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ABSTRACT Cognitive radio is a technology that allows the unlicensed users to access the licensed spectrum based on sensing information perceived by the users. Cognitive radio user generally attempts to access the unutilized band of licensed user. However, the emergence/reappearance of primary users during ongoing cognitive user's transmission creates the interference at the primary receiver and data loss at cognitive receiver. In this context, the spectrum monitoring is a vital process of the cognitive radio network during data transmission to continuously monitor the primary user's presence or absence on a licensed channel. In this manuscript, we have utilized the spectrum monitoring technique on earlier existing MAC protocol for CR network to detect the emergence/reappearance of primary users throughout the data transmission period of cognitive users. The spectrum monitoring is employed to reduce the interference at primary receiver, which is measured in terms of interference power (IP) and data loss at CU receiver. The closed form expressions of data loss and IP for the monitoring and without monitoring scenarios are provided. In addition, the probability of monitoring error's (which causes misinterpretation of channel's status by the cognitive users) impact on these performance parameters is also provided and simulated. Further, earlier MAC protocol has significant amount of bandwidth wastage in case primary user disappears or reappears during cognitive user's data transmission, which is shown mathematically and simulated as well. However, incorporation of the monitoring phenomenon even with significant amount of monitoring error has reduced bandwidth wastage, which is measured in bps. In addition, the throughput calculation of the proposed system and the effect of monitoring error on the throughput of the proposed system are also seen. As per the results, the maximum data loss and IP occurred at licensed channel's busy probability value of 0.5, which is 23.22 bps and 3.487 W, respectively, at maximum monitoring error value. However, minimum data loss and IP are achieved at licensed busy channel's probability values of zero and one. In addition, the resource wastage at licensed channel's busy probability value of 0.5 is varying from 2.322 to 23.22 bps for varying the monitoring error value from 0.1 to 1, respectively, giving highest resource wastage at maximum monitoring error value. [ABSTRACT FROM AUTHOR]

Cloud service providers deliver computing services on demand using the Infrastructure as a Service (IaaS) model. In a cloud data center, several virtual machines (VMs) can be hosted on a single physical machine (PM) with the help of virtualization. The virtual machine placement (VMP) involves assigning VMs across various physical machines, which is a crucial process impacting energy draw and resource usage in the cloud data center. Nonetheless, finding an effective settlement is challenging owing to factors like hardware heterogeneity and the scalability of cloud data centers. This paper proposes an efficient algorithm named VMP-ER aimed at optimizing power consumption and reducing resource wastage. Our algorithm achieves this by decreasing the number of running physical machines, and it gives priority to energy-efficient servers. Additionally, it improves resource utilization across physical machines, thus minimizing wastage and ensuring balanced resource allocation. [ABSTRACT FROM AUTHOR]

Cloud service providers deliver computing services on demand using the Infrastructure as a Service (IaaS) model. In a cloud data center, several virtual machines (VMs) can be hosted on a single physical machine (PM) with the help of virtualization. The virtual machine placement (VMP) involves assigning VMs across various physical machines, which is a crucial process impacting energy draw and resource usage in the cloud data center. Nonetheless, finding an effective settlement is challenging owing to factors like hardware heterogeneity and the scalability of cloud data centers. This paper proposes an efficient algorithm named VMP-ER aimed at optimizing power consumption and reducing resource wastage. Our algorithm achieves this by decreasing the number of running physical machines, and it gives priority to energy-efficient servers. Additionally, it improves resource utilization across physical machines, thus minimizing wastage and ensuring balanced resource allocation.

Fog-cloud computing is a promising distributed model for hosting ever-increasing Internet of Things (IoT) applications. IoT applications should meet different characteristics such as deadline, frequency rate, and input file size. Fog nodes are heterogeneous, resource-limited devices and cannot accommodate all the IoT applications. Due to these difficulties, designing an efficient algorithm to deploy a set of IoT applications in a fog-cloud environment is very important. In this paper, a fuzzy approach is developed to classify applications based on their characteristics then an efficient heuristic algorithm is proposed to place applications on the virtualized computing resources. The proposed policy aims to provide a high quality of service for IoT users while the profit of fog service providers is maximized by minimizing resource wastage. Extensive simulation experiments are conducted to evaluate the performance of the proposed policy. Results show that the proposed policy outperforms other approaches by improving the average response time up to 13%, the percentage of deadline satisfied requests up to 12%, and the resource wastage up to 26%. [ABSTRACT FROM AUTHOR]

Cloud Computing has been economically famous for sharing the resources of third-party applications. There may be an increase in the exploitation of the prevailing Cloud resources and their vulnerabilities as a result of the aggressive growth of Cloud Computing. In the Cache Side Channel Attack (CSCA), the attackers can leak sensitive information of a Virtual Machine (VM) which is co-located in a physical machine due to inadequate logical isolation. The Cloud Service Provider (CSP) has to modify either at the hardware level to isolate their VM or at the software, level to isolate their applications. The hardware isolation requires changes in hardware design and it also leads to performance degradation. The existing works challenge to eliminate CSCA. But our solution prevents CSCA by securing the VM placement in the energy-efficient server. This paper proposes a heuristic secure VM placement policy by using different VM placement techniques (Least VM, Multi-Objective Ant Colony System (MOACO), Previously Assigned Physical Host First (PAPHF)) based on user behaviours to prevent co-resident. The Co-Resident Rate for the three VM placement policies (Least VM, MOACO, PAPHF) has been compared with the existing First Fit Decreasing (FFD) algorithm and Discrete Firefly Algorithm-VM Placement (DFA-VMP) policy in the CloudSim environment. The Power Consumption, Resource Wastage, and Co-Resident probability are compared with the existing FFD and DFA-VMP Systems. Our Multi-Objective Secure Optimal VM Placement (MOSOVMP) proposed system proved that the Co-Resident Rate is reduced with improved server utilization. [ABSTRACT FROM AUTHOR]

The goal of data centers in the cloud computing environment is to provision the workloads and the computing resources as demanded by the users without the intervention of the providers. To achieve this, virtualization based server consolidation acts as a vital part in virtual machine placement process. Consolidating the Virtual Machines (VMs) on the Physical Machines (PMs) cuts down the unused physical servers, decreasing the energy consumption, while keeping the constraints for CPU and memory utilization. This technique also reduces the resource wastage and optimizes the available resources efficiently. Ant Colony Optimization (ACO) that is a well-known multi objective heuristic algorithm and Grey Wolf Algorithm (GWO) has been used to consolidate the servers used in the virtual machine placement problem. The proposed Fuzzy HAGA algorithm outperforms the other algorithms MMAS, ACS, FFD and Fuzzy ACS compared against it as the number of processors and memory utilization are lesser than these algorithms.

A massive number of daily spam demands a method of anti-spam filtering that can efficiently detect unsolicited emails. Traditional spam filtering techniques block spam at receiver's side, unable to stop their propagation on the network, resulting in wastage of network bandwidth, processing power, and disk space. To address these issues, this paper proposes a methodology for sender oriented anti-spamming that identifies and filters spam at its origin, thus not only discourages spam but also reduces the resource wastage. To the best of our knowledge, addressing this wastage by spam has not been done before. Along with blocking spam at origin, this research proposes a diagonal hash algorithm to optimize image spam filtering, and email hash token generator to promote email authentication and discourage zombie accounts. Experiments suggest that the proposed technique has the potential to achieve better performance and accuracy compared to the current approaches. [ABSTRACT FROM AUTHOR]

Cloud computing technology has transformed the information and communication technology industry by authorizing on-demand resource delivery to the cloud users. Datacenters are the major resource storage places from where the resources are disseminated to the requesters. When several requests are received by datacenters, the available resources are to be handled in an optimized way; otherwise the datacenters suffer from resource wastage. Virtualization is the technology that helps the cloud providers to handle several requests in an optimized way. In this regard, virtual machine placement, i.e., the process of mapping virtual machines to physical machines is considered to be the major research issue. In this paper, we propose to apply fuzzy hybrid bio-inspired meta-heuristic techniques for solving the virtual machine placement problem. The cuckoo search technique is hybridized with the fuzzy ant colony optimization and fuzzy firefly colony optimization technique. The experimental results obtained show competing performance of the proposed algorithms.

This paper presents a novel application of hypercube framework of ant colony optimization to the virtual machine (VM) placement problem, with the objective of minimizing the power consumption and resource wastage. The aim of the VM placement is to develop an optimal placement strategy to allocate the VM's to physical servers such that the usage of physical resource is minimized. The hypercube framework of ACO differs from its usual ACO implementation in the hyperspace for the pheromone values. In other words, in the hypercube framework, the updating of pheromone values is constrained to lie between 0 to1. This constrained pheromone updating has an advantage of automatically handling the scaling of objective function values and further leads to robust version pf ACO procedure. Here, we experimentally investigate the influence of hypercube framework of ACO for virtual machine placement using three ACO variants, namely, Ant system, Max-Min Ant System, and Ant colony system. [ABSTRACT FROM AUTHOR]

Objectives. To investigate the hypothetical benefits of the IV2TM flow medical device. Background. Intravenous fluid administration is a standard hospital procedure with assumed inadequacies. The IV-Event Study [Fraser N, Nel G, Snyman J & Wessels F (2004) IV-EVENT Study: Intravenous Infusion Therapy – Management and Adverse Events. Data on File: Varori International (Pty) Ltd., Centurion, South Africa] quantified these inadequacies; The ‘Stargait’ intervention trial investigated the effectiveness and possible cost-benefit of the IV2TM flow. The IV2TM flow is intended for routine use with gravitational intravenous infusion sets. The IV2TM flow should reduce the incidence rate of adverse events and maintain a set flow rate. Method. Nursing staff assisted by study assessors captured relevant data. Consented patients were enrolled for the period of their prescribed infusions. Intervention. The Stargait Trial compared the treatment group (standard gravitational sets with the IV2TM flow) with the control group (standard gravitational infusion sets without IV2TM flow). The difference in observed events and the cost benefit derived from this were measured. Results. A total of 2387 drip hours were observed in 52 patients. The adverse event rates were: Control group (without IV2TM flow) 33·8%. The treatment group (IV2TM flow) 15·4%. This 55% reduction is statistically significant ( p = 0·0069). Adverse event related monetary wastage (labour and consumables) is reduced by 76% for infusion bags in the intervention group (with IV2TM flow). There was a significant difference between the treatment group and control group as far as deviation in flow rate was concerned ( p = 0·00818). The mean deviation of the IV2TM flow group was just more than 5 ml per hour. The standard line group had a mean deviation of more than 30 ml per hour. Conclusion. Gravitational intravenous therapy compromises quality of patient care. The Stargait Trial has proven the care-effectiveness and cost-effectiveness of the IV2TM flow. Relevance to clinical practice. The IV2TM flow improves quality of patient care and reduces associated wastage. [ABSTRACT FROM AUTHOR]