Dependability modeling and analysis for the virtual clusters

Reliability assessment represent a key issue in any advanced technology application where the systems must satisfy high level of reliability in order to guarantee ambient, personnel and system safety. This paper is focused on Reliability Block Diagram (RBD) technique with the aim of providing a reliability prediction for complex systems. In particular the case study is focused on gas turbine auxiliary systems. These systems are made up of hydraulic items (valves, pumps, filters, etc.) and electronic ones (sensors, instruments, control logic); these complex structures can be decomposed to a collection of blocks and interconnections in order to achieve reliability performance of the whole system. The aim of this paper is to introduce a new approach to assess standby redundant architectures widely used many field of applications and allow project engineers to focus system design on reliability assessment: in this way project engineers can reduce time-delivery, reduce time for improvements, achieve reliability targets and guarantee availability performance to the costumers. Furthermore a dedicated tool is shown, RBDesigner, that semi-automatically generates a RBD starting from the sketch of thermal-hydraulic systems and provides the most important reliability parameters. The strengths of the developed tools were provided by a comparison with other commercial software solutions. © 2015 IEEE.

Virtual organization clusters are systems comprised of virtual machines that provide dedicated computing clusters for each individual virtual organization. The design of these clusters allows individual virtual machines to be independent of the underlying physical hardware, potentially allowing virtual clusters to span multiple grid sites. A major challenge in using virtual organization clusters as a grid computing abstraction arises from the need to schedule and provision physical resources to run the virtual machines. This paper describes a virtual cluster scheduler implementation based on the Condor high throughput computing system. By means of real-time monitoring of the Condor job queue, virtual machines that belong to individual virtual organizations are provisioned and booted. Jobs belonging to each virtual organization are then run on the organization-specific virtual machines, which form a cluster dedicated to the specific organization. Once the queued jobs have executed, the virtual machines are terminated, thereby allowing the physical resources to be re-claimed. Tests of this system were conducted using synthetic workloads, demonstrating that dynamic provisioning of virtual machines preserves system throughput for all but the shortest-running of grid jobs, without undue increase in scheduling latency.

Based on the structure parameter and operating principle of the traction drive system for high-speed electric multiple units (EMUs), the reliability block diagram(RBD) models of traction drive system are established. According to the theory of system reliability and the failure statistic datum of the traction drive system in CRH3 high-speed EMUs which running in Beijing-Tianjin and Wuhan-Guangzhou high-speed railway, the law of reliability about components in the traction drive system are proved. At the same time, the methods of calculation about reliability evaluation indexes are given, the failure rate and the reliability of the various components are calculated. Based on the RBD model of the traction drive system, the reliability of the whole traction drive system in Beijing-Tianjin and Wuhan-Guangzhou high-speed railway are evaluated. The research results provide reference for safe utilization and maintenance and repair of the traction drive system of CRH3 high-speed EMUs.

In cloud computing, virtualization is accomplished by equipping with a virtual switch to connect between physical machine and virtual machine. The virtual switch which is usually written coded in software provides a flexible configuration and management. In this paper, we use kernel based virtual machine (KVM) to deploy the virtual server cluster and apply Open vSwitch for the virtualized network environment. Our platform provides service with single entry point so that the virtual server cluster is fully transparent to end users. The implementation of load balancing is carried out by using Open vSwitch with an Openflow controller. Openflow controller dynamically manages the flow table in Open vSwitch through secure channel by Openflow protocol. With the platform, we can also deploy a virtual cluster across two Local Area Networks (LANs) through Generic Routing Encapsulation tunnel protocol. Upon a request, the Open vSwitch forwards the traffic to the virtual server cluster upon the loading policy to achieve load balancing.

Through analysis on 220kV longitudinal differential line relay protection device in smart substation, a new method for quantitative evaluation of relay protection system reliability of smart substation is proposed, considering the influence of power circuit and synchronous clock on secondary devices, the RBD(Reliability Block Diagram) models of 220kV longitudinal differential line relay protection and 110kV line relay protection system are established and evaluated; The reliability of the integration mode of merging unit and smart terminal is also quantitatively evaluated, based on which the reliability improvement strategy is proposed.

The quality of the scheduling method for virtualization clusters directly affects the performance of the entire cluster. Improper scheduling can even eliminate the advantages of virtualization clusters and reduce the performance of the entire system. Aiming at the problems of low resource utilization and poor performance caused by improper resource scheduling in cluster mode, a comprehensive heuristic resource scheduling method based on virtualization cluster - Resource Type Priority List Scheduling (RTPLS) algorithm is proposed. The entire algorithm includes two stages: resource monitoring and virtual machine scheduling. On the basis of considering multiple threshold triggering conditions such as CPU, memory, and network in the host load judgment during virtual machine scheduling, the host’s resource oscillation granularity and resource weight are introduced to comprehensively evaluate the resource load situation of the entire host, avoiding frequent migration of virtual machines caused by the oscillation of host resource load. In the selection of target virtual machine migration, in order to minimize the number of virtual machine migrations as much as possible, the compatibility coefficient λ of virtual machines is introduced, and the migration is sorted according to the priority list of λ to reduce the server load and energy consumption caused by virtual machine migration with the minimum number of virtual machine migrations. When selecting the target server, in order to minimize the load on the target virtual machine, target server relevance is introduced. Finally, by testing the algorithm in practical environments, it was found that the RTPLS algorithm can significantly improve the efficiency of host load balancing adjustments and enhance resource utilization.

Multi-Valued Reliability Block Diagrams (MVRBD) are introduced as a generalization of classical reliability block diagrams (RBD) commonly used in system analysis. MVRBD offer the advantage of allowing systems and subsystem components to be modeled with arbitrary hazard rate relationships. MVRBD are based upon a multiple-valued discrete switching algebra that is functionally complete with constants thereby affording a corresponding model to be formulated for any system that is capable of being modeled in a reliability block diagram form. The utility of this new model is that system and reliability analysis can be performed without restricting component hazard rate relationships to the binary case of either failure or fully operational. The incorporation of any desired number of degraded states into the MVRBD model allows for non-parametric probability mass functions (pmf) to be used. Any arbitrary number of intermediate system states of degraded may be incorporated into the MVRBD model without significant increase in the complexity of the analyses methodologies.

As server consolidations based on the virtualization techniques become popular and cloud services continue to grow rapidly, more and more data centers are being built to accommodate a number of virtual clusters running various workloads. Since these virtual clusters often share the resources provided by physical machines (PMs), it is more likely that the interferences between virtual machines (VMs) affect the performance of applications running on top of the virtual clusters. While a lot of studies have proposed different virtual machine placement algorithms to investigate this issue, the problem caused by network performance variability still remains as a challenging issue. Since they usually ignore the CPU overhead to process the communications between VMs, the network bandwidth allocated to a VM cannot be fully utilized when a PM has not enough CPU resources to cover the CPU overhead for VM networking functions. This results in unpredictable application performance running on the virtual clusters. This paper proposes a virtual machine placement algorithm that considers the CPU overhead incurred to reserve network bandwidth in a virtualized data center environment. In order to decide the CPU overhead necessary to guarantee the network bandwidth allocated to a VM, a performance model based on standard linear regression using the data collected from a real environment is used. By comparing the amount of CPU resource available in the driver domain with the CPU overhead obtained from the performance model, the proposed algorithm decides whether the network bandwidth for the VM can be provided or not and selects an appropriate location for VM placement. The benchmarking results show that the proposed algorithm guarantees the network bandwidth allocated to each VM without violations when the CPU resources are shared by multiple VMs.

Reliability block diagram (RBD) is an effective tool for modeling and evaluating system reliability. During operation, a system’s reliability may decrease significantly due to the failure of certain critical nodes and thus should be reconfigured. This paper presents a framework for system reliability evaluation and dynamic optimization based on RBD, designed from the perspective of system users. First, we improve the classic RBD model with a new encoding scheme and develop an accurate RBD computation algorithm that is easily recognized by computers and highly efficient. Second, we create an optimization algorithm based on Tabu Search to reconfigure the system after node failure, striking a balance between system reliability recovery and RBD variation amplitude. Finally, we provide some numerical examples and a computational experiment based on a practical instance from a navy fleet to demonstrate the correctness and effectiveness of our proposed methods.

Recently, virtualization has become more and more important in the cloud computing to support efficient flexible resource provisioning. However, performance interference among virtual machines (VMs) has become a challenge which may affect the effectiveness of resource provisioning. In a virtual cluster which runs the MapReduce applications, the performance interference can also affect the performance of the Map and Reduce tasks and thus cause a performance degradation of the MapReduce job. Accordingly, this paper presents a MapReduce scheduling framework to mitigate this performance degradation caused by the performance interference. The framework includes a performance interference prediction module and an interference aware scheduling algorithm. To verify its effectiveness, we have done a set of experiments on a 24-node virtual MapReduce cluster. The experiments illustrate that the proposed framework can achieve a performance improvement in the virtualized environment compared with other MapReduce schedulers.

Recently, virtualization of hardware resources to run multiple instances of independent virtual machines over physical hosts has gained popularity due to an industry-wide focus on the need to reduce the cost of operation of an enterprise computing infrastructure. Xen is an open source hypervisor that provides a virtual machine abstraction layer which is very similar to the underlying physical machine. Using multiple physical hosts, each hosting multiple virtual machines over a VMM like Xen, system administrators can setup a high-availability virtual cluster to meet the ever-increasing demands of their data centers. In such an environment, the Xen hypervisor enables live migration of individual virtual machine instances from one physical node to another without significantly affecting the performance of the applications running on a target virtual machine. This paper describes a scalable Virtual Cluster Manager that provides such application agnostic cluster management capabilities to the system administrators maintaining virtual clusters over Xen powered virtual nodes.

A virtualized cloud platform may host several cloud services/applications simultaneously. Each hosted cloud service usually runs on a virtual cluster, consisting of several virtual machines (VMs) with a virtual network. In many cases, the virtual clusters share the overall physical network bandwidth. Thus, the quality of the cloud services sharing the same physical networks can be seriously affected when one of the cloud services produces a high traffic load. In this paper, we propose a mechanism to solve the mentioned issue. The proposed mechanism utilizes a logically centralized QoS controller to allocate network bandwidth to virtual clusters dynamically. The QoS controller uses a monitor to continuously gather network usage of each VM, and calculates the bandwidth consumption in the granule of virtual clusters. A Rate Limiter is bundled to each physical machine for triggering bandwidth re-distribution. The proposed mechanism is implemented on the OpenStack-based cloud platform, SAMEVED, which is developed to support on-line network security experiments. We implemented several network bandwidth re-distribution strategies on the proposed mechanism. We emulate the real network flows in the platform, and the experiment results show that, the proposed approach can effectively reduce nearly one third of packet loss rate when running a video streaming service in a high network traffic scenario.

To address the virtual machine cluster deployment issues in cloud computing environment, a novel MCSA (Min-cut segmentation algorithm) of virtual machine cluster is proposed with resource and communication bandwidth constraints. In this paper, the basic idea is based on the fully consideration on the CPU, memory, hard-disk and other resource constraints between virtual machine cluster and physical host, as well as the communication bandwidth constraints between the virtual machine. We quantified the virtual machine cluster constructed an undirected graph. In the undirected graph, the nodes represent the virtual machine, so the weight of a node represents the value of resources, and the edges represent the communication bandwidth, so the weight of the edge represents the value of communication bandwidth. Base on the above transformations, the resources and bandwidth constrained optimization problem is transformed into the graph segmentation problem. Next we segment the undirected graph by minimum cut algorithm, and computing the matching degree of physical machines. Last we obtained the approximate solution. To validate the effectiveness of the new algorithm, we carried out extensive experiments based on the CloudSim platform.

Electrical systems have evolved toward greater complexity, demanding dynamic methods to assess their reliability. Traditional reliability analysis methods often fail to account for operational changes that a system undergoes over time, which affects the importance and criticality of its components. Variability in component reliability and failure probabilities can alter a system's internal structure, changing the relevance of certain components at different times. This study proposes an integrated methodology based on importance measures to evaluate reliability in systems with dependent components. Given that conventional Reliability Block Diagram (RBD) models are typically static and do not consider standby redundancy, importance measures were incorporated into the reliability analysis. This approach enabled the identification of critical components and the prioritization of maintenance strategies. The measures used include the Birnbaum importance index, improvement potential, risk reduction, and criticality. The methodology allows modeling the survival signature of components and the complete system at a given time, incorporating a Monte Carlo algorithm to analyze system reliability while accounting for dependencies between elements. This method was applied to a hydroelectric power plant, facilitating informed maintenance decision-making.

Metaverse as-a-Service (MaaS) enables <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Meta</i>verse tenants to execute their <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">APP</i>lications (MetaAPP) by allocating Metaverse resources in the form of Metaverse service functions (MSF). Usually, each MSF is deployed in a virtual machine (VM) for better resiliency and security. However, these MSFs along with VMs and virtual machine monitors (VMM) running them will encounter software aging after prolonged continuous operation. Then, there is a decrease in MetaAPP dependability, namely, the dependability of the MSF chain (MSFC), consisting of MSFs allocated to MetaAPP. This paper aims to investigate the impact of both software aging and rejuvenation techniques on MetaAPP dependability in the scenarios, where both active components (MSF, VM and VMM) and their backup components are subject to software aging. We develop a hierarchical model to capture behaviors of aging, failure, and recovery by applying Semi-Markov process and reliability block diagram. Numerical analysis and simulation experiments are conducted to evaluate the approximation accuracy of the proposed model and dependability metrics. We then identify the key parameters for improving the MetaAPP/MSFC dependability through sensitivity analysis. The investigation is also made about the influence of various parameters on MetaAPP/MSFC dependability.