Centralized computing

Abstract: This study empirically develops a taxonomy that has implications for matching information technology (IT) and organizational structures. The taxonomy of IT structure is based on the degree of centralization of computer processing, capability to support communications, and the ability to share resources. By using a multistep cluster analysis, both the membership and number of groups are derived from the responses of 313 firms. Four IT structures are identified: centralized (centralized processing, low communication, low sharing), decentralized (decentralized processing, low communication, low sharing), centralized cooperative (centralized processing, high communication, high sharing), and distributed cooperative computing (decentralized processing, high communication, high sharing). Centralized computing is related to functional organizational forms with low integration and centralized decision making. Decentralized computing is related to product organizational forms with decentralized decision making. Centralized cooperative computing is related to functional organizational forms with high integration. Distributed cooperative computing is related to both matrix and product organizational forms with high integration. The ability to identify and understand the implications of IT structure is of critical importance to both academic and management practitioners.

Abstract: Commoditization and virtualization of wireless networks are changing the economics of mobile networks to help network providers (e.g., MNO, MVNO) move from proprietary and bespoke hardware and software platforms toward an open, cost-effective, and flexible cellular ecosystem. Cloud radio access network is a novel architecture that perform the required base band and protocol processing on a centralized computing resources or a cloud infrastructure. This replaces traditional base stations with distributed (passive) radio elements with much smaller footprints than the traditional base station and a remote pool of base band units allowing for simpler network densification. This paper investigates three critical issues for the cloudification of the current LTE/LTE-A radio access network. Extensive experimentations have been performed based on the OpenAirInterface simulators to characterise the base band processing time under different conditions. Based on the results, an accurate model is proposed to compute the total uplink and downlink processing load as a function of bandwidth, modulation and coding scheme, and virtualization platforms. The results also reveal the feasible virtualization approach towards a cloud-native radio access network.

Abstract: With the development of internet of things (IoT), capabilities of computing, networking infrastructure, storage of data and management have come very close to the edge of networks. This has accelerated the necessity of Fog computing paradigm. Due to availability of Internet, most of our business operations are integrated with IoT platform. Fog computing has enhanced the strategy of collecting and processing, huge amount of data. On the other hand, attacks and malicious activities has adverse consequences on the development of IoT, Fog, and cloud computing. This has led to development of many security models using fog computing to protect IoT network. Therefore, for dynamic and highly scalable IoT environment, a distributed architecture based intrusion detection system (IDS) is required that can distribute the existing centralized computing to local fog nodes and can efficiently detect modern IoT attacks. This paper proposes a novel distributed ensemble design based IDS using Fog computing, which combines k-nearest neighbors, XGBoost, and Gaussian naive Bayes as first-level individual learners. At second-level, the prediction results obtained from first level is used by Random Forest for final classification. Most of the existing proposals are tested using KDD99 or NSL-KDD dataset. However, these datasets are obsolete and lack modern IoT-based attacks. In this paper, UNSW-NB15 and actual IoT-based dataset namely, DS2OS are used for verifying the effectiveness of the proposed system. The experimental result revealed that the proposed distributed IDS with UNSW-NB15 can achieve higher detection rate upto 71.18% for Backdoor, 68.98% for Analysis, 92.25% for Reconnaissance and 85.42% for DoS attacks. Similarly, with DS2OS dataset, detection rate is upto 99.99% for most of the attack vectors.

Abstract: Data service has been considered as one the most prominent characteristics for Industrial Internet of Things (IIoT). This paper studies how to design an optimal computing manner for a general IIoT system. On the theory end, we analyze the relationship between the data processing and the energy consumption through investigating the content correlation of the captured data. Importantly, we derive an exact expression for the performance of IIoT by combining computation with intelligence. On the application end, we design an efficient way to obtain a threshold by approximating the performances of different computing manners, and show how to apply it to practical IIoT applications. We believe that the proposed computation rules hold great significance for the IIoT designer, that is, it is better to use distributed computing manner when the content correlation is high, otherwise, centralized computing manner is better.