scholarly journals Energy-Efficient Resource Provisioning Using Adaptive Harmony Search Algorithm for Compute-Intensive Workloads with Load Balancing in Datacenters

2020 ◽  
Vol 10 (7) ◽  
pp. 2323
Author(s):  
T. Renugadevi ◽  
K. Geetha ◽  
K. Muthukumar ◽  
Zong Woo Geem
Keyword(s):  
Energy Consumption ◽  
Load Balancing ◽  
Power Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Resource Provisioning ◽  
Total Power

Drastic variations in high-performance computing workloads lead to the commencement of large number of datacenters. To revolutionize themselves as green datacenters, these data centers are assured to reduce their energy consumption without compromising the performance. The energy consumption of the processor is considered as an important metric for power reduction in servers as it accounts to 60% of the total power consumption. In this research work, a power-aware algorithm (PA) and an adaptive harmony search algorithm (AHSA) are proposed for the placement of reserved virtual machines in the datacenters to reduce the power consumption of servers. Modification of the standard harmony search algorithm is inevitable to suit this specific problem with varying global search space in each allocation interval. A task distribution algorithm is also proposed to distribute and balance the workload among the servers to evade over-utilization of servers which is unique of its kind against traditional virtual machine consolidation approaches that intend to restrain the number of powered on servers to the minimum as possible. Different policies for overload host selection and virtual machine selection are discussed for load balancing. The observations endorse that the AHSA outperforms, and yields better results towards the objective than, the PA algorithm and the existing counterparts.

Energy and Quality Aware Multi-Objective Resource Allocation Algorithm in Cloud

2021 ◽  
pp. 2150052
Author(s):  
Koné Kigninman Désiré ◽  
Eya Dhib ◽  
Nabil Tabbane ◽  
Olivier Asseu
Keyword(s):  
Resource Allocation ◽  
Energy Consumption ◽  
Search Algorithm ◽  
Fitness Function ◽  
Video Quality ◽  
Minimum Energy ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Energy Aware ◽  
Resource Allocation Algorithm

Cloud gaming has become the new service provisioning prototype that hosts the video games in the cloud and broadcasts the interactive game streaming to the players through the Internet. Here, the cloud must use massive resources for video representation and its streaming when several simultaneous players reach a particular point. Alternatively, various players may have separate necessities on Quality-of Experience, like low delay, high-video quality, etc. The challenging task is providing better service by the fixed cloud resource. Hence, there is a necessity for an energy-aware multi-resource allocation in the cloud. This paper devises a Fractional Rider-Harmony search algorithm (Fractional Rider-HSA) for resource allocation in the cloud. The Fractional Rider-HSA combines fractional calculus, Rider Optimization algorithm (ROA), and HSA. Moreover, the fitness function, like mean opinion score (MOS), gaming experience loss, fairness, energy consumption, and network parameters, is considered to determine the optimal resource allocation. The proposed model produces the maximal MOS of 0.8961, maximal gaming experience loss (QE) of 0.998, maximal fairness of 0.9991, the minimum energy consumption of 0.3109, and minimal delay 0.2266, respectively.

scholarly journals Low Power Consumption on Cloud Data Centers Using HSA Algorithm

2020 ◽  
pp. 78-84
Author(s):  
Louay Al Nuaimy ◽  
Tadele Debisa Deressa ◽  
Mohammad Mastan ◽  
Syed Umar
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Search Algorithm ◽  
Rapid Development ◽  
Electricity Consumption ◽  
Cloud Infrastructure ◽  
Virtual Machine Migration ◽  
Cloud Data ◽  
The Right

The rapid development of knowledge and communication has created a new processing style called cloud computing. One of the key issues facing cloud infrastructure providers is minimizing costs and maximizing profitability. Power management in cloud centres is very important to achieve this. Energy consumption can be reduced by releasing inactive nodes or by reducing the migration of virtual machines. The second is one of the challenges of choosing the virtual machine deployment method to migrate to the right node. This article proposes an approach to reduce electricity consumption in cloud centres. This approach adapts Harmony's search algorithm to move virtual machines. Positioning is done by sorting nodes and virtual machines according to their priorities in descending order. Priority is calculated based on the workload. The proposed procedure is envisaged. The evaluation results show less virtual machine migration, greater efficiency and lower energy consumption.

Energy Aware Better Implementation of Load Balancing in Cloud Computing

2020 ◽  
Vol 13 ◽  
Author(s):  
Alekhya Orugonda ◽  
V. Kiran Kumar
Keyword(s):  
Energy Consumption ◽  
Load Balancing ◽  
Power Consumption ◽  
Virtual Machines ◽  
Average Power ◽  
Power Saving ◽  
System Level ◽  
Energy Aware ◽  
Physical Machine ◽  
Average Power Consumption

Background: It is important to minimize bandwidth that improves battery life, system reliability and other environmental concerns and energy optimization.It also do everything within their power to reduce the amount of data that flows through their pipes.To increase resource exertion, task consolidation is an effective technique, greatly enabled by virtualization technologies, which facilitate the concurrent execution of several tasks and, in turn, reduce energy consumption. : MaxUtil, which aims to maximize resource exertion, and Energy Conscious Task Consolidation which explicitly takes into account both active and idle energy consumption. Method: In this paper an Energy Aware Cloud Load Balancing Technique (EACLBT) is proposed for the performance improvement in terms of energy and run time. It predicts load of host after VM allocation and if according to prediction host become overloaded than VM will be created on different host. So it minimize the number of migrations due to host overloading conditions. This proposed technique results in minimize bandwidth and energy utilization. Results: The result shows that the energy efficient method has been proposed for monitor energy exhaustion and support static and dynamic system level optimization.The EACLBT can reduce the number of power-on physical machine and average power consumption compare to other deploy algorithms with power saving.Besides minimization in bandwidth along with energy exertion, reduction in the number of executed instructions is also achieved. Conclusion: This paper comprehensively describes the EACLBT (Energy Aware Cloud Load Balancing Technique) to deploy the virtual machines for power saving purpose. The average power consumption is used as performance metrics and the result of PALB is used as baseline. The EACLBT can reduce the number of power-on physical machine and average power consumption compare to other deploy algorithms with power saving. It shown that on average an idle server consumes approximately 70% of the power consumed by the server running at the full CPU speed.The performance holds better for Common sub utterance elimination. So, we can say the proposed Energy Aware Cloud Load Balancing Technique (EACLBT) is effective in bandwidth minimization and reduction of energy exertion.

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.

scholarly journals Intelligent SLA-Aware VM Allocation and Energy Minimization Approach with EPO Algorithm for Cloud Computing Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jitendra Kumar Samriya ◽  
Subhash Chandra Patel ◽  
Manju Khurana ◽  
Pradeep Kumar Tiwari ◽  
Omar Cheikhrouhou
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Large Scale ◽  
Virtual Machines ◽  
Search Algorithm ◽  
Cloud Service ◽  
Resource Provisioning ◽  
Emperor Penguin ◽  
Cloud Environment ◽  
Machine Allocation

Cloud computing is the most prominent established framework; it offers access to resources and services based on large-scale distributed processing. An intensive management system is required for the cloud environment, and it should gather information about all phases of task processing and ensuring fair resource provisioning through the levels of Quality of Service (QoS). Virtual machine allocation is a major issue in the cloud environment that contributes to energy consumption and asset utilization in distributed cloud computing. Subsequently, in this paper, a multiobjective Emperor Penguin Optimization (EPO) algorithm is proposed to allocate the virtual machines with power utilization in a heterogeneous cloud environment. The proposed method is analyzed to make it suitable for virtual machines in the data center through Binary Gravity Search Algorithm (BGSA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). To compare with other strategies, EPO is energy-efficient and there are significant differences. The results of the proposed system have been evaluated through the JAVA simulation platform. The exploratory outcome presents that the proposed EPO-based system is very effective in limiting energy consumption, SLA violation (SLAV), and enlarging QoS requirements for giving capable cloud service.

scholarly journals Hybrid and adaptive harmony search algorithm for optimizing energy efficiency in VMP problem in cloud environment

2022 ◽  
Vol 11 (2) ◽  
pp. 113-126
Author(s):  
Amol C. Adamuthe ◽  
Smita M. Kagwade
Keyword(s):  
Energy Consumption ◽  
Data Centers ◽  
Search Strategy ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Energy Usage ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Local Search Strategy

Data Center energy usage has risen dramatically because of the rapid growth and demand for cloud computing. This excessive energy usage is a challenge from an economic and environmental point. Virtual Machine Placement (VMP) along with virtualization technologies is widely used to manage power utilization in data centers. The assignment of virtual machines to physical machines affects energy consumption. VMP is a process of mapping VMs onto a set of PMs in a data center to minimize total power consumption and maximize resource utilization. The VMP is an NP-hard problem due to its constraints and huge combinations. In this paper, we formulated the problem as a single objective optimization problem in which the objective is to minimize the energy consumption in cloud data centers. The main contribution of this paper is hybrid and adaptive harmony search algorithm for optimal placements of VMs to PMs. HSA with adaptive PAR settings, simulated annealing and local search strategy aims at minimizing energy consumption in cloud data centers with satisfying given constraints. Experiments are conducted to validate the performance of these variations. Results show that these hybrid HSA variations produce better results than basic HSA and adaptive HSA. Hybrid HS with simulated annealing, and local search strategy gives better results than other variants for 80 percent datasets.

scholarly journals Novel load balancing approach based on ant colony optimization technique in cloud computing

2021 ◽  
Vol 10 (4) ◽  
pp. 2320-2326
Author(s):  
Yasameen A. Ghani Alyouzbaki ◽  
Muaayed F. Al-Rawi
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Load Balancing ◽  
Ant Colony Optimization ◽  
Virtual Machine ◽  
Execution Time ◽  
Virtual Machines ◽  
Service Providers ◽  
Ant Colony ◽  
Time And Energy

The cloud is the framework in which communication is connected with virtual machines, data centers, hosts, and brokers. The broker searches for a highly reliable cloudlet virtual machine for execution. Vulnerability can occur in the network because of which framework gets overburden. A research strategy is introduced in this article to expand the fault tolerance of the framework. The proposed approach improvement depends on the algorithm of ant colony optimization (ACO) that can choose the better virtual machine on which is to migrate the cloudlet to reduce the execution time and energy consumption. The efficiency of the proposed approach simulated in terms of execution time, energy consumption and examined with CloudSim. The introduction is provided in this article with a detailed description of cloud computing and, in addition, green cloud computing with its models. This article also discussed the virtual machine (VM) in more depth in the introduction section, which allows cloud service providers to supervise cloud resources competently while dispensing with the need for human oversight. Then the article submitted and explained the related works with their discussion and then it explained the novel proposed load balancing based on ACO technique and concluded that the execution time and energy consumption of the proposed technique is better than the three-threshold energy saving algorithm (TESA) technique that is commonly used in cloud load balancing.

scholarly journals RSHSC-Routing Algorithm Based on Simplified Harmony Search and Coding for UWSNs

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Meiju Li ◽  
Xiujuan Du ◽  
Chunyan Peng
Keyword(s):  
Energy Consumption ◽  
Routing Protocol ◽  
Energy Efficient ◽  
Search Algorithm ◽  
Routing Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Energy Efficient Routing ◽  
Data Packets ◽  
Network Encoding

With the development of wireless networks and increasingly interest of people in underwater resources and environment, UWSNs are being paid more and more attention. Because of the characteristics of underwater channel and acoustic signal, the protocols used in the terrestrial networks cannot be directly used in UWSNs. In this paper, a reliable and energy-efficient routing protocol based on SHS and coding, called RSHSC, is proposed. Firstly, regular nodes are assigned to cluster heads according to simplified harmony search algorithm. Secondly, partial network encoding is introduced and the next two-hop information is considered when data packets are transmitted to sink nodes from the source node. Only the best next-hop forwards data packets. All data packets from neighbor nodes are used for decoding. Thirdly, two schemes of updating routing are designed and compared. Lastly, extensive simulations prove RSHSC is effective in improving reliability and decreasing energy consumption.

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