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A Break in the Clouds: Towards a Cloud Definition

The Analysis of Time Series: An Introduction, 4th edn. By C. Chatfield. ISBN 0 412 31820 2. Chapman and Hall, London, 1989. 242 pp. £13.50.

The Analysis of Time Series: An Introduction

Fast Fourier Transform

Network slicing has been identified as the backbone of the rapidly evolving 5G technology. However, as its consolidation and standardization progress, there are no literatures that comprehensively discuss its key principles, enablers, and research challenges. This paper elaborates network slicing from an end-to-end perspective detailing its historical heritage, principal concepts, enabling technologies and solutions as well as the current standardization efforts. In particular, it overviews the diverse use cases and network requirements of network slicing, the pre-slicing era, considering RAN sharing as well as the end-to-end orchestration and management, encompassing the radio access, transport network and the core network. This paper also provides details of specific slicing solutions for each part of the 5G system. Finally, this paper identifies a number of open research challenges and provides recommendations toward potential solutions.

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Network Slicing and Softwarization: A Survey on Principles, Enabling Technologies, and Solutions

Wireless sensor networks (WSNs) are networks of distributed autonomous devices that can sense or monitor physical or environmental conditions cooperatively. WSNs face many challenges, mainly caused by communication failures, storage and computational constraints and limited power supply. Paradigms of computational intelligence (CI) have been successfully used in recent years to address various challenges such as data aggregation and fusion, energy aware routing, task scheduling, security, optimal deployment and localization. CI provides adaptive mechanisms that exhibit intelligent behavior in complex and dynamic environments like WSNs. CI brings about flexibility, autonomous behavior, and robustness against topology changes, communication failures and scenario changes. However, WSN developers are usually not or not completely aware of the potential CI algorithms offer. On the other side, CI researchers are not familiar with all real problems and subtle requirements of WSNs. This mismatch makes collaboration and development difficult. This paper intends to close this gap and foster collaboration by offering a detailed introduction to WSNs and their properties. An extensive survey of CI applications to various problems in WSNs from various research areas and publication venues is presented in the paper. Besides, a discussion on advantages and disadvantages of CI algorithms over traditional WSN solutions is offered. In addition, a general evaluation of CI algorithms is presented, which will serve as a guide for using CI algorithms for WSNs.

Computational Intelligence in Wireless Sensor Networks: A Survey

Mobile ad hoc network (MANET) is a group of mobile nodes which communicates with each other without any supporting infrastructure. Routing in MANET is extremely challenging because of MANETs dynamic features, its limited bandwidth and power energy. Nature-inspired algorithms (swarm intelligence) such as ant colony optimization (ACO) algorithms have shown to be a good technique for developing routing algorithms for MANETs. Swarm intelligence is a computational intelligence technique that involves collective behavior of autonomous agents that locally interact with each other in a distributed environment to solve a given problem in the hope of finding a global solution to the problem. In this paper, we propose a hybrid routing algorithm for MANETs based on ACO and zone routing framework of bordercasting. The algorithm, HOPNET, based on ants hopping from one zone to the next, consists of the local proactive route discovery within a node's neighborhood and reactive communication between the neighborhoods. The algorithm has features extracted from ZRP and DSR protocols and is simulated on GlomoSim and is compared to AODV routing protocol. The algorithm is also compared to the well known hybrid routing algorithm, AntHocNet, which is not based on zone routing framework. Results indicate that HOPNET is highly scalable for large networks compared to AntHocNet. The results also indicate that the selection of the zone radius has considerable impact on the delivery packet ratio and HOPNET performs significantly better than AntHocNet for high and low mobility. The algorithm has been compared to random way point model and random drunken model and the results show the efficiency and inefficiency of bordercasting. Finally, HOPNET is compared to ZRP and the strength of nature-inspired algorithm is shown.

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HOPNET: A hybrid ant colony optimization routing algorithm for mobile ad hoc network

In this study, we design, develop, and simulate a cloud resources pricing model that satisfies two important constraints: the dynamic ability of the model to provide a high satisfaction guarantee measured as Quality of Service (QoS) - from users perspectives, profitability constraints - from the cloud service providers perspectives We employ financial option theory and treat the cloud resources as underlying assets to capture the realistic value of the cloud compute commodities (C3). We then price the cloud resources using our model. We discuss the results for four different metrics that we introduce to guarantee the quality of service and price as follows: (a) Moore's law based depreciation of asset values, (b) new technology based volatility measures in capturing price changes, (c) a new financial option pricing based model combining the above two concepts, and (d) the effect of age of resources and depreciation of cloud resource on QoS. We show that the cloud parameters can be mapped to financial economic model and we discuss the results of cloud compute commodity pricing for various parameters, such as the age of the resource, quality of service, and contract period.

Pricing Cloud Compute Commodities: A Novel Financial Economic Model

The number of IoT sensors and physical objects accommodated on the Internet is increasing day by day, and traditional Cloud Computing would not be able to host IoT data because of its high latency Being challenged of processing all IoT big data on Cloud facilities, there is not enough study on automating components to deal with the big data and real-time tasks in the IoT–Fog–Cloud ecosystem For instance, designing automatic data transfer from the fog layer to cloud layer, which contains enormous distributed devices is challenging Considering fog as the supporting processing layer, dealing with decentralized devices in the IoT and fog layer leads us to think of other automatic mechanisms to manage the existing heterogeneity The big data and heterogeneity challenges also motivated us to design other automatic components for Fog resiliency, which we address as the third challenge in the ecosystem Fog resiliency makes the processing of IoT tasks independent to the Cloud layer This survey aims to review, study, and analyze the automatic functions as a taxonomy to help researchers, who are implementing methods and algorithms for different IoT applications We demonstrated the automatic functions through our research in accordance to each challenge The study also discusses and suggests automating the tasks, methods, and processes of the ecosystem that still process the data manually

Process Automation in an IoT–Fog–Cloud Ecosystem: A Survey and Taxonomy

Vehicular Ad hoc Networks (VANET) exhibit highly dynamic behavior with high mobility and random network topologies. The performance of Transmission Control Protocols (TCP) in such wireless ad hoc networks is plagued by a number of problems: frequent link failures, scalability, multi-hop data transmission and data loss. In this work, we make use of the vehicle's movement pattern, vehicle density, vehicle velocity and vehicle fading conditions to develop a hybrid, multi-path ant colony based routing algorithm, Mobility Aware Zone based Ant Colony Optimization Routing for VANET (MAZACORNET). that exhibits locality and scalability. We use ACO to find multiple routes between nodes in the network to aid in link failures. To achieve scalability we partition the network into multiple zones. We use proactive approach to find a route within a zone and reactive approach to find routes between zones using the local information stored in each zone thereby trying to reduce broadcasting and congestion. Our proposed algorithm makes effective use of the network bandwidth, is scalable and is robust to link failures. The results show that the algorithm works well for dense networks. The algorithm produces better delivery ratio and is scalable for zones beyond four. When compared to other existing VANET algorithms, the hybrid algorithm proved to be more efficient in terms of packet delivery ratio and end to end delay. To our knowledge this is the first ant based routing algorithm for VANET that uses the concept of zones.

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MAZACORNET: Mobility aware zone based ant colony optimization routing for VANET

We investigate the performance of a highly parallel Particle Swarm Optimization (PSO) algorithm implemented on the GPU. In order to achieve this high degree of parallelism we implement a collaborative multi-swarm PSO algorithm on the GPU which relies on the use of many swarms rather than just one. We choose to apply our PSO algorithm against a real-world application: the task matching problem in a heterogeneous distributed computing environment. Due to the potential for large problem sizes with high dimensionality, the task matching problem proves to be very thorough in testing the GPUs capabilities for handling PSO. Our results show that the GPU offers a high degree of performance and achieves a maximum of 37 times speedup over a sequential implementation when the problem size in terms of tasks is large and many swarms are used.

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Parimala Thulasiraman

Papers

HOPNET: A hybrid ant colony optimization routing algorithm for mobile ad hoc network

Pricing Cloud Compute Commodities: A Novel Financial Economic Model

Process Automation in an IoT–Fog–Cloud Ecosystem: A Survey and Taxonomy

MAZACORNET: Mobility aware zone based ant colony optimization routing for VANET

Collaborative multi-swarm PSO for task matching using graphics processing units