Cloud computing is a network model of on-demand access for sharing configurable computing resource pools. Compared with conventional service architectures, cloud computing introduces new security challenges in secure service management and control, privacy protection, data integrity protection in distributed databases, data backup, and synchronization. Blockchain can be leveraged to address these challenges, partly due to the underlying characteristics such as transparency, traceability, decentralization, security, immutability, and automation. We present a comprehensive survey of how blockchain is applied to provide security services in the cloud computing model and we analyze the research trends of blockchain-related techniques in current cloud computing models. During the reviewing, we also briefly investigate how cloud computing can affect blockchain, especially about the performance improvements that cloud computing can provide for the blockchain. Our contributions include the following: (i) summarizing the possible architectures and models of the integration of blockchain and cloud computing and the roles of cloud computing in blockchain; (ii) classifying and discussing recent, relevant works based on different blockchain-based security services in the cloud computing model; (iii) simply investigating what improvements cloud computing can provide for the blockchain; (iv) introducing the current development status of the industry/major cloud providers in the direction of combining cloud and blockchain; (v) analyzing the main barriers and challenges of integrated blockchain and cloud computing systems; and (vi) providing recommendations for future research and improvement on the integration of blockchain and cloud systems.
Cloud Computing is one of the upcoming Internet based technology. It is been considered as the next generation computing model for its advantages. It is the latest computational model after distributed computing, parallel processing and grid computing. To be effective they need to tap all available sources of supply, both internal and external. The system has facilities where prospective candidates can upload their CV’s and other academic achievements. Earlier recruitment was done manually and it was all at a time-consuming work. Now it is all possible in a fraction of second. Better recruitment and selection strategies result in improved organizational outcomes. With reference to this context, the research paper entitled Recruitment and Selection has been prepared to put a light on Recruitment and Selection process.
The development of cloud infrastructures inspires the emergence of cloud-native computing. As the most promising architecture for deploying microservices, serverless computing has recently attracted more and more attention in both industry and academia. Due to its inherent scalability and flexibility, serverless computing becomes attractive and more pervasive for ever-growing Internet services. Despite the momentum in the cloud-native community, the existing challenges and compromises still wait for more advanced research and solutions to further explore the potentials of the serverless computing model. As a contribution to this knowledge, this article surveys and elaborates the research domains in the serverless context by decoupling the architecture into four stack layers: Virtualization, Encapsule, System Orchestration, and System Coordination. Inspired by the security model, we highlight the key implications and limitations of these works in each layer, and make suggestions for potential challenges to the field of future serverless computing.
Serverless computing has emerged as an attractive deployment option for cloud applications in recent times. The unique features of this computing model include rapid auto-scaling, strong isolation, fine-grained billing options and access to a massive service ecosystem which autonomously handles resource management decisions. This model is increasingly being explored for deployments in geographically distributed edge and fog computing networks as well, due to these characteristics. Effective management of computing resources has always gained a lot of attention among researchers. The need to automate the entire process of resource provisioning, allocation, scheduling, monitoring and scaling, has resulted in the need for specialized focus on resource management under the serverless model. In this article, we identify the major aspects covering the broader concept of resource management in serverless environments and propose a taxonomy of elements which influence these aspects, encompassing characteristics of system design, workload attributes and stakeholder expectations. We take a holistic view on serverless environments deployed across edge, fog and cloud computing networks. We also analyse existing works discussing aspects of serverless resource management using this taxonomy. This article further identifies gaps in literature and highlights future research directions for improving capabilities of this computing model.
Current monitoring methods of transmission line operation status can not obtain real-time data of distributed distribution transmission line, which leads to a large error in monitoring results. Therefore, a multi-state on-line monitoring method based on power Internet of Things is proposed. Using the gateway of power internet of things to set up network control access mode, build edge computing model, and using AD chip of ADS8365W5300 to obtain the real-time data of massive distributed distribution network, then make a decision on the fault after processing and analyzing the data. This paper constructs an edge computing model which can complete the data processing and analysis in the edge node, and designs the deployment of the edge computing model. By evaluating the faults in the dynamic incremental fault set, the risk state of transmission line in the danger control area is obtained, and a multi-state on-line monitoring method is designed. The experimental results show that the proposed method can monitor the transmission line running state accuratel
Our computers today, from sophisticated servers to small smartphones, operate based on the same computing model, which requires running a sequence of discrete instructions, specified as an algorithm. This sequential computing paradigm has not yet led to a fast algorithm for an NP-complete problem despite numerous attempts over the past half a century. Unfortunately, even after the introduction of quantum mechanics to the world of computing, we still followed a similar sequential paradigm, which has not yet helped us obtain such an algorithm either. Here a completely different model of computing is proposed to replace the sequential paradigm of algorithms with inherent parallelism of physical processes. Using the proposed model, instead of writing algorithms to solve NP-complete problems, we construct physical systems whose equilibrium states correspond to the desired solutions and let them evolve to search for the solutions. The main requirements of the model are identified and quantum circuits are proposed for its potential implementation.