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Failure Prediction has long known to be a challenging problem. With the evolving trend of technology and growing complexity of high-performance cloud data centre infrastructure, focusing on failure becomes very vital particularly when designing systems for the next generation. The traditional runtime fault-tolerance (FT) techniques such as data replication and periodic check-pointing are not very effective to handle the current state of the art emerging computing systems. This has necessitated the urgent need for a robust system with an in-depth understanding of system and component failures as well as the ability to predict accurate potential future system failures. In this paper, we studied data in-production-faults recorded within a five years period from the National Energy Research Scientific computing centre (NERSC). Using the data collected from the Computer Failure Data Repository (CFDR), we developed an effective failure prediction model focusing on high-performance cloud data centre infrastructure. Using the Auto-Regressive Moving Average(ARMA), our model was able to predict potential future failures in the system. Our results also show a failure prediction accuracy of 95%, which is good. We thus believe that our strategy is practical and can be adapted to use in existing real-time systems.

Cloud fault tolerance is an important issue in cloud computing platforms and applications. In the event of an unexpected system failure or malfunction, a robust fault-tolerant design may allow the cloud to continue functioning correctly possibly at a reduced level instead of failing completely. To ensure high availability of critical cloud services, the application execution, and hardware performance, various fault-tolerant techniques exist for building self-autonomous cloud systems. In comparison with current approaches, this paper proposes a more robust and reliable architecture using optimal checkpointing strategy to ensure high system availability and reduced system task service finish time. Using pass rates and virtualized mechanisms, the proposed smart failover strategy (SFS) scheme uses components such as cloud fault manager, cloud controller, cloud load balancer, and a selection mechanism, providing fault tolerance via redundancy, optimized selection, and checkpointing. In our approach, the cloud fault manager repairs faults generated before the task time deadline is reached, blocking unrecoverable faulty nodes as well as their virtual nodes. This scheme is also able to remove temporary software faults from recoverable faulty nodes, thereby making them available for future request. We argue that the proposed SFS algorithm makes the system highly fault tolerant by considering forward and backward recovery using diverse software tools. Compared with existing approaches, preliminary experiment of the SFS algorithm indicates an increase in pass rates and a consequent decrease in failure rates, showing an overall good performance in task allocations. We present these results using experimental validation tools with comparison with other techniques, laying a foundation for a fully fault-tolerant infrastructure as a service cloud environment. Copyright © 2017 John Wiley & Sons, Ltd.

As financial institutions navigate an increasingly complex cyber threat landscape and regulatory ecosystem, there is a pressing need for a robust and adaptive security architecture. This paper introduces a comprehensive, Zero Trust model-based framework specifically tailored for the finance industry. It encompasses identity and access management (IAM), data protection, and device and network security and introduces trust through blockchain technology. This study provides a literature review of existing Zero Trust paradigms and contrasts them with cybersecurity solutions currently relevant to financial settings. The research adopts a mixed methods approach, combining extensive qualitative analysis through a literature review and assessment of security assumptions, threat modelling, and implementation strategies with quantitative evaluation using a prototype banking application for vulnerability scanning, security testing, and performance testing. The IAM component ensures robust authentication and authorisation processes, while device and network security measures protect against both internal and external threats. Data protection mechanisms maintain the confidentiality and integrity of sensitive information. Additionally, the blockchain-based trust component serves as an innovative layer to enhance security measures, offering both tamper-proof verification and increased integrity. Through analysis of potential threats and experimental evaluation of the Zero Trust model's performance, the proposed framework offers financial institutions a comprehensive security architecture capable of effectively mitigating cyber threats and fostering enhanced consumer trust. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jss.2010.03.080 Byline: Mohamed Alnas, Irfan Awan, R.D.W. Holton Keywords: Mobile IPv6; Fast Handover; Mobility Management; Link-Layer Information; Handover Latency Abstract: Handover latency is the primary cause of packet loss resulting in performance degradation of standard Mobile IPv6. Mobile IPv6 with fast Handover enables a Mobile Node (MN) to quickly detect at the IP layer that it has moved to a new subnet by receiving link-related information from the link-layer; furthermore it gathers anticipative information about the new Access Point (AP) and the associated subnet prefix when the MN is still connected to the previous Corresponding Node (CN). This paper proposes an enhancement to Fast Mobile IPv6 handover (FMIPv6), based on link layer information, we also present performance evaluations in terms of the packet loss and handover latency using evaluation models. Author Affiliation: Mobile Computing, Networks and Security Research Group, School of Informatics, University of Bradford, Bradford, West Yorkshire, BD7 1DP, UK Article History: Received 9 November 2009; Revised 18 January 2010; Accepted 30 March 2010

Efficient and adaptive buffer management will play crucial role in the future integrated mobile network (e.g., 4th Generation). One of the main features of the future mobile networks will be their capability of providing different levels of quality of service to the different types of services. Some of these services will be delay and delay variation sensitive such as real-time voice and video. Others will be loss sensitive such as data transfer. But the most challenging will be the sort of multimedia traffic that owns both types of sensitivity. Another source of difficulty stems from the heterogeneous wireless air interface technologies that will act as border nodes to the network providing different traffic streams with different data rates and QoS requirements (e.g, WLAN provides data rates of 11Mbbps while UMTS can only provide 384Kbps). Although improved call admission control algorithms on the border of the network will provide a sort of protection from extensive traffic over the network, however, they will only operate on mean traffic volumes and will not cope with the variability of actual instantaneous traffic entering the network. Furthermore, as the core part of the mobile network will be packet switched based on IP, the QoS can only be maintained through efficient buffer management at the border and inside the network. This paper proposes and analytically evaluates a buffer management scheme that is based on multi-level priority and Partial Buffer Sharing (PBS) policy for all buffers at the border and inside the wireless network. The analytical model is based on the G/G/1/N censored queue with single server and R ( R2) priority classes under the Head of Line (HOL) service rule for the PBS scheme. The traffic is modelled using the Generalised Exponential distribution. The paper presents an analytical solution based on the approximation using the Maximum Entropy (ME) principle. The numerical results show the capability of the space prioritised buffer management scheme to provide higher QoS for the higher priority service classes [ABSTRACT FROM AUTHOR]