Browsing by Author "Gaurav Baranwal"

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A Blockchain Framework for Efficient Resource Allocation in Edge Computing
(Institute of Electrical and Electronics Engineers Inc., 2024) Gaurav Baranwal; Dinesh Kumar; Amit Biswas; Ravi Yadav
Edge computing provides low-latency computing services. Since Edge computing Service Providers (ESPs) are competitors, mutual distrust and distrust towards the platform may exist if a centralized resource allocation platform is used. To address trust issues, we propose a blockchain framework for resource allocation in edge computing that ensures decentralization and transparency in resource allocation. We also offer a novel consensus mechanism for the framework, Proof of Efficient Resource Allocation (PoERA), where ESPs compete to give the best solution to the resource allocation problem to become the leader and earn rewards. The framework addresses the trust issue, single-point failure, and the biased nature of the centralized platform. PoERA includes a novel self-stabilizing leader election algorithm, ensuring no forking, final consensus and consistency, which is lacking in most existing works. The work encourages the participation of both leader and non-leader ESPs by rewarding them based on the quality of their solutions. We perform CAP theorem analysis, demonstrating that Consistency (C) and Availability (A) are more important for the proposed framework than Partition tolerance (P). We conduct experiments to show that the work ensures decentralization, fair competition, and fair reward distribution to miners, eventually improving resource allocation in edge computing. © 2004-2012 IEEE.
A Blockchain-Based Framework to Resolve the Oligopoly Issue in Cloud Computing
(Institute of Electrical and Electronics Engineers Inc., 2024) Amit Biswas; Gaurav Baranwal; Abhinav Kumar
Cloud computing is one of the foundation technologies of Industry 4.0. Cloud 2.0 is the upcoming cloud technology that addresses several bottlenecks of Cloud 1.0. For instance, the presence of small service providers is threatened by the dominance of a few giant service providers in today's cloud market in Cloud 1.0. Under this circumstance, the small service providers must work together to compete with the giant competitors to survive in the market. For that, small service providers require a transparent, fair, cost-effective, fault-tolerant, and easily scalable platform that can provide reliable and quality services to customers. This work introduces a blockchain-based framework to provide such a platform for cloud service providers and their customers. Here, a new consensus mechanism is proposed to maintain the system's fairness, decentralization, and consistency. A consensus-based service monitoring concept is also introduced to assess the service quality. If a service provider does not deliver the committed quality of service (QoS), a penalty is imposed on the service provider. This framework is designed so that the service providers are always bound to provide committed QoS to the customers. Finally, we performed several experiments, and the experimental results corroborate our claims regarding the proposed framework. © 2013 IEEE.
A blockchain-enabled private parking space allocation with improved parking space utilization
(Elsevier Ltd, 2024) Keshab Kumar Gaurav; Gaurav Baranwal
With the rapid development of the world's infrastructure, we are moving towards making cities smart, known as smart cities. To make smart cities sustainable, designing efficient solutions is needed to reduce traffic congestion due to the lack of parking spaces within the constrained land areas of many metropolitan city centres. Parking problems in urban areas can be reduced by sharing vacant private parking spaces with others. In this work, we propose a decentralized and shared private parking management system using blockchain, which helps private parking owners and users fulfil their demands without the involvement of central authority. The proposed parking allocation method assigns parking to a single user from multiple parking providers, but the requester does not need to relocate between different parking spaces. The proposed method significantly improves private parking space utilization compared to a state-of-the-art parking allocation system. Hence, the system satisfies more user requests and generates more profit for parking owners than the state-of-the-art parking allocation system. The proposed system is a winning strategy because it maximizes the utilization of private parking spaces for the entire community's benefit and helps to reduce traffic congestion. Experiments conducted in the simulated environment validate the benefits of the proposed model and show that it outperforms the state-of-the-art parking allocation system. A prototype of the proposed system is also developed in the Ethereum blockchain to validate the work, and the gas cost used to deploy the proposed model is analyzed. © 2024 Elsevier Ltd
A Consensus Model to Manage Unavailability of Decision-Makers in Group Decision Making
(Institute of Electrical and Electronics Engineers Inc., 2023) Manisha Singh; Gaurav Baranwal; Anil Kumar Tripathi
All the known Group Decision Making (GDM) models assume the continuous availability of all decision-makers (DMs) during the Consensus Reaching Process (CRP). Factually, the constant presence of a DM means that the concerned DMs are interested in adequately contributing to the decision-making process, and the technical support continuously enables their support. However, in a realistic situation, one or more DMs may be unavailable at times in CRP iterations due to technical or non-technical reasons. This paper considers such a scenario wherein the DMs are sparsely present. Hence, working out a model to take care of such pertinent absences that eventually make GDM possible. The bounded confidence of an individual DM is used to facilitate CRP in evaluating the opinions of the unavailable DMs. We propose to assign weight to a DM based on their cumulative presence in the decision process. Consideration of the opinion of a DM in a particular iteration based on the opinion in the previous iterations in case of the absence of the concerned DM in an implementation shown here is shown to be helpful. © 2023 IEEE.
A framework for IoT service selection
(Springer, 2020) Gaurav Baranwal; Manisha Singh; Deo Prakash Vidyarthi
IoT is getting popular as it makes human life comfortable. The industry giants such as IBM, Microsoft, Cisco and Amazon have started offering IoT assistance in form of services. Numerous IoT applications exist today with different roles to play in day-to-day life. Because of application diversity and a good number of IoT service providers, it is difficult for IoT users to select the best one as per the requirement and expected quality of service, QoS. To address this, QoS metrics related to major IoT components, i.e., communication, computing and things, are designed to assess the alternative services. IoT users can express their requirements regarding QoS, while service providers exhibit their offerings. Because of three major IoT components, service selection is considered as multi-criteria group decision-making (MCGDM) problem. This work proposes a new MCGDM framework to rank the IoT services that considers rank reversal problem, judgments of decision makers in linguistic term and the uncertainty and risk-attitudinal characteristics in human decision-making. The proposed framework is validated by comparing it with an existing MCGDM model. A case study on IoT health-care application is provided besides the sensitivity analysis to demonstrate the effectiveness of the proposed framework. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.
A Multi-Criteria Framework for Smart Parking Recommender System
(Institute of Electrical and Electronics Engineers Inc., 2020) Gaurav Baranwal; Dinesh Kumar; Deo Prakash Vidyarthi
Parking has become a real challenge in cities, especially in metros, due to exponential increase in number of vehicles. A significant amount of time wasted in locating the parking space results in traffic congestion, pollution and fuel consumption. Recommending parking spot is an important service towards intelligent transportation system. Evolution in Internet of Things (IoT), Fog and Cloud Computing, and Sensor technologies can be better utilized to explore parking details such as parking occupancy, traffic congestion in parking path etc. in real time and an efficient and effective Parking Recommender System (PRS) can be designed. Parkers may have different expectations from PRS such as walking distance between the destination and the parking spot, pricing, safety etc. Therefore, a personalized recommender system is warranted in which a parker specifies its preference to various quality parameters related to parking. Considering parkers as human being, uncertainty in decision making over the preference cannot be ruled out. This work proposes a framework for multiple quality parameters/criteria based smart parking spot recommender system. It also provides various quality parameters, related to parking, to help parkers to express their need which helps in recommendation. As the boundaries between the parametric values are not crisp, fuzzy logic is utilized in parking recommender method to handle the uncertainty in human decision making. A case study, along with sensitivity analysis, demonstrates the effectiveness of the proposed model. © 2020 IEEE.
A novel 2-phase consensus with customized feedback based group decision-making involving heterogeneous decision-makers
(Springer, 2023) Manisha Singh; Gaurav Baranwal; Anil Kumar Tripathi
In Group Decision-Making (GDM), decision-makers (DMs) who are experts take wise decisions. But in systems such as smart cities, IoT, and e-democracy, the acceptance and survival of the decision given by the experts depend on the experience of citizens and end-users. Hence, an attempt can be made to use the citizens' perceptions. A potential solution to improve the acceptance and survival of the decision is to include citizens' opinions too in the decision-making. In this work, a novel GDM model is proposed that involves non-experts along with the experts to understand the opinions of non-experts also by the experts. Two phases of the consensus reaching process (CRP) are defined: the inter-consensus reaching phase, where consensus between experts and non-experts will be achieved, and the intra-consensus reaching phase, where the experts negotiate among themselves to attain the consensus. In existing GDM models, CRP overcomes the conflicts in the opinions of the DMs by providing feedback to DMs for modifying their preferences to achieve the required consensus. However, multiple feedback rounds increase the cost of CRP. The proposed GDM gives customized feedback to the experts only once at each phase, reducing the feedback cost in attaining the consensus. A numerical example is discussed to explain the effectiveness of the proposed model. The proposed approach is tested on different consensus thresholds to verify its practicality. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
A Novel Tolerance-Based Moderator Guided Heterogeneous Group Decision-Making Involving Experts and End-Users
(World Scientific, 2023) Manisha Singh; Gaurav Baranwal; Anil Kumar Tripathi
This study focuses on two issues of group decision-making (GDM). First, the multiple rounds of feedback recommendations in the consensus reaching process (CRP) make GDM inefficient. The second is no involvement of heterogeneous decision-makers (DMs), possibly end-users as stakeholders apart from the experts. To address the first issue, a novel threshold-based feedback mechanism is introduced to improve the efficiency of the CRP that helps the experts reach consensus in at most one round of feedback. To address the second issue, end-users are invited to participate in decision-making where their majority group opinion is used. Then, a novel concept of tolerance degree of the moderator is proposed to obtain the final decision considering the consensual opinion of experts and cumulative opinion of end-users. The effectiveness of the proposed method is demonstrated through a case of healthcare service selection. Further, various experiments are conducted to show how the proposed work outperforms the existing works. © 2023 World Scientific Publishing Company.
A Study on Integration of Trust Management and Application Placement in Fog Computing
(Institute of Electrical and Electronics Engineers Inc., 2023) Ravi Yadav; Gaurav Baranwal
Fog computing enables placement of applications on fog nodes present in the proximity of Internet of Things (IoT) users, leading to improved performance such as fast response time, low bandwidth consumption, low power consumption, etc. Thus, IoT users experienced enhanced satisfaction. Quality of Experience (QoE) is a well-known metric used to meter the user satisfaction level and can be enhanced with improvement in the performance of IoT applications. However, application placement in presence of malicious fog environment can lead to numerous harmful effects such as inaccurate placement results, reduced user satisfaction, and increased user relinquish rate. Thus, we integrate a trust management framework in QoE-aware application placement framework to study the effect of trust in reducing the harmful effects created in malicious environment. The trust management framework evaluates and manages the trust of fog nodes and consider them with other QoE-dependent metrics to make placement decisions. Simulation results show that our proposed placement mechanism performs satisfactorily in malicious fog environments. © 2023 IEEE.
A Study on Privacy-Preserving Auctions Considering Possible Collusions
(Institute of Electrical and Electronics Engineers Inc., 2023) Anubhav Yadav; Gaurav Baranwal
An auction can be used in various domains, such as cloud computing, data trading, energy trading, and spectrum allocation, which can help to identify the true value of selling goods. The introduction of the Internet has already paved the way for electronic auction (e-auction). Electronic auctions have several benefits over traditional physical auctions; however, in e-auctions, privacy becomes a major issue. Many works are reported in the literature to address this issue. Collusion of various entities involved in the auction, such as bidder, seller, auctioneer, etc. However, most works related to privacy-preserving auctions lack the consideration of collusion of involved entities. In this paper, we have discussed various important works related to privacy-preserving auctions and their associated issues. Problems created due to the collusion of entities in the works are also discussed. We also summarized works in allied disciplines where privacy-preserving auctions are applied. We have also discussed the future research directions in brief. © 2023 IEEE.
A Survey on Auction based Approaches for Resource Allocation and Pricing in Emerging Edge Technologies
(Springer Science and Business Media B.V., 2022) Dinesh Kumar; Gaurav Baranwal; Deo Prakash Vidyarthi
The advancements in sensing technologies, smart devices, wearable gadgets, and communication paradigm enable the vision of the internet of things, smart city, virtual and augmented reality, pervasive healthcare, to name a few. These applications have strict requirements of low latency delivery, high data rate, and instant response. To support this, various new technologies, such as fog computing, mobile edge computing, cloudlet, Micro, and Nano centers, mini and micro clouds, etc., have emerged. The entire set of emerging edge computing paradigms are commonly referred as "edge technologies" in which computational resources and storage are closer to the user/terminal devices somewhere between the device and the cloud data center. The edge technologies aim to deliver computing services with minimal delay by reducing the downward and upward time and data traffic volume. Like cloud service providers, edge service providers are emerging, and a market of edge computing resources has been created. Therefore, Auction theory, a subfield of Economics, is being widely applied for the allocation of resources in emerging edge technologies. This work presents a comprehensive survey on auction-based resource allocation and pricing approaches in emerging edge technologies. An overview of edge technologies and auction theory is given, followed by a thorough review and comparison of the existing auction-based approaches applied in edge technologies for resource allocation and pricing in terms of economic properties. Various open research issues have been deliberated to set the future research direction at the end. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.
A survey on nature-inspired techniques for computation offloading and service placement in emerging edge technologies
(Springer, 2022) Dinesh Kumar; Gaurav Baranwal; Yamini Shankar; Deo Prakash Vidyarthi
Internet of Things (IoT) aims to make an environment more innovative and productive by connecting physical things to the internet. Processing generated data from IoT devices and actuation warranted in real-time requires computational infrastructure near the edge to get the outcome without delay. Emerging edge technologies such as Fog computing, Multi-Access Edge Computing, and Cloudlet provide computing resources near the edge, i.e. closer to the IoT devices, where devices can place their services/applications or offload their computational job for processing. The utilization of computing resources provided by emerging edge technologies addresses the issue of delay in the outcome and increases the battery life of IoT devices/End-user devices. Computational resources provided by the edge technologies, i.e. edge/fog nodes, can be heterogeneous, dynamic and mobile. Therefore, service placement and computation offloading on edge/fog nodes are challenging issues, and the problem to finding the best suitable fog/edge nodes is NP-Hard. Nature-inspired algorithms provide robust solutions to NP-Hard problems. Nowadays, nature-inspired algorithms have been widely applied for resource allocation for service placement and computation offloading in emerging edge technologies. In this work, we provide a detailed study on the applications of nature-inspired algorithms in emerging edge computing domains. We provide an overview of emerging edge technologies, related quality parameters and nature-inspired algorithms followed by the basic formulation of service placement and computation offloading in emerging edge computing systems. In this work, we classify the works in emerging edge computing applying nature-inspired algorithms into two categories: works related to service placement and works related to offloading. We provide a thorough review and comparison of the existing nature-inspired approaches in each category. We discuss various open issues at the end to set future research directions. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
A Survey on Spot Pricing in Cloud Computing
(Springer New York LLC, 2018) Dinesh Kumar; Gaurav Baranwal; Zahid Raza; Deo Prakash Vidyarthi
Amazon offers spot instances to cloud customers using an auction-like mechanism. These instances are dynamically priced and offered at a lower price with less guarantee of availability. Observing the popularity of Amazon spot instances among the cloud users, research has intensified on defining the users’ and providers’ behavior in the spot market. This work presents an exhaustive survey of spot pricing in cloud ecosystem. An insight into the Amazon spot instances and its pricing mechanism has been presented for better understanding of the spot ecosystem. Spot pricing and resource provisioning problem, modeled as a market mechanism, is discussed from both computational and economics perspective. A significant amount of important research papers related to price prediction and modeling, spot resource provisioning, bidding strategy designing etc. are summarized and categorized to evaluate the state of the art in the context. All theoretical frameworks, developed for cloud spot market, are illustrated and compared in terms of the techniques and their findings. Finally, research gaps are identified and various economic and computational challenges in cloud spot ecosystem are discussed as a guide to the future research. © 2017, Springer Science+Business Media, LLC, part of Springer Nature.
A truthful combinatorial double auction-based marketplace mechanism for cloud computing
(Elsevier Inc., 2018) Dinesh Kumar; Gaurav Baranwal; Zahid Raza; Deo Prakash Vidyarthi
Designing market-based mechanism that benefits both the cloud customer and cloud provider in a cloud market is a fundamental but complex problem. Double auction is one such mechanism to allocate resources that prevents monopoly and is used to design an unbiased optimal market strategy for cloud market. This work proposes a truthful combinatorial double auction mechanism for allocation and pricing of computing resources in cloud. For resource allocation, utilitarian social welfare maximization problem is formulated using Integer Linear Programming (ILP) and a near optimal solution is obtained using Linear Programming based padded method. For payment, truthful and novel schemes are designed for both customers and providers. Moreover, the proposed mechanism is individual rational, computationally tractable, weakly budget-balance and asymptotic efficient. Performance evaluation and comparative study exhibit that the proposed mechanism is effective on various performance metrics such as utilitarian social welfare, total utility, customers’ satisfaction, providers’ revenue and hence is applicable in real cloud environments. © 2018 Elsevier Inc.
ABAC: Alternative by alternative comparison based multi-criteria decision making method
(Elsevier Ltd, 2022) Amit Biswas; Gaurav Baranwal; Anil Kumar Tripathi
Decision-making appears as a complex and challenging task when it requires finding the most suitable alternative among the numerous alternatives in the presence of multiple, usually conflicting criteria. At the same time, stakeholders expect a simple, transparent, and traceable decision-making method. Multi-Criteria Decision-Making (MCDM) methods rank the alternatives considering multiple criteria. The rank reversal problem is an important issue in most existing conventional MCDM methods. This paper proposes a new alternative by alternative comparison-based MCDM Method (ABAC) that addresses the rank reversal problem. We prove that ABAC is free from the rank reversal problem. To illustrate and validate ABAC, we have taken the cloud service selection problem as an application. Further, to show the effectiveness of ABAC, we have provided several case studies covering various domains. We perform several experiments by simulating the ABAC method. We have compared ABAC and existing MCDM methods. The experimental results support that the ABAC method is a rank reversal free MCDM method. We also carry out sensitivity analysis for ABAC. Salient features of ABAC over existing MCDM methods are (i) it is simple; (ii) it is rank reversal free; (iii) it is more scalable. © 2022 Elsevier Ltd
Admission Control Policies in Fog Computing Using Extensive Form Game
(Institute of Electrical and Electronics Engineers Inc., 2022) Gaurav Baranwal; Deo Prakash Vidyarthi
Due to emergence of Internet of Things (IoT), fog computing is gaining momentum in the IT industry. The fog nodes are owned by the fog service providers (FSPs) and usually are not intended to provide their services for free. If FSP charges high for its services or does not stick with its promised quality of service, existing users of that FSP may leave early or may churn to some other FSP. In such competitive scenario, to survive and to maximize the profit in the long run, FSPs should accept the requests of the users considering both technical and non-technical parameters. Since both FSP and IoT users are strategic decision makers, game theoretic analysis may help FSPs to maximize their payoffs. With the change in the strategy of the player, equilibrium solution may change and therefore this dynamic scenario is formulated as an extensive game form. A subgame perfect equilibrium, obtained for this game using backward induction, makes admission control policies suitable for different environment which helps the FSPs in maximizing their profit in the long run. A comparative analysis of the proposed work with state of art indicates that the proposed work outperforms and generates better revenue to the FSPs. © 2013 IEEE.
An Efficient Trust Management using Feedback Credibility Evaluation Method in Fog Computing
(Elsevier B.V., 2022) Ravi Yadav; Gaurav Baranwal
Trust of a fog node in fog computing plays a vital role when a user is not familiar with the fog node and can be calculated using the feedback provided by other users. Malicious feedbacks lead to degraded user satisfaction, degraded reputation of good fog nodes, and various attacks such as bad-mouthing attacks, ballot stuffing, Sybil attacks, whitewashing attacks, etc. This work proposes a novel checkers-based feedback credibility evaluation method to identify malicious feedbacks and omits them while evaluating the trust of a fog node. Checkers are the users who have already taken service from the fog node and are honest. The method uses aggregated opinion of checkers and aging factor of checkers to decide the credibility of feedbacks. The proposed method limits the impact of various possible attacks caused by malicious feedbacks. Performance evaluation shows that the method performs well even when malicious feedbacks are present in the system. © 2022 Elsevier B.V.
Auction based resource provisioning in cloud computing
(Springer, 2018) Gaurav Baranwal; Dinesh Kumar; Zahid Raza; Deo Prakash Vidyarthi
[No abstract available]
Auction theory
(Springer, 2018) Gaurav Baranwal; Dinesh Kumar; Zahid Raza; Deo Prakash Vidyarthi
Auctioning has a long history and is reported to have been used in Babylon as early as 500 B.C. The entire Roman Empire was sold off using auction in 193 A.D. [83]. With time, auction theory has evolved with more sophisticated and mature auction procedures. Auction is considered as an efficient and fair mechanism as it provides equal opportunity to both the seller and the buyer. The price of the resources is decided on the basis of the value of the resources for the bidders that makes higher revenue. In early auction days and even during its evolution, only antiques and art matters were sold using auction but now various commodities, e.g. fish, bond, spectrum, computing resources, are sold using auction because of its multifaceted benefits. After the introduction of mechanism design, auction has become a great success in economics for the resource allocation. Mechanism design and information and communication technologies (ICTs) are the two major responsible factors that made game theory and optimization an effective tool for auction design to achieve specific goals. In economics, the rich literature and practical implementation on auction are available. Because of involvement of pricing in resource allocation in Cloud computing, good possibilities can be explored for applying auction in Cloud computing. Academicians have proposed various types of auction models that can be applied in different scenarios in Cloud computing. Spot market has been become a milestone for both, academicians and professionals, to explore auction in greater depth. Amazon, a giant in the Cloud computing market, practically gave a push to implement dynamic pricing using auction. Plenty of works on auction theory, variants of auction as well as its applications for suitable scenario are available in the form of books, survey papers, etc. The aim of this chapter is to provide a detailed description of most important findings of the auction so that academicians and researchers can work their way through these findings in Cloud computing. © The Author(s) 2018.
BARA: A blockchain-aided auction-based resource allocation in edge computing enabled industrial internet of things
(Elsevier B.V., 2022) Gaurav Baranwal; Dinesh Kumar; Deo Prakash Vidyarthi
The recent emergence of Industrial Internet of Things (IIoT), a novel subset of Internet of Things (IoT), integrates sensors and intelligent devices with industry applications to develop a self-organizing system for creating enhanced and adaptive industrial environments. IIoT devices usually have limited computational power, while IIoT applications are mostly mission-critical and/or safety-critical. Therefore, these applications need computing resources that are closer to the devices. This work proposes a decentralized auction-based resource allocation mechanism in edge computing enabled IIoT using consortium blockchain and smart contract that shelves the involvement of a trusted third party, i.e. auctioneer. Various quality parameters are considered during resource allocation to address the mobility of both IIoT devices and edge resources, heterogeneity of edge servers, false assurance of edge servers, reliability, delay in results, and responsiveness of edge servers. Utility functions for IIoT devices are designed to calculate their degree of satisfaction depending on various quality parameters. The proposed blockchain-aided auction based mechanism fulfills various auction-based resource allocation requirements such as seal bidding, no impersonation of the bidder, no modification in any bid or result of allocation by the auctioneer, and proof for winners. The proposed work encourages edge servers for truthful bidding and furnishes the allocation results in polynomial time. Performance evaluation of the proposed model exhibits encouraging results. © 2022 Elsevier B.V.