Cloud computing

Abstract: There has been significant research in incorporating both blockchain and intrusion detection to improve data privacy and detect existing and emerging cyberattacks, respectively. In these approaches, learning-based ensemble models can facilitate the identification of complex malicious events and concurrently ensure data privacy. Such models can also be used to provide additional security and privacy assurances during the live migration of virtual machines (VMs) in the cloud and to protect Internet-of-Things (IoT) networks. This would allow the secure transfer of VMs between data centers or cloud providers in real time. This article proposes a deep blockchain framework (DBF) designed to offer security-based distributed intrusion detection and privacy-based blockchain with smart contracts in IoT networks. The intrusion detection method is employed by a bidirectional long short-term memory (BiLSTM) deep learning algorithm to deal with sequential network data and is assessed using the data sets of UNSW-NB15 and BoT-IoT. The privacy-based blockchain and smart contract methods are developed using the Ethereum library to provide privacy to the distributed intrusion detection engines. The DBF framework is compared with peer privacy-preserving intrusion detection techniques, and the experimental outcomes reveal that DBF outperforms the other competing models. The framework has the potential to be used as a decision support system that can assist users and cloud providers in securely migrating their data in a timely and reliable manner.

Abstract: The near future evolution of the cloud computing can be hypothesized in three subsequent stages: stage 1 "Monolithic" (now), cloud services are based on independent proprietary architectures; stage 2 "Vertical Supply Chain", cloud providers will leverage cloud services from other providers; stage 3 "Horizontal Federation", smaller, medium, and large cloud providers will federate themselves to gain economies of scale and an enlargement of their capabilities. Currently, the major clouds are planning the transition to the stage 2, but how to achieve the stage 3 is unclear because some architectural limitations have to be overcome. In this paper, considering a general cloud architecture, we highlight such limitations and propose some enhancements which add new federation capabilities. In order to address such concerns we propose a solution based on the Cross-Cloud Federation Manager, a new component placeable inside the cloud architectures, allowing a cloud to establish the federation with other clouds according to a three-phase model: discovery, match-making and authentication.

Abstract: Fog computing is a promising architecture to provide economical and low latency data services for future Internet of Things (IoT)-based network systems. Fog computing relies on a set of low-power fog nodes (FNs) that are located close to the end users to offload the services originally targeting at cloud data centers. In this paper, we consider a specific fog computing network consisting of a set of data service operators (DSOs) each of which controls a set of FNs to provide the required data service to a set of data service subscribers (DSSs). How to allocate the limited computing resources of FNs to all the DSSs to achieve an optimal and stable performance is an important problem. Therefore, we propose a joint optimization framework for all FNs, DSOs, and DSSs to achieve the optimal resource allocation schemes in a distributed fashion. In the framework, we first formulate a Stackelberg game to analyze the pricing problem for the DSOs as well as the resource allocation problem for the DSSs. Under the scenarios that the DSOs can know the expected amount of resource purchased by the DSSs, a many-to-many matching game is applied to investigate the pairing problem between DSOs and FNs. Finally, within the same DSO, we apply another layer of many-to-many matching between each of the paired FNs and serving DSSs to solve the FN-DSS pairing problem. Simulation results show that our proposed framework can significantly improve the performance of the IoT-based network systems.

Abstract: Osmotic computing is a new paradigm to support the efficient execution of Internet of Things (IoT) services and applications at the network edge. This paradigm is founded on the need for a holistic distributed system abstraction enabling the deployment of lightweight microservices on resource-constrained IoT platforms at the network edge, coupled with more complex microservices running on large-scale datacenters. This paradigm is driven by the significant increase in resource capacity/capability at the network edge, along with support for data transfer protocols that enable such resources to interact more seamlessly with datacenter-based services. This installment of "Blue Skies" discusses osmotic computing features, challenges, and future directions.