Dynamic Virtual Machine Consolidation Algorithms for Energy-Efficient Cloud Resource Management: A Review

One way to conserve energy in cloud data centers is to transition idle servers into a power saving state during periods of low utilization. Dynamic virtual machine (VM) consolidation (VMC) algorithms are proposed to create idle times by periodically repacking VMs on the least number of physical machines (PMs). Existing works mostly apply VMC on top of centralized, hierarchical, or ring-based system topologies which result in poor scalability and/or packing efficiency with increasing number of PMs and VMs. In this paper, we propose a novel fully decentralized dynamic VMC schema based on an unstructured peer-to-peer (P2P) network of PMs. The proposed schema is validated using three well known VMC algorithms: First-Fit Decreasing (FFD), Sercon, V-MAN, and a novel migration-cost aware ACO-based algorithm. Extensive experiments performed on the Grid'5000 testbed show that once integrated in our fully decentralized VMC schema, traditional VMC algorithms achieve a global packing efficiency very close to a centralized system. Moreover, the system remains scalable with increasing number of PMs and VMs. Finally, the migration-cost aware ACO-based algorithm outperforms FFD and Sercon in the number of released PMs and requires less migrations than FFD and V-MAN.

With the rapid development of integration in blockchain and IoT, virtual machine consolidation (VMC) has become a heated topic because it can effectively improve the energy efficiency and service quality of cloud computing in the blockchain. The current VMC algorithm is not effective enough because it does not regard the load of the virtual machine (VM) as an analyzed time series. Therefore, we proposed a VMC algorithm based on load forecast to improve efficiency. First, we proposed a migration VM selection strategy based on load increment prediction called LIP. Combined with the current load and load increment, this strategy can effectively improve the accuracy of selecting VM from the overloaded physical machines (PMs). Then, we proposed a VM migration point selection strategy based on the load sequence prediction called SIR. We merged VMs with complementary load series into the same PM, effectively improving the stability of the PM load, thereby reducing the service level agreement violation (SLAV) and the number of VM migrations due to the resource competition of the PM. Finally, we proposed a better virtual machine consolidation (VMC) algorithm based on the load prediction of LIP and SIR. The experimental results show that our VMC algorithm can effectively improve energy efficiency.

Minimizing energy consumption of data centers is important to reduce carbon emissions. Virtual machines (VMs) consolidation is a typical technique to utilize the available data center resources and thus improve energy efficiency. The cooling systems consume up to 50% of the total data center electricity. In this work, we investigate the joint energy optimization of cooling systems and VM consolidations in cloud data centers. We propose a cooling-aware VM consolidation (CAVC for short) algorithm to the problem. The CAVC algorithm is a two-stage solution: 1) we first relax the constraints of the problem and determine an optimal number of physical machines (PMs) and an optimal CPU utilization of the PMs that yields the minimum cooling power; and 2) based on the initial solution of the first stage, we consolidate the VMs into the predetermined PMs with the predetermined CPU utilization ratio as much as possible. To the best of authors' knowledge, this is the first work that jointly considers the VM consolidation and the cooling systems in minimizing energy consumption of cloud data centers. We derive an approximation ratio of CAVC over the optimal solution. The real-world data set (i.e., Google cluster data) is adopted in the simulations and the results show that the CAVC algorithm yields very close energy consumption to the theoretical lower bound.

Virtual Machine (VM) consolidation technique plays an important role in energy management and load-balancing of cloud computing systems. Dynamic VM consolidation is a promising consolidation approach in this direction, which aims at using least active physical machines (PMs) through appropriately migrating VMs to reduce resource consumption. The resulting optimization problem is well-acknowledged to be NP-hard optimization problems. In this paper, we propose a novel merge-and-split-based coalitional game-theoretic approach for VM consolidation in heterogeneous clouds. The proposed approach first partitions PMs into different groups based on their workload levels, then employs a coalitional-game-based VM consolidation algorithm (CGMS) in choosing members from such groups to form effective coalitions, performs VM migrations among the coalition members to maximize the payoff of every coalition, and finally keeps PMs running in a high energy-efficiency state. The simulation results based on three scenarios clearly suggest that our proposed approach outperforms traditional ones in terms of energy-saving, and also achieve a fair level of load balance.

Server consolidation technique plays an important role in energy management and load-balancing of cloud computing systems. Dynamic virtual machine (VM) consolidation is a promising consolidation approach in this direction, which aims at using least active physical machines (PMs) through appropriately migrating VMs to reduce resource consumption. The resulting optimization problem is well-acknowledged to be NP-hard optimization problems. In this paper, we propose a novel merge-and-split-based coalitional game-theoretic approach for VM consolidation in heterogeneous clouds. The proposed approach first partitions PMs into different groups based on their load levels, then employs a coalitional-game-based VM consolidation algorithm (CGMS) in choosing members from such groups to form effective coalitions, performs VM migrations among the coalition members to maximize the payoff of every coalition, and close PMs with low energy-efficiency. Experimental results based on multiple cases clearly demonstrate that our proposed approach outperforms traditional ones in terms of energy-saving and level of load fairness.

Embedding individualized machine learning prediction models for energy efficient VM consolidation within Cloud data centers

Application of virtual machine consolidation in cloud computing systems

I-Neat: An intelligent framework for adaptive virtual machine consolidation

Adaptive selection of dynamic VM consolidation algorithm using neural network for cloud resource management

High energy consumption of cloud data centers is a matter of great concern. Dynamic consolidation of Virtual Machines (VMs) presents a significant opportunity to save energy in data centers. A VM consolidation approach uses live migration of VMs so that some of the under-loaded Physical Machines (PMs) can be switched-off or put into a low-power mode. On the other hand, achieving the desired level of Quality of Service (QoS) between cloud providers and their users is critical. Therefore, the main challenge is to reduce energy consumption of data centers while satisfying QoS requirements. In this paper, we present a distributed system architecture to perform dynamic VM consolidation to reduce energy consumption of cloud data centers while maintaining the desired QoS. Since the VM consolidation problem is strictly NP-hard, we use an online optimization metaheuristic algorithm called Ant Colony System (ACS). The proposed ACS-based VM Consolidation (ACS-VMC) approach finds a near-optimal solution based on a specified objective function. Experimental results on real workload traces show that ACS-VMC reduces energy consumption while maintaining the required performance levels in a cloud data center. It outperforms existing VM consolidation approaches in terms of energy consumption, number of VM migrations, and QoS requirements concerning performance.

Dynamic Virtual Machine (VM) consolidation is an effective manner to reduce energy consumption and balance the resource load of physical machines (PMs) in cloud data centers that guarantees efficient power consumption while maintaining the quality of service requirements. Reducing the number of active PMs using VM live migration leads to prevent inefficient usage of resources. However, high frequency of VM consolidation has a negative effect on the system reliability and we need to deal with the trade-off between energy consumption and system reliability. In recent years many research work has been done to optimize energy management using power management techniques. Although these methods are very efficient from the point of view of energy management, but they ignore the negative impact on the system reliability. In this paper, a novel approach is proposed to achieve a reliable VM consolidation method. In this way, a Markov chain model is designed to determine the reliability of PMs and then it has been prioritized PMs based on their CPU utilization level and reliability status. Two algorithms are presented to determining source and destination servers. The efficiency of our proposed approach is validated by conducting extensive simulations. The results of the evaluation clearly show that the proposed approach significantly improve energy consumption while avoiding the inefficient VM migrations.

Bayesian network-based Virtual Machines consolidation method

The virtualization and Virtual Machine (VM) Consolidation are the effective solutions for energy consumption reduction. The VM consolidation algorithms designed recently shows the efficiency in terms of energy consumption, Service Level Agreement Violations (SLAV) etc. The methods still suffered from the challenges to achieve the trade-off between QoS and Energy efficiency at VM consolidation. The design of VM consolidation should be efficient in terms of VM’s allocation, migration, and workload management. In this paper, we proposed the design of novel VM consolidation approach in cloud data center to address the challenges of recent methods. First the Multi Resources based (CPU, RAM, network Traffic, bandwidth etc.) dynamic Overload Decision Algorithm (MR-ODA). For best VM selection, we used the policies of CloudSim framework. After selection of VMs to migrate, the efficient VM placement algorithm proposed based on optimization method called Particle Swarm Optimization (PSO). The algorithms designed to achieve the minimize energy consumption, QoS, reliability with minimum SLAV. The outcome of this paper is the new framework for VM consolidation to optimize the CloudSim tool.

An intelligent ensemble learning approach for energy efficient and interference aware dynamic virtual machine consolidation

Dynamic virtual machine (VM) consolidation is considered an effective approach for improving power consumption and computing resource utilization in cloud-based data centers. However, the ever-changing workload in a data center makes it difficult for VM consolidation to prevent service level agreement (SLA) violations and optimize power consumption. The detection of overutilized and underutilized physical machines (PMs) plays a significant role in effective VM consolidation, immediately improving resource utilization, SLA violations, and power consumption. This paper proposes the dynamic threshold-based fuzzy approach (DTFA) for detecting overloaded and underutilized PMs, and the Lowest Interdependence Factor Exponent Multiple Resources Predictive (LIFE-MP) approach for VM placement, by considering multiple computing resources being used simultaneously in a cloud environment. The DTFA is a fuzzy threshold-based approach used to adjust the threshold values of PMs in a cloud environment. The LIFE-MP approach selects a PM at which to place the migrating VM, based on the PM with the lowest correlation coefficient value among the already-running VMs and the migrating VM to reduce performance degradation because of the VM migration. The comparison between LIFE-MP scheme and a power-aware best-fit decreasing (PABFD) scheme shows that the proposed scheme reduces power consumption by 22.52, SLA violations by 45.63, and the number of VM migrations by 56.68.