Metaverse as-a-Service (MaaS) enablesMetaverse tenants to execute theirAPPlications (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.

Convergent encryption is an effective technique to achieve cross-user deduplication of encrypted data in cloud storage. However, it is vulnerable to brute-force attacks for data with low min-entropy. Moreover, once the content of the target data is successfully constructed through the aforementioned attacks, the corresponding index can also be obtained, leading to the risk of violating privacy during the process of data downloading. To address these challenges, we propose a key transferring-based secure deduplication (KTSD) scheme for cloud storage with support for ownership verification, which significantly improves the security against brute-force attacks during the ciphertext deduplication and downloading. Specifically, we introduce a randomly generated key in data encryption and downloading index generation to prevent the results from being inferred. And define a deduplication request index and a key request index by using the bloom filter to achieve brute-force attack resistant key transferring. An RSA-based ownership verification scheme is designed for the downloading process to effectively prevent privacy leakage. Finally, we prove the security of our schemes by security analysis and perform the performance evaluation experiments, the results of which show that compared to the state-of-the art, the cloud storage overhead can be reduced by 6.01% to 20.49% under KTSD.

Container technology, as the key enabler behind microservice architectures, is widely applied in Cloud and Edge Computing. A long and continuous running of operating system (OS) hosting container-based services can encounter software aging that leads to performance deterioration and even causes system failures. OS rejuvenation techniques can mitigate the impact of software aging but the rejuvenation trigger interval needs to be carefully determined to reduce the downtime cost due to rejuvenation. This paper proposes a comprehensive semi-Markov-based approach to quantitatively evaluate the effect of OS rejuvenation on the dependability and the performance of a container-based service. In contrast to the existing studies, we neither restrict the distributions of time intervals of events to be exponential nor assume that backup resources are always available. Through the numerical study, we show the optimal container-migration trigger intervals that can maximize the dependability or minimize the performance of a container-based service.

Both blockchain and oracle are among key technologies which are leveraged in Web 3.0 to empower the internet industry. Oracle aims to provision blockchain with real world data and support external connectivity for closed blockchain systems. Compared with a centralized oracle system, a distributed oracle system can tackle the issues of single point failure and untrusted data. This paper explores analytical modeling techniques to quantitatively study the dependability (availability and reliability) of the distributed oracle system with arbitrary number of heterogeneous oracle nodes. We first develop a Markov model to describe oracle system dynamics. Then we derive both the formula of system availability and the formula of mean time to failure (MTTF) to study the system reliability. The experimental results indicate 1) the system availability is mainly affected by mean node failure time when it is smaller than 10 days, 2) the system recovery ability has critical impact on availability, and 3) MTTF can be significantly improved by introducing more nodes.

Bitcoin is the largest PoW blockchain, which currently uses the longest-chain protocol for chain selection and is vulnerable to various attacks like stubborn mining attack. As a variant of selfish mining attack, stubborn mining attack usually has 7 types of strategies, each of which does damage to the blockchain system. GHOST is another chain-selection protocol, which has been demonstrated to make the blockchain system more secure than the longest-chain protocol under selfish mining attack. There were studies on stubborn mining in perfect GHOST blockchains and they only studied two types of stubborn mining strategies. But it is a fact of life that the blockchain is an imperfect network due to ubiquitous network congestion and/or attacks. This paper aims to explore a simulation-based approach to quantitatively evaluate the threat capability of all 7 stubborn mining strategies. We first develop all stubborn strategies in imperfect GHOST blockchains. Then we evaluate miner revenues and system throughput over different network conditions. The results show that the lead-fork-stubborn strategy is the dominant strategy for attackers when they have more than 33% total computing power. The stubborn attackers with less than 20% total computing power lose their revenue whichever stubborn mining strategy is used. The blockchain with high network quality still has the risk of significant throughput downgrade. Our work can help the stubborn mining attack detection and secure blockchain system design.

This paper aims to use analytical modeling technique to quantitatively study the dependability of Vehicle Platooning Application, which consists of Multiple Sub-Services (VPP-MSS) to achieve its functionality. Each sub-service (SS), based on network function virtualization technology, is executed in a container. Both SSes and OSes which SSes run on can suffer from software aging after a long and continuous running, reducing VPP-MSS dependability. Rejuvenation techniques are usually used to combat software aging, but they require the support of backup components. Quantitative study of VPP-MSS dependability enables in-depth understanding of the effectiveness of rejuvenation techniques based on analytical models. In contrast to the existing studies, we develop a semi-Markov process (SMP) model to jointly analyze the impact of rejuvenation technique trigger intervals (RTTIs), backup components’ behaviors, time-dependent interactions between various behaviors and the number of active SSes deployed on an OS on the effectiveness of rejuvenation technique. Sensitivity analysis helps identify key parameters for improving the dependability of VPP-MSS. Extensive numerical experiments demonstrate the necessity of considering backup components’ behaviors and investigating non-exponentially distributed failure times. We also determine both the optimal RTTI combination and the optimal combination of SSes and OSes, which can maximize VPP-MSS dependability.

The Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV) integrated architecture is a key enabling platform for 5G to run multiple customized services in the form of service function chain (SFC) configured as an ordered set of service functions (SFs). However, memory-related software aging in the SF that can be exploited by attackers becomes a new threat to the dependability of MEC-SFC services. To provide dependable MEC-SFC services, proactive rejuvenation techniques to counteract the SF aging problem are essential. In this paper, we develop a semi-Markov model to quantitatively investigate the transient availability and steady-state dependability (availability and reliability) of MEC-SFC services. Our model enables the analysis of a MEC-SFC with any number of SFs, and can capture complex time-dependent behaviors of aging, failure, and recovery. The approximate accuracies of the presented model on dependability measures are comprehensively evaluated through comparative studies with simulation experiments. We then detect potential bottlenecks for a MEC-SFC system through sensitivity analysis and further analyze the impact of event-time interval distributions on steady-state dependability. Finally, we investigate the transient behaviors of a MEC-SFC service when varying system parameters during MEC-SFC operation.

Multi-accessedgecomputing (MEC)-enabledInternetofThings (IoT) is considered as a promising paradigm to deliver computation-intensive and delay-sensitive services to users. IoT service requests can be served by multiplemicroservices (MSs) that form a chain, called amicroservicechain (MSC). However, the high complexity of MSs and security threats in MEC-enabled IoT pose new challenges to MSC dependability. Proactive rejuvenation techniques can mitigate the impact of resource degradation of MSs and hostoperatingsystems (OSes) executing them. In this article, we develop a multi-dimensional semi-Markov model to investigate the effectiveness of proactive rejuvenation techniques in improving the dependability (availability and reliability) of a dynamic and heterogeneous MSC. The results of numerical experiments firstly reveal how MSs can be effectively combined, in different deployment configurations, with host OSes to improve MSC dependability, secondly jointly optimize the rejuvenation trigger intervals of host OS and MSs running on it, and finally show the impact of time-varying parameters. We also identify the bottlenecks for MSC dependability improvement by sensitivity analysis, and give the ranges of important parameter values guaranteeing five-nines availability. In addition, the superiority of our model is demonstrated by comparison with the continuous-time Markov chain model.

With the rapid and wide development and deployment of system virtualization, service availability analysis has become increasingly important in a virtualized system (VS) which suffers from software aging. Software rejuvenation techniques can be applied to improve service availability but its effectiveness depends on the rejuvenation policy, which defines when and where to rejuvenate, and which rejuvenation technique to be triggered. This article aims to analyze the optimal inspection time interval for maximizing application service (AS) availability under a three-level rejuvenation policy, in which rejuvenation techniques are deployed at each level, namely, AS, virtual machine (VM), and virtual machine monitor (VMM) levels. We first apply Markov regenerative process to construct an analytical model for the VS. Experiments of injecting memory leaks are conducted to measure aging-related parameters. Furthermore, numerical analysis is carried out to study the quantitative relationship between AS availability and inspection time interval, and determine the approximate optimal inspection time interval.