Data replication approach with consistency guarantee for data grid

Data grids have been adopted by many scientific communities that need to share, access, transport, process, and manage geographically distributed large data collections. Data replication is one of the main mechanisms used in data grids whereby identical copies of data are generated and stored at various distributed sites to either improve data access performance or reliability or both. However, when data updates are allowed, it is a great challenge to simultaneously improve performance and reliability while ensuring data consistency of such huge and widely distributed data. In this paper, we address this problem. We propose a new quorum-based data replication protocol with the objectives of minimizing the data update cost, providing high availability and data consistency. We compare the proposed approach with two existing approaches using response time, data consistency, data availability, and communication costs. The results show that the proposed approach performs substantially better than the benchmark approaches

Large iterative multitier ensemble classifiers for security of big data

This paper introduces and investigates large iterative multitier ensemble (LIME) classifiers specifically tailored for big data. These classifiers are very large, but are quite easy to generate and use. They can be so large that it makes sense to use them only for big data. They are generated automatically as a result of several iterations in applying ensemble meta classifiers. They incorporate diverse ensemble meta classifiers into several tiers simultaneously and combine them into one automatically generated iterative system so that many ensemble meta classifiers function as integral parts of other ensemble meta classifiers at higher tiers. In this paper, we carry out a comprehensive investigation of the performance of LIME classifiers for a problem concerning security of big data. Our experiments compare LIME classifiers with various base classifiers and standard ordinary ensemble meta classifiers. The results obtained demonstrate that LIME classifiers can significantly increase the accuracy of classifications. LIME classifiers performed better than the base classifiers and standard ensemble meta classifiers

Secure Object Tracking Protocol for the Internet of Things

In this paper, we propose a secure object tracking protocol to ensure the visibility and traceability of an object along the travel path to support the Internet of Things (IoT). The proposed protocol is based on radio frequency identification system for global unique identification of IoT objects. For ensuring secure object tracking, lightweight cryptographic primitives and physically unclonable function are used by the proposed protocol in tags. We evaluated the proposed protocol both quantitatively and qualitatively. In our experiment, we modeled the protocol using security protocol description language (SPDL) and simulated SPDL model using automated claim verification tool Scyther. The results show that the proposed protocol is more secure and requires less computation compared to existing similar protocols

A multi-protocol security framework to support Internet of Things

In this paper we are proposing a multi-protocol security framework for sensors and actuators used in Internet of Things (IoT). This is to make sure that IoT security framework is capable of accommodating a number of secure communication protocols to support diverse need of IoT systems. The proposed framework will extend scope of combining common security functionalities like mutual authentication, malware injection of all integrated secure communication protocols and will make these services universally available to them. The IoT provision requires all diverse actuators and sensor networks connected together. The aim of the proposed security framework is to secure this connected diverse networks universally with least amount of performance tradeoff. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

A multi-protocol security framework to support Internet of Things

In this paper we are proposing a multi-protocol security framework for sensors and actuators used in Internet of Things (IoT). This is to make sure that IoT security framework is capable of accommodating a number of secure communication protocols to support diverse need of IoT systems. The proposed framework will extend scope of combining common security functionalities like mutual authentication, malware injection of all integrated secure communication protocols and will make these services universally available to them. The IoT provision requires all diverse actuators and sensor networks connected together. The aim of the proposed security framework is to secure this connected diverse networks universally with least amount of performance tradeoff. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

PGSW-OS: a novel approach for resource management in a semantic web operating system based on a P2P grid architecture

A web operating system is an operating system that users can access from any hardware at any location. A peer-to-peer (P2P) grid uses P2P communication for resource management and communication between nodes in a grid and manages resources locally in each cluster, and this provides a proper architecture for a web operating system. Use of semantic technology in web operating systems is an emerging field that improves the management and discovery of resources and services. In this paper, we propose PGSW-OS (P2P grid semantic Web OS), a model based on a P2P grid architecture and semantic technology to improve resource management in a web operating system through resource discovery with the aid of semantic features. Our approach integrates distributed hash tables (DHTs) and semantic overlay networks to enable semantic-based resource management by advertising resources in the DHT based upon their annotations to enable semantic-based resource matchmaking. Our model includes ontologies and virtual organizations. Our technique decreases the computational complexity of searching in a web operating system environment. We perform a simulation study using the Gridsim simulator, and our experiments show that our model provides enhanced utilization of resources, better search expressiveness, scalability, and precision. © 2014 Springer Science+Business Media New York

Guest editorial (14th International Conference on Computer and Information Technology)

No abstract

Affinity replica selection in distributed systems

Replication is one of the key techniques used in distributed systems to improve high data availability, data access performance and data reliability. To optimize the maximum benefits from file replication, a systems that includes replicas need a strategy for selecting and accessing suitable replicas. A replica selection strategy determines the available replicas and chooses the most access files. In most of these access frequency based solutions or popularity of files are assuming that files are independent of each other. In contrast, distributed systems such as peer-to-peer file sharing, and mobile database, files may be dependent or correlated to one another. Thus, this paper focused on the combination of popularity and affinity files as the most important parameters in selecting replicas in distributed environments. Herein, a replica selection is proposed focusing on popular files and affinity files. The idea is to improve data availability in distributed data replica selection strategy. A P2P simulator, PeerSim, is used to evaluate the performance of the dynamic replica selection strategy. The simulation results provided a proof that the proposed affinity replica selection has contributed towards a new dimension of replica selection strategy that incorporates the affinity and popularity of file replicas in distributed systems