Virtual Machine Downtime Research Articles

VM performance-aware virtual machine migration method based on ant colony optimization in cloud environment

Many virtual machine (VM) allocation methods have been proposed to reduce the number of physical machines (PMs), improve resource utilization for cloud service providers. If VMs are migrated on the same PM, then there will be substantial resource competition among these VMs, which results in VM performance reduction. Many VM migration (VMM) methods neglect VM performance reduction. Although some performance-aware VM allocation methods were proposed, the performance optimization objective of these methods mainly aimed at guaranteeing service level agreement or reducing VM downtime during migration, and they did not scientifically analyze how the VM performance degrades. Therefore, how to minimize the VM performance reduction for users when migrating VMs remains a major challenge. This paper proposes a VM Performance-Aware VMM method (PAVMM) for both users and cloud service providers. To maximize VM performance for users, it utilizes the VM performance model, which was built in our previous works, to predict the VM performance after migrating VMs. It then establishes an optimization objective of maximizing VM performance for users. Meanwhile, minimizing the number of active PMs and the total migration cost are regarded as additional optimization objectives for cloud service providers. Therefore, we formulate VMM as a multi-objective optimization problem, which tries to maximize VM performance for users and minimize the number of active PMs and the total migration cost for cloud service providers simultaneously. Then an ant colony optimization (ACO)-based algorithm is proposed to solve the NP-hard VMM problem. Lastly, the experiments are conducted to evaluate PAVMM, and the results verify its efficiency.

Cloud-BlackBox: Toward practical recording and tracking of VM swarms for multifaceted cloud inspection

Given the widening scope of the utilization and application of cloud computing services from general to mission-critical systems such as strategic military, financial, and the information systems of governmental agencies, the need for the development of improved methods to ensure the stability and security of cloud data and services is being increasingly emphasized. Various approaches have been developed to improve the security and stability of cloud infrastructure. In particular, the continuous inspection of the memory of Virtual Machine (VM) instances in the cloud platform has been an important factor in identifying the causes of security incidents related to zero-day vulnerabilities and critical system faults in cloud infrastructure. However, despite numerous studies in the field of continuous memory inspection, it is difficult to find a practical solution that is deployable in commercial-off-the-shelf cloud platforms. For instance, continuous memory snapshots generally cause various problems such as increased VM downtime occurrences, user-obstructive latency for memory snapshots, VM performance degradation, and massive data generation. To alleviate these limitations, we propose Cloud-BlackBox, which enables the recording of the memory of VM swarms running on cloud platforms that require a very high level of stability and security, and facilitates the flexible analysis of the recorded memory on a large-scale. A VM swarm refers to an environment in which multiple VMs are run in parallel. The proposed Cloud-BlackBox method provides the following benefits. First, by clustering VM swarm kernel memory, the amount of computation required to capture memory snapshots and the size of the generated snapshot images are minimized. Further, we propose a mechanism to merge kernel memory by rapidly identifying the homogeneity of the memory layout through analysis of the underlying base image and introspection of the running VM. The application of the proposed mechanism led to a storage reduction by a factor of 12.85. Second, a cognitive-scale bitmap was designed to track changes in the memory of VM swarms. The cognitive-scale bitmap is a mechanism that can dynamically manage the tracking of memory change information by recognizing the memory usage patterns of component VMs. With the designed cognitive-scale bitmap, the time required for the collection of a memory snapshot was reduced by more than 14.85 times, and the VM input/output (I/O) performance degradation was reduced by 50%. Third, a synchronized accessible memory interchange (SAMI) mechanism is proposed to facilitate the agile in-depth analysis of large-scale memory resources. Cloud-BlackBox tracks and records memory change information. Therefore, a procedure for restoring the recorded memory to a raw-memory analyzable form is required to analyze the recorded memory. The SAMI mechanism assists the analyst in ensuring consistent memory restoration performance when arbitrarily selecting recorded memory. Furthermore, SAMI is useful for reducing the scope of analysis without memory restoration simply by analyzing recorded metadata. Consequently, the revised schemes inside Cloud-BlackBox have several applications in various fields, such as advanced detection of malicious activities, service error recovery, malware analysis, and antivirus functions. In addition, the proposed approach has been implemented on a campus-wide cloud computing service called SysCore-Cloud.

IConSnap: An Incremental Continuous Snapshots System for Virtual Machines

The reliability of data and services hosted on a virtual machine (VM) is a top concern in cloud environments. The Continuous Snapshots can reduce the data loss in case of failures and thus is prevailing for protecting long-running systems. However, existing methods suffer from long VM downtime, long snapshot interval and significant performance loss. In this paper, we present iConsnap, a system designed to take fine-grained continuous snapshots of virtual machines without compromising VM performance. First, iConsnap adopts the copy-on-write (COW) mechanism to save the memory pages on-demand, and thus decreases the downtime to about 200 milliseconds. Second, we extend the idea of COW and propose a lazily incremental approach to save the delta data between two successive snapshots only once, thereby reducing the snapshot duration and snapshot data a lot. Third, we propose several scheduling mechanisms to mitigate the VM performance penalty issue. Last, we introduce a method combined of compression and time-aware multi-granularity reclamation strategy to reduce the storage costs without losing performance and availability. We implement iConsnap on QEMU/KVM and evaluate it through a set of experiments. The results show that iConsnap outperforms existing approaches in terms of downtime, duration, storage costs and VM performance.

Live Migration of Virtual Machines Using a Mamdani Fuzzy Inference System

Efforts were exerted to enhance the live virtual machines (VMs) migration, including performance improvements of the live migration of services to the cloud. The VMs empower the cloud users to store relevant data and resources. However, the utilization of servers has increased significantly because of the virtualization of computer systems, leading to a rise in power consumption and storage requirements by data centers, and thereby the running costs. Data center migration technologies are used to reduce risk, minimize downtime, and streamline and accelerate the data center move process. Indeed, several parameters, such as non-network overheads and downtime adjustment, may impact the live migration time and server downtime to a large extent. By virtualizing the network resources, the infrastructure as a service (IaaS) can be used dynamically to allocate the bandwidth to services and monitor the network flow routing. Due to the large amount of filthy retransmission, existing live migration systems still suffer from extensive downtime and significant performance degradation in cross-data-center situations. This study aims to minimize the energy consumption by restricting the VMs migration and switching off the guests depending on a threshold, thereby boosting the residual network bandwidth in the data center with a minimal breach of the service level agreement (SLA). In this research, we analyzed and evaluated the findings observed through simulating different parameters, like availability, downtime, and outage of VMs in data center processes. This new paradigm is composed of two forms of detection strategies in the live migration approach from the source host to the destination source machine.

Выбор факторов для прогнозирования характеристик живой миграции виртуальных машин

Virtual machine migration is widely used in cloud data centers to scale and maintain the stability of cloud services. However, the performance metrics of virtual machine (VM) applications during migration that are set in the Service Level Agreements may deteriorate. Before starting a migration, it is necessary to evaluate the migration characteristics that affect the quality of service. These characteristics are the total migration time and virtual machine downtime, which are random variables that depend on a variety of factors. The prediction is based on the VM monitoring data. In this paper, we select the most suitable factors for forecasting five types of migrations: precopy migration, postcopy migration, and modification of precopy migration such as CPU throttling, data compression, and delta compression of modified memory pages. To do this, we analyzed a dataset that includes data on five types of migrations, approximately 8000 records of each type. Using correlation analysis, the factors that mostly affect the total migration time and the VM downtime are chosen. These characteristics are predicted using machine learning methods such as linear regression and the support vector machine. It is shown that the number of factors can be reduced almost twice with the same quality of the forecast. In general, linear regression provides relatively high accuracy in predicting the total migration time and the duration of virtual machine downtime. At the same time, the observed nonlinearity in the correlations shows that it is advisable to use the support vector machine to improve the quality of the forecast.

A Hypervisor Approach to Enable Live Migration with Passthrough SR-IOV Network Devices

Single-Root I/O Virtualization (SR-IOV) is a specification that allows a single PCI Express (PCIe) device (physical function or PF) to be used as multiple PCIe devices (virtual functions or VF). In a virtualization system, each VF can be directly assigned to a virtual machine (VM) in passthrough mode to significantly improve the network performance. However, VF passthrough mode is not compatible with live migration, which is an essential capability that enables many advanced virtualization features such as high availability and resource provisioning. To solve this problem, we design SRVM which provides hypervisor support to ensure the VF device can be correctly used by the migrated VM and the applications. SRVM is implemented in the hypervisor without modification in guest operating systems or guest VM drivers. SRVM does not increase VM downtime. It only costs limited resources (an extra CPU core only during the live migration pre-copy phase), and there is no significant runtime overhead in VM network performance.

A Multi-Objective Load Balancing System for Cloud Environments

Virtual machine (VM) live migration has been applied to system load balancing in cloud environments for the purpose of minimizing VM downtime and maximizing resource utilization. However, the migra ...

Energy conservation in cloud data centers by minimizing virtual machines migration through artificial neural network

Cloud computing is one of the most attractive cost effective technologies for provisioning information technology (IT) resources to common IT consumers. These resources are provided as service through internet in pay per usage manner, which are mainly classified into application, platform and infrastructure. Cloud provides its services through data centers that possess high configuration servers. The conservation of data centers energy give benefits to both cloud providers and consumers in terms of service time and cost. One of the fundamental services of cloud is infrastructure as a service that provides virtual machines (VMs) as a computing resource to consumers. The VMs are created in data center servers as the machine instances, which could work as a dedicated computer system for consumers. As cloud provides the feature of elasticity, the consumers can change their resource demand during service. This characteristics leads VMs migration is unavoidable in cloud environment. The increased down time of VMs in migration affects the efficiency of cloud service. The minimization of VMs migration reduces the processing time that ultimately saves the energy of data centers. The proposed methodology in this work utilizes genetically weight optimized artificial neural network to predict the near future availability of data center servers. Based on the future availability of resources the VMs management activities are performed. The implementation results demonstrated that the proposed methodology significantly reduces the processing time of data centers and the response time of customer applications by minimizing VMs migration.

SRVM

Single-Root I/O Virtualization (SR-IOV) is a specification that allows a single PCI Express (PCIe) device (ysical function or PF) to be used as multiple PCIe devices (virtual functions or VF). In a virtualization system, each VF can be directly assigned to a virtual machine (VM) in passthrough mode to significantly improve the network performance. However, VF passthrough mode is not compatible with live migration, which is an essential capability that enables many advanced virtualization features such as high availability and resource provisioning. To solve this problem, we design SRVM which provides hypervisor support to ensure the VF device can be correctly used by the migrated VM and the applications. SRVM is implemented in the hypervisor without modification in guest operating systems or guest VM drivers. Our experimental results show that SRVM can effectively migrate all memory state, and there is no data loss or corruption in applications after live migration. SRVM does not increase VM downtime. It only costs limited resources (an extra CPU core), and there is no significant runtime overhead in VM network performance. In fact, since the VF can continue to be used during the pre-copy ase, it offers network throughput which is 9.6 times and network latency which is 98% lower compared to other solutions that switch to para-virtualization mode during live migration.

Task-Based System Load Balancing in Cloud Computing Using Particle Swarm Optimization

Live virtual machine (VM) migration is a technique for achieving system load balancing in a cloud environment by transferring an active VM from one physical host to another. This technique has been proposed to reduce the downtime for migrating overloaded VMs, but it is still time- and cost-consuming, and a large amount of memory is involved in the migration process. To overcome these drawbacks, we propose a Task-based System Load Balancing method using Particle Swarm Optimization (TBSLB-PSO) that achieves system load balancing by only transferring extra tasks from an overloaded VM instead of migrating the entire overloaded VM. We also design an optimization model to migrate these extra tasks to the new host VMs by applying Particle Swarm Optimization (PSO). To evaluate the proposed method, we extend the cloud simulator (Cloudsim) package and use PSO as its task scheduling model. The simulation results show that the proposed TBSLB-PSO method significantly reduces the time taken for the load balancing process compared to traditional load balancing approaches. Furthermore, in our proposed approach the overloaded VMs will not be paused during the migration process, and there is no need to use the VM pre-copy process. Therefore, the TBSLB-PSO method will eliminate VM downtime and the risk of losing the last activity performed by a customer, and will increase the Quality of Service experienced by cloud customers.