Energy Harvesting based Mobile Cloud Network in Latency and QoS Improvement using 5G Systems by Energy Routing Optimization

D2D communication technology enables the User Equipment (UE) in 5G networks to instantly connect with other UEs, with or without partial infrastructure involvement. In a Cloud Assisted energy harvesting system, it has improved user numbers and data transmission between mobile nodes. This research propose energy harvesting for mobile cloud computing in enhancing the QoS and latency of the network. The main aim of this research is to enhance energy optimization using discrete energy efficient offloading algorithm. The routing has been optimized using fuzzy logic cognitive Bellman-Ford routing algorithm. To identify the failing node and find an alternative node to deliver the seamless services, an unique weight-based approach has been presented. The method relies on two working node parameters: execution time and failure rate. Threshold values are specified for the parameters of the chosen master node. By contrasting the values with the threshold values, the alternative node is chosen. The experimental results shows comparative analysis in terms of throughput of 96%, QoS of 96%, latency of 28%, energy consumption of 51%, end-end delay of 41%, average power consumption of 41% and PDR of 85

Statistical Review of Health Monitoring Models for Real-Time Hospital Scenarios

Health Monitoring System Models (HMSMs) need speed, efficiency, and security to work. Cascading components ensure data collection, storage, communication, retrieval, and privacy in these models. Researchers propose many methods to design such models, varying in scalability, multidomain efficiency, flexibility, usage and deployment, computational complexity, cost of deployment, security level, feature usability, and other performance metrics. Thus, HMSM designers struggle to find the best models for their application-specific deployments. They must test and validate different models, which increases design time and cost, affecting deployment feasibility. This article discusses secure HMSMs' application-specific advantages, feature-specific limitations, context-specific nuances, and deployment-specific future research scopes to reduce model selection ambiguity. The models based on the Internet of Things (IoT), Machine Learning Models (MLMs), Blockchain Models, Hashing Methods, Encryption Methods, Distributed Computing Configurations, and Bioinspired Models have better Quality of Service (QoS) and security than their counterparts. Researchers can find application-specific models. This article compares the above models in deployment cost, attack mitigation performance, scalability, computational complexity, and monitoring applicability. This comparative analysis helps readers choose HMSMs for context-specific application deployments. This article also devises performance measuring metrics called Health Monitoring Model Metrics (HM3) to compare the performance of various models based on accuracy, precision, delay, scalability, computational complexity, energy consumption, and security

Big data y NoSQL: su rol en la revolución del cloud computing y sus retos hacia la estandarización

El auge en la utilización de las tecnologías de información (TI) orientadas a cloud computing ha generado grandes cantidades y variedades de datos, o Big Data, en los centros de datos de las organizaciones proveedoras de estos servicios, generando retos a los desarrolladores y administradores de las TI para el manejo eficiente de esos recursos. Frente a este fenómeno, las bases de datos relacionales son consideradas por los profesionales de las TI como una obstrucción para ofrecer las características esenciales del cloud computing. Esto ha impulsado una serie de nuevos enfoques y tecnologías para la gestión de datos, conocidos como bases de datos no relacionales o NoSQL, diseñadas para proveer rapidez, escalabilidad, alta disponibilidad y elasticidad, facilitando la administración de datos en las soluciones tecnológicas en cloud computing. En este artículo se exponen los conceptos de Big Data, sus principales herramientas tecnológicas para la gestión de datos, el movimiento NoSQL, el rol de estos en la revolución del cloud computing y los retos de estas tecnologías hacia la estandarización

Toward Customizable Multi-tenant SaaS Applications

abstract: Nowadays, Computing is so pervasive that it has become indeed the 5th utility (after water, electricity, gas, telephony) as Leonard Kleinrock once envisioned. Evolved from utility computing, cloud computing has emerged as a computing infrastructure that enables rapid delivery of computing resources as a utility in a dynamically scalable, virtualized manner. However, the current industrial cloud computing implementations promote segregation among different cloud providers, which leads to user lockdown because of prohibitive migration cost. On the other hand, Service-Orented Computing (SOC) including service-oriented architecture (SOA) and Web Services (WS) promote standardization and openness with its enabling standards and communication protocols. This thesis proposes a Service-Oriented Cloud Computing Architecture by combining the best attributes of the two paradigms to promote an open, interoperable environment for cloud computing development. Mutil-tenancy SaaS applicantions built on top of SOCCA have more flexibility and are not locked down by a certain platform. Tenants residing on a multi-tenant application appear to be the sole owner of the application and not aware of the existence of others. A multi-tenant SaaS application accommodates each tenant’s unique requirements by allowing tenant-level customization. A complex SaaS application that supports hundreds, even thousands of tenants could have hundreds of customization points with each of them providing multiple options, and this could result in a huge number of ways to customize the application. This dissertation also proposes innovative customization approaches, which studies similar tenants’ customization choices and each individual users behaviors, then provides guided semi-automated customization process for the future tenants. A semi-automated customization process could enable tenants to quickly implement the customization that best suits their business needs.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Doctor of Philosophy

dissertationIn the past few years, we have seen a tremendous increase in digital data being generated. By 2011, storage vendors had shipped 905 PB of purpose-built backup appliances. By 2013, the number of objects stored in Amazon S3 had reached 2 trillion. Facebook had stored 20 PB of photos by 2010. All of these require an efficient storage solution. To improve space efficiency, compression and deduplication are being widely used. Compression works by identifying repeated strings and replacing them with more compact encodings while deduplication partitions data into fixed-size or variable-size chunks and removes duplicate blocks. While we have seen great improvements in space efficiency from these two approaches, there are still some limitations. First, traditional compressors are limited in their ability to detect redundancy across a large range since they search for redundant data in a fine-grain level (string level). For deduplication, metadata embedded in an input file changes more frequently, and this introduces more unnecessary unique chunks, leading to poor deduplication. Cloud storage systems suffer from unpredictable and inefficient performance because of interference among different types of workloads. This dissertation proposes techniques to improve the effectiveness of traditional compressors and deduplication in improving space efficiency, and a new IO scheduling algorithm to improve performance predictability and efficiency for cloud storage systems. The common idea is to utilize similarity. To improve the effectiveness of compression and deduplication, similarity in content is used to transform an input file into a compression- or deduplication-friendly format. We propose Migratory Compression, a generic data transformation that identifies similar data in a coarse-grain level (block level) and then groups similar blocks together. It can be used as a preprocessing stage for any traditional compressor. We find metadata have a huge impact in reducing the benefit of deduplication. To isolate the impact from metadata, we propose to separate metadata from data. Three approaches are presented for use cases with different constrains. For the commonly used tar format, we propose Migratory Tar: a data transformation and also a new tar format that deduplicates better. We also present a case study where we use deduplication to reduce storage consumption for storing disk images, while at the same time achieving high performance in image deployment. Finally, we apply the same principle of utilizing similarity in IO scheduling to prevent interference between random and sequential workloads, leading to efficient, consistent, and predictable performance for sequential workloads and a high disk utilization

Performance of Machine Learning and Big Data Analytics paradigms in Cybersecurity and Cloud Computing Platforms

The purpose of the research is to evaluate Machine Learning and Big Data Analytics paradigms for use in Cybersecurity. Cybersecurity refers to a combination of technologies, processes and operations that are framed to protect information systems, computers, devices, programs, data and networks from internal or external threats, harm, damage, attacks or unauthorized access. The main characteristic of Machine Learning (ML) is the automatic data analysis of large data sets and production of models for the general relationships found among data. ML algorithms, as part of Artificial Intelligence, can be clustered into supervised, unsupervised, semi-supervised, and reinforcement learning algorithms

A Lightweight Attribute-Based Access Control System for IoT.

The evolution of the Internet of things (IoT) has made a significant impact on our daily and professional life. Home and office automation are now even easier with the implementation of IoT. Multiple sensors are connected to monitor the production line, or to control an unmanned environment is now a reality. Sensors are now smart enough to sense an environment and also communicate over the Internet. That is why, implementing an IoT system within the production line, hospitals, office space, or at home could be beneficial as a human can interact over the Internet at any time to know the environment. 61% of International Data Corporation (IDC) surveyed organizations are actively pursuing IoT initiatives, and 6.8% of the average IT budgets is also being allocated to IoT initiatives. However, the security risks are still unknown, and 34% of respondents pointed out that data safety is their primary concern [1]. IoT sensors are being open to the users with portable/mobile devices. These mobile devices have enough computational power and make it di cult to track down who is using the data or resources. That is why this research focuses on proposing a dynamic access control system for portable devices in IoT environment. The proposed architecture evaluates user context information from mobile devices and calculates trust value by matching with de ned policies to mitigate IoT risks. The cloud application acts as a trust module or gatekeeper that provides the authorization access to READ, WRITE, and control the IoT sensor. The goal of this thesis is to offer an access control system that is dynamic, flexible, and lightweight. This proposed access control architecture can secure IoT sensors as well as protect sensor data. A prototype of the working model of the cloud, mobile application, and sensors is developed to prove the concept and evaluated against automated generated web requests to measure the response time and performance overhead. The results show that the proposed system requires less interaction time than the state-of-the-art methods