Bio: Gaurav Baranwal is an academic researcher from Banaras Hindu University. The author has contributed to research in topics: Cloud computing & Computer science. The author has an hindex of 10, co-authored 29 publications receiving 347 citations. Previous affiliations of Gaurav Baranwal include Jawaharlal Nehru University & Madan Mohan Malaviya University of Technology.
TL;DR: This work proposes a multi-attribute combinatorial double auction for the allocation of Cloud resources, which not only considers the price but other quality of service parameters also, which reflects the usefulness of the method.
Abstract: Recently, Cloud computing has emerged as a market where computing related resources are treated as a utility and are priced. There is a big competition among the Cloud service providers and therefore, the providers offer the services strategically. Auction, a market based resource allocation strategy, has received the attention among the Cloud researchers recently. The auction principal of resource allocation is based on demand and supply. This work proposes a multi-attribute combinatorial double auction for the allocation of Cloud resources, which not only considers the price but other quality of service parameters also. Auctioneer extends some of the parameters to the offered bids from the bidders in order to provide fairness and robustness. In case of not meeting the assured quality, a penalty is imposed on the provider and customer is compensated. The reputation of the provider also diminishes in the forthcoming rounds. Performance study of the proposed model is done by simulation which reflects the usefulness of the method.
••01 Feb 2018
TL;DR: This work identifies and describes various QoS metrics of IoT keeping the fact in mind that Computing, Communication and Things are three pillars of IoT.
Abstract: The Internet of Things (IoT) has emerged with an ultimate goal of automating human life by providing its services. This incredible technology has a huge potential by which it can make human life simpler and easier. To increase the popularity of any type of services, first, their quality metrics need to be defined clearly. In IoT also, Quality of Service (QoS) metrics are needed to be defined first then only user will be able to understand and express their requirement using these metrics. This work is a step in this direction. This work identifies and describes various QoS metrics of IoT keeping the fact in mind that Computing, Communication and Things are three pillars of IoT. This work helps IoT providers to describe their services, users to describe their needs and researchers and professionals to develop the models in IoT considering the importance of QoS metrics.
TL;DR: A Truthful Multi-Unit Double Auction mechanism (TMDA) is proposed that would help researchers to understand how a truthful double auction mechanism can be designed and would also encourage researchers to contribute in this emerging area.
Abstract: A detailed study of the double auction mechanisms in cloud is provided.A framework for double auction in cloud is proposed for a future cloud market.A model TMDA is proposed to infer how a truthful double auction can be designed.TMDA is asymptotically efficient, individual rational, truthful and budget-balanced.Various challenges and future scope in double auction in cloud are also presented. The cloud system is designed, implemented and conceptualized as a marketplace where resources are traded. This demands efficient allocation of resources to benefit both the cloud users and the cloud service providers. Accordingly, market based resource allocation models for cloud computing have been proposed. These models apply economy based approaches e.g. auction, negotiation etc. This work makes a detailed study of the double auction mechanisms and their applicability for the cloud markets. A framework for a future cloud market using double auction is also proposed. As most of the existing works in double auction confines only resource allocation, therefore, a Truthful Multi-Unit Double Auction mechanism (TMDA) is proposed that would help researchers to understand how a truthful double auction mechanism can be designed. TMDA is proven to be asymptotically efficient, individual rational, truthful and budget-balanced. TMDA would also encourage researchers to contribute in this emerging area. The performance of TMDA, which addresses the interests of both the cloud user and the provider, has been validated through simulation study. Various challenges in the realization of double auction mechanisms in cloud computing along-with the future possibilities are also presented.
••27 Mar 2014
TL;DR: This paper identifies QoS metrics and defines it in such a way that user and provider both can express their expectation and offers respectively into quantified form.
Abstract: Cloud computing provides computing resources on demand. It is a promising solution for utility computing. Increasing number of cloud service providers having similar functionality poses a problem to cloud users of its selection. To assist the users, for selection of a best service provider as per user's requirement, it is necessary to create a solution. User may provide its QoS expectation and service providers may also express the offers. Experience of existing users may also be beneficial in selection of best cloud service provider. This paper identifies QoS metrics and defines it in such a way that user and provider both can express their expectation and offers respectively into quantified form. A dynamic and flexible framework using Ranked Voting Method is proposed which takes requirement of user as an input and provides a best provider as output.
TL;DR: An exhaustive survey of spot pricing in cloud ecosystem is presented and an insight into the Amazon spot instances and its pricing mechanism has been presented for better understanding of the spot ecosystem.
Abstract: 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.
TL;DR: This paper identifies which quality factors, research and contribution facets have been underutilised in the state of the art of proposed QoS approaches in the IoT.
Abstract: In an Internet of Things (IoT) environment, the existence of a huge number of heterogeneous devices, which are potentially resource-constrained and/or mobile has led to quality of service (QoS) concerns. Quality approaches have been proposed at various layers of the IoT architecture and take into consideration a number of different QoS factors. This paper evaluates the current state of the art of proposed QoS approaches in the IoT, specifically: (1) What layers of the IoT architecture have had the most research on QoS? (2) What quality factors do the quality approaches take into account when measuring performance? (3) What types of research have been conducted in this area? We have conducted a systematic mapping using a number of automated searches from the most relevant academic databases to address these questions. This mapping has identified a number of state of the art approaches which provides a good reference for researchers. The paper also identifies a number of gaps in the research literature at specific layers of the IoT architecture. It identifies which quality factors, research and contribution facets have been underutilised in the state of the art.
TL;DR: The effect of linear and nonlinear mapping approaches in DispEn are investigated and fluctuation-based DispEn (FDispEn) is developed as a measure to deal with only the fluctuations of time series to discriminate deterministic from stochastic time series.
Abstract: Dispersion entropy (DispEn) is a recently introduced entropy metric to quantify the uncertainty of time series. It is fast and, so far, it has demonstrated very good performance in the characterisation of time series. It includes a mapping step, but the effect of different mappings has not been studied yet. Here, we investigate the effect of linear and nonlinear mapping approaches in DispEn. We also inspect the sensitivity of different parameters of DispEn to noise. Moreover, we develop fluctuation-based DispEn (FDispEn) as a measure to deal with only the fluctuations of time series. Furthermore, the original and fluctuation-based forbidden dispersion patterns are introduced to discriminate deterministic from stochastic time series. Finally, we compare the performance of DispEn, FDispEn, permutation entropy, sample entropy, and Lempel–Ziv complexity on two physiological datasets. The results show that DispEn is the most consistent technique to distinguish various dynamics of the biomedical signals. Due to their advantages over existing entropy methods, DispEn and FDispEn are expected to be broadly used for the characterization of a wide variety of real-world time series. The MATLAB codes used in this paper are freely available at http://dx.doi.org/10.7488/ds/2326.
TL;DR: In this article, the authors used generalized additive models (GAM) to better support marketing decision makers in identifying risky customers by using non-linear fits to the data, which can improve marketing decision making.
Abstract: Nowadays, companies are investing in a well-considered CRM strategy. One of the cornerstones in CRM is customer churn prediction, where one tries to predict whether or not a customer will leave the company. This study focuses on how to better support marketing decision makers in identifying risky customers by using Generalized Additive Models (GAM). Compared to Logistic Regression, GAM relaxes the linearity constraint which allows for complex non-linear fits to the data. The contributions to the literature are three-fold: (i) it is shown that GAM is able to improve marketing decision making by better identifying risky customers; (ii) it is shown that GAM increases the interpretability of the churn model by visualizing the non-linear relationships with customer churn identifying a quasi-exponential, a U, an inverted U or a complex trend and (iii) marketing managers are able to significantly increase business value by applying GAM in this churn prediction context.
TL;DR: This paper uses a Multi-Objective Particle Swarm Optimization based on Crowding Distance (MOPSO-CD) to solve the problem of service allocation in the cloud computing, and uses fuzzy set theory to specify the best compromise solution.
Abstract: Cloud computing is an emerging Internet-based computing paradigm, with its built-in elasticity and scalability. In cloud computing field, a service provider offers a large number of resources like computing units, storage space, and software for customers with a relatively low cost. As the number of customer increases, fulfilling their requirements may become an important yet intractable matter. Therefore, service allocation is one of the most challenging issues in the cloud environments. The problem of service allocation in the cloud computing is thought to be a combinatorial optimization problem to a large company for numbers of their customers and owned resources could be huge enough. This paper considers three conflicting objectives, namely maximizing revenue for users and providers as well as finding the optimal solution at desired time. We use a Multi-Objective Particle Swarm Optimization based on Crowding Distance (MOPSO-CD) to solve the problem because MOPSO-CD is highly competitive in converging towards the Pareto front and generates a well-distributed set of non-dominated solutions. In addition, fuzzy set theory is employed to specify the best compromise solution. We simulate the proposed method using Matlab and compare the performance of the method against the performance of two other multi-objective algorithms, in order to prove that the proposed method is highly competitive with respect to them. Finally, the experiments results show that the method improves the speed of the execution of the resources allocation algorithm while generating high revenue for both the users and the providers and increasing the resource utilization.
TL;DR: The current state of art of the functional pillars of IoT and its emerging applications are presented to motivate academicians and researches to develop real-time, energy-efficient, scalable, reliable, and secure IoT applications.
Abstract: Internet of Things (IoT) is an integration of the Sensor, Embedded, Computing, and Communication technologies. The purpose of the IoT is to provide seamless services to anything, anytime at any place. IoT technologies play a crucial role everywhere, which brings the fourth revolution of disruptive technologies after the internet and Information and Communication Technology (ICT). The Research & Development community has predicted that the impact of IoT will be more than the internet and ICT on society, which improves the well-being of society and industries. Addressing the predominant system-level design aspects like energy efficiency, robustness, scalability, interoperability, and security issues result in the use of a potential IoT system. This paper presents the current state of art of the functional pillars of IoT and its emerging applications to motivate academicians and researches to develop real-time, energy-efficient, scalable, reliable, and secure IoT applications. This paper summarizes the architecture of IoT, with the contemporary status of IoT architectures. Highlights of the IoT system-level issues to develop more advanced real-time IoT applications have been discussed. Millions of devices exchange information using different communication standards, and interoperability between them is a significant issue. This paper provides the current status of the communication standards and application layer protocols used in IoT with the detailed analysis. The computing paradigms like Cloud, Cloudlet, Fog, and Edge computing facilitate IoT with various services like data offloading, resource and device management, etc. In this paper, an exhaustive analysis of Edge Computing in IoT with different edge computing architectures and existing status are deliberated. The widespread adoption of IoT in society has resulted in privacy and security issues. This paper emphasizes on analyzing the security challenges, privacy and security threats, conventional mitigation techniques, and further scope for IoT security. The features like fewer memory footprints, scheduling, real-time task execution, fewer interrupt, and thread switching latency of Real-Time Operating Systems (RTOS) enables the development of time critical IoT applications. Also, this review offers the analysis of the RTOS’s suitable for IoT with the current status and networking stack. Finally, open research issues in IoT system development are discussed.