Energy-aware VM Allocation on an Opportunistic Cloud Infrastructure

2013 ◽  
Author(s):  
C. O. Diaz ◽  
H. Castro ◽  
M. Villamizar ◽  
J. E. Pecero ◽  
P. Bouvry
Keyword(s):  
Cloud Infrastructure ◽  
Energy Aware ◽  
Vm Allocation

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.

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.

Heuristics for Energy-Aware VM Allocation in HPC Clouds

2014 ◽  
pp. 248-261 ◽  
Author(s):  
Nguyen Quang-Hung ◽  
Duy-Khanh Le ◽  
Nam Thoai ◽  
Nguyen Thanh Son
Keyword(s):  
Energy Aware ◽  
Vm Allocation

scholarly journals Energy-Aware Virtual Machine Clustering for Consolidation in Multi-tenant IaaS Public Clouds

2019 ◽  
pp. 1123-1136
Author(s):  
Kenga Mosoti Derdus ◽  
Vincent Oteke Omwenga ◽  
Patrick Job Ogao
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Service Providers ◽  
Cloud Service ◽  
Resource Usage ◽  
Energy Aware ◽  
Allocation Policy ◽  
Cloud Data ◽  
Vm Allocation ◽  
Vm Consolidation

Cloud computing has gained a lot of interest from both small and big academic and commercial organizations because of its success in delivering service on a pay-as-you-go basis. Moreover, many users (organizations) can share server computing resources, which is made possible by virtualization. However, the amount of energy consumed by cloud data centres is a major concern. One of the major causes of energy wastage is the inefficient utilization of resources. For instance, in IaaS public clouds, users select Virtual Machine (VM) sizes set beforehand by the Cloud Service Providers (CSPs) without the knowledge of the kind of workloads to be executed in the VM. More often, the users overprovision the resources, which go to waste. Additionally, the CSPs do not have control over the types of applications that are executed and thus VM consolidation is performed blindly. There have been efforts to address the problem of energy consumption by efficient resource utilization through VM allocation and migration. However, these techniques lack collection and analysis of active real cloud traces from the IaaS cloud. This paper proposes an architecture for VM consolidation through VM profiling and analysis of VM resource usage and resource usage patterns, and a VM allocation policy. We have implemented our policy on CloudSim Plus cloud simulator and results show that it outperforms Worst Fit, Best Fit and First Fit VM allocation algorithms. Energy consumption is reduced through efficient consolidation that is informed by VM resource consumption.

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