Cloud/fog computing resource management and pricing for blockchain networks

The mining process in blockchain requires solving a proof-of-work puzzle, which is resource expensive to implement in mobile devices due to the high computing power and energy needed. In this paper, we, for the first time, consider edge computing as an enabler for mobile blockchain. In particular, we study edge computing resource management and pricing to support mobile blockchain applications in which the mining process of miners can be offloaded to an edge computing service provider. We formulate a two-stage Stackelberg game to jointly maximize the profit of the edge computing service provider and the individual utilities of the miners. In the first stage, the service provider sets the price of edge computing nodes. In the second stage, the miners decide on the service demand to purchase based on the observed prices. We apply the backward induction to analyze the sub-game perfect equilibrium in each stage for both uniform and discriminatory pricing schemes. For the uniform pricing where the same price is applied to all miners, the existence and uniqueness of Stackelberg equilibrium are validated by identifying the best response strategies of the miners. For the discriminatory pricing where the different prices are applied to different miners, the Stackelberg equilibrium is proved to exist and be unique by capitalizing on the Variational Inequality theory. Further, the real experimental results are employed to justify our proposed model.Comment: 16 pages, double-column version, accepted by IEEE Internet of Things Journa

Role of Optical Network in Cloud/Fog Computing

This chapter is a study of exploring the role of the optical network in the cloud/fog computing environment. With the growing network issues, unified and cost-effective computing services and efficient utilization of optical resources are required for building smart applications. Fog computing provides the foundation platform for implementing cyber-physical system (CPS) applications which require ultra-low latency. Also, the digital revolution of fog/cloud computing using optical resources has upgraded the education system by intertwined VR using the fog nodes. Presently, the current technologies face many challenges such as ultra-low delay, optimum bandwidth, and minimum energy consumption to promote virtual reality (VR)-based and electroencephalogram (EEG)-based gaming applications. Ultra-low delay, optimum bandwidth, and minimum energy consumption. Therefore, an Optical-Fog layer is introduced to provide a novel, secure, highly distributed, and ultra-dense fog computing infrastructure. Also, for optimum utilization of optical resources, a novel concept of OpticalFogNode is introduced that provides computation and storage capabilities at the Optical-Fog layer in the software defined networking (SDN)-based optical network. It efficiently facilitates the dynamic deployment of new distributed SDN-based OpticalFogNode which supports low-latency services with minimum energy as well as bandwidth usage. Therefore, an EEG-based VR framework is also introduced that uses the resources of the optical network in the cloud/fog computing environment

Improving quality-of-service in cloud/fog computing through efficient resource allocation

Recently, a massive migration of enterprise applications to the cloud has been recorded in the IT world. One of the challenges of cloud computing is Quality-of-Service management, which includes the adoption of appropriate methods for allocating cloud-user applications to virtual resources, and virtual resources to the physical resources. The effective allocation of resources in cloud data centers is also one of the vital optimization problems in cloud computing, particularly when the cloud service infrastructures are built by lightweight computing devices. In this paper, we formulate and present the task allocation and virtual machine placement problems in a single cloud/fog computing environment, and propose a task allocation algorithmic solution and a Genetic Algorithm Based Virtual Machine Placement as solutions for the task allocation and virtual machine placement problem models. Finally, the experiments are carried out and the results show that the proposed solutions improve Quality-of-Service in the cloud/fog computing environment in terms of the allocation cost

Managing Service-Heterogeneity using Osmotic Computing

Computational resource provisioning that is closer to a user is becoming increasingly important, with a rise in the number of devices making continuous service requests and with the significant recent take up of latency-sensitive applications, such as streaming and real-time data processing. Fog computing provides a solution to such types of applications by bridging the gap between the user and public/private cloud infrastructure via the inclusion of a "fog" layer. Such approach is capable of reducing the overall processing latency, but the issues of redundancy, cost-effectiveness in utilizing such computing infrastructure and handling services on the basis of a difference in their characteristics remain. This difference in characteristics of services because of variations in the requirement of computational resources and processes is termed as service heterogeneity. A potential solution to these issues is the use of Osmotic Computing -- a recently introduced paradigm that allows division of services on the basis of their resource usage, based on parameters such as energy, load, processing time on a data center vs. a network edge resource. Service provisioning can then be divided across different layers of a computational infrastructure, from edge devices, in-transit nodes, and a data center, and supported through an Osmotic software layer. In this paper, a fitness-based Osmosis algorithm is proposed to provide support for osmotic computing by making more effective use of existing Fog server resources. The proposed approach is capable of efficiently distributing and allocating services by following the principle of osmosis. The results are presented using numerical simulations demonstrating gains in terms of lower allocation time and a higher probability of services being handled with high resource utilization.Comment: 7 pages, 4 Figures, International Conference on Communication, Management and Information Technology (ICCMIT 2017), At Warsaw, Poland, 3-5 April 2017, http://www.iccmit.net/ (Best Paper Award

Secure Fog Computing System using Emoticon Technique

Fog computing is a distributed computing infrastructure in whichsome application services are provided at the edge of the network in smart devices( IoT devices) and some applications are handled in cloud. Fog computing operates on network ends instead of working entirely from a centralized cloud. It facilitates the operation of storage, compute, analysis and other services between edge devices mostly IoT devices and cloud computing data centres. The main objective of Fog computing is to process the data close to the edge devices .The problem that occurs in Fog is confidentiality and security of data . To overcome this problem, we have proposed the Dual-Encryption to data using Emoticon Technique which is combination of Cryptography and Steganography. In this method, first data is encrypted and then encrypted data is hidden with the cover text like emoticons. So Dual-Encryption enhances data security and reliability. Even if the covered text is accessed by unauthorized person, only encrypted data of original data can be viewed not the actual data

FIT A Fog Computing Device for Speech TeleTreatments

There is an increasing demand for smart fogcomputing gateways as the size of cloud data is growing. This paper presents a Fog computing interface (FIT) for processing clinical speech data. FIT builds upon our previous work on EchoWear, a wearable technology that validated the use of smartwatches for collecting clinical speech data from patients with Parkinson's disease (PD). The fog interface is a low-power embedded system that acts as a smart interface between the smartwatch and the cloud. It collects, stores, and processes the speech data before sending speech features to secure cloud storage. We developed and validated a working prototype of FIT that enabled remote processing of clinical speech data to get speech clinical features such as loudness, short-time energy, zero-crossing rate, and spectral centroid. We used speech data from six patients with PD in their homes for validating FIT. Our results showed the efficacy of FIT as a Fog interface to translate the clinical speech processing chain (CLIP) from a cloud-based backend to a fog-based smart gateway.Comment: 3 pages, 5 figures, 1 table, 2nd IEEE International Conference on Smart Computing SMARTCOMP 2016, Missouri, USA, 201