Optimizing the Topology and Energy-Aware VM Migration in Cloud Computing

2020 ◽  
Vol 11 (3) ◽  
pp. 42-65
Author(s):  
Nitin S. More ◽  
Rajesh B. Ingle
Keyword(s):  
Energy Consumption ◽  
Spanning Tree ◽  
Tree Topology ◽  
Superior Performance ◽  
Cloud Infrastructure ◽  
Virtual Machine Migration ◽  
Energy Aware ◽  
Migration Cost ◽  
And Migration ◽  
The Cost

The advancements in virtual machine migration (VMM) have been trending due to its effective load balancing features in cloud infrastructure. Previously, data centers were used for handling VMs organized in racks. These racks are arranged in a spanning tree topology with a high bandwidth. Thus, the cost for moving the data between servers is highest when the racks are far from each other. This work addresses this issue and proposed VMM strategy based on self-adaptive D-Crow algorithm (S-DCrow) that incorporates adaptive constants in Dragonfly-based Crow (D-Crow) optimization algorithm based on the proposed topology model. The proposed S-DCrow describes a migrating model, which is based on topology, energy consumption, load, and migration cost. Here, the network is organized in a spanning tree topology and is adapted by proposed S-DCrow for optimal VMM. The performance of the proposed S-DCrow shows superior performance in terms of load, energy consumption, and migration cost with the values of 0.1417, 0.1009, and 0.1220, respectively.

Energy-aware VM migration using dragonfly–crow optimization and support vector regression model in Cloud

2018 ◽  
Vol 09 (06) ◽  
pp. 1850050 ◽  
Author(s):  
Nitin S. More ◽  
Rajesh B. Ingle
Keyword(s):  
Energy Consumption ◽  
Support Vector Regression ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Support Vector ◽  
Load Prediction ◽  
Energy Aware ◽  
Support Vector Regression Model ◽  
Migration Cost ◽  
And Migration

Nowadays, virtual machine migration (VMM) is a trending research since it helps in balancing the load of the Cloud effectively. Several VMM-based strategies defined in the literature have considered various metrics, such as load, energy, and migration cost for balancing the load of the model. This paper introduces a novel VMM strategy by considering the load of the Cloud network. Two important aspects of the proposed scheme are the load prediction through the support vector regression (SVR) and the optimal VM placement through the proposed dragonfly-based crow (D-Crow) optimization algorithm. The proposed D-Crow optimization algorithm is developed by incorporating crow search algorithm (CSA) into dragonfly algorithm (DA). Also, the proposed VMM strategy defines a load balancing model based on the energy consumption, load, and the migration cost to achieve the energy-aware VMM. The simulation of the proposed VMM strategy is done based on the metrics such as load, energy consumption, and the migration cost. From the results, it can be shown that the proposed VMM strategy surpassed other comparative models by achieving the minimum values of 7.3719%, 10.0368%, and 11.0639% for the load, energy consumption, and migration cost, respectively.

scholarly journals A novel energy efficient virtual machine configuration and migration technique

2018 ◽  
Vol 7 (4) ◽  
pp. 2391
Author(s):  
L Srinivasa Rao ◽  
I Raviprakash Reddy
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Energy Efficient ◽  
Virtual Machines ◽  
The Novel ◽  
Virtual Machine Migration ◽  
Machine Configuration ◽  
And Migration ◽  
The Cost ◽  
Migration Technique

The recent growth in the data centre usage and the higher cost of managing virtual machines clearly demands focused research in reducing the cost of managing and migrating virtual machines. The cost of virtual machine management majorly includes the energy cost, thus the best available virtual machine management and migration techniques must have the lowest energy consumption. The management of virtual machine is solely dependent on the number of applications running on that virtual machine, where there is a very little scope for researchers to improve the energy. The second parameter is migration in order to balance the load, where a number of researches are been carried out to reduce the energy consumption. This work addresses the issue of energy consumption during virtual machine migration and proposes a novel virtual machine migration technique with improvement of energy consumption. The novel algorithm is been proposed in two enhancements as VM selection and VM migration, which demonstrates over 47% reduction in energy consumption.  

scholarly journals Deployment of a Hybrid Multicast Switch in Energy-Aware Data Center Network: A Case of Fat-Tree Topology

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Tosmate Cheocherngngarn ◽  
Jean Andrian ◽  
Deng Pan
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Data Center ◽  
Tree Topology ◽  
Data Center Network ◽  
Bandwidth Utilization ◽  
Shared Resources ◽  
Energy Aware ◽  
Deployment Strategy ◽  
It Systems

Recently, energy efficiency or green IT has become a hot issue for many IT infrastructures as they attempt to utilize energy-efficient strategies in their enterprise IT systems in order to minimize operational costs. Networking devices are shared resources connecting important IT infrastructures, especially in a data center network they are always operated 24/7 which consume a huge amount of energy, and it has been obviously shown that this energy consumption is largely independent of the traffic through the devices. As a result, power consumption in networking devices is becoming more and more a critical problem, which is of interest for both research community and general public. Multicast benefits group communications in saving link bandwidth and improving application throughput, both of which are important for green data center. In this paper, we study the deployment strategy of multicast switches in hybrid mode in energy-aware data center network: a case of famous fat-tree topology. The objective is to find the best location to deploy multicast switch not only to achieve optimal bandwidth utilization but also to minimize power consumption. We show that it is possible to easily achieve nearly 50% of energy consumption after applying our proposed algorithm.

Small-GEAR in the Specific Scenarios of Small Network

2013 ◽  
Vol 475-476 ◽  
pp. 936-944
Author(s):  
Xun Wang ◽  
Ling Hua Zhang
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Routing Protocol ◽  
Residual Energy ◽  
Wireless Sensor ◽  
Energy Aware ◽  
Hop Count ◽  
Energy Aware Routing ◽  
The Cost ◽  
Entire Network

GEAR is an important geographic and energy aware routing protocol in wireless sensor network. As the GEAR is short of enough topology knowledge and the nodes energy is limited, routing void and routing loop will be arisen. This paper presents a smart energy aware routing protocol based on the geographic (SGEAR), which is suitable for the specific scenarios of small network. In the specific scenarios of small network, there are three major nodes to concentrate on, (1) the selected (2) the void (3) the residual energy is less than threshold. The SGEAR modifies the cost functions based on the residual energy, escaping the routing loop caused by the broadcast delay. From the simulations, the conclusions can be drawn that the smaller hop count doesnt indicate the less energy consumption, and SGEAR can reduce the void number, reducing the energy consumption of the entire network, which further prolongs the life of the network to satisfy the need of the specific scenarios of small network.

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.

scholarly journals A history-based energy-efficient routing protocol for opportunistic networks

2021 ◽  
Author(s):  
Khuram Khalid
Keyword(s):  
Energy Consumption ◽  
Routing Protocol ◽  
Energy Efficient ◽  
Opportunistic Networks ◽  
Superior Performance ◽  
Opportunistic Network ◽  
Energy Aware ◽  
Energy Efficient Routing ◽  
One Simulator ◽  
Overhead Ratio

In this thesis, a history-based energy-efficient routing protocol (called AEHBPR) for opportunistic networks (OppNets) is proposed, which saves the energy consumption by avoiding unnecessary packets transmission in the network and by clearing the buffer of nodes carrying the copies of the already delivered packets. The proposed AEHBPR protocol is evaluated using the Opportunistic NEtwork (ONE) simulator with both synthetic and real mobility traces, showing a superior performance compared to the History-Based Prediction for Routing (HBPR) protocol and AEProphet, in terms of average remaining energy, number of dead nodes, number of delivered messages, and overhead ratio, where AEProphet is the ProPHet routing protocol for OppNets on which the same energy-aware mechanism has been implemented.

scholarly journals ENERDGE: Distributed Energy-Aware Resource Allocation at the Edge

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 660
Author(s):  
Marios Avgeris ◽  
Dimitrios Spatharakis ◽  
Dimitrios Dechouniotis ◽  
Aris Leivadeas ◽  
Vasileios Karyotis ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Mobile Applications ◽  
Cloud Infrastructure ◽  
Energy Aware ◽  
Total Energy Consumption ◽  
Optimal Resource ◽  
Prediction Mechanism ◽  
The Cost ◽  
Different Characteristics ◽  
Task Offloading

Mobile applications are progressively becoming more sophisticated and complex, increasing their computational requirements. Traditional offloading approaches that use exclusively the Cloud infrastructure are now deemed unsuitable due to the inherent associated delay. Edge Computing can address most of the Cloud limitations at the cost of limited available resources. This bottleneck necessitates an efficient allocation of offloaded tasks from the mobile devices to the Edge. In this paper, we consider a task offloading setting with applications of different characteristics and requirements, and propose an optimal resource allocation framework leveraging the amalgamation of the edge resources. To balance the trade-off between retaining low total energy consumption, respecting end-to-end delay requirements and load balancing at the Edge, we additionally introduce a Markov Random Field based mechanism for the distribution of the excess workload. The proposed approach investigates a realistic scenario, including different categories of mobile applications, edge devices with different computational capabilities, and dynamic wireless conditions modeled by the dynamic behavior and mobility of the users. The framework is complemented with a prediction mechanism that facilitates the orchestration of the physical resources. The efficiency of the proposed scheme is evaluated via modeling and simulation and is shown to outperform a well-known task offloading solution, as well as a more recent one.

Direct and Semi-Direct Frame Partitioning Mapping for Computation and Energy-Aware HEVC Transrating

2020 ◽  
Vol 15 (1) ◽  
pp. 1-8
Author(s):  
Thiago Alves Bubolz ◽  
Ruhan Conceição ◽  
Mateus Grellert ◽  
Bruno Zatt ◽  
Luciano Agostini ◽  
...  
Keyword(s):  
Energy Consumption ◽  
High Energy ◽  
Energy Aware ◽  
Time Saving ◽  
Compression Efficiency ◽  
Efficiency Loss ◽  
Processing Times ◽  
Direct Mapping ◽  
Common Operation ◽  
The Cost

The most common operation to maintain various versions of the same video sequence under different bitrates in live and streaming video applications is video transrating. The typical transrating operation is comprised of a decoding and a complete encoding step in sequence, which results in long processing times and high energy consumption. This work proposes two early-termination schemes based on frame partitioning inheritance between decoding and encoding to accelerate the complex quadtree structure decisions performed during HEVC transrating. The proposed Direct Mapping scheme achieved time saving results of 48.5%, with a compression efficiency loss of 0.818%, on average. The Semi-Direct Mapping reaches 40.2% in time savings at the cost of an average compression efficiency loss of 0.724% in comparison to the original transcoder. Energy consumption was reduced in 50.4%, on average, considering the Direct Mapping approach.

scholarly journals A Novel Cost Based Model for Energy Consumption in Cloud Computing

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
A. Horri ◽  
Gh. Dastghaibyfard
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Electrical Energy ◽  
Cloud Environment ◽  
Cloud Infrastructure ◽  
Cloud Data ◽  
Proposed Model ◽  
Energy Consumption Model ◽  
Cloud Data Centers ◽  
The Cost

Cloud data centers consume enormous amounts of electrical energy. To support green cloud computing, providers also need to minimize cloud infrastructure energy consumption while conducting the QoS. In this study, for cloud environments an energy consumption model is proposed for time-shared policy in virtualization layer. The cost and energy usage of time-shared policy were modeled in the CloudSim simulator based upon the results obtained from the real system and then proposed model was evaluated by different scenarios. In the proposed model, the cache interference costs were considered. These costs were based upon the size of data. The proposed model was implemented in the CloudSim simulator and the related simulation results indicate that the energy consumption may be considerable and that it can vary with different parameters such as the quantum parameter, data size, and the number of VMs on a host. Measured results validate the model and demonstrate that there is a tradeoff between energy consumption and QoS in the cloud environment. Also, measured results validate the model and demonstrate that there is a tradeoff between energy consumption and QoS in the cloud environment.

scholarly journals A history-based energy-efficient routing protocol for opportunistic networks

2021 ◽  
Author(s):  
Khuram Khalid
Keyword(s):  
Energy Consumption ◽  
Routing Protocol ◽  
Energy Efficient ◽  
Opportunistic Networks ◽  
Superior Performance ◽  
Opportunistic Network ◽  
Energy Aware ◽  
Energy Efficient Routing ◽  
One Simulator ◽  
Overhead Ratio

In this thesis, a history-based energy-efficient routing protocol (called AEHBPR) for opportunistic networks (OppNets) is proposed, which saves the energy consumption by avoiding unnecessary packets transmission in the network and by clearing the buffer of nodes carrying the copies of the already delivered packets. The proposed AEHBPR protocol is evaluated using the Opportunistic NEtwork (ONE) simulator with both synthetic and real mobility traces, showing a superior performance compared to the History-Based Prediction for Routing (HBPR) protocol and AEProphet, in terms of average remaining energy, number of dead nodes, number of delivered messages, and overhead ratio, where AEProphet is the ProPHet routing protocol for OppNets on which the same energy-aware mechanism has been implemented.

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