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Table Of Integrals Series And Products

Expression and Meaning.

: Today's smartphones operate independently of each other, using only local computing, sensing, networking, and storage capabilities and functions provided by remote Internet services. It is generally difficult or expensive for one smartphone to share data and computing resources with another. Data is shared through centralized services, requiring expensive uploads and downloads that strain wireless data networks. Collaborative computing is only achieved using ad hoc approaches. Coordinating smartphone data and computing would allow mobile applications to utilize the capabilities of an entire smartphone cloud while avoiding global network bottlenecks. In many cases, processing mobile data in-place and transferring it directly between smartphones would be more efficient and less susceptible to network limitations than off loading data and processing to remote servers. We have developed Hyrax, a platform derived from Hadoop that supports cloud computing on Android smartphones. Hyrax allows client applications to conveniently utilize data and execute computing jobs on networks of smartphones and heterogeneous networks of phones and servers. By scaling with the number of devices and tolerating node departure, Hyrax allows applications to use distributed resources abstractly, oblivious to the physical nature of the cloud. The design and implementation of Hyrax is described, including experiences in porting Hadoop to the Android platform and the design of mobile specific customizations. The scalability of Hyrax is evaluated experimentally and compared to that of Hadoop. Although the performance of Hyrax is poor for CPU-bound tasks, it is shown to tolerate node-departure and offer reasonable performance in data sharing. A distributed multimedia search and sharing application is implemented to qualitatively evaluate Hyrax from an application development perspective.

/pdf/hyrax-cloud-computing-on-mobile-devices-using-mapreduce-481j5iqy0h.pdf

Hyrax: Cloud Computing on Mobile Devices using MapReduce

Internet of Things (IoT) is an emerging technology that makes people’s lives smart by conquering a plethora of diverse application and service areas. In near future, the fifth-generation (5G) wireless networks provide the connectivity for this IoT ecosystem. It has been carefully designed to facilitate the exponential growth in the IoT field. Network slicing is one of the key technologies in the 5G architecture that has the ability to divide the physical network into multiple logical networks (i.e., slices) with different network characteristics. Therefore, network slicing is also a key enabler of realisation of IoT in 5G. Network slicing can satisfy the various networking demands by heterogeneous IoT applications via dedicated slices. In this survey, we present a comprehensive analysis of the exploitation of network slicing in IoT realisation. We discuss network slicing utilisation in different IoT application scenarios, along with the technical challenges that can be solved via network slicing. Furthermore, integration challenges and open research problems related to the network slicing in the IoT realisation are also discussed in this paper. Finally, we discuss the role of other emerging technologies and concepts, such as blockchain and Artificial Intelligence/Machine Learning (AI/ML) in network slicing and IoT integration.

https://researchrepository.ucd.ie/bitstream/10197/12083/2/IEEE_COMST_Slicing___IoT_survey__Final_Submission__%20%281%29.pdf

Survey on Network Slicing for Internet of Things Realization in 5G Networks

A method of bandwidth allocation for delivery of stored digital content from at least one server device to at least one client device by way of a network is disclosed. The method begins by prescribing a control variable which represents a target flow rate from the server device to each client device. Next, time-varying constraints on the flow rate of the content are determined. A cost function of the control variable for each client is determined. The cost function corresponds to a maximized value of the control variable. Finally, bandwidth is prescribed to each client based upon the value of the control variable maximized by the cost function. In this respect, the method achieves optimal allocation of bandwidth between the server and the respective clients.

Method for connection acceptance control and rapid determination of optimal multi-media content delivery over networks

Existing cellular communications and the upcoming 5G mobile network requires meeting high-reliability standards, very low latency, higher capacity, more security, and high-speed user connectivity. Mobile operators are looking for a programmable solution that will allow them to accommodate multiple independent tenants on the same physical infrastructure and 5G networks allow for end-to-end network resource allocation using the concept of Network Slicing (NS). Data-driven decision making will be vital in future communication networks due to the traffic explosion and Artificial Intelligence (AI) will accelerate the 5G network performance. In this paper, we have developed a ‘DeepSlice’ model by implementing Deep Learning (DL) Neural Network to manage network load efficiency and network availability, utilizing in-network deep learning and prediction. We use available network Key Performance Indicators (KPIs) to train our model to analyze incoming traffic and predict the network slice for an unknown device type. Intelligent resource allocation allows us to use the available resources on existing network slices efficiently and offer load balancing. Our proposed DeepSlice model will be able to make smart decisions and select the most appropriate network slice, even in case of a network failure.

DeepSlice: A Deep Learning Approach towards an Efficient and Reliable Network Slicing in 5G Networks

Network Slicing will play a vital role in enabling a multitude of 5G applications, use cases, and services. Network slicing functions will provide an end-to-end isolation between slices with an ability to customize each slice based on the service demands (bandwidth, coverage, security, latency, reliability, etc.). Maintaining isolation of resources, traffic flow, and network functions between the slices is critical in protecting the network infrastructure system from Distributed Denial of Service (DDoS) attack. The 5G network demands and new feature sets to support ever-growing and complex business requirements have made existing approaches to network security inadequate. In this paper, we have developed a Neural Network based ‘Secure5G’ Network Slicing model to proactively detect and eliminate threats based on incoming connections before they infest the 5G core network. ‘Secure5G’ is a resilient model that quarantines the threats ensuring end-to-end security from device(s) to the core network, and to any of the external networks. Our designed model will enable the network operators to sell network slicing as-a-service to serve diverse services efficiently over a single infrastructure with high security and reliability.

Secure5G: A Deep Learning Framework Towards a Secure Network Slicing in 5G and Beyond

Overloads that occur during times of network stress result in blocked access to all users, independent of importance. These overloads can occur because of degraded resource availability or abnormally high demand. Public broadband networks must dynamically recognize some multimedia connections as having greater importance than others and allocate resources accordingly. A new approach to connection admission control is proposed that uses an upper limit policy to optimize the admission of connections based on the weighted sum of blocking across traffic classes. This results in a simple algorithm suitable for multimedia and packet networks. This work is also the first to demonstrate that the use of an upper limit policy is superior to traditional approaches of adding extra capacity or partitioning capacity, both in terms of the amount of resources required and sensitivity to load variations. An upper limit policy can also be deployed much faster when a large overload occurs from a disaster event.

Prioritized resource allocation for stressed networks

In wireless networks employing carrier-sense multiple-access with collision avoidance (CSMA/CA), correlations between the service processes of different nodes arise as a result of competition for common wireless channels and the dependencies between upstream and downstream traffic flows. These dependencies make the development of tractable performance models extremely difficult. To address this purpose, we present a new continuous-time model for CSMA wireless networks where we combine a node model and a channel model in order to capture correlation. Simplification methods are presented that make our models computationally tractable for large networks with minimal loss of accuracy. The model can be used for both single and multi-hop wireless networks and takes into account non-saturated queues, backoff-stage dependence of collision probabilities, and the correlation between departure processes and arrival processes of adjacent nodes. The model can be used to compute probabilistic quality of service guarantees to optimize end-to-end throughput and end-to-end delay by adjusting arrival and backoff rates along various paths.

Analytical models for understanding space, backoff, and flow correlation in CSMA wireless networks

The purpose of this paper is to present a new method for the study of the competition and co- operation relationships among nodes in a network us- ing a CSMA (Carrier Sense Multiple Access) proto- col implemented with an exponential backoff process. Self-organized behavior is proposed. Standard topolo- gies, such as tandem, traffic splitting and merging, are studied with variables: 1) backoff rate, 2) topology po- sition, 3) traffic splitting. Several interesting phenom- ena are reported. We propose a “Channel Utilization Model” with a new derived variable “channel access rate” which can be used for all traffic loads. These mod- els are applied with different optimization methods. Special strategies, for example “MAC Friendliness”, are also presented for serving special traffic like emergency traffic.

http://perpetualinnovation.net/ojs/index.php/ijngc/article/download/166/57

Competition, Cooperation, and Optimization in Multi-Hop CSMA Networks with Correlated Traffic

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