Towards Semi-Markov Model-based Dependability Evaluation of VM-based Multi-Domain Service Function Chain

In NFV networks, service functions (SFs) can be deployed on virtual machines (VMs) across multiple domains and then form a service function chain (MSFC) for end-to-end network service provision. However, any software component in a VM-based MSFC must experience software aging issue after a long period of operation. This paper quantitatively investigates the capability of proactive rejuvenation techniques in reducing the damage of software aging on a VM-based MSFC. We develop a semi-Markov model to capture the behaviors of SFs, VMs and virtual machine monitors (VMMs) from software aging to recovery under the condition that failure times and recovery times follow general distributions. We derive the formulas for calculating the steady-state availability and reliability of the VM-based MSFC composed of multiple SFs running on VMs hosted by VMMs. Sensitivity analysis is also conducted to identify potential dependability bottlenecks

In situ evaluation of podocin in normal and glomerular diseases

In situ evaluation of podocin in normal and glomerular diseases.BackgroundMutations of the NPHS2 gene are responsible for autosomal-recessive steroid-resistant nephrotic syndrome. Its product, podocin, faces the slit diaphragm area with its two ends in the cytoplasm of foot processes.MethodsWe generated rabbit polyclonal antibodies against conjugated peptides from human podocin N- and C-termini, and studied podocin and synaptopodin using kidney tissues of normal humans and those with glomerular diseases.ResultsAntipodocin antibodies detected the original 42 kD fragment and an extra smaller fragment by Western blot analysis using human isolated mature glomeruli. RNA analysis showed two bands, the original and the other of a decreased length. Immunohistochemically, podocin was detected in a linear pattern along the glomerular capillary loop. Antipodocin antibody (C-terminal) stained the smooth muscles of renal arterioles and aorta. Among 42 patients, podocin was normally expressed in glomeruli in purpura nephritis, IgA nephropathy (IgAN), and minimal-change disease (MCD), while it was either decreased or absent in most subjects with focal segmental glomerulosclerosis (FSGS). The expression of synaptopodin was similar to that of podocin, although some discrepancy existed.ConclusionAlthough indirect, our data suggest the existence of a vascular isoform of podocin with a different molecular mass. We propose that examination of podocin expression may help differentiate MCD from FSGS

Enhancing the Reliability of Perception Systems using N-version Programming and Rejuvenation

peer reviewedMachine Learning (ML) has become indispensable for real-world complex systems, such as perception systems of autonomous systems and vehicles. However, ML-based systems are sensitive to input data, faults, and malicious threats that can degrade output quality and compromise the complete system's correctness. Ensuring a reliable output of ML-based components is crucial, especially for safety-critical systems. In this paper, we investigate architectures of perception systems using N-version programming for ML to mitigate the dependence on a singular ML component and combine it with a time-based rejuvenation mechanism to maintain a healthy system over extended periods. We propose models and functions to evaluate the reliability of N-version perception systems subject to faults, malicious threats, and rejuvenation. Our numerical experiments show that a rejuvenation mechanism could benefit a multiple-version system, with a reliability improvement superior to 13%. Also, the results indicate that rejuvenation could improve output reliability when ML modules' accuracy is high

Availability modeling and analysis of a virtualized system

This paper develops an availability model of a virtualized system. We construct non-virtualized and virtualized two hosts system models using a two-level hierarchical approach in which fault trees are used in the upper level and homogeneous continuous time Markov chains (CTMC) are used to represent sub-models in lower level. In the models, we incorporate not only hardware failures (e.g., CPU, memory, power, etc) but also software failures including Virtual Machine Monitor (VMM), Virtual Machine (VM), and application failures. We also incorporate high availability (HA) service and VM live migration in the virtualized system. Metrics we use are system steady state availability, downtime in minutes per year and capacity oriented availability

Modeling and analysis of software rejuvenation in a server virtualized system

As server virtualization is used as an essential software infrastructure of various software services such as cloud computing, availability management of server virtualized system is becoming more significant. Although time-based software rejuvenation is useful to postpone/prevent failures due to software aging in a server virtualized system, the rejuvenation schedules for virtual machine (VM) and virtual machine monitor (VMM) need to be determined in a proper way for the VM availability, since VMM rejuvenation affects VMs running on the VMM. This paper presents analytic models using stochastic reward nets for three time-based rejuvenation techniques of VMM; (i) Cold-VM rejuvenation in which all VMs are shut down before the VMM rejuvenation, (ii) Warm-VM rejuvenation in which all VMs are suspended before the VMM rejuvenation and (iii) Migrate-VM rejuvenation in which all VMs are moved to the other host server during the VMM rejuvenation. We compare the three techniques in terms of steady-state availability and the number of transactions lost in a year. We find the optimal combination of rejuvenation trigger intervals for each rejuvenation technique by a gradient search method. The numerical analysis shows the interesting result that Warm-VM rejuvenation does not always outperform Cold-VM rejuvenation in terms of steady-state availability depending on rejuvenation trigger intervals. Migrate-VM rejuvenation is better than the other two as long as live VM migration rate is large enough and the other host server has a capacity to accept the migrated VM

Virtual Machine Replication on Achieving Energy-Efficiency in a Cloud

The rapid growth in cloud service demand has led to the establishment of large-scale virtualized data centers in which virtual machines (VMs) are used to handle user requests for service. A user’s request cannot be completed if the VM fails. Replication mechanisms can be used to mitigate the impact of failures. Further, data centers consume a large amount of energy resulting in high operating costs and contributing to significant greenhouse gas (GHG) emissions. In this paper, we focus on Infrastructure as a Service (IaaS) cloud where user job requests are processed by VMs and analyze the effectiveness of VM replications in terms of job completion time performance as well as energy consumption. Three different schemes: cold, warm, and hot replications are considered. The trade-offs between job completion time and energy consumption in different replication schemes are characterized through comprehensive analytical models which capture VM state transitions and associated power consumption patterns. The effectiveness of replication schemes are demonstrated through experimental results. To verify the validity of the proposed analytical models, we extend the widely used cloud simulator CloudSim and compare the simulation results with analytical solutions