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.

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.

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.

In blockchain-based systems, participants can be malicious. Therefore, this work first characterizes several properties expected in systems where the honest behaviour of involved parties plays significant role in the success. Considering these properties, a new consensus mechanism, Proof of Karma (PoK), is proposed based on karma (actions) of nodes. PoK incorporates a self-stabilizing leader election algorithm based on karma score to ensure consistency in the system. In PoK, both new and existing nodes get a fair chance to earn profit by becoming a leader and adding a valid block to the blockchain. PoK gives incentives and imposes penalties to encourage and discourage the nodes’ honest and malicious actions, respectively. PoK is analyzed with respect to the CAP theorem. The work provides security analysis to demonstrate the resistance of PoK against various blockchain specific attacks and karma specific attacks. Several experiments are also performed to assess the performance of PoK and compare it with the baseline model. The results show the feasibility, effectiveness, usability and scalability of PoK. PoK is also compared based on the characterized properties with various existing consensus mechanisms that consider malicious actions of nodes. PoK achieves consensus finality, decentralization and fairness, outperforming existing works.

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.

Resource procurement using reverse auction in Cloud computing is an interesting but a complex problem as it involves many attributes and constraints. Reverse auction is a mechanism in which a customer prepares call for proposal of resource requirement and publicizes it in order to get the attention of eligible service providers. This work proposes a multi-attribute combinatorial reverse auction for Cloud resource procurement which considers price as well as non-price attributes such as quality of service parameters, reputation etc. in the determination of winning service providers. For this, the problem is formulated using approximation algorithm and near optimal solution is obtained in polynomial time. Auction mechanism allows providers to reveal true information in order to maximize their profit. It also imposes a penalty on the providers who cheat i.e., do not offer the agreed upon services. This makes the system robust as it maintains the utility of the customer. It also maintains a healthy competition among the providers. Performance evaluation and a comparative study with some base line models exhibit that the proposed method performs better.

Summary Cloud computing is an upcoming and promising solution for utility computing that provides resources on demand. As it has grown into a business model, a large number of cloud service providers exist today in the cloud market, which further is expanding exponentially. Many cloud service providers, with almost similar functionality, pose a selection problem to the cloud users. To assist the users in the best service selection, as per its requirement, a framework has been developed in which users list their quality of service (QoS) expectation, while service providers express their offerings. Experience of the existing cloud users is also taken into account in order to select the best cloud service provider. This work identifies some new QoS metrics, besides few existing ones, and defines it in a way that eases both the user and the provider to express their expectations and offers, respectively, in a quantified manner. Further, a dynamic and flexible model, using a variant of ranked voting method, is proposed that considers users' requirement and suggests the best cloud service provider. Case studies affirm the correctness and the effectiveness of the proposed model. Copyright © 2016 John Wiley & Sons, Ltd.