Energy Efficient Multi-Level Network Resources Management in Cloud Computing Data Centers

The article presents an efficient energy optimization framework based on dynamic resource scheduling for VM migration in cloud data centers. This increasing number of cloud data centers all over the world are consuming a vast amount of power and thus, exhaling a huge amount of CO2 that has a strong negative impact on the environment. Therefore, implementing Green cloud computing by efficient power reduction is a momentous research area. Live Virtual Machine (VM) migration, and server consolidation technology along with appropriate resource allocation of users' tasks, is particularly useful for reducing power consumption in cloud data centers. In this article, the authors propose algorithms which mainly consider live VM migration techniques for power reduction named “Power_reduction” and “VM_migration.” Moreover, the authors implement dynamic scheduling of servers based on sequential search, random search, and a maximum fairness search for convenient allocation and higher utilization of resources. The authors perform simulation work using CloudSim and the Cloudera simulator to evaluate the performance of the proposed algorithms. Results show that the proposed approaches achieve around 30% energy savings than the existing algorithms.

Cloud computing is a new computation technology that provides services to consumers and businesses. The main idea of Cloud computing is to present software and hardware services through the Internet to the users and organizations at all levels. In Cloud computing, the users pay for the services, which means a usage-based payment system is used in this technology. Using virtualization technology in computation resources enables the appropriate utilization of resources in cloud computing. One of the most significant challenging issues in virtualization technology is the placement of optimal virtual machines on physical machines in cloud data centers. The placement of virtual machines comprises a process wherein virtual machines are mapped onto physical machines in cloud data centers. Optimal deployment leads to the reduction in power consumption, optimal use of resources, traffic reduction in data centers, costs reduction, and efficiency enhancement of the data center in the cloud. The present article proposed a new approach using a combination of the Sine–Cosine Algorithm and Salp Swarm Algorithm as discrete multi-objective and chaotic functions for optimal virtual machine placement. The first goal of the proposed algorithm was to reduce the power consumption in cloud data centers by condensing the number of active physical machines. The second goal was to reduce the waste of resources and manage it by optimally virtual machine placement on physical machines in cloud data centers. The third objective was to minimize and reduce Service Level Agreement among the active physical machines in cloud data centers. The proposed method prevent the increase in the migration of virtual machines onto physical machines. Ultimately, the results obtained from the proposed algorithm were compared with those of previous akin algorithms in the literature, including First Fit, Virtual Machine Placement Ant Colony System, and Modified Best Fit Decreasing. The proposed scheme is tested using Amazon EC2 Instances and the result indicated that the proposed algorithm performs better than the existing algorithms for various performance metrics.

Software aging affects the availability of Virtual Machine Monitor (VMM), one of the main components of virtualized environments. Software aging causes internal software degradation due to bugs activation and accumulation during its execution. Software rejuvenation implemented through Virtual Machine (VM) Live Migration may be applied to cope with software aging effects on cloud computing system. Another relevant problem is the power consumption of using software rejuvenation techniques in the virtualized environments. This paper presents availability models based on Stochastic Petri Nets to evaluate two VM Live Migration approaches. These approaches are based on the redundancy schemes: Warm-Standby and Cold-Standby. In the Cold-Standby migration, the migration target machine is started only before the VM Live Migration. In the Warm-Standby Migration, the migration target machine runs along with the source migration machine. Results show that VM Live Migration causes a significant improvement in system availability. Scenarios with a heavy workload present an annual downtime reduction of 164 h. The availability comparison between two approaches reveals that the Cold-Standby approach has a slightly better result due to the decrease in the total number of VM Live Migrations. The power consumption results show that Cold-Standby approach is more efficient in power consumption. In all the observed scenarios, the costs savings by using Cold-Standby approach exceed 40%. The highlights of this paper are: i) a comprehensive model for availability evaluation of cloud computing with VMM rejuvenation through VM migration scheduling; ii) sensitivity analysis to define proper rejuvenation scheduling to maximize system availability and iii) power consumption comparison of the two adopted migration approaches.

Virtualization techniques effectively handle the growing demand for computing, storage, and communication resources in large-scale Cloud Data Centers (CDC). It helps to achieve different resource management objectives like load balancing, online system maintenance, proactive fault tolerance, power management, and resource sharing through Virtual Machine (VM) migration. VM migration is a resource-intensive procedure as VM’s continuously demand appropriate CPU cycles, cache memory, memory capacity, and communication bandwidth. Therefore, this process degrades the performance of running applications and adversely affects efficiency of the data centers, particularly when Service Level Agreements (SLA) and critical business objectives are to be met. Live VM migration is frequently used because it allows the availability of application service, while migration is performed. In this paper, we make an exhaustive survey of the literature on live VM migration and analyze the various proposed mechanisms. We first classify the types of Live VM migration (single, multiple and hybrid). Next, we categorize VM migration techniques based on duplication mechanisms (replication, de-duplication, redundancy, and compression) and awareness of context (dependency, soft page, dirty page, and page fault) and evaluate the various Live VM migration techniques. We discuss various performance metrics like application service downtime, total migration time and amount of data transferred. CPU, memory and storage data is transferred during the process of VM migration and we identify the category of data that needs to be transferred in each case. We present a brief discussion on security threats in live VM migration and categories them in three different classes (control plane, data plane, and migration module). We also explain the security requirements and existing solutions to mitigate possible attacks. Specific gaps are identified and the research challenges in improving the performance of live VM migration are highlighted. The significance of this work is that it presents the background of live VM migration techniques and an in depth review which will be helpful for cloud professionals and researchers to further explore the challenges and provide optimal solutions.

Performance-controlled server consolidation for virtualized data centers with multi-tier applications

Live migration of virtual machine (VM) is a promising technology that helps physical machines (PMs) adapt to load changes and guarantees Quality of Service (QoS) in cloud data center. Many individual-based VM migration studies ignore the association between VMs, resulting in high communication cost. Some research on multiple VMs migration migrates the VM group as a whole, which is likely to result in ineffective migration and increase the network burden. In this paper, a VM migration algorithm based on group selection (VMMAGS) is proposed, which takes into account the migration cost, communication cost, and VM heat to optimize migration performance. The appropriate VM groups are selected as migration options, and the optimal migration scheme is obtained according to the integration cost of partitions of selected VM groups. Extensive experiments show that our algorithm can effectively reduce the migration cost and communication cost, improve the system reliability compared with other related algorithms.

Modern data centers must provide performance assurance for complex system software such as multi-tier web applications. In addition, the power consumption of data centers needs to be minimized to reduce operating costs and avoid system overheating. Various power-efficient performance management strategies have been proposed based on dynamic voltage and frequency scaling (DVFS). Virtualization technologies have also made it possible to consolidate multiple virtual machines (VMs) onto a smaller number of active physical servers for even greater power savings, but at the cost of a higher overhead. This paper proposes a performance-controlled power optimization solution for virtualized data centers with multi-tier applications. While existing work relies on either DVFS or server consolidation in a separate manner, our solution utilizes both strategies for maximized power savings by integrating feedback control with optimization strategies. At the application level, a multi-input-multi-output controller is designed to achieve the desired performance for applications spanning multiple VMs, on a short time scale, by reallocating the CPU resources and DVFS. At the data center level, a power optimizer is proposed to incrementally consolidate VMs onto the most power-efficient servers on a longer time scale. Empirical results on a hardware testbed demonstrate that our solution can effectively achieve performance-assured power savings. Extensive simulation results, based on a trace file of 5,415 real servers, demonstrate the efficacy of our solution in large-scale data centers.

A balanced virtual machine scheduling method for energy-performance trade-offs in cyber-physical cloud systems

More than bin packing: Dynamic resource allocation strategies in cloud data centers

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

Green technology is the emerging new sustainable technology which paves the way for lowering the raise in the earth’s temperature. The increase in toxic waste from the electronic devices in every level of usage increases the global warming and thus the need of green technology is inevitable. The contributions towards green computing in global community will save the environment by minimizing the energy consumption. Emerging technologies in Internet of things, pervasive applications, cloud computing provides effective consumer, scientific and business domains. Due to the massive data storage and computation of cloud computing, there is an exponential increment in the demand of cloud computing. The growth of enormous amount of data brings a huge challenge to the cloud computing environment. Data centers are the main module of the cloud computing environment, but the major issue is the management of power computing in those data centers. Green data centers are established with limited power consumption and carbon-di-oxide emission. The research work mainly focusses on the delay time of the data centers in Internet cloud which in turn increases the usage of power consumption in data centers. The analysis is simulated with Internet cloud and data centers.

In the IT industry, Cloud Technology has ushered in a revolution. The sole drawback of a cloud environment is the high power consumption. As a result, to improve the efficiency of the cloud environment, cloud servers must be able to execute more work at the same time while using less power. The majority of existing energy optimization algorithms set an upper and lower threshold for underload and overload detection, lowering energy usage while lowering performance. Using the Energy Optimization Algorithm, we primarily focus on optimizing energy without sacrificing performance in this study (EOA). We identify the overload in our approach by looking at the entire workload utilization of the data center. The performance-to-power ratio is improved with this approach. Reduced live migration of virtual machines is required to achieve high throughput. The primary goal of this EOA is to reduce the number of live migrations while maintaining performance.

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.

bwSlicer: A bandwidth slicing framework for cloud data centers

One of the major challenges that cloud providers face is minimizing power consumption of their data centers. To this point, majority of current research focuses on energy efficient management of resources in the Infrastructure as a Service model and through virtual machine consolidation. However, containers are increasingly gaining popularity and going to be major deployment model in cloud environment and specifically in Platform as a Service. This paper focuses on improving the energy efficiency of servers for this new deployment model by proposing a framework that consolidates containers on virtual machines. We first formally present the container consolidation problem and then we compare a number of algorithms and evaluate their performance against metrics such as energy consumption, Service Level Agreement violations, average container migrations rate, and average number of created virtual machines. Our proposed framework and algorithms can be utilized in a private cloud to minimize energy consumption, or alternatively in a public cloud to minimize the total number of hours the virtual machines leased.