware Virtual Machine Allocation for Heterogeneous Cloud Data Centers

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2880
Author(s):  
Abbas Akbari ◽  
Ahmad Khonsari ◽  
Seyed Mohammad Ghoreyshi
Keyword(s):  
Energy Consumption ◽  
Resource Management ◽  
Virtual Machine ◽  
Data Center ◽  
Data Centers ◽  
Greedy Algorithms ◽  
Heterogeneous Data ◽  
Management Problem ◽  
Vm Allocation ◽  
Machine Allocation

In recent years, a large and growing body of literature has addressed the energy-efficient resource management problem in data centers. Due to the fact that cooling costs still remain the major portion of the total data center energy cost, thermal-aware resource management techniques have been employed to make additional energy savings. In this paper, we formulate the problem of minimizing the total energy consumption of a heterogeneous data center (MITEC) as a non-linear integer optimization problem. We consider both computing and cooling energy consumption and provide a thermal-aware Virtual Machine (VM) allocation heuristic based on the genetic algorithm. Experimental results show that, using the proposed formulation, up to 30 % energy saving is achieved compared to thermal-aware greedy algorithms and power-aware VM allocation heuristics.

A Survey and Taxonomy of Energy Efficient Resource Management Techniques in Platform as a Service Cloud

2017 ◽  
pp. 410-454 ◽  
Author(s):  
Sareh Fotuhi Piraghaj ◽  
Amir Vahid Dastjerdi ◽  
Rodrigo N. Calheiros ◽  
Rajkumar Buyya
Keyword(s):  
Energy Consumption ◽  
Resource Management ◽  
Energy Efficient ◽  
Power Efficiency ◽  
Large Scale ◽  
Data Centers ◽  
Cloud Service ◽  
Efficient Resource ◽  
Management Techniques ◽  
Increasing Demand

The numerous advantages of cloud computing environments, including scalability, high availability, and cost effectiveness have encouraged service providers to adopt the available cloud models to offer solutions. This rise in cloud adoption, in return encourages platform providers to increase the underlying capacity of their data centers so that they can accommodate the increasing demand of new customers. Increasing the capacity and building large-scale data centers has caused a drastic growth in energy consumption of cloud environments. The energy consumption not only affects the Total Cost of Ownership but also increases the environmental footprint of data centers as CO2 emissions increases. Hence, energy and power efficiency of the data centers has become an important research area in distributed systems. In order to identify the challenges in this domain, this chapter surveys and classifies the energy efficient resource management techniques specifically focused on the PaaS cloud service models.

scholarly journals An Intelligent Approach to Resource Allocation on Heterogeneous Cloud Infrastructures

2021 ◽  
Vol 11 (21) ◽  
pp. 9940
Author(s):  
Jack Marquez ◽  
Oscar H. Mondragon ◽  
Juan D. Gonzalez
Keyword(s):  
Resource Allocation ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Service Providers ◽  
Cloud Service ◽  
Data Placement ◽  
Memory Access ◽  
Heterogeneous Hardware ◽  
Machine Allocation ◽  
Virtual Machine Allocation

Cloud computing systems are rapidly evolving toward multicloud architectures supported on heterogeneous hardware. Cloud service providers are widely offering different types of storage infrastructures and multi-NUMA architecture servers. Existing cloud resource allocation solutions do not comprehensively consider this heterogeneous infrastructure. In this study, we present a novel approach comprised of a hierarchical framework based on genetic programming to solve problems related to data placement and virtual machine allocation for analytics applications running on heterogeneous hardware with a variety of storage types and nonuniform memory access. Our approach optimizes data placement using the Hadoop File System on heterogeneous storage devices on multicloud systems. It guarantees the efficient allocation of virtual machines on physical machines with multiple NUMA (nonuniform memory access) domains by minimizing contention between workloads. We prove that our solutions for data placement and virtual machine allocation outperform other state-of-the-art approaches.

scholarly journals Resource Management for Minimizing Energy and Cost of Geo-Distributed Data Centers

2021 ◽  
Vol 18 (13) ◽  
Author(s):  
Moh Moh THAN
Keyword(s):  
Resource Allocation ◽  
Energy Consumption ◽  
Resource Management ◽  
Energy Efficient ◽  
Real Life ◽  
Cost Effective ◽  
Turnaround Time ◽  
Electricity Price ◽  
Resource Demand ◽  
Demand Prediction

Geo-distributed data centers (GDCs) house computing resources and provide cloud services across the world. As cloud computing flourishes, energy consumption and electricity cost for powering servers of GDCs also soar high. Energy consumption and cost minimization for GDCs has become the main challenge for the cloud service providers. This paper proposes a resource management framework that accomplishes resource demand prediction, ensuring service level objective (SLO), electricity price prediction, and energy-efficient and cost-effective resource allocation through GDCs. This paper also proposes an energy-efficient and cost-effective resource allocation (EECERA) algorithm which deploys energy efficiency factors and incorporates the electricity price diversity of GDCs. Extensive evaluations were performed based on real-world workload traces and real-life electricity price data of GDC locations. The evaluation results showed that the resource demand prediction model could predict the right amount of dynamic resource demand while achieving SLO, and also, the electricity price prediction model could provide promising accuracy. The performances of resource allocation algorithms were evaluated on CloudSim. This work contributes to minimizing the energy consumption and the average turnaround time taken to complete the task and offers cost-saving. HIGHLIGHTS SLO guaranteed, energy-efficient and cost-effective resource management framework Energy-efficient and cost-effective resource allocation (EECERA) algorithm Extensive evaluations based on real-world workload traces and real-life electricity price data of GDC locations Performances of resource allocation algorithms evaluated on CloudSim Minimizing the energy consumption and the average turnaround time taken to complete the task and also cost-saving GRAPHICAL ABSTRACT

scholarly journals Energy Efficient Resource Allocation for Virtual Services Based on Heterogeneous Shared Hosting Platforms in Cloud Computing

2017 ◽  
Vol 17 (3) ◽  
pp. 47-58
Author(s):  
Nguyen Minh Nhut Pham ◽  
Van Son Le ◽  
Ha Huy Cuong Nguyen
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Service Providers ◽  
Resource Provisioning ◽  
Cloud Computing Service ◽  
Efficient Resource ◽  
Optimal Resource ◽  
Energy Efficient Resource Allocation ◽  
Virtual Services

Abstract This paper is an extended and updated version, presented at the INDIA 2017 conference. Optimal resource provisioning for virtual services in the Cloud computing is one of the concerns nowadays. For cloud computing service providers, reducing the number of physical machines providing resources for virtual services in cloud computing is one of the efficient ways to reduce the amount of energy consumption, which in turn enhances the performance of data centers. Multi-dimensional resource provisioning on a Heterogeneous Shared Hosting Platforms for virtual services is known as a NP-hard problem. Therefore, it is necessary to apply the metaheuristic algorithms for estimating the outcome of the problem. In this study, we propose the resource allocation problem for reducing the energy consumption. ECRA-SA algorithms were designed to solve it and were evaluated through CloudSim simulation tool compared with FFD algorithm. The experimental results show that the proposed ECRA-SA algorithm yields a better performance than FFD algorithm.

scholarly journals Energy Efficient Multiresource Allocation of Virtual Machine Based on PSO in Cloud Data Center

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
An-ping Xiong ◽  
Chun-xiang Xu
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Data Center ◽  
Energy Efficient ◽  
Heuristic Algorithms ◽  
Fitness Function ◽  
Cloud Data Center ◽  
Allocation Model ◽  
Cloud Data ◽  
Machine Allocation

Presently, massive energy consumption in cloud data center tends to be an escalating threat to the environment. To reduce energy consumption in cloud data center, an energy efficient virtual machine allocation algorithm is proposed in this paper based on a proposed energy efficient multiresource allocation model and the particle swarm optimization (PSO) method. In this algorithm, the fitness function of PSO is defined as the total Euclidean distance to determine the optimal point between resource utilization and energy consumption. This algorithm can avoid falling into local optima which is common in traditional heuristic algorithms. Compared to traditional heuristic algorithms MBFD and MBFH, our algorithm shows significantly energy savings in cloud data center and also makes the utilization of system resources reasonable at the same time.

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