E2SVM: Electricity-Efficient SLA-aware Virtual Machine Consolidation approach in cloud data centers.

Virtual machine (VM) consolidation in Cloud computing environments provides a great opportunity for energy saving. However, the obligation of providing suitable quality of service to end users leads to the necessity in dealing with energy-performance tradeoff. In this paper, we propose a redesigned energy-aware heuristic framework for VM consolidation to achieve a better energy-performance tradeoff. The main contribution of us is a Service Level Agreement (SLA) violation decision algorithm, which is used to decide a host is overload with SLA violation or not. Finally, we have evaluated our framework through simulation on large-scale experiments driven by workload traces from more than a thousand VMs, and the results show that our framework outperforms previous work, which has 11.8%~27% decrease in energy consumption, 57.9%~78.4% decrease in SLA violation, 63.2%~84.1% decrease in energy-performance metric which is a product metric of the energy consumption and SLA violation.

Virtual machine (VM) consolidation is an effective way to improve resource utilization and reduce energy consumption in cloud data centers. Most existing studies have considered VM consolidation as a bin-packing problem, but the current schemes commonly ignore the long-term relationship between VMs and hosts. In addition, there is a lack of long-term consideration for resource optimization in the VM consolidation, which results in unnecessary VM migration and increased energy consumption. To address these limitations, a VM consolidation method based on multi-step prediction and affinity-aware technique for energy-efficient cloud data centers (MPaAFVMC) is proposed. The proposed method uses an improved linear regression prediction algorithm to predict the next-moment resource utilization of hosts and VMs, and obtains the stage demand of resources in the future period through multi-step prediction, which is realized by iterative prediction. Then, based on the multi-step prediction, an affinity model between the VM and host is designed using the first-order correlation coefficient and Euclidean distance. During the VM consolidation, the affinity value is used to select the migration VM and placement host. The proposed method is compared with the existing consolidation algorithms on the PlanetLab and Google cluster real workload data using the CloudSim simulation platform. Experimental results show that the proposed method can achieve significant improvement in reducing energy consumption, VM migration costs, and service level agreement (SLA) violations.

Virtual Machine (VM) consolidation provides a promising approach to save energy and improve resource utilization in cloud data centers. However, aggressive consolidation of VMs may lead to performance degradation of data centers and even Service Level Agreement (SLA) violation. Therefore, it is very important to solve the tradeoff between reduction of SLA violation level and energy costs. In this paper, we propose a Host State 3-order Markov Chain prediction model for SLA-aware and energy efficient consolidation of VMs in cloud data centers. We propose two adaptive utilization thresholds based on a robust statistical method for host state estimate, and our model uses them to build a 3-order Markov Chain Transition Matrix manually for host state detection. We implement our algorithms on Cloudsim simulator for comparative study. The experimental results show that our model can significantly reduce SLA violation rates while keeping energy cost efficient, it can reduce the metric of SLA Violation by at most 98.69% and the metric of Energy by at most 21.88% for real world workload.

Cloud computing providers have to deal with the energy-performance trade-off: minimizing energy consumption, while meeting service level agreement (SLA) requirements. This paper proposes a new heuristic approach for the dynamic consolidation of virtual machines (VMs) in cloud data centers. The fast best-fit decreasing (FBFD) algorithm for intelligent VMs allocating into hosts and dynamic utilization rate (DUR) algorithm for utilization space and VM migration are successfully proposed. We performed simulations using PlanetLab and GWDG data center workloads to compare our approach against the existing models. It has been observed that the FBFD heuristic algorithm produces better results compared to modified BFD algorithm in terms of energy consumption and SLA violation. Additionally, the time complexity of FBFD algorithm is significantly improved from the order of O(\(m\,*\,n\)) to O(\(m\,*\,\log _2{n}\)). Furthermore, leaving some rates of capacity in the physical machines by the proposed DUR algorithm for VMs to be extended reduces the number of migrations which in turn improves the energy consumption and SLA violation. Our heuristic approach is evaluated using CloudSim and the results show that it performs better than the current state-of-the-art approaches.

Energy consumption in data center is currently the main focus of many large-scale enterprises and cloud service providers. Dynamic virtual machine (VM) consolidation technologies are widely used to improve resource utilization of data centers and reduce energy. They try to identify the poorly utilized physical hosts and make the most of the resources. Then idle hosts can be switched to sleep or active mode, meanwhile considering the real-time fluctuation of service workload. In this paper, we propose a Reinforcement Learning (RL) based Virtual Machine Consolidation (RL-VMC) framework with an application to the cloud data center operation. The RL-VMC method uses an agent corresponding to a VM planner to interact with the environment, which encapsulates the data center running state, and learns the optimal policy to determine the migration mapping from VMs to physical hosts. Specifically, we adopt the on-policy method State-Action-Reward-State-Action (SARSA) in RL-VMC, which learns from experience to get the optimal VMs migration strategy and manage the host power mode as well. Additionally, we use the PBRS technology to speed up the convergence of the RL-VMC. Experimental results show that the RL-VMC method can adapt to the dynamic workload maintaining an improved balance between energy consumption and Service Level Agreements (SLA).

The rapid growth in cloud computing encourages the cloud provider to create more reliable, scalable and efficient cloud environment. For providing such a scalable environment, cloud provider develops large-scale Cloud Data Centers (CDC). Such CDCs consume a large amount of electrical energy for operational purpose and cooling purpose to keep the machines operating at a right temperature, but it also emits a large amount of carbon-dioxide. Thus to decrease the energy consumption, Virtual Machines (VMs) are consolidated to fewer numbers of active servers through VM migration techniques, this leads to Service Level Agreement (SLA) violation and performance degradation. Therefore to retain the energy-performance tradeoff, we should perform an optimum number of VM migrations. There are two cases when the VM migration performed: First when a hot spot is found and Second when a cold spot is found. But in both the cases we do not directly migrate the VM instead first we check whether it is required to migrate the VM. After that, the algorithm finds an appropriate destination host on which the VM will be migrated. In this paper, we implement three approaches to select destination host: First-fit, Best-fit, and Worst-fit. Finally compare the result of a number of VM migration performed, energy consumption and SLA violations with existing one.

Cloud computing is currently playing an essential role in supporting emerging sectors such as smart energy, intelligent transportation, and large-scale distributed systems. The scalability and adaptability features of cloud computing are efficiently enable resource utilization and continuous data exchange in dynamic and heterogeneous environments. However, the increasing demand for cloud services has raised the energy consumption of cloud data centers, which has a critical environmental impact. Therefore, sustainable resource management tactics have become crucial in today's world. Dynamic Virtual Machine (VM) consolidation is one of the major tactics for sustainable resource management in the green cloud computing environment. Herein, a novel dynamic Energy-Aware Cosine Similarity Learning Network (ECSLN) is proposed to predict the overutilized host. Further, an Impact Factor-Based VM Selection (IFBVMS) method is proposed to select VMs for migration, and an ECSLN-Packed Placement method for VM placement into hosts. The main aim of the proposed VM consolidation is to maximize energy efficiency while preserving compliance with Service Level Agreements (SLAs) for sustainable environments. The experimental evaluation using real-world traces like PlanetLab, Bitbrains, and Alibaba Cluster 2020 workload validates that the proposed VM consolidation methods significantly reduce energy consumption and SLA violation compared to the existing methods for sustainable environments. The evaluation shows that the proposed ECSLN-based consolidation framework enables green cloud infrastructure by intelligently balancing energy consumption, SLA violation, and performance of cloud data centers.

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.

Improving the energy efficiency while guaranteeing quality of services (QoS) is one of the main challenges of efficient resource management of large-scale data centers. Dynamic virtual machine (VM) consolidation is a promising approach that aims to reduce the energy consumption by reallocating VMs to hosts dynamically. Previous works mostly have considered only the current utilization of resources in the dynamic VM consolidation procedure, which imposes unnecessary migrations and host power mode transitions. Moreover, they select the destinations of VM migrations with conservative approaches to keep the service-level agreements , which is not in line with packing VMs on fewer physical hosts. In this paper, we propose a regression-based approach that predicts the resource utilization of the VMs and hosts based on their historical data and uses the predictions in different problems of the whole process. Predicting future utilization provides the opportunity of selecting the host with higher utilization for the destination of a VM migration, which leads to a better VMs placement from the viewpoint of VM consolidation. Results show that our proposed approach reduces the energy consumption of the modeled data center by up to 38% compared to other works in the area, guaranteeing the same QoS. Moreover, the results show a better scalability than all other approaches. Our proposed approach improves the energy efficiency even for the largest simulated benchmarks and takes less than 5% time overhead to execute for a data center with 7600 physical hosts.

Cloud computing provides utility oriented services and pay-as-you-go computing model for its users. Massive adoption of Cloud services leads to build a sustainable environment and also demands for green Cloud services. With the advancement in Cloud services, the issue of carbon dioxide emission becomes significant. There is also a challenge for Cloud provider to satisfy the customer by guaranteeing the services with performance and availability in Service Level Agreement (SLA). Hence, energy consumption and SLA violation become important issues for Cloud providers. These issues can be addressed in many facets viz. efficient provisioning of resources, reducing energy consumption in datacenters, server consolidation, virtual machine (VM) migration, minimizing SLA violation or also by providing SLA with green requirements. In this paper, we survey various techniques which address green SLA with Quality of Service (QoS) requirements by using different resource management and consolidation procedures. Further, we propose our work by considering the multi-criteria method for resource management which selects the host as probable migration destination having highest predicted utilization after allocating a VM on the host. Hence, we aim to address the issue of maintaining the trade-off between energy consumption and SLA violation.

The demand for cloud computing has increased manifold in the recent past. More specifically, on-demand computing has seen a rapid rise as organizations rely mostly on cloud service providers for their day-to-day computing needs. The cloud service provider fulfills different user requirements using virtualization - where a single physical machine can host multiple Virtual Machines. Each virtual machine potentially represents a different user environment such as operating system, programming environment, and applications. However, these cloud services use a large amount of electrical energy and produce greenhouse gases. To reduce the electricity cost and greenhouse gases, energy efficient algorithms must be designed. One specific area where energy efficient algorithms are required is virtual machine consolidation. With virtual machine consolidation, the objective is to utilize the minimum possible number of hosts to accommodate the required virtual machines, keeping in mind the service level agreement requirements. This research work formulates the virtual machine migration as an online problem and develops optimal offline and online algorithms for the single host virtual machine migration problem under a service level agreement constraint for an over-utilized host. The online algorithm is analyzed using a competitive analysis approach. In addition, an experimental analysis of the proposed algorithm on real-world data is conducted to showcase the improved performance of the proposed algorithm against the benchmark algorithms. Our proposed online algorithm consumed 25% less energy and performed 43% fewer migrations than the benchmark algorithms.

The Cloud Computing (CC) has vast amount of data centers that consists of many computing nodes and consumes a huge amount of electrical energy. Hence the researchers found that the high service-level agreements (SLAs) violations and Energy Consumption (EC) are the major challenging issues in CC. The various traditional approaches reduced the EC, but ignored the SLA violation during the selection of Virtual Machine (VM) from overloaded hosts. In order to effectively deal with these issues, this paper proposed the Recursive Ant Colony Optimization (RACO) in 2 different workloads. The main aim of the RACO is to minimize the high EC and SLA violations by the movement of best ant, which is random and identified the distance between their movements. The algorithm consists of 3 major steps includes tracking and updating the pheromone and finally city selection. The proposed method simulated on Cloud Sim to validate the efficiency and stability of the proposed RACO and their performance compared to that of other existing techniques. The results showed that RACO reduced the EC by 40 - 42 % (approx.) which is less than the traditional ACO algorithm in Planet Lab data. HIGHLIGHTS Reduced EC and SLA Violation of Data Center are done via RACO VM Allocations are done by IQR and utilises micro, small, medium and extra-large instances Pheromone Tracking, updating and city selection are the endeavours of RACO RACO reduced EC by 40 - 42 % and SLA Violation

Cloud computing is very promising technology to deliver computing resources like infrastructure (IaaS), platform (PaaS), software (SaaS) etc. in form of services over the Internet. Server consolidation is the mechanism to minimize active (running) physical servers in the data center of cloud service provider. Consolidation helps the service provider to make efficient usage data center resources and in turn to minimize running cost of data center. Users have to pay as per service usage. SLA is like contract between cloud service provider and users. Service Level Agreement (SLA) is the mechanism of providing guaranteed services to cloud users. SLA Violation may cause high penalties to service provider. In this paper it is shown that if consolidation is maximized, it may cause more number of SLA violation. A balanced approach of server consolidation with minimal SLA violations is suggested in this paper.

Maximizing the number of virtual infrastructures spawned out of a data center is a prime concern of cloud service providers to improve their revenue and the customers' quality-of-experience. Optimal placement of topology sensitive virtual data centers on its physical counterparts increases the resource utilization and thereby the revenue. However, provisioning, scaling, and de-provisioning of virtual data centers, over the time, leaves the cloud data center fragmented. Embedding new virtual data centers on a fragmented data center would require migration of virtual machines and virtual network, already mapped to it. However, these migrations are costly in terms of the resource usage and possible violations of the terms of service level agreement. The costs of these migrations associated with a new embedding should be controlled to increase the profit. In this paper, the problem of finding virtual machine migrations with minimum cost is formally defined and two meta-heuristic solutions are proposed for it. We consider the memory size, the rate of page dirtying, the class of application running on the VM, and the bandwidth of the migration path for modeling the cost of migration. The objective is to find a pattern of virtual machine migrations so that the total cost for embedding a new virtual data center is minimized. Experimental results show that the proposed technique can reduce the average migration time upto 30% and the penalty for corresponding service level agreement violation by 200%.

Cloud computing provides online services to users from anywhere in a pay‐as‐you‐go basis. This leads to the construction of different forms of data centers around the world, which usually consumes huge amount of energy and produce pollution (heat and carbon dioxide footprint). Many efforts have focused on managing cloud resources to decrease their energy consumption. Since servers are the highest energy consumers in data centers, most researchers have focused on optimizing their energy consumption while other resources such as network devices and cooling systems have received less attention. In this paper, we propose a network‐aware virtual machine (VM) relocation algorithm, which optimizes the distribution of the VMs running on the data center in order to conserve energy in both servers and network devices (switches). The proposed technique targets the communicated VMs and places them closer to each other. This would reduce the traffic overhead between any communicating VMs so that fewer network devices are involved in the communication process. This also increases the VMs consolidation to some servers while leaving other servers idle. Switching off the idle servers and network devices would significantly reduce the total energy consumed by the data center. The proposed algorithm is evaluated using simulation and the performance of the proposed algorithm is compared against different previously proposed algorithms in the literature. Performance metrics used are the energy consumed by the host and by the network switches, the service level agreement violations, and the number of VM migrations. The impact of different packet sizes, the efficiency of switching the network elements to sleep mode, and the impact of resource homogeneity and heterogeneity are also studied and discussed. Simulation results show that the proposed algorithm outperforms the existing algorithms in terms of all evaluation metrics.