The role and importance of solid storage devices (SSD) is rapidly increasing for the purpose of data storage. The SSDs are rapidly replacing the old fashioned and traditional magnetic storage medium. Few factors responsible for this metamorphic turnaround are due to the better performance and low power requirement of SSDs. But, as with every pros there are some associated cons. One limiting factor of SSDs in replacing traditional magnetic storage media is its low endurance. These solid state devices can be re-programmed for a limited number of times. Unlike, magnetic storage media, the SSDs can be reprogrammed for a limited number of times. Internally, SSDs are organized either in bytes or blocks. Generally, for memory access operations, either the byte or blocks are used. With each write operation, the byte gets worn out and its lifetime decreases. To guard this limiting factor of SSD, wear leveling mechanism is used. Wear leveling mechanism is provided as a feature in flash type SSDs, which evenly distributes the write operation throughout the flash memory and prevents the early wear out of memory. The existing wear leveling mechanism is limited only to flash type SSDs. In this paper, we propose a wear leveling algorithm for SSD and precisely NOR type SSD. NAND types SSDs are well equipped with algorithms to enhance their life and reliability. The algorithm proposed will provide a mechanism that will enhance the life, endurance and reliability of SSD of NOR type.

No abstract

It is time for the healthcare industry to move from the era of "analyzing our health history" to the age of "managing the future of our health." In this article, we illustrate the importance of real-time analytics across the healthcare industry by providing a generic mechanism to reengineer traditional analytics expressed in the R programming language into Storm-based real-time analytics code. This is a powerful abstraction, since most data scientists use R to write the analytics and are not clear on how to make the data work in real-time and on high-velocity data. Our paper focuses on the applications necessary to a healthcare analytics scenario, specifically focusing on the importance of electrocardiogram (ECG) monitoring. A physician can use our framework to compare ECG reports by categorization and consequently detect Arrhythmia. The framework can read the ECG signals and uses a machine learning-based categorizer that runs within a Storm environment to compare different ECG signals. The paper also presents some performance studies of the framework to illustrate the throughput and accuracy trade-off in real-time analytics.

Organizations face a challenge of accurately analyzing network data and providing automated action based on the observed trend. This trend-based analytics is beneficial to minimize the downtime and improve the performance of the network services, but organizations use different network management tools to understand and visualize the network traffic with limited abilities to dynamically optimize the network. This research focuses on the development of an intelligent system that leverages big data telemetry analysis in Platform for Network Data Analytics (PNDA) to enable comprehensive trendbased networking decisions. The results include a graphical user interface (GUI) done via a web application for effortless management of all subsystems, and the system and application developed in this research demonstrate the true potential for a scalable system capable of effectively benchmarking the network to set the expected behavior for comparison and trend analysis. Moreover, this research provides a proof of concept of how trend analysis results are actioned in both a traditional network and a software-defined network (SDN) to achieve dynamic, automated load balancing.

No abstract