Backup Virtual Machine Research Articles

Overbooking-Enabled Task Scheduling and Resource Allocation in Mobile Edge Computing Environments

Mobile Edge Computing (MEC) is proposed to solve the needs of Internet of Things (IoT) users for high resource utilization, high reliability and low latency of service requests. However, the backup virtual machine is idle when its primary virtual machine is running normally, which will waste resources. Overbooking the backup virtual machine under the above circumstances can effectively improve resource utilization. First, these virtual machines are deployed into slots randomly, and then some tasks with cooperative relationship are offloaded to virtual machines for processing. Different deployment locations have different resource utilization and average service response time. We want to find a balanced solution that minimizes the average service response time of the IoT application while maximizing resource utilization. In this paper, we propose a task scheduler and exploit a Task Deployment Algorithm (TDA) to obtain an optimal virtual machine deployment scheme. Finally, the simulation results show that the TDA can significantly increase the resource utilization of the system, while reducing the average service response time of the application by comparing TDA with the other two classical methods. The experimental results confirm that the performance of TDA is better than that of other two methods.

Downtime Distribution, Risk Volatility, and Optimal Backup VMs Allocation in Cloud Services

Service Level Agreement (SLA) is an important basis to ensure the availability of cloud service. Cloud service providers seek to not only fulfill the SLA, but also simultaneously minimize the risk uncertainty. Under such a mechanism, service providers look forward to determine the optimal number of backup virtual machines (VMs) to minimize operational costs. In this paper, we propose a data-driven risk measurement method under multi-stage SLA and empirically portray the likelihood and risk values of compensation events at different stages under several service periods. Furthermore, a multi-objective optimization function is established to balance the provisioning cost and risk volatility for cloud service providers with different risk preferences. This paper provides a practical method to cloud service providers by addressing the risk uncertainty in multi-stage SLA from cloud computing resource management perspective.

Timely Reliability Analysis of Virtual Machines Considering Migration and Recovery in an Edge Server.

For the edge computing network, whether the end-to-end delay satisfies the delay constraint of the task is critical, especially for delay-sensitive tasks. Virtual machine (VM) migration improves the robustness of the network, whereas it also causes service downtime and increases the end-to-end delay. To study the influence of failure, migration, and recovery of VMs, we define three states for the VMs in an edge server and build a continuous-time Markov chain (CTMC). Then, we develop a matrix-geometric method and a first passage time method to obtain the VMs timely reliability (VTR) and the end-to-end timely reliability (ETR). The numerical results are verified by simulation based on OMNeT++. Results show that VTR is a monotonic function of the migration rate and the number of VMs. However, in some cases, the increase in task VMs (TVMs) may conversely decrease VTR, since more TVMs also brings about more failures in a given time. Moreover, we find that there is a trade-off between TVMs and backup VMs (BVMs) when the total number of VMs is limited. Our findings may shed light on understanding the impact of VM migration on end-to-end delay and designing a more reliable edge computing network for delay-sensitive applications.

Cloud Service Reliability Enhancement via Virtual Machine Placement Optimization

With rapid adoption of the cloud computing model, many enterprises have begun deploying cloud-based services. Failures of virtual machines (VMs) in clouds have caused serious quality assurance issues for those services. VM replication is a commonly used technique for enhancing the reliability of cloud services. However, when determining the VM redundancy strategy for a specific service, many state-of-the-art methods ignore the huge network resource consumption issue that could be experienced when the service is in failure recovery mode. This paper proposes a redundant VM placement optimization approach to enhancing the reliability of cloud services. The approach employs three algorithms. The first algorithm selects an appropriate set of VM-hosting servers from a potentially large set of candidate host servers based upon the network topology. The second algorithm determines an optimal strategy to place the primary and backup VMs on the selected host servers with k-fault-tolerance assurance. Lastly, a heuristic is used to address the task-to-VM reassignment optimization problem, which is formulated as finding a maximum weight matching in bipartite graphs. The evaluation results show that the proposed approach outperforms four other representative methods in network resource consumption in the service recovery stage.

An Efficient Survivable Design With Bandwidth Guarantees for Multi-Tenant Cloud Networks

In cloud data centers (DCs), where hosted applications share the underlying network resources, network bandwidth guarantees have shown to improve predictability of application performance and cost. However, recent empirical studies have also shown that often DC devices and links are not all that reliable and that failures may cause service outages, rendering significant revenue loss for the affected tenants, as well as the cloud operator. Accordingly, cloud operators are pressed to offer both reliable and predictable performance for the hosted applications. While much work has been done on solving both problems separately, this paper seeks to develop a joint framework by which cloud operators can offer both performance and availability guarantees for the hosted tenants. In particular, this paper considers a simple model to abstract the bandwidth guarantees requirement for the tenant and presents a protection plan design which consists of backup virtual machines (VMs) placement and bandwidth provisioning to optimize the internal DC traffic. We show through solid motivational examples that finding the optimal protection plan design is highly perplexing, and encompasses several constituent challenges. Owing to its complexity, we decompose it into two subproblems, and solve them separately. First, we invoke a placement subproblem of the minimum number of backup VMs and then we explore the most efficient correspondence between backup and primary VMs (i.e., protection plan) which minimizes the bandwidth redundancy. Further, we study the design of various facets of such a plan by exploiting bandwidth sharing opportunities in multi-tenant cloud networks.

A cost-effective strategy for Cloud system maintenance

To guarantee the high availability of modern Cloud-based applications, service backup is commonly used to maintain the failed services. However, an ineffective maintenance strategy based on virtual machine (VM) backup will cause a high budget and low resource utilization. This paper proposes a cost-effective multi-VM maintenance strategy to minimize maintenance cost with limited backup VMs in the Cloud. Different from the single-system maintenance strategies that pursue local optima by considering only one system each time, the proposed strategy considers all the running systems simultaneously from a global perspective. We formalize the multi-system maintenance as a restless multi-armed bandit problem, propose the concept of Healthy Indices of VMs with Lagrangian relaxation and develop an index based policy for maintenance scheduling. Given the limited backup VMs, the experiment results show that the proposed cost-effective multi-VM maintenance strategy significantly outperforms single-system maintenance policies in terms of total maintenance cost and system availability.

A Heuristic Time Sharing Policy for Backup Resources in Cloud System

Cloud computing promises high performance and cost-efficiency. However, most cloud infrastructures operate at a low utilization, which greatly adheres cost effectiveness. Previous works focus on seeking efficient virtual machine (VM) consolidation strategies to increase the utilization of virtual resources in production environment, but overlook the under-utilization of backup virtual resources. We propose a heuristic time sharing policy of backup VMs derived from the restless multi-armed bandit problem. The proposed policy achieves increasing backup virtual resources utilization and providing high availability. Both the results in simulation and prototype system experiments show that the traditional 1:1 backup provision can be extended to 1:M (M≫1) between the backup VMs and the service VMs, and the utilization of backup VMs can be enhanced significantly.

Enhancing Survivability in Virtualized Data Centers: A Service-Aware Approach

In this paper, we propose a service-aware approach to enhance survivability in virtualized data centers. The idea is to create and maintain a Survivable Virtual Infrastructure (SVI) for each service or tenant, which includes Virtual Machines (VMs) hosting the corresponding application and their backup VMs. A fundamental problem is to determine how to map each SVI to a data center network with minimum operational costs while satisfying each VM's resource requirements and bandwidth demands between VMs before and after failures. This problem can be naturally divided into two subproblems: VM Placement (VMP) and Virtual Link Mapping (VLM). We first present a general optimization framework. Then we propose an efficient algorithm for VMP, and a polynomial-time optimal algorithm for VLM, which can be used as subroutines in the framework. We also present an effective heuristic algorithm that jointly solves two subproblems. It has been shown by extensive simulation results based on the real VM workload traces collected from Syracuse University's green data center that compared to the First Fit Decreasing (FFD) and shortest path routing based baseline algorithm, the proposed algorithms significantly reduce the reserved bandwidth, and yield comparable results in terms of the number of active servers. \begin{keywords}Cloud Computing, Data Center, Service-aware, Survivability, Virtual Machine Management. \end{keywords}

Optimizing the Performance of Virtual Machine Synchronization for Fault Tolerance

Hypervisor-based fault tolerance (HBFT), which synchronizes the state between the primary VM and the backup VM at a high frequency of tens to hundreds of milliseconds, is an emerging approach to sustaining mission-critical applications. Based on virtualization technology, HBFT provides an economic and transparent fault tolerant solution. However, the advantages currently come at the cost of substantial performance overhead during failure-free, especially for memory intensive applications. This paper presents an in-depth examination of HBFT and options to improve its performance. Based on the behavior of memory accesses among checkpointing epochs, we introduce two optimizations, read-fault reduction and write-fault prediction, for the memory tracking mechanism. These two optimizations improve the performance by 31 percent and 21 percent, respectively, for some applications. Then, we present software superpage which efficiently maps large memory regions between virtual machines (VM). Our optimization improves the performance of HBFT by a factor of 1.4 to 2.2 and achieves about 60 percent of that of the native VM.

The design of a practical system for fault-tolerant virtual machines

We have implemented a commercial enterprise-grade system for providing fault-tolerant virtual machines, based on the approach of replicating the execution of a primary virtual machine (VM) via a backup virtual machine on another server. We have designed a complete system in VMware vSphere 4.0 that is easy to use, runs on commodity servers, and typically reduces performance of real applications by less than 10%. In addition, the data bandwidth needed to keep the primary and secondary VM executing in lockstep is less than 20 Mbit/s for several real applications, which allows for the possibility of implementing fault tolerance over longer distances. An easy-to-use, commercial system that automatically restores redundancy after failure requires many additional components beyond replicated VM execution. We have designed and implemented these extra components and addressed many practical issues encountered in supporting VMs running enterprise applications. In this paper, we describe our basic design, discuss alternate design choices and a number of the implementation details, and provide performance results for both micro-benchmarks and real applications