Ceph is a unified distributed storage system,which can provide storage services of 3 types of interfaces:block,file and object.Different from the traditional distributed storage system,it adopts the metadata management method without central node,so it has good scalability and linear growth performance.After more than ten years of development,Ceph has been widely used in cloud computing and big data storage systems.As the underlying platform of cloud computing,Ceph not only provides storage service for virtual machines,but also directly provides the object storage service and NAS file service.Ceph supports storage requirements of various operating systems and applications in cloud computing systems.Its performance has a great influence on virtual machines and applications running on it.Therefore,the performance optimization of the Ceph storage system has been a research hotspot in academia and industry.This paper first introduces the architecture and characteristics of Ceph,then summarizes existing performance optimization technologies from 3 aspects,including internal mechanism improvement,new hardware-orien-ted and application-based optimization and reviews the recent research on Ceph storage and optimization.Finally,it prospects the future work,hoping to provide a valuable reference for researchers in the performance optimization of distributed storage system.

Service-oriented software engineering with the fusion of the services and cloud computing paradigms not only offers many advantages for large-scale distributed software development and applications,but also brings new challenges.The biggest challenge in cloud computing is the lack of a de facto standard or single architectural design method,which can meet the requirements of an enterprise cloud approach to help deliver software as a service over the Internet.First,according to the business cha-racteristics of enterprise cloud computing,a generic and abstract model for Enterprise Cloud Service Architecture (ECSA) is proposed.The model consists of nine components,including the cloud services,service mode,service consumers,management,processes,quality attributes,service matching and interactive matching.The model components and their relationships are analysed,and their roles are discussed.Then,a four-phase software architecture improvement process that considers cloud services as the first class modeling elements is also presented.By decoupling the cloud service mode from their implementation on target component configurations,the process supports exploration of multiple architectures utilizing the same set of services.Finally,the application instance of ECSA is introduced,which hopes to provide recommendations and reference for enterprise cloud service system development and application integration.

With the development of new generation,direct-current transmission,electric energy storage and other technologies,flexible load such as new energy generation and electric vehicles and energy storage devices with charge-discharge ability are constantly integrated into the power grid,which makes the traditional distribution network architecture change greatly.Due to the great instability of the new type of source grid load storage,it brings great challenges to the distribution network dispatching,especially the extra power loss in scheduling which is difficult to control.With the construction of Ubiquitous Power Internet of Things (UPIoT),real-time information collection and data analysis of source grid load storage can be realized,which provides an opportunity for real-time data-driven collaborative scheduling of Source-Grid-Load-Storage.The collaborative scheduling of Source-Grid-Load-Storage in distribution network has a natural distributed characteristic.Therefore,a distributed state awareness system can be built which can bring low latency and high precision for the collaborative real-time scheduling of Source-Grid-Load-Storage.The distribution network structure under the background of UPIoT is analyzed in this paper,then the source grid load storage and their interaction methods in a distributed environment are modeled.This model is based on the premise that the feeder nodes have certain computing and communication capabilities,and it stipulates the data interaction method of all the nodes in entire distribution network,which can effectively reflect the effect of collaborative scheduling in the distribution network.A collaborative scheduling mechanism of Source-Grid-Load-Storage with distributed state awareness under Power Internet of Things is proposed,and the response strategy of each end of source grid load storage is defined in this paper,thus realizing the goal of peak load shifting and scheduling loss reduction.Based on some real data of the power grid,a simulation verification experiment is designed.The experimental results verify the effectiveness of the collaborative scheduling mechanism of Source-Grid-Load-Storage.

Microservice architecture is an emerging style of service architecture,which is characterized by efficient operation and flexible deployment when dealing with complex service systems.Compared with monolithic architecture,it can provide better business management and service support.In view of the complex case of the procuratorate affair,it is necessary to combine and encapsulate the services to form new value-added services to meet the needs of users.However,quality-of-service driven service encapsulation alone cannot meet the needs of procuratorate affair.Therefore,combining service functions and quality of service,an improved graphplan under microservice architecture (IGMA) is proposed.Firstly,the method establishes a mathematical model for the service and user request,then integrates the functional and non-functional requirements of the service,and provides users with a variety of combination schemes under different case types.Finally,the service workflow is established to complete the case service encapsulation.This method can intelligently judge the branch structures in the service composition structure and establish different composition schemes for different branch structures.Experimental results show that the proposed method improves the timeliness and accuracy of service encapsulation.

MapReduce is an important parallel computing framework for large data processing,which greatly improves the performance of data processing by performing multiple tasks in parallel on a large number of cluster nodes.However,since the intermediate data needs to wait until the Mapper task is completed,it can be sent to the Reducer task.The massive transmission delay becomes an important bottleneck of the MapReduce framework performance.To this end,an intermediate data transmission pipeline mechanism for MapReduce is proposed.It decouples the effective computation from intermediate data transmission,overlaps each stage in pipeline mode,and effectively hides data transmission delay.The execution mechanism and implementation strategy of the approach are given,including pipeline partition,data subdivision,data merging and data transmission granularity.The proposed mechanism is evaluated on public data sets.When the Shuffle data volume is large,the overall performance improves by 60.2% compared with the default framework.

Convolution operation plays a vital role in statistics,signal processing,image processing and deep learning.It is also an important operation in deepneural networks where it is the basis of information filter and characteristics extraction.The exploration of methods to speed up the convolutional operations has become an open research topic in recent years.Many studies have pointed out that distributed computing framework may improve the computational time of convolution operations and hence optimize the training efficiency for deep learning.But stragglers in distributed systems may slow down the overall system.This paper proposes a distributed coding based Winograd algorithm for implementing 2D convolution operations.In this algorithm,the Winograd algorithm can effectively accelerate the speed of 2D convolution calculation,while distributed encoding can mitigate the impact of stragglers by using a redundancy-based encoding strategy.Therefore,the proposed distributed 2D convolution algorithm can effectively mitigate the straggler problem in distributed systems while improving the 2D convolution calculation,hence it may effectively improve the computational efficiency of distributed 2D convolution algorithms.

The problem of UAV formation confrontation has always been a hot topic in scientific research,and there are few related studies on the deployment of UAV group defense.Based on the protection of defensive UAV against common UAV,such as civil,commercial,reconnaissance,cruise and exploration,the coding and decoding scheme of existing heterogeneous UAV formation is improved.The fitness function is established from the missile flight distance and the safety of unarmed drones,and the genetic algorithm is used to optimize the defense formation of the drone.According to the situation of enemy UAV of different sizes and various formations,the formation of our UAV is optimized.The solution results show that the genetic algorithm can converge to the optimal value in different enemy formations at a high speed within 30 iterations,and the corresponding optimized formation is given.Finally,by evaluating the probability effect and drawing the loss curve of five combat situations,it can be seen that the defense deployment strategy designed in this paper is effective.The maximum loss quantity of our UAVs is 6,minimum loss quantity is 0,average loss quantity is 3,and average loss rate is 18.75%.This method is of great significance for the research of UAV group defense deployment.

As the number of wireless network users increases dramatically,the network topology of traditional cellular networks can't meet the performance requirements of all users.In order to improve the uplink coverage probability in the cell-edge area,an uplink amplify-and-forward (AF) relay network model in which relays are deployed around the base station is established.The base stations and relays are respectively modeled as the Poisson point process (PPP) and the truncated Thomas cluster process (TCP),and relay node amplifies and forwards the data of cell-edge users to the base station.We drive a fine-grained performance analysis of the network model,i.e.,SIR meta distribution which is the distribution of the conditional coverage probability (CCP).The moments of the CCP in the relays network are analytically derived and the approximation of SIR meta distribution is presented in semiclosed-form expression.In contrast to the conventional performance analysis based on the coverage probability,meta distribution can intuitively show the proportion of uplinks in the network whose CCP is greater than a certain value.And the accuracy of the theoretical analysis is verified by simulations.Besides,the effect of the relay distribution parameters on the meta distribution is studied by adjusting the parameters of the relay distribution,such as radius and variance.Finally,the effects of power compensation factor of the uplink power control on the network coverage probability are compared,which provides help for the later research on network performance optimization.

The assembly of metagenomic sequences faces huge challenge in computing and storage.SpaRC (Spark Reads Clustering) is a metagenomic sequence fragment clustering algorithm based on Apache Spark,which provides a scalable solution for clustering of billions of sequencing fragments.However,setting SpaRC parameters is a very challenging task.SpaRC algorithm has many hyperparameters that have a great impact on the performance of the algorithm.Choosing the appropriate hyperparameter set is crucial to the performance of SpaRC algorithm.In order to improve the performance of SpaRC algorithm,a hyperpara-meter optimization method based on Tree Parzen Estimator (TPE) is explored,which can use prior knowledge to efficiently adjust the parameters,accelerate the search for the optimal parameters by reducing the calculation task to achieve the optimal clustering effect,thus avoding expensive parameter exploration.After experiments with long-reads(PacBio) and short-reads(CAMI2),the results show that the proposed method has a great effect on improving the performance of SpaRC algorithm.