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In recent years, network coding has received extensive attention and has been applied to various computer network systems, since it has been mathematically proven to enhance the network robustness and maximize the network throughput. However, it is well-known that network coding is extremely vulnerable to pollution attacks. Certificateless network coding scheme (CLNS) is a recently proposed mechanism to defend against pollution attacks for network coding, which avoids tedious management of certificates and key-escrow attack. Until now, only a few constructions were presented, and more ones should be given so as to enrich this field. In this paper, for the first time, we study the general construction of CLNS from certificateless public auditing protocol (CL-PAP), although the two areas seem to be quite different in their nature and are studied independently. Since there are many candidates of CL-PAPs, we can naturally obtain abundant constructions of CLNSs according to our systematic way. In addition, in order to show the power of the general construction, we also present a concrete implementation given a specific CL-PAP. The performance analysis and experimental results show that the implemented CLNS is competitive in the existing network coding schemes. [ABSTRACT FROM AUTHOR]

The optimization of traditional FTL(Flash Translation Layer) algorithm is mainly aimed at the average response time of flash memory read and write operation on a flash chip, because of the Out-of-place update, traditional FTL algorithm writes data in a new free page each time, and a new block is assigned when a block is full. Therefore, when the flash memory is almost full, a written request will lead to a garbage collection, in which there will be many write copies, which will lead to a significant decrease in response time. In this paper, we optimize the traditional FTL algorithm and propose an FTL algorithm that guarantees real-time performance by shortening the worst response time of the request as a measure of the guaranteed flash Qos (Quality of Service) under the condition that the address mapping is provided the upper layer. The algorithm adopts the idea of space-for-time and spreads the written copy of flash memory to each write request so that the worst response time of the write request is significantly reduced. The algorithm also wears balancing considerations to the algorithm. After experimental verification, the proposed algorithm has advantages over existing FTL algorithms in terms of real-time performance and has a performance improvement of more than 11% in terms of average response time and wear balance. By the way, we add the wear-level module into an algorithm. According to improving the algorithm, the worst response time has shorten 47.6%. [ABSTRACT FROM AUTHOR]

With the rapid development of cloud computing technology and the broad application of the Internet, more and more user data are stored in the cloud storage system online. Data copy technology is an effective technical means to manage various data in cloud storage systems, which has the advantages of improving data access speed and enhancing data availability in cloud storage systems. Since a data copy management strategy has an important impact on the performance of cloud storage systems, how to design a data copy management strategy is of great significance to achieving high-quality cloud storage management. Firstly, this paper analyzes the HDFS random copy placement strategy and its shortcomings. Secondly, proposes a copy placement strategy based on evaluation value and load balancing. Thirdly, gives the evaluation function of rack and node by considering multiple evaluation indicators such as node disk space load capacity, node memory utilization, node CPU utilization, rack load rate and network distance between racks. Fourthly, finds suitable nodes through computer shelf evaluation values and node evaluation values when placing replicas. Finally, through simulation experiments, the experimental results of the HDFS random replica placement strategy and the evaluation results and load balancing-based replica placement strategy are compared and analyzed. [ABSTRACT FROM AUTHOR]

As a fast-rising storage model in recent years, cloud storage has adopted a "pay-as-you-go" approach to provide users with highly reliable, highly available, low-cost, and secure storage services that have received widespread attention and use within enterprises and individuals. Data replica management technology, as an essential part of cloud storage systems, has irreplaceable advantages in improving cluster fault tolerance and availability, so it has become the focus of many experts and scholars. It replicates multiple data blocks and places them in various nodes in the cluster; this makes the data more secure and reliable and improves the access rate while ensuring system load balance. Data replica technology runs through the process from replica creation to consistency maintenance. Each part of it has an essential impact on the performance of the cloud storage system. This article focuses on the dynamic decision of the number of data replicas in the cloud storage system. Considering the shortcomings of the static replica strategy, a dynamic decision strategy for the number of replicas based on the popularity of the data is proposed. By using the gray prediction model GM (1, 1) to predict the future data access frequency and using the Markov model to modify the prediction result, the data can be divided into hot data and non-hot data according to the predicted value, thereby determining the data replica number. Finally, through simulation experiments, the experimental results of the static replica strategy and the data hot-based replica number dynamic decision strategy are compared and analyzed. [ABSTRACT FROM AUTHOR]

Cloud computing platform is a data-centric architecture, which utilize virtualization resource scheduling technology to integrate and distribute the processing power of distributed server cluster and other network devices distributed over the network, and uses service-oriented architecture to provide users with highly reliable, highly available, and efficient application data services. Although the cloud platform application based on various requirements has become a research hotspot in cloud computing technology, in some high Real-time requirements application scenarios, the research on how to guarantee the real time performance of cloud platform is few. Compared to traditional resource virtualization such as CPU and memory, Docker only provides a small portion of network resource virtualization. The four network modes provided by Docker officially have a single function, which makes it very difficult to apply to cloud platform scenarios with complex network bandwidth requirements and high real-time requirements. Based on Docker's traditional NAT communication method, this paper proposes a network virtualization solution based on SDN and Docker containers. Through comparison experiments, it is proved that the scheme can guarantee the Real-time performance of the container cloud platform. [ABSTRACT FROM AUTHOR]

As user-generated data diversifies, an increasing number of big data tasks now encompass unstructured data forms, including audio, imagery, and video content. The processing of these data often requires the support of GPU devices in the cluster. However, most of the existing cluster resource management frameworks lack effective decoupling of CPU resources and GPU resources, and the management granularity of GPU resources is too coarse, resulting in poor GPU sharing and low resource utilization. Given, for the inadequacy of the existing cluster resource management framework that cannot support batch stream integration and CPU-GPU resource scheduling, we design and implement a Hybrid Heterogeneous Resource Management (H-HRM) for CPU-GPU heterogeneous clusters. The resource queue binding mechanism on computing nodes provides flexible binding of CPU resources to GPU resources, which solves the problem that CPU resources and GPU resources in the cluster are difficult to decouple. According to the different uses of CPU or GPU as the main resource, a Hybrid Domain Resource Fairness (HDRF) model is proposed to realize the reasonable allocation of CPU resources and GPU resources. Through the queue stacking technology and the mechanism that multiple executors can run simultaneously on the queue, fine-grained sharing of GPU resources is realized and the utilization rate of GPU resources is improved. Finally, this paper realizes the docking of H-HRM and Spark programming framework and conducts real load tests, and after comparing with the performance of Mesos, it is proved that H-HRM can handle the mixed scenarios of batch jobs and stream processing jobs in the cluster, and the HDRF algorithm also greatly improves the utilization of the GPU. [ABSTRACT FROM AUTHOR]

Internet of Medical Things (IoMT) technology is widely used in intelligent medical treatment; however, massive mobile data transmission makes the cellular network overburden. The combination of medical Internet of Things technology and cloud storage can greatly improve this problem. Most of the existing medical Internet of Things systems cannot be adapted to the environment with limited resources after considering security and privacy, or after ensuring implementation efficiency, it will lead to reduced system security; that is, the mutual authentication function of the system is easily destroyed. The combination of medical Internet of Things technology and medical cloud storage technology can greatly improve the current smart medical environment. We propose an escrow-free identity-based scheme (EF-IDS) to ensure the function of mutual authentication between system entities, propose a secure lightweight cloud-centric smart medical system based on the medical Internet of Things based on EF-IDS and prove that our system ensures the privacy and security of users' personal health information. It also provides the ability to publicly verify the integrity of the data stored on the medical cloud server. Finally, the performance analysis shows that our proposed system has significant advantages in both communication overhead and computational cost. [ABSTRACT FROM AUTHOR]

Provable data possession (PDP) protocol is a mechanism that guarantees the integrity of user's cloud data, and many efficient protocols have been proposed. Many of them ignore data's privacy against the third-party auditor (TPA) and also suffer from intricate management of certificates, which heavily relies on the public key infrastructure (PKI). In order to overcome the two shortcomings, Li et al. recently proposed an "identity-based" (IB) PDP protocol with the privacy-preserving property (IEEE Syst J, https://doi.org/10.1109/JSYST.2020.2978146). However, we find out that (1) their protocol has great communication overhead, (2) a PKI-based signature scheme is used as a building block, which results in their protocol is not completely identity-based. Hence, in this paper, we try to improve the performance of this protocol. Concretely, by adopting flexible data-splitting and tag-aggregating techniques, we can greatly reduce its communication overhead. A concrete example shows that the total communication overhead can be reduced over 99%. Moreover, by replacing with an identity-based signature, we can twist this protocol into a complete IB-PDP protocol. [ABSTRACT FROM AUTHOR]