Live Migration Of Virtual Machines Research Articles

EMotion: An SGX extension for migrating enclaves

Software Guard Extensions (SGX) is a good candidate to address sensitive information disclosure in cloud computing because SGX creates enclaves for applications that protect security sensitive code and data from malicious access. However, existing SGX-enabled Virtual Machine Managers (VMMs) do not provide live migration of SGX-enabled Virtual Machines (VMs). This management operation is impossible because the VMM cannot directly access the Enclave Page Cache (EPC) pages where the VM’s enclaves reside. SGX supports the EPC page swapping mechanism that evicts the EPC pages into the untrusted memory which the VMM can access. However, this mechanism has the limitations to be applied to enclave migration. In this paper, we propose an SGX extension for migrating enclaves called eMotion that adds additional instructions and migration support to the SGX architecture for enabling the secure managed migration of running enclaves. eMotion allows that the participating hosts establish a key used in enclave migration and the VMMs in the hosts migrate running enclaves using the established key. We implement a prototype on top of OpenSGX, an open source SGX emulator, to demonstrate the operations of eMotion and to estimate the impact on enclave migration.

Lambda response reflects the color and luminance information processing

This work uses the open source codes and PHP web programming to implement a resource management system with power saving method for virtual machines. We propose a system integrated with open source software, such as KVM and Libvirt, to construct a virtual cloud management platform. This system can detect the status of cloud resources via SNMP, calculate the operation efficiency of the overall system, allocate virtual machines through the live migration technology, and turn off extra machines in the cloud to save energy. According to our proposed power saving method, we have constructed a power efficient virtualization management platform in the cloud. Our objective is to provide enterprises or end users with power saving private cloud solutions. In this work we have also built a web page to allow users to easily access and control the cloud virtualization resources, i.e., users can manage virtual machines and monitor the status of resources via the web interface. From analysis of the experimental results of live migration of virtual machines, this work demonstrates that efficient use of hardware resources is realized by the power saving method, and the aim of power saving for cloud computing is achieved.

Energy-Efficient Cloudlet Management for Privacy Preservation in Wireless Metropolitan Area Networks

Currently, cloudlet based wireless metropolitan area network (WMAN) is emerging as an effective paradigm to extend the performance of mobile devices, which enables the execution of computational intensive mobile applications. But the normal operation of cloudlets consumes a large amount of energy, which brings about carbon dioxide emissions, aggravation of the greenhouse effect, etc. Meanwhile the data transmission of various mobile users among cloudlets would cause leakage of personal privacy. In view of this challenge, we propose an energy-efficient cloudlet management method, named ECM, for privacy preservation in WMAN. Technically, an optimization model is designed to formalize our problem. Then, based on live virtual machine (VM) migration technique, a corresponding privacy-aware VM scheduling method for energy saving is designed to determine which VMs should be migrated and where they should be migrated. Finally, experimental data demonstrate that the proposed method is both efficient and effective.

Захист віртуальних машин на основі інструкцій нового покоління процесорів AMD Zen

It is demonstrated the development of a virtualization environment security subsystem with the help of hardware-accelerated AMD Zen CPU cryptography API and its instruction set for security tasks, including but not limited to: protection against unauthorized memory access, data leaks, hypervisor breach, external attacks and malware spread via the Internet. The method in question utilizes real-time memory encryption and decryption, with the memory bandwidth and computing power sufficient for seamless hypervisor and server operation, virtual machine live migration and secure export, and demonstrates capabilities of ARM Cortex A5 on-board cryptography processor core for mentioned tasks, as well as providing secure asymmetric key exchange invisible and inaccessible to any software beside internal Trusted Platform Module and its inner DRAM memory controller, to guarantee high level of virtual environment security and sufficient resistance to most active attacks with minimum computation overhead, suitable for most real-life virtualization-based workload scenarios. The example subsystem specifically targets Microsoft Windows 10 operating system, however software support for different operating systems (including UNIX-based) may already be provided by appropriate vendors, including enterprise-ready solutions, such as Cisco, Dell, HP, etc.

IOFollow: Improving the performance of VM live storage migration with IO following in the cloud

VM live migration becomes critical to deployment in the Cloud, as to enable fast and transparent workload rescheduling among unevenly utilized physical nodes, aimed at high performance and energy efficient utilization of computer server resources. Nevertheless, VM I/O intensity can significantly impact the performance of VM live storage migration due to contention for the shared disk bandwidth and system resources. Based on this observation, this paper proposes the IOFollow scheme to improve simultaneously the VM performance and migration performance. IOFollow mitigates the I/O interference between VM I/O requests and migration I/O requests, by scheduling the migration sequence according to the access pattern of VM I/O requests. In addition, it improves the cache management efficiency by exploiting the semantic information from the VMs and migration threads. Results obtained from trace-based experiments on a lightweight prototype implementation of IOFollow indicate that IOFollow system is highly competitive, improving both the VM performance and migration performance significantly when compared to existing design.

A Live Migration Algorithm for Containers Based on Resource Locality

With the wide application of cloud computing, the scale of cloud data center network is growing. The virtual machine (VM) live migration technology is becoming more crucial in cloud data centers for the purpose of load balance, and efficient utilization of resources. The lightweight virtualization technique has made virtual machines more portable, efficient and easier to management. Different from virtual machines, containers bring more lightweight, more flexible and more intensive service capabilities to the cloud. Researches on container migration is still in its infancy, especially live migration is still very immature. In this paper, we present the locality live migration model where we take into account the distance, available bandwidth and costs between containers. Furthermore, we conduct comprehensive experiments on a cluster. Extensive simulation results show that the proposed method improves the utilization of resources of servers, and also improves the balance of all kinds of resources on the physical machine.

Models for availability and power consumption evaluation of a private cloud with VMM rejuvenation enabled by VM Live Migration

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.

Dynamic VM Consolidation Enhancement for Designing and Evaluation of Energy Efficiency in Green Data Centers Using Regression Analysis

Enhancement of dynamic Virtual Machines (VM) consolidation is an efficient means to improve the energy efficiency via effective resources utilization in Cloud data centers. In this paper, we propose an algorithm, Energy Conscious Greeny Cloud Dynamic Algorithm, which considers multiple factors such as CPU, memory and bandwidth utilization of the node for empowering VM consolidation by using regression analysis model. This algorithm is the combination of several adaptive algorithms such as EnCoReAn (UPReAn) for Predicting the Utility of a host), Overload and Under-load detection), VM Selection and Allocation algorithms, which helps to achieve live VM migration by switching-off unused servers to low-power mode (i.e., sleep or hibernation), thus saves energy and efficient resource utilization. This approach reduces the operational cost, computation time and increase the scalability. The experimental result proves that, the proposed algorithm attains significant percentage in reduction of energy consumption rather than existing VM consolidation strategies.

Effective live migration of virtual machines using partitioning and affinity aware-scheduling

During maintenance and disaster recovery scenarios, Virtual Machine (VM) inter-site migrations usually take place over limited bandwidth—typically Wide Area Network (WAN)—which is highly affected by the amount of inter-VM traffic that becomes separated during the migration process. This causes both a degradation of the Quality of Service (QoS) of inter-communicating VMs and an increase in the total migration time due to congestion of the migration link. We consider the problem of scheduling VM migration in those scenarios. In the first stage, we resort to graph partitioning theory in order to partition the VMs into groups with high intra-group communication. In the second stage, we devise an affinity-based scheduling algorithm for controlling the order of the migration groups by considering their inter-group traffic. Comprehensive simulations and real-life experimental results show that our approach is able to decrease the volume of separated traffic by a factor larger than 30%.

Ada-Things: An adaptive virtual machine monitoring and migration strategy for internet of things applications

Internet of Things (IoT) applications running on mobile devices are subject to the low storage capacity and short battery lifetime. Edge clouds (EC) provide an approach to offload computation tasks and reduce network latency for these applications. The main challenge in such ecosystems is how to efficiently monitor and allocate VM resources to realize load balancing among edge clouds. In this paper, we propose Ada-Things, an adaptive VM monitoring and live migration Strategy for IoT applications in edge cloud architecture. The basic idea of Ada-Things is that the migration method of a VM should be determined by its workload characteristics. Specifically, based on the variation of current memory dirty page rate in IoT applications, Ada-Things can adaptively select the most appropriate migration method to copy memory pages, thus addressing the two limitations (application generality and performance imbalance) of existing VM migration methods in edge cloud. Evaluation results show, compared with traditional methods, Ada-Things can significantly reduce the total migration time by 21%, the VM downtime by 38% and the amount of pages transferred by 29% in average.

A Distributed API for Live VM Migration in Cloudlets

A Distributed API for Live VM Migration in Cloudlets

Dynamic Workload Migration Over Backbone Network to Minimize Data Center Electricity Cost

As more organizations adopt cloud services, energy consumption in data centers (DCs) keeps increasing. Today, information and communication technology (ICT) has become a major consumer of energy worldwide. A large portion of ICT energy consumption is used to power servers running in DCs and the network they use to communicate. In this paper, we consider that energy cost at a particular DC is often related to the electricity price regulated by independent system operators/regional transmission organizations. As these prices vary in time and depend on the geographical locations of the DCs, recent studies have shown that the spatio-temporal variations of electricity price can be exploited to reduce electricity cost. In particular, as workloads tend to change, often unpredictably, adaptive workload placement and migration can help to serve the workloads in regions with lower electricity costs. While most prior works consider a quasi-static scenario with known workload patterns, this paper proposes dynamic workload-aware algorithms which exploit the spatio-temporal variations of electricity costs to minimize the energy cost in ICT. Although prior studies focused on power consumption from power consumers such as servers, cooling systems, etc., recent studies have shown that the network elements consume a significant portion of the energy. Hence, while reducing DC energy cost, this paper also considers electricity cost of the backbone network. Algorithms introduced in this paper use dynamic request re-routing and live virtual machine (VM) migration to move workloads to DCs with lower electricity cost. We consider VM migration cost (including electricity cost at backbone network nodes), bandwidth constraints for migration, VM consolidation, constraints from service level agreement, and administrative overhead of VM migration. Our simulation studies show that the proposed algorithms reduce operational cost of DCs significantly.

Type-aware virtual machine management for energy efficient cloud data centers

To meet the growing demands on cloud services and applications, a sizeable number of large scale cloud data centers, hosting thousands of heterogeneous servers, is established by cloud service providers. The ever growth in establishing cloud data centers is accompanied by consuming enormous amounts of energy. Thus, proposing efficient management approaches to reduce the energy consumption in cloud data centers becomes a top priority for ensuring the scalability of the cloud computing architecture. In general, the main source of energy overconsumption in today’s data centers is due to the inefficient use of the physical servers’ resources, which results in poor server utilization patterns. So, the key aspect is to utilize the physical resources optimally while serving the cloud user demands. This paper investigates the design and implementation of virtual machine management strategies for energy efficient cloud data centers. Particularly, it considers the processes of virtual machine placement and virtual machine consolidation in enhancing the energy efficiency in cloud infrastructures. While addressing the virtual machine placement problem is important, virtual machine consolidation is even more important to enable continuous reorganization of the already-placed virtual machines on the least number of physical machines. This results in reducing the number of active physical machines by leveraging live virtual machine migration enabled by the virtualization concept. Moreover, since the virtual machine migration operations consume additional energy, the frequency of VM migrations needs to be limited and controlled as well. The paper presents a distributed approach to an energy-efficient dynamic virtual machine consolidation mechanism. This approach determines, based on novel algorithms, which virtual machines to migrate, and when. Then, the placement of the virtual machines selected for migration is achieved based on a generalization of the Knapsack Problem known as the Multiple Choice Knapsack Problem. The placement process suits both static and dynamic virtual machine placement. The results of the performance evaluation demonstrate that the proposed new algorithms are able to enhance the energy efficiency in cloud data centers.

SWARE: A Methodology for Software Aging and Rejuvenation Experiments

Reliability and availability are mandatory requirements for numerous applications. Technical apparatus to study system dependability is essential to support software deployment and maintenance. Software aging is a related issue in this context. Software aging is a cumulative process which leads systems with long-running execution to hangs or failures. Software rejuvenation is used to prevent software aging problems. Software rejuvenation actions comprise system reboot or application restart to bringing software to a stable fresh state. This paper proposes a methodology to conduct software aging and software rejuvenation experiments. The approach has three phases: (i) Stress Phase - stress environment with the accelerated workload to induce bugs activation; (ii) Wait Phase - stop workload submission to observe the system state after workload submission; (iii) Rejuvenation Phase - find the impacts caused by the software rejuvenation. We named our methodology as SWARE (Stress-Wait-Rejuvenation). To validate the SWARE methodology, we present a case study. This case study consists of an experiment of VM Live Migration as rejuvenation mechanism for VMM software aging. The considered testbed is a Private Cloud with OpenNebula and KVM 1.0. The obtained results show that VM live migration is useful as rejuvenation for VMM software aging.

VM Live Migration At Scale

Uninterrupted uptime is a critical aspect of Virtual Machines (VMs) offered by cloud hosting providers. Google's VMs run on top of rapidly changing infrastructure: we regularly update hardware and host software, and we must quickly respond to failing hardware. Frequent change is critical to both development velocity---deploying new versions of services and infrastructure---and the ability to respond rapidly to defects, including critical security fixes. Typically these updates would be disruptive, resulting in VM termination or restart. In this paper we present how we use VM live migration at scale to eliminate this disruption with minimal impact to the guest, performing over 1,000,0001migrations monthly in our production fleet, with 50ms median blackout, 300ms 99th percentile blackout.

Load monitoring and system-traffic-aware live VM migration-based load balancing in cloud data center using graph theoretic solutions

This paper proposes solutions to monitor the load and to balance the load of cloud data center. The proposed solutions work in two phases and graph theoretical concepts are applied in both phases. In the first phase, cloud data center is modeled as a network graph. This network graph is augmented with minimum dominating set concept of graph theory for monitoring its load. For constructing minimum dominating set, this paper proposes a new variant of minimum dominating set (V-MDS) algorithm and is compared with existing construction algorithms proposed by Rooji and Fomin. The V-MDS approach of querying cloud data center load information is compared with Central monitor approach. The second phase focuses on system and network-aware live virtual machine migration for load balancing cloud data center. For this, a new system and traffic-aware live VM migration for load balancing (ST-LVM-LB) algorithm is proposed and is compared with existing benchmarked algorithms dynamic management algorithm (DMA) and Sandpiper. To study the performance of the proposed algorithms, CloudSim3.0.3 simulator is used. The experimental results show that, V-MDS algorithm takes quadratic time complexity, whereas Rooji and Fomin algorithms take exponential time complexity. Then the V-MDS approach for querying Cloud Data Center load information is compared with the Central monitor approach and the experimental result shows that the proposed approach reduces the number of message updates by half than the Central monitor approach. The experimental results show on load balancing that the developed ST-LVM-LB algorithm triggers lesser Virtual Machine migrations, takes lesser time and migration cost to migrate with minimum network overhead. Thus the proposed algorithms improve the service delivery performance of cloud data center by incorporating graph theoretical solutions in monitoring and balancing the load.

SLA Management For Virtual Machine Live Migration Using Machine Learning with Modified Kernel and Statistical Approach

Application of cloud computing is rising substantially due to its capability to deliver scalable computational power. System attempts to allocate a maximum number of resources in a manner that ensures that all the service level agreements (SLAs) are maintained. Virtualization is considered as a core technology of cloud computing. Virtual machine (VM) instances allow cloud providers to utilize datacenter resources more efficiently. Moreover, by using dynamic VM consolidation using live migration, VMs can be placed according to their current resource requirements on the minimal number of physical nodes and consequently maintaining SLAs. Accordingly, non optimized and inefficient VMs consolidation may lead to performance degradation. Therefore, to ensure acceptable quality of service (QoS) and SLA, a machine learning technique with modified kernel for VMs live migrations based on adaptive prediction of utilization thresholds is presented. The efficiency of the proposed technique is validated with different workload patterns from Planet Lab servers.

LayerMover: Fast virtual machine migration over WAN with three-layer image structure

Live Virtual Machine (VM) migration across data centers is an important technology to facilitate cloud management and deepen the cooperation between cloud providers. Without the support of a shared storage system between data centers, migrating the storage data (i.e. virtual disk) of a VM becomes the bottleneck of live VM migration over Wide Area Network (WAN) due to the contradiction between the low bandwidth of the Internet and the comparatively large size of VM storage data. According to previous studies, many inter- and intra-VM duplicated blocks exist between VM disk images. Therefore, data deduplication is widely used for accelerating VM storage data migration. However, it must make a trade-off between computation cost and transmission benefit. Existing approaches are fragile as they explore only the static data feature of image files without much consideration on data semantics. They may adversely influence on migration performance when the benefit resulting from data deduplication cannot remedy its computation overhead. In this paper, we propose a new space-efficient VM image structure—three-layer structure. According to different functions and features, the data of a VM are separated into an Operating System (OS) layer, a Working Environment (WE) layer, and a User Data (UD) layer. Based on this structure, we design a novel VM migration system—LayerMover. It mainly focuses on improving migration performance through optimizing the data deduplication technique. Our experimental results show that three-layer image structure can improve data sharing between VMs, and the similarity ratio between WE images can reach 70%. The tests for LayerMover indicate that it can be significantly beneficial to VM migration across data centers, especially when multiple VMs which share base images are migrated.

A combined forecast-based virtual machine migration in cloud data centers

Live virtual machine (VM) migration improves the performance of cloud data center in terms of energy efficiency, fault tolerance, and availability. The workload handled by cloud data center is dynamic in nature. This increases the resource requirement of either the migrated virtual machine or collocated virtual machine at any time leading to further migration. Inappropriately handled live VM migration imposes severe application performance degradation. In this paper, a combined forecasting technique to predict the resource requirement of any virtual machine is proposed. Based on the current and predicted resource utilization, live migration is performed by Combined Forecast Load-Aware technique. Experiments were carried out to evaluate the performance of the proposed technique on live VM migration. The outcomes indicate that the proposed approach has minimized the number of migrations, energy usage, and the message overhead when compared with the existing state-of-art technique.

Energy-efficient Tasks Scheduling Heuristics with Multi-constraints in Virtualized Clouds

Reducing energy consumption has become an important task in cloud datacenters. Many existing scheduling approaches in cloud datacenters try to consolidate virtual machines (VMs) to the minimum number of physical hosts and hence minimize the energy consumption. VM live migration technique is used to dynamically consolidate VMs to as few PMs as possible; however, it introduces high migration overhead. Furthermore, the cost factor is usually not taken into account by existing approaches, which will lead to high payment cost for cloud users. In this paper, we aim to achieve energy reduction for cloud providers and payment saving for cloud users, and at the same time, without introducing VM migration overhead and without compromising deadline guarantees for user tasks. Motivated by the fact that some of the tasks have relatively loose deadlines, we can further reduce energy consumption by proactively postponing the tasks without waking up new physical machines (PMs). A heuristic task scheduling algorithm called Energy and Deadline Aware with Non-Migration Scheduling (EDA-NMS) algorithm is proposed, which exploits the looseness of task deadlines and tries to postpone the execution of the tasks that have loose deadlines in order to avoid waking up new PMs. When determining the VM instant types, EDA-NMS selects the instant types that are just sufficient to guarantee task deadline to reduce user payment cost. The results of extensive experiments show that our algorithm performs better than other existing algorithms on achieving energy efficiency without introducing VM migration overhead and without compromising deadline guarantees.