Load dispersion-aware VM placement in favor of energy-performance tradeoff

2016 ◽  
Vol 73 (4) ◽  
pp. 1547-1566 ◽  
Author(s):  
Ali Nadjar ◽  
Saeid Abrishami ◽  
Hossein Deldari
Keyword(s):  
Energy Performance ◽  
Vm Placement ◽  
Performance Tradeoff

scholarly journals CoLocateMe: Aggregation-based, Energy, Performance and Cost Aware VM Placement and Consolidation in Heterogeneous IaaS Clouds

2021 ◽  
Author(s):  
Muhammad Zakarya ◽  
Lee Gillam ◽  
Khaled Salah ◽  
Omer F. Rana ◽  
Santosh Tirunagari ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Empirical Evaluation ◽  
Energy Performance ◽  
Vm Placement ◽  
Placement Decisions ◽  
New Directions ◽  
And Performance ◽  
And Migration ◽  
Placement Policies ◽  
The Impact

In many production clouds, with the notable exception of Google, aggregation-based VM placement policies are used to provision datacenter resources energy and performance efficiently. However, if VMs with similar workloads are placed onto the same machines, they might suffer from contention, particularly, if they are competing for similar resources. High levels of resource contention may degrade VMs performance, and, therefore, could potentially increase users' costs and infrastructure's energy consumption. Furthermore, segregation-based methods result in stranded resources and, therefore, less economics. The recent industrial interest in segregating workloads opens new directions for research. In this paper, we demonstrate how aggregation and segregation-based VM placement policies lead to variabilities in energy efficiency, workload performance, and users' costs. We, then, propose various approaches to aggregation-based placement and migration. We investigate through a number of experiments, using Microsoft Azure and Google's workload traces for more than twelve thousand hosts and a million VMs, the impact of placement decisions on energy, performance, and costs. Our extensive simulations and empirical evaluation demonstrate that, for certain workloads, aggregation-based allocation and consolidation is ~9.61% more energy and ~20.0% more performance efficient than segregation-based policies. Moreover, various aggregation metrics, such as runtimes and workload types, offer variations in energy consumption and performance, therefore, users' costs.<br>

scholarly journals Efficient virtual machine placement algorithms for consolidation in cloud data centers

2020 ◽  
Vol 17 (1) ◽  
pp. 29-50
Author(s):  
Loiy Alsbatin ◽  
Gürcü Öz ◽  
Ali Ulusoy
Keyword(s):  
Virtual Machine ◽  
Service Level Agreement ◽  
Energy Performance ◽  
Service Level ◽  
Vm Placement ◽  
Cloud Data ◽  
Successful Approach ◽  
Cloud Data Centers ◽  
Placement Algorithms ◽  
Best Fit

Dynamic Virtual Machine (VM) consolidation is a successful approach to improve the energy efficiency and the resource utilization in cloud environments. Consequently, optimizing the online energy-performance tradeoff directly influences quality of service. In this study, algorithms named as CPU Priority based Best-Fit Decreasing (CPBFD) and Dynamic CPU Priority based Best-Fit Decreasing (DCPBFD) are proposed for VM placement. A number of VM placement algorithms are implemented and compared with the proposed algorithms. The algorithms are evaluated through simulations with real-world workload traces and it is shown that the proposed algorithms outperform the known algorithms. The simulation results clearly show that CPBFD and DCPBFD provide the least service level agreement violations, least VM migrations, and efficient energy consumption.

scholarly journals CoLocateMe: Aggregation-based, Energy, Performance and Cost Aware VM Placement and Consolidation in Heterogeneous IaaS Clouds

2021 ◽  
Author(s):  
Muhammad Zakarya ◽  
Lee Gillam ◽  
Khaled Salah ◽  
Omer F. Rana ◽  
Santosh Tirunagari ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Empirical Evaluation ◽  
Energy Performance ◽  
Vm Placement ◽  
Placement Decisions ◽  
New Directions ◽  
And Performance ◽  
And Migration ◽  
Placement Policies ◽  
The Impact

In many production clouds, with the notable exception of Google, aggregation-based VM placement policies are used to provision datacenter resources energy and performance efficiently. However, if VMs with similar workloads are placed onto the same machines, they might suffer from contention, particularly, if they are competing for similar resources. High levels of resource contention may degrade VMs performance, and, therefore, could potentially increase users' costs and infrastructure's energy consumption. Furthermore, segregation-based methods result in stranded resources and, therefore, less economics. The recent industrial interest in segregating workloads opens new directions for research. In this paper, we demonstrate how aggregation and segregation-based VM placement policies lead to variabilities in energy efficiency, workload performance, and users' costs. We, then, propose various approaches to aggregation-based placement and migration. We investigate through a number of experiments, using Microsoft Azure and Google's workload traces for more than twelve thousand hosts and a million VMs, the impact of placement decisions on energy, performance, and costs. Our extensive simulations and empirical evaluation demonstrate that, for certain workloads, aggregation-based allocation and consolidation is ~9.61% more energy and ~20.0% more performance efficient than segregation-based policies. Moreover, various aggregation metrics, such as runtimes and workload types, offer variations in energy consumption and performance, therefore, users' costs.<br>

Optimum Utilization of Resources Through Restricted Virtual Machine Migration and Efficient VM Placement in Cloud Data Center

2018 ◽  
Vol 9 (4) ◽  
pp. 1-19
Author(s):  
Subhadra Bose Shaw ◽  
Anil Kumar Singh ◽  
Shailesh Tripathi
Keyword(s):  
Energy Consumption ◽  
Virtual Machines ◽  
Energy Performance ◽  
Migration Decision ◽  
Performance Gain ◽  
Virtual Machine Migration ◽  
Vm Placement ◽  
Cloud Data Center ◽  
Network Bandwidth ◽  
Optimum Utilization

In infrastructure-as-a-service (IAAS) cloud platforms, it is a real challenge to provide high performance gain by the optimum utilization of resources while maintaining minimum consumption of energy. The existing research works show that reduction in energy consumption causes violation of service level agreement (SLA). In this article, the concept of probability has been used to take the migration decision of virtual machines (VM) from over-utilized as well as under-utilized nodes. A novel method has also been proposed for selecting the destination server where a migrated VM will be placed. This method is based on the current utilization of CPU, memory and network bandwidth. The proposed scheme maintains a balance between energy consumption and performance gain. Results obtained through trace driven simulation demonstrate that the probability-based migration scheme achieves energy-performance trade-off whereas the VM placement method shows a very high gain in performance.

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