Efficient and Fair Bandwidth Scheduling in Cloud Environments

Hundreds of thousands of servers from data centers are operated to provide users with pay-as-yougo infrastructure as a service, platform as a service, and software as a service. Many different types of virtual machine (VM) instances hosted on these servers oftentimes need to efficiently communicate with data movement under current bandwidth capacity. This motivates providers to seek for a bandwidth scheduler to satisfy objectives, namely assuring the minimum bandwidth per VM for the guaranteed deadline and eliminating network congestion as much as possible. Based on some rigorous mathematical models, we formulated a cloud-based bandwidth scheduling algorithm which enables dynamic and fair bandwidth management by categorizing the total bandwidth into several categories and adjusting the allocated bandwidth limit per VM for both upstream and downstream traffics in real time. The simulation showed that paradigm was able to utilize the total assigned bandwidth more efficiently compared to algorithms such as bandwidth efficiency persistence proportional sharing (PPS), PPS, and PS at the network level

Analysis of Computer Network Security Storage System Based on Cloud Computing Environment

A fundamental component of cloud computers from a business perspective is that users are allowed to use any desire and pay with a product that desire. Its cloud services were accessible anytime and anywhere consumers needed them. As a result, consumers are free to purchase whatever IT services they want, and they don't have to worry about how easy things can be managed. The remote server is used in a new information storage computing architecture that is considered an Internet generation. Ensuring security, material at resource providers' sites is a challenge that must be addressed in cloud technology. Thus, rather than reliance on a single provider for knowledge storing, this research implies developing construction for protection of knowledge stockpiling with a variation of operations, in which knowledge is scrambled and divided into numerous cipher frames and distributed across a large number of provider places. This support was applied to provide greater security, scalability, or reliability that was suggested according to the new structure. This paper, presented an encoded model for the cloud environment to improve security. The proposed model comprises the parity metadata for the database management provision to the provider. In the developed encoder chunks parity is not stored within the single resources with the provision of the available information chunks. The constructed security architecture in the RAID layer increases the dependability of the data with the deployment of the RAID 10 deployment. The developed RAID-based encoder chunks exhibit improved efficiency for the higher uptime at a minimal cost

Runtime methods for energy-efficient, image processing using significance driven learning.

Ph. D. Thesis.Image and Video processing applications are opening up a whole range of opportunities for processing at the "edge" or IoT applications as the demand for high accuracy processing high resolution images increases. However this comes with an increase in the quantity of data to be processed and stored, thereby causing a significant increase in the computational challenges. There is a growing interest in developing hardware systems that provide energy efficient solutions to this challenge. The challenges in Image Processing are unique because the increase in resolution, not only increases the data to be processed but also the amount of information detail scavenged from the data is also greatly increased. This thesis addresses the concept of extracting the significant image information to enable processing the data intelligently within a heterogeneous system. We propose a unique way of defining image significance, based on what causes us to react when something "catches our eye", whether it be static or dynamic, whether it be in our central field of focus or our peripheral vision. This significance technique proves to be a relatively economical process in terms of energy and computational effort. We investigate opportunities for further computational and energy efficiency that are available by elective use of heterogeneous system elements. We utilise significance to adaptively select regions of interest for selective levels of processing dependent on their relative significance. We further demonstrate that exploiting the computational slack time released by this process, we can apply throttling of the processor speed to effect greater energy savings. This demonstrates a reduction in computational effort and energy efficiency a process that we term adaptive approximate computing. We demonstrate that our approach reduces energy in a range of 50 to 75%, dependent on user quality demand, for a real-time performance requirement of 10 fps for a WQXGA image, when compared with the existing approach that is agnostic of significance. We further hypothesise that by use of heterogeneous elements that savings up to 90% could be achievable in both performance and energy when compared with running OpenCV on the CPU alone

Cloud Computing Adoption in Afghanistan: A Quantitative Study Based on the Technology Acceptance Model

Cloud computing emerged as an alternative to traditional in-house data centers that businesses can leverage to increase the operation agility and employees\u27 productivity. IT solution architects are tasked with presenting to IT managers some analysis reflecting cloud computing adoption critical barriers and challenges. This quantitative correlational study established an enhanced technology acceptance model (TAM) with four external variables: perceived security (PeS), perceived privacy (PeP), perceived connectedness (PeN), and perceived complexity (PeC) as antecedents of perceived usefulness (PU) and perceived ease of use (PEoU) in a cloud computing context. Data collected from 125 participants, who responded to the invitation through an online survey focusing on Afghanistan\u27s main cities Kabul, Mazar, and Herat. The analysis showed that PEoU was a predictor of the behavioral intention of cloud computing adoption, which is consistent with the TAM; PEoU with an R2 = .15 had a stronger influence than PU with an R2 = .023 on cloud computing behavior intention of adoption and use. PeN, PeS, and PeP significantly influenced the behavioral intentions of IT architects to adopt and use the technology. This study showed that PeC was not a significant barrier to cloud computing adoption in Afghanistan. By adopting cloud services, employees can have access to various tools that can help increase business productivity and contribute to improving the work environment. Cloud services, as an alternative solution to home data centers, can help businesses reduce power consumption and consecutively decrease in carbon dioxide emissions due to less power demand

Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities

[EN] Energy efficiency is a significant characteristic of battery-run devices such as sensors, RFID and mobile phones. In the present scenario, this is the most prominent requirement that must be served while introducing a communication protocol for an IoT environment. IoT network success and performance enhancement depend heavily on optimization of energy consumption that enhance the lifetime of IoT nodes and the network. In this context, this paper presents a comprehensive review on energy efficiency techniques used in IoT environments. The techniques proposed by researchers have been categorized based on five different layers of the energy architecture of IoT. These five layers are named as sensing, local processing and storage, network/communication, cloud processing and storage, and application. Specifically, the significance of energy efficiency in IoT environments is highlighted. A taxonomy is presented for the classification of related literature on energy efficient techniques in IoT environments. Following the taxonomy, a critical review of literature is performed focusing on major functional models, strengths and weaknesses. Open research challenges related to energy efficiency in IoT are identified as future research directions in the area. The survey should benefit IoT industry practitioners and researchers, in terms of augmenting the understanding of energy efficiency and its IoT-related trends and issues.Kumar, K.; Kumar, S.; Kaiwartya, O.; Cao, Y.; Lloret, J.; Aslam, N. (2017). Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities. Energies. 10(12):1-40. https://doi.org/10.3390/en10122073S1401012Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of Things for Smart Cities. 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