Energy-efficient resource allocation scheme based on enhanced flower pollination algorithm for cloud computing data center

Cloud Computing (CC) has rapidly emerged as a successful paradigm for providing ICT infrastructure. Efficient and environmental-friendly resource allocation mechanisms, responsible for allocatinpg Cloud data center resources to execute user applications in the form of requests are undoubtedly required. One of the promising Nature-Inspired techniques for addressing virtualization, consolidation and energyaware problems is the Flower Pollination Algorithm (FPA). However, FPA suffers from entrapment and its static control parameters cannot maintain a balance between local and global search which could also lead to high energy consumption and inadequate resource utilization. This research developed an enhanced FPA-based energy efficient resource allocation scheme for Cloud data center which provides efficient resource utilization and energy efficiency with less probable Service Level Agreement (SLA) violations. Firstly, an Enhanced Flower Pollination Algorithm for Energy-Efficient Virtual Machine Placement (EFPA-EEVMP) was developed. In this algorithm, a Dynamic Switching Probability (DSP) strategy was adopted to balance the local and global search space in FPA used to minimize the energy consumption and maximize resource utilization. Secondly, Multi-Objective Hybrid Flower Pollination Resource Consolidation (MOH-FPRC) algorithm was developed. In this algorithm, Local Neighborhood Search (LNS) and Pareto optimisation strategies were combined with Clustering algorithm to avoid local trapping and address Cloud service providers conflicting objectives such as energy consumption and SLA violation. Lastly, Energy-Aware Multi-Cloud Flower Pollination Optimization (EAM-FPO) scheme was developed for distributed Multi-Cloud data center environment. In this scheme, Power Usage Effectiveness (PUE) and migration controller were utilised to obtain the optimal solution in a larger search space of the CC environment. The scheme was tested on MultiRecCloudSim simulator. Results of the simulation were compared with OEMACS, ACS-VMC, and EA-DP. The scheme produced outstanding performance improvement rate on the data center energy consumption by 20.5%, resource utilization by 23.9%, and SLA violation by 13.5%. The combined algorithms have reduced entrapment and maintaned balance between local and global search. Therefore, based on the findings the developed scheme has proven to be efficient in minimizing energy consumption while at the same time improving the data center resource allocation with minimum SLA violation

Energy-efficient virtual machine allocation technique using interior search algorithm for cloud datacenter

Cloud Computing is revolutionizing how Computing power is generated and consumed over the Internet on a pay-peruse basis over the past few years. The broader acceptance of Cloud technologies has led to the establishment of datacenters. Over the years, high energy consumption by datacenters has become a major interest as a result of increasing demands of resources and services by enterprise and scientific applications. Consequently, datacenter infrastructure turns out to be not only expensive to sustain, but also unfavorable to the surrounding environment due to their huge carbon emission. Thus, energy efficient virtual machine allocation techniques are required to overcome high energy consumption due to improper resource allocation within the data centers. This paper proposes Energy-Efficient Virtual Machine allocation technique using Interior Search Algorithm (ISA) that reduces the datacenter energy consumption and resource underutilization. The results shows that, the energy consumption of GA and BFD is 90%-95% as compare to the proposed EE-IS which around 65%. On average 30% of energy has been save using EE-IS as well the utilization of the resources which has also improved

VANETs Multipath Video Data Streaming Considering Road Features

Multipath video streaming in Vehicular Ad-hoc Networks (VANETs) is an evolving research topic. The adoption of video transmission in VANETs communication has become essential due to the comprehensiveness and applicability of video data for on-road advertisement and infotainment. Meanwhile, several research studies have considered how to apply and improve the transmission of the video quality. Due to this, the concurrent multipath transmission has been employed in order to achieve load balancing and path diversity, because of the high data rate of the video data.  However, the main nature of the road, which is the pathway for VANET nodes has not been considered explicitly. In this paper, the road features are considered for VANETs multipath video streaming based on the greedy geographical routing protocol. Thus, VANETs Multipath Video Streaming based on Road Features (VMVS-RF) protocol has been proposed. The protocol was compared with an ordinary Multipath Video Streaming (MVS). The result demonstrates that the proposed VMVS-RF protocol outperforms the MVS in terms of Data Receiving Rate (DRR), Structural Similarity (SSIM) index and Packet Loss Ratio (PLR)

Modified low energy adaptive clustering hierarchy protocol for efficient energy consumption in wireless sensor networks

In this paper, we propose a Modified Low-Energy Adaptive Clustering Hierarchy (MoLEACH) protocol to improve energy consumption in in Wireless Sensor Networks. The novelty of MoLEACH is that, unlike the original LEACH that uses the residual energy of the network, it considers the residual energy of each node for calculation of the threshold value for the next round in cluster head selection. We make comparative simulation analysis between the MoLEACH and LEACH in testing different parameters such as first node dead, half node dead, and the effect of the number of nodes to the network lifetime. The simulation results show that the number of nodes affects the network lifetime in which increments of number of nodes decrease the network lifetime. In small area, minimum number of nodes is better for network lifetime in both MoLEACH and LEACH protocols. Hence, MoLEACH shows improvement of energy efficiency over the LEAC

Energy-efficient Virtual Machine Allocation Technique Using Flower Pollination Algorithm in Cloud Datacenter: A Panacea to Green Computing

Cloud computing has attracted significant interest due to the increasing service demands from organizations offloading computationally intensive tasks to datacenters. Meanwhile, datacenter infrastructure comprises hardware resources that consume high amount of energy and give out carbon emissions at hazardous levels. In cloud datacenter, Virtual Machines (VMs) need to be allocated on various Physical Machines (PMs) in order to minimize resource wastage and increase energy efficiency. Resource allocation problem is NP-hard. Hence finding an exact solution is complicated especially for large-scale datacenters. In this context, this paper proposes an Energy-oriented Flower Pollination Algorithm (E-FPA) for VM allocation in cloud datacenter environments. A system framework for the scheme was developed to enable energy-oriented allocation of various VMs on a PM. The allocation uses a strategy called Dynamic Switching Probability (DSP). The framework finds a near optimal solution quickly and balances the exploration of the global search and exploitation of the local search. It considers a processor, storage, and memory constraints of a PM while prioritizing energy-oriented allocation for a set of VMs. Simulations performed on MultiRecCloudSim utilizing planet workload show that the E-FPA outperforms the Genetic Algorithm for Power-Aware (GAPA) by 21.8%, Order of Exchange Migration (OEM) ant colony system by 21.5%, and First Fit Decreasing (FFD) by 24.9%. Therefore, E-FPA significantly improves datacenter performance and thus, enhances environmental sustainability

Cryptanalytic attacks on Rivest, Shamir, and Adleman (RSA) cryptosystem: issues and challenges

RSA cryptosystem is an information security algorithm used for encrypting and decrypting of digital data in order to protect the content of the data and to ensure its privacy. Prior research studies have shown that RSA algorithm is very successful in protecting enterprises commercial services and systems as well as web servers and browsers to secure web traffic. In an email application, it's utilized to ensure the privacy and authenticity of email message. Some studies have also shown the efficiency of RSA algorithm in securing remote login sessions, and electronic credit-card payment systems. Generally RSA algorithm gain a security support because of it’s frequently use in most applications where security of digital data is mostly a concern. Its strength lies with its ability of withstanding many forms of attacks. While many studies focus on proving that RSA algorithm is breakable under certain cryptanalytic attacks, yet there are some confrontations on the circumstances of applying those attacks. This paper presents the issues and challenges on some key aspects of cryptanalytic attacks on RSA algorithm. The paper also explores the perceived vulnerabilities of implementing RSA algorithm which can render a cryptanalyst easier means of attack

Road-based multi-metric forwarder evaluation for multipath video streaming in urban vehicular communication

In video streaming over vehicular communication, optimal selection of a video packet forwarder is a daunting issue due to the dynamic nature of Vehicular Ad-hoc NETworks (VANETs)and the high data rates of video. In most of the existing studies, extensive considerations of the essential metrics have not been considered. In order to achieve quality video streaming in vehicular network, important metrics for link connectivity and bandwidth efficiency need to be employed to minimize video packet error and losses. In order to address the aforementioned issues, a Road-based Multi-metric Forwarder Evaluation scheme for Multipath Video Streaming (RMF-MVS) has been proposed. The RMF-MVS scheme is adapted to be a Dynamic Self-Weighting score (DSW) (RMF-MVS+DSW) for forwarder vehicle selection. The scheme is based on multipath transmission. The performance of the scheme is evaluated using Peak Signal to Noise Ratio (PSNR), Structural SIMilarity index (SSIM), Packet Loss Ratio (PLR) and End-to-End Delay (E2ED) metrics. The proposed scheme is compared against two baseline schemes including Multipath Solution with Link and Node Disjoint (MSLND) and Multimedia Multi-metric Map-aware Routing Protocol (3MRP) with DSW (3MRP+DSW). The comparative performance assessment results justify the benefit of the proposed scheme based on various video streaming related metrics

Energy-efficient Nature-Inspired techniques in Cloud computing datacenters

Cloud computing is a systematic delivery of computing resources as services to the consumers via the Internet. Infrastructure as a Service (IaaS) is the capability provided to the consumer by enabling smarter access to the processing, storage, networks, and other fundamental computing resources, where the consumer can deploy and run arbitrary software including operating systems and applications. The resources are sometimes available in the form of Virtual Machines (VMs). Cloud services are provided to the consumers based on the demand, and are billed accordingly. Usually, the VMs run on various datacenters, which comprise of several computing resources consuming lots of energy resulting in hazardous level of carbon emissions into the atmosphere. Several researchers have proposed various energy-efficient methods for reducing the energy consumption in datacenters. One such solutions are the Nature-Inspired algorithms. Towards this end, this paper presents a comprehensive review of the state-of-the-art Nature-Inspired algorithms suggested for solving the energy issues in the Cloud datacenters. A taxonomy is followed focusing on three key dimension in the literature including virtualization, consolidation, and energy-awareness. A qualitative review of each techniques is carried out considering key goal, method, advantages, and limitations. The Nature-Inspired algorithms are compared based on their features to indicate their utilization of resources and their level of energy-efficiency. Finally, potential research directions are identified in energy optimization in data centers. This review enable the researchers and professionals in Cloud computing datacenters in understanding literature evolution towards to exploring better energy-efficient methods for Cloud computing datacenters

Optimization of neural network through genetic algorithm searches for the prediction of international crude oil price based on energy products prices

This study investigated the prediction of crude oil price based on energy product prices using genetically optimized Neural Network (GANN). It was found from experimental evidence that the international crude oil price can be predicted based on energy product prices. The comparison of the prediction performance accuracy of the propose GANN with Support Vector Machine (SVM), Vector Autoregression (VAR), and Feed Forward NN (FFNN) suggested that the propose GANN was more accurate than the SVM, VAR, and FFNN in the prediction accuracy and time computational complexity. The propose GANN was able to improve the performance accuracy of the comparison algorithms. Our approach can easily be modified for the prediction of similar commodities

Computational intelligence techniques with application to crude oil price projection: A literature survey from 2001-2012

This paper is an attempt to survey the applications of computational intelligence techniques for predicting crude oil prices over a period of ten years. The purpose of this research is to provide an exhaustive overview of the existing literature which may assist prospective researchers. The reviewed literature covers a spectrum of publications on the proposed model, source of experimental data, period of data collection, year of publication and contributors. The overall trend of the publications in this area of research issued within the last decade is also addressed. The existing body of research has been analyzed and new research directions have been outlined that have been previously ignored. It is expected that researchers across the globe may thus be encouraged to re–direct their attention and resources in order to keep on searching for an optimum solution