A Green Network-Aware VMs Placement Mechanism

Neural Computing and Applications

Energy efficiency is a critical issue in the management and operation of cloud data centers, which form the backbone of cloud computing. Virtual machine (VM) placement has a significant impact on energy-efficiency improvement for virtualized data centers. Among various methods to solve the VM-placement problem, the genetic algorithm (GA) has been well accepted for the quality of its solution. However, GA is also computationally demanding, particularly in the computation of its fitness function. This limits its application in large-scale systems or specific scenarios where a fast VM-placement solution of good quality is required. Our analysis in this paper reveals that the execution time of the standard GA is mostly consumed in the computation of its fitness function. Therefore, this paper designs a data structure extended from a previous study to reduce the complexity of the fitness computation from quadratic to linear one with respect to the input size of the VM-placement problem. ...

Cluster Computing, 2020

One of the most important issues in the context of cloud computing concerns the placement of virtual machines (VMs). The purpose of multi-objective virtual machine placement (MO-VMP) is to find the best place of VMs on physical machines (PMs) so as to reach predetermined goals. In this regard, a fundamental goal is maximizing the utilization of available resources while minimizing energy consumption. It is clear that inefficient use of computing resources (for instance CPU, memory, storage capacity, and bandwidth) could cause increased energy wastage. On the other hand, with optimal placement of VMs on PMs, one may prevent migrating them from one PM to another in the future, itself a secondary cause of increased energy consumption. Concerning the MO-VMP, there are very serious challenges in previous studies. Some of these works have attempted to minimize the number of active PMs. Others have investigated minimizing rack link traffic and optimizing communication and VM migration costs regarding routing goals. Since the MO-VMP is an NP-hard problem and involves high spatial and temporal complexities, heuristic and meta-heuristic methods have been widely used to solve the problem in the past decade. In the present research, we use the non-dominated sorting genetic algorithm (NSGA-III) to determine the optimal MO-VMP. To this end, a multi-objective optimizing problem is designed, and after introducing a non-linear convex optimization solution, we solve it with the NSGA-III method. Our main purpose is to minimize overall resource loss while minimizing power consumption as well as decreasing the number of active PMs. The simulation results on the CloudSim simulator confirm the superiority of the proposed method over basic methods such as first-fit decreasing (FFD) and exact mathematical approaches in terms of significant criteria such as execution time, utilization, resource wastage, and energy consumption. Keywords Virtual machine placement (VMP) Á Energy consumption Á Multi-objective optimization Á Meta-heuristic method Á Non-dominated sorting genetic algorithm (NSGA-III)

Cluster Computing, 2020

Cloud providers offer computing services based on user demands using the Infrastructure as a Service (IaaS) service model. In a cloud data center, it is possible that multiple Virtual Machines (VMs) run on a Physical Machine (PM) using virtualization technology. Virtual Machine Placement (VMP) problem is the mapping of virtual machines across multiple physical ones. This process plays a vital role in defining energy consumption and resource usage efficiency in the cloud data center infrastructure. However, providing an efficient solution is not trivial due to difficulties such as machine heterogeneity, multi-dimensional resources, and large scale cloud data centers. In this paper, we propose an efficient heuristic algorithm that focuses on power consumption and resource wastage optimization to solve the aforementioned problem. The proposed algorithm, called MinPR, minimizes the total power consumption by reducing the number of active physical machines and prioritizing the power-efficient ones. Also, it reduces resource wastage by maximizing and balancing resource utilization among physical machines. To achieve these goals, we propose a new Resource Usage Factor model that manages virtual machine placement on physical machines using reward and penalty mechanisms. Simulations based on cloud user-customized VMs and Amazon EC2 Instances workloads illustrate that the proposed algorithm outperforms existing approaches. In particular, the proposed algorithm reduces total energy consumption by up to 15% for cloud user-customized VMs and by up to 10% for Amazon EC2 Instances.

Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of data centers. Virtual machine placement is the key in the server consolidation.

Lecture Notes in Computer Science, 2013

Due to the increasing use of Cloud computing services and the amount of energy used by data centers, there is a growing interest in reducing energy consumption and carbon footprint of data centers. Cloud data centers use virtualization technology to host multiple virtual machines (VMs) on a single physical server. By applying efficient VM placement algorithms, Cloud providers are able to enhance energy efficiency and reduce carbon footprint. Previous works have focused on reducing the energy used within a single or multiple data centers without considering their energy sources and Power Usage Effectiveness (PUE). In contrast, this paper proposes a novel VM placement algorithm to increase the environmental sustainability by taking into account distributed data centers with different carbon footprint rates and PUEs. Simulation results show that the proposed algorithm reduces the CO2 emission and power consumption, while it maintains the same level of quality of service compared to other competitive algorithms.

Expert Systems with Applications, 2020

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Proceedings of the 7th International Conference on Cloud Computing and Services Science, 2017

Reducing energy consumption in cloud data center is a complex task, where both computation and network related effects must be taken into account. While existing solutions aim to reduce energy consumption considering separately computational and communication contributions, limited attention has been devoted to models integrating both parts. We claim that this lack leads to a sub-optimal management in current cloud data centers, that will be even more evident in future architectures characterized by Software-Defined Network approaches. In this paper, we propose a joint computation-plus-communication model for Virtual Machines (VMs) allocation that minimizes energy consumption in a cloud data center. The contribution of the proposed model is threefold. First, we take into account data traffic exchanges between VMs capturing the heterogeneous connections within the data center network. Second, the energy consumption due to VMs migrations is modeled by considering both data transfer and computational overhead. Third, the proposed VMs allocation process does not rely on weight parameters to combine the two (often conflicting) goals of tightly packing VMs to minimize the number of powered-on servers and of avoiding an excessive number of VM migrations. An extensive set of experiments confirms that our proposal, which considers both computation and communication energy contributions even in the migration process, outperforms other approaches for VMs allocation in terms of energy reduction.

2021

the increasing demand for cloud computing services has led to the adoption of large-scale cloud data centers (DCs) to meet the user's requirements. Efficiency and managing of such DCs have become a challenging problem. Consequently, energy-efficient solutions to optimize the whole DC energy consumption, optimize the application's performance and reduce the cloud provider operational cost are crucial and needed. This paper addressed the problem of Virtual Machines (VMs) placement of multi-tier applications to maximize the compute resources utilization, minimize energy consumption, and reduce network traffic inside modern large-scale cloud DCs. The VM placement problem with communication dependencies among the VMs is modeled as an optimization problem. In this context, to solve the proposed problem, that formulated as a variant of a multiple knapsack problem, an adaptive genetic algorithm is implemented to find a near-optimal solution to the NP-complete modeled optimization problem. To validate the efficacy of the proposed model, extensive simulations are conducted using CloudSimSDN simulator. The experimental results validate the usefulness of the proposed model and its effectiveness in reducing DC energy consumption and optimize network traffic inside DC.

Computer Engineering and Applications Journal, 2015

The increasing energy consumption has become a major concern in cloud computing due to its cost and environmental damage. Virtual Machine placement algorithms have been proven to be very effective in increasing energy efficiency and thus reducing the costs. In this paper we have introduced a new priority routing VM placement algorithm and have compared it with PABFD (power-aware best fit decreasing) on CoMon dataset using CloudSim for simulation. Our experiments show the superiority of our new method with regards to energy consumption and level of SLA violations measures and prove that priority routing VM placement algorithm can be effectively utilized to increase energy efficiency in the clouds.

Lecture Notes in Computer Science, 2014

Cloud infrastructures are designed to simultaneously service many, diverse applications that consist of collections of Virtual Machines (VMs). The policy used to map applications onto physical servers (placement policy) has important effects in terms of application performance and resource efficiency. This paper proposes enhancing placement policies with network-aware optimizations trying to simultaneously improve application performance, resource efficiency and, as a consequence, power efficiency. The per-application placement decision is formulated as a bi-objective optimization problem (minimizing communication cost and minimizing the number of physical servers assigned to the application) whose solution is searched using an evolutionary algorithm with problemspecific crossover and mutation operators. Experiments carried out with a simulator demonstrate how a low-cost optimization technique results in improved placements that achieve all the target objectives.