Portability Responsive System for VM Migration in Cloud DCN

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.

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.

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.

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 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.

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.

[Purpose] Server virtualization technology is the basic form for the cloud data center to serve customers. While the cloud data center reduces the SLA violations in the early stage, the increase in the utilization of resources such as virtual machine servers and network equipment and decrease in energy consumption have become an important issue for reduction of operating costs. Therefore, on the background of the cloud data center network based on SDN technology, this paper studies a cloud data center resource utilization and energy consumption optimization strategy through virtual machine migration and integration. [Method] The technology based on SDN is firstly introduced, the approximate optimal solution FFD algorithm for the multidimensional vector bin packing problem is then presented, the Partheno-Genetic Algorithm (PGA) is then recommended, finally a Hybrid Partheno genetic Algorithm (HPGA) based on the both is established and a mathematic model is created. [Results] By combining the advantages of FFD and PGA, this paper improves the utilization of the resources of the entire cloud data center, reduces energy consumption, and prevents the occurrence of computing “hot spots”. [Conclusion] Experiments in CloudSim prove the effectiveness of the algorithm. Compared with FFD and PGA algorithms, it is able to enhance the efficiency of the Data Center effectively, and obtain the optimal solution more quickly, thereby balancing the physical resources of the server.

SummaryThis paper focus on providing a secure and trustworthy solution for virtual machine (VM) migration within an existing cloud provider domain, and/or to the other federating cloud providers. The infrastructure‐as‐a‐service (IaaS) cloud service model is mainly addressed to extend and complement the previous Trusted Computing techniques for secure VM launch and VM migration case. The VM migration solution proposed in this paper uses a Trust_Token based to guarantee that the user VMs can only be migrated and hosted on a trustworthy and/or compliant cloud platforms. The possibility to also check the compliance of the cloud platforms with the pre‐defined baseline configurations makes our solution compatible with an existing widely accepted standards‐based, security‐focused cloud frameworks like FedRAMP. Our proposed solution can be used for both inter‐ and intra‐cloud VM migrations. Different from previous schemes, our solution is not dependent on an active (on‐line) trusted third party; that is, the trusted third party only performs the platform certification and is not involved in the actual VM migration process. We use the Tamarin solver to realize a formal security analysis of the proposed migration protocol and show that our protocol is safe under the Dolev‐Yao intruder model. Finally, we show how our proposed mechanisms fulfill major security and trust requirements for secure VM migration in cloud environments.

Impact of Factors Affecting Pre-copy Virtual Machine Migration Technique for Cloud Computing

Moving Target Defense (MTD) is a defensive mechanism based on dynamic system reconfiguration to prevent or thwart cyberattacks. In the last years, considerable progress has been made regarding MTD approaches for virtualized environments, and Virtual Machine (VM) migration is the core of most of these approaches. However, VM migration produces system downtime, meaning that each MTD reconfiguration affects system availability. Therefore, a method for a combined evaluation of availability and security is of utmost importance for VM migration-based MTD design. In this paper, we propose a Stochastic Reward Net (SRN) for the probability of attack success and availability evaluation of an MTD based on VM migration scheduling. We study the MTD system under different conditions regarding 1) VM migration scheduling, 2) VM migration failure probability, and 3) attack success rate. Our results highlight the tradeoff between availability and security when applying MTD based on VM migration. The approach and results may provide inputs for designing and evaluating MTD policies based on VM migration.

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.

In Infrastructure-as-a-Service (IaaS) clouds, users manage the systems in virtual machines (VMs) called user VMs through remote management systems (RMSes). To allow users to manage their VMs during failures inside the VMs, IaaS usually provides out-of-band remote management. This management is performed indirectly via an RMS server in a privileged VM called the management VM. However, it is discontinued when a user VM is migrated. This is because an RMS server in the management VM at the source host is terminated on VM migration. Even worse, pending data is lost between an RMS client and a user VM. In this paper, we propose D-MORE for continuing out-of-band remote management across VM migration. D-MORE provides a privileged and migratable VM called DomR and performs out-of-band remote management of a user VM via DomR. During VM migration, it synchronously co-migrates DomR and its target VM and transparently maintains the connections between an RMS client, DomR, and its target VM. We have implemented D-MORE in Xen and confirmed that a remote user could manage his VM via DomR after the VM has been migrated. Our experiments showed that input data was not lost during VM migration and the overhead of D-MORE was acceptable.

We propose two VM migration methods that improve the total sum of program correct by dynamically moving VMs to appropriate edge node. We assume a multi-hierarchy information processing system that the cloud data center and the edge node VM (Virtual Machine) perform processing at the same time, the data center completes all processing, and the VM has the required response time and returns a result that the processing has progressed (called accuracy). The first method is “VM sweeping method” that allocates more CPU time to VMs that perform high accuracy processing. The second methods is “VM sweeping method” that allocates more CPU time to VMs that perform high accuracy processing that improves per unit CPU time (AIR), and “VM number averaging method” that attempts to move VMs so that the assignment status of the CPU time in all edge nodes is equal. The accuracy improvement effect of the proposed VM migration method was evaluated by a simulator. Evaluations changed the AIR from the Early-blooming type where the accuracy greatly increases in the first half to the Late-blooming type where the accuracy greatly increases in the second half, and comparing it with the case where the VM is not moved. It was clarified that a big effect can be expected in the VM sweeping method, and the VM number averaging method in the Earlyblooming type program.

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.

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