Dynamic Virtual Machine Consolidation for Energy Efficient Cloud Data Centers

As cloud computing models have evolved from clusters to large-scale data centers, reducing the energy consumption, which is a large part of the overall operating expense of data centers, has received much attention lately. From a cluster-level viewpoint, the most popular method for an energy efficient cloud is Dynamic Right Sizing (DRS), which turns off idle servers that do not have any virtual resources running. To maximize the energy efficiency with DRS, one of the primary adaptive resource management strategies is a Virtual Machine (VM) migration which consolidates VM instances into as few servers as possible. In this paper, we propose a Two Phase based Adaptive Resource Management (TP-ARM) scheme that migrates VM instances from under-utilized servers that are supposed to be turned off to sustainable ones based on their monitored resource utilizations in real time. In addition, we designed a Self-Adjusting Workload Prediction (SAWP) method to improve the forecasting accuracy of resource utilization even under irregular demand patterns. From the experimental results using real cloud servers, we show that our proposed schemes provide the superior performance of energy consumption, resource utilization and job completion time over existing resource allocation schemes.

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.

Cloud providers generally run one or more Virtual Machine (VM) instances on the same physical machine. Though it increases data center utilization, it exposes VM to a co-location attack. VM placement and migration are the two strategies adopted for mitigating co-locations. Current methods for VM placement or VM migration consider only security as decision criteria and do not consider other factors like Quality-of-Service degradation, data center utilization, etc. This work proposes a placement and migration strategy for mitigation of co-location attacks with joint consideration of multi objectives like QoS, data center utilization, energy consumption, and security risks. A security-driven multi-criteria optimization -based VM placement policy is proposed. A joint consideration of multiobjective performance optimization along with co-location security risk minimization is done to design a novel VM placement policy based on user categorization. The policy can reduce the likelihood of co-location target VM with attacker VM without much degradation to the performance of VM and data center utilization. The solution mitigates co-location risks without much compromise to the performance of VM and data center resource utilization. The co-residence risk is mitigated by the categorization of users into three levels i.e. unlabeled, risky, and safe, and physical machines into two groups as safe and unsafe. The PMs available in data center is grouped into three different VM placement policies, they are undecided pool, safe pool and unsafe pool.

Nowadays cloud computing provides an effective way of implementing infrastructure as a service (IaaS). However virtualized data centers still face many challenges, such as low resource utilization of physical machines (PMs) and imbalanced server loads. Virtual machine (VM) consolidation based on live migration allows administrator to dynamically redeploy VMs into PMs for better resource utilization. Common VM consolidation methods usually focus on one challenge, and pay little attention to others or just ignore them, while effective VM redeployment should make tradeoffs between these challenges, and more importantly, should not let other challenges become worse. On the other hand, since VM live migration leads to performance degradation of applications, consolidation work should control migration cost. In this paper, we provide a manner to comprehensively consider power consumption, load balancing, communication delay and migration cost during VM redeployment. And we formalize VM consolidation as a multiobjective optimization problem, then solve this problem with an improved genetic algorithm. Simulation experiments based on real world workload trace show that compared with single objective optimization approaches our method effectively make tradeoffs between optimized objectives and has better overall performance, which is more practical in real data centers.

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.

Recent developments in virtualization and communication technologies have transformed the way data centers are designed and operated by providing new tools for better sharing and control of data center resources. In particular, Virtual Machine (VM) migration is a powerful management technique that gives data center operators the ability to adapt the placement of VMs in order to better satisfy performance objectives, improve resource utilization and communication locality, mitigate performance hotspots, achieve fault tolerance, reduce energy consumption, and facilitate system maintenance activities. Despite these potential benefits, VM migration also poses new requirements on the design of the underlying communication infrastructure, such as addressing and bandwidth requirements to support VM mobility. Furthermore, devising efficient VM migration schemes is also a challenging problem, as it not only requires weighing the benefits of VM migration, but also considering migration costs, including communication cost, service disruption, and management overhead. This chapter provides an overview of VM migration benefits and techniques and discusses its related research challenges in data center environments. Specifically, the authors first provide an overview of VM migration technologies used in production environments as well as the necessary virtualization and communication technologies designed to support VM migration. Second, they describe usage scenarios of VM migration, highlighting its benefits as well as incurred costs. Next, the authors provide a literature survey of representative migration-based resource management schemes. Finally, they outline some of the key research directions pertaining to VM migration and draw conclusions.

Live Virtual Machine (VM) migration has significantly improved the flexibility of modern Data Centers (DC). However, seamless live migration of a VM between geo-distributed DCs faces several challenges due to difficulties in preserving the network configuration after the migration paired with a large network convergence time. Although SDN-based approaches can speed up network convergence time, these techniques have two limitations. First, they typically react to the new topology by installing new flow rules once the migration is finished. Second, because the WAN is typically not under SDN control, they result in sub-optimal routing thus severely degrading the network performance once the VM is attached at the new location. In this paper, we identify networking challenges for VM migration across geo-distributed DCs. Based on those observations, we design a novel long-haul VM migration scheme that overcomes those limitations. First, instead of reactively restoring connectivity after the migration, our SDN-based approach proactively restores flows across the WAN towards the new location with the help of EVPN and VXLAN overlay technologies. Second, the SDN controller accelerates the network convergence by announcing the migration to other controllers using MP-BGP control plane messages. Finally, the SDN controller resolves the sub-optimal routing problem that arises as a result of migration implementing a distributed anycast gateway. We implement our approach as extensions to the OpenDaylight controller. Our evaluation shows that our approach outperforms existing approaches in reducing the downtime by 400 ms and increasing the application performance up to 12 times.

The big data era is presenting unprecedented opportunities for generating new revenues in various sectors ranging from health care, economics, life science, to manufacturing. Datacenters (DCs) are widely deployed to provision various application services as well as to process big data. Since more and more servers are installed in DCs, the cost of electricity incurs a financial burden for the DC operators. Many DCs are equipped with renewable energy to reduce the electricity bill. However, the locations of the energy demands do not match the locations of the renewable energy generation. This mismatch may be addressed by virtual machine (VM) migration. The DCs, which lack renewable energy, can migrate their workloads to other DCs, which have abundant renewable energy. In addition, DC operators always want to maximize revenue and minimize operation cost. In this paper, the problem of maximizing revenue and minimizing operation cost of a green DC network enabled with and without VM migration is formulated by integer linear programming. Simulation results show that optimal results can be reached for small size problems. Two heuristic algorithms are proposed to efficiently solve large size problems. To our best knowledge, this is the first study of the revenue driven VM management problem in green DC networks towards big data with VM migration.

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).

In modern data centres (DCs), energy consumption and resource management are challenging tasks. So in large virtualized DCs, performance of applications is highly dependent on energy-aware DC architecture and smooth network communication between virtual machines (VMs), while minimizing the communication burden to avoid congestion, latency etc. The communication cost of a network can be reduced by minimizing the VMs migration between physical machines (PMs). Therefore customer as well as service providers need cloud computing solutions that not only minimize operational costs, but also total network load. Minimizing the use of energy/network communication overhead with maximum resource utilization in large DCs is a challenging task as computing applications and data are growing so quickly for which increasing larger servers and disks are required to process them fast enough within the required time period. In order to minimize network load and maximize resource utilization in cloud DCs, we simulated our proposed network load-aware scheduling algorithm that ensures minimum VMs migration while delivering the negotiated Quality-of-Service (QoS). Our simulation also shows results with different bit rates.

CBase: Fast Virtual Machine storage data migration with a new data center structure

With the blooming of cloud computing, the demand for data centers has been rising greatly in recent years. Their energy consumption and environmental impact has become much more significant due to the continuous growth of data center supply. It is possible to reduce the amount of energy consumed by a data center by shutting down unnecessary servers and maintaining only a subset running, such that it is enough to fulfill the resource demand. With recent advances in virtualization technology, it even became possible to consolidate the workload of multiple under-utilized servers into a single server. However, too aggressive consolidation may lead to significant degradation of data center performance. Therefore, the problem of simultaneously minimizing energy consumption and performance degradation in a data center is a complex and challenging problem. In this paper, a novel multi-objective Boolean optimization encoding for virtual machine consolidation is proposed and several approaches to solve it are described and compared. Moreover, this encoding is extended to consider anti-collocation constraints and the migration of virtual machines that are initially placed. This work is in part motivated by the great improvements in the performance of Boolean optimization solvers, thus increasing their applicability and effectiveness for a wider spectrum of complex problems. In this case, specific techniques are applied to further boost the performance, namely search space reduction by symmetry breaking and heuristic reduction of the instance size. An extensive experimental evaluation shows the suitability of the proposed solution in comparison to the state-of-the-art approaches based on stochastic methods.

Cloud data centers present a challenge to environmental sustainability because of their significant energy consumption. Additionally, performance degradation resulting from energy management solutions, such as virtual machine (VM) consolidation, impacts service level agreements (SLAs) between cloud service providers and users. Thus, to achieve a balance between efficient energy consumption and avoiding SLA violations, we propose a novel VM consolidation algorithm. Conventional algorithms result in unnecessary migrations when improperly selecting VMs. Therefore, our proposed E2SVM algorithm addresses this issue by selecting VMs with high load fluctuations and minimal resource usage from overloaded servers. These selected VMs are then placed on normally loaded servers, considering their stability index. Moreover, our approach prevents server underutilization by either applying all or no VM migrations. Simulation results demonstrate a 12.9% decrease in maximum energy consumption compared with the minimum migration time VM selection policy. In addition, a 47% reduction in SLA violations was observed when using the medium absolute deviation as the overload detection policy. Therefore, this approach holds promise for real-world data centers because it minimizes energy waste and maintains low SLA violations.

Virtual machine (VM) consolidation is an emerging solution for energy saving in cloud data centers. An appropriate VM consolidation policy must change accordingly, depending on the situation. To easily deploy a VM consolidation solution, this paper proposes a VM consolidation framework for Apache CloudStack, which is a popular open source cloud platform software suite. We separate the VM consolidation mechanism from the policy used to decide which VMs should be moved to somewhere more appropriate to run. This framework is a modular architecture that combines various resource monitoring systems, power control mechanisms, and VM packing algorithms. Using the proposed framework, we have demonstrated that a Web application can consolidate and deconsolidate VMs so as to balance the load of CPU utilization among physical machines in response to the number of physical machines in operation. Experimental results reveal the proposed framework is preliminary, but provides the required functionality.

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.