Storage Management Strategy of Flash-Based Platform

Storage management strategy can be expressed by a file system. Commercial file system for embedded application is generally complicated and resource wasted. In this paper, a specified file system adapted to embedded system with flash-based memory is developed. To guarantee the average usage of flash storage sectors, the strategy of wear leveling and adaptive damage management is introduced, in which a dynamic storage space management mode and the strategy of first in first out (FIFO) are adopted. Moreover, the strategy of redundancy design and fast-calculation and tracing is also adopted to extend the life of kernel sector, which can guarantee the reliable service of system booting. The practical application in an embedded CNC (computerized numerical control) platform proves that the file system has effective performance. Furthermore, the flash file system can be transplanted to different embedded platforms by changing a few bottom hardware parameters with universality.

Financial Derivatives Market and Storage Management

It’s known to all that under ideal condition the s to rage cost is kept in lower level when storage management be arranged by Economic Order Quantity(EOQ).Does this mean that any companies should set up their own storing system in proportion to the scale of the commodities’ producing or sell ing Furthermore, even if they manage storage in EOQ, because of different oper ation scale, geographical condition or ability borrowing money from financial ma rket, different companies pay unequal cost in storing the same commodity.In thi s paper, except for supplying commodities from our own storage system, the autho rs have analyzed other two supplying ways without whole storage system, they are forward contracts and futures contracts.The authors have discussed variable su pply cost for above different supply measures.According to the cost of each sup ply way, the managers can choose the most economical way in supplying the commod ity and predict the price of futures from storage management arranged by EOQ.Th e summary content is as follow: 1. The comparing of supply cost between forward contracts and storing system a rranged by EOQ. (1) The supply cost from forward contracts (2) The supply cost from storage system arranged by Economic Order Quantity (3) The application example for comparing cost in different supply way 2.The comparing of supply cost between futures going physical and storing syst em arranged by Economic Order Quantity. (1) The supply cost from futures going physical (2) The correlation between futures contracts and storage management arranged b y EOQ (3) The application example for comparing cost in different supply way 3.How does storing system of scale economic affect the price of forward and fu tures contracts (1) How does the price of forward and futures contracts fluctuate (2) How do we calculate the price of a commodity at future point from the cost of scale economic storing (3) How do we operate efficiently in derivatives market by using the cost of sc ale economic storing (4) The application example for analyzing the price of futures 4.The correlation among storage managementforward contracts and futures mark et.

Fuzzy and IRLNC-based routing approach to improve data storage and system reliability in IoT

Internet of Things(IoT)based sensor network is largely utilized in various field for transmitting huge amount of data due to their ease and cheaper installation.While performing this entire process,there is a high possibility for data corruption in the mid of transmission.On the other hand,the network performance is also affected due to various attacks.To address these issues,an efficient algorithm that jointly offers improved data storage and reliable routing is proposed.Initially,after the deployment of sensor nodes,the election of the storage node is achieved based on a fuzzy expert system.Improved Random Linear Network Coding(IRLNC)is used to create an encoded packet.This encoded packet from the source and neighboring nodes is transmitted to the storage node.Finally,to transmit the encoded packet from the storage node to the destination shortest path is found using the Destination Sequenced Distance Vector(DSDV)algorithm.Experimental analysis of the proposed work is carried out by evaluating some of the statistical metrics.Average residual energy,packet delivery ratio,compression ratio and storage time achieved for the proposed work are 8.8%,0.92%,0.82%,and 69 s.Based on this analysis,it is revealed that better data storage system and system reliability is attained using this proposed work.

Smart energy management system framework for population dynamics modelling and suitable energy trajectories identification in islanded micro-grids

In an increasingly electrified and connected world,renewable energy production and robust distribution as well as sobriety paradigm,both for the individual and the society,will most likely play a central role regarding global systems stability.Consequently,while being able to conceive efficient storage systems coupled with robust energy management strategies present significant interests,a number of related studies often consider the human behaviour factor separately.While not decisive in large industrial factories,human demeanor impact cannot be overlooked in residential areas.As such,this work proposes an innovative and flexible dynamic population model,inspired from epidemiological methods,that allows simulation of a vast spectrum of social scenarios.By pairing this formalization with a smart energy management strategy,a complete framework is proposed.In particular,beyond the theoretical identification of sustainable parameters in a wide diversity of configurations,our experiments demonstrate the relevance of reinforcement learning agents as efficient energy management policies.Depending on the scenario,the trained agent enables an increase of the sustainability areas over baseline strategies up to 200%,thus hinting at ultimately softer societal impact.

HAG： An Energy-Proportional Data Storage Scheme for Disk Array Systems

Energy consumption has been a critical issue for data storage systems, especially for modern data centers. A recent survey has showed that power costs amount to about 50%of the total cost of ownership in a typical data center, with about 27% of the system power being consumed by storage systems. This paper aims at providing an effective solution to reducing the energy consumed by disk storage systems, by proposing a new approach to reduce the energy consumption. Differing from previous approaches, we adopt two new designs. 1） We introduce a hotness-aware and group-based system model （HAG） to organize the disks, in which all disks are partitioned into a hot group and a cold group. We only make file migration between the two groups and avoid the migration within a single group, so that we are able to reduce the total cost of file migration. 2） We use an on-demand approach to reorganize files among the disks that is based on workload change as well as the change of data hotness. We conduct trace-driven experiments involving two real and nine synthetic traces and we make detailed comparisons between our method and competitor methods according to different metrics. The results show that our method can dynamically select hot files and disks when the workload changes and that it is able to reduce energy consumption for all the traces. Furthermore, its time performance is comparable to that of the compared algorithms. In general, our method exhibits the best energy e？ciency in all experiments, and it is capable of maintaining an improved trade-off between performance and energy consumption.

Energy storage resources management:Planning,operation,and business model

With the acceleration of supply-side renewable energy penetration rate and the increasingly diversified and complex demand-side loads,how to maintain the stable,reliable,and efficient operation of the power system has become a challenging issue requiring investigation.One of the feasible solutions is deploying the energy storage system(ESS)to integrate with the energy system to stabilize it.However,considering the costs and the input/output characteristics of ESS,both theinitial configuration process and the actual operation process require efficient management.This study presents a comprehensive reviewof managing ESs from the perspectives of planning,operation,and business model.First of all,in terms of planning and configuration,it is investigated from capacity planning,location planning,as well as capacity and location combined planning.This process is generally the first step in deploying ESS.Then,it explores operation management of ESS from the perspectives of state assessment and operation optimization.The so-called state assessment refers to the assessment of three aspects:The state of charge(SOC),the state of health(SOH),and the remaining useful life(RUL).The operation optimization includes ESS operation strategy optimization and joint operation optimization.Finally,it discusses the business models of ESS.Traditional business models involve ancillary services and load transfer,while emerging business models include electric vehicle(EV)as energy storage and shared energy storage.

FARMER:A novel approach to file access correlation mining and evaluation reference model

File semantic has proven effective in optimizing large scale distributed file system.As a consequence of the elaborate and rich I/O interfaces between upper layer applications and file systems,file system can provide useful and insightful information about semantic.Hence,file semantic mining has become an increasingly important practice in both engineering and research community.Unfortunately,it is a challenge to exploit file semantic knowledge because a variety of factors coulda ffect this information exploration process.Even worse,the challenges are exacerbated due to the intricate interdependency between these factors,and make it difficult to fully exploit the potentially important correlation among various semantic knowledges.This article proposes a file access correlation miming and evaluation reference（FARMER） model,where file is treated as a multivariate vector space,and each item within the vector corresponds a separate factor of the given file.The selection of factor depends on the application,examples of factors are file path,creator and executing program.If one particular factor occurs in both files,its value is non-zero.It is clear that the extent of inter-file relationships can be measured based on the likeness of their factor values in the semantic vectors.Benefit from this model,FARMER represents files as structured vectors of identifiers,and basic vector operations can be leveraged to quantify file correlation between two file vectors.FARMER model leverages linear regression model to estimate the strength of the relationship between file correlation and a set of influencing factors so that the ＂bad knowledge＂ can be filtered out.To demonstrate the ability of new FARMER model,FARMER is incorporated into a real large-scale object-based storage system as a case study to dynamically infer file correlations.In addition FARMER-enabled optimize service for metadata prefetching algorithm and object data layout algorithm is implemented.Experimental results show that is FARMER-enabled prefetching algorithm is shown to reduce the metadata operations latency by approximately 30%-40% when compared to a state-of-the-art metadata prefetching algorithm and a commonly used replacement policy.

Energy cost minimization through optimization of EV, home and workplace battery storage

Besides grid-to-vehicle(G2 V) and vehicle-to-grid(V2 G) functions, the battery of an electric vehicle(EV) also has the specific feature of mobility. This means that EVs not only have the potential to utilize the storage of cheap electricity for use in high energy price periods, but can also transfer energy from one place to another place. Based on these special features of an EV battery, a new EV energy scheduling method has been developed and is described in this article. The approach is aimed at optimizing the utilization EV energy for EVs that are regularly used in multiple places. The objective is to minimize electricity costs from multiple meter points. This work applies real data in order to analyze the effectiveness of the method. The results show that by applying the control strategy presented in this paper at locations where the EVs are parked, the electricity cost can be reduced without shifting the demand and lowering customer's satisfaction. The effects of PV size and number of EVs on our model are also analyzed in this paper. This model has the potential to be used by energy system designers as a new perspective to determine optimal sizes of generators or storage devices in energy systems.

A new hybrid AI optimal management method for renewable energycommunities

In this study, we propose a hybrid AI optimal method to improve the efficiency of energy managementin a smart grid such as Renewable Energy Community. This method adopts a Time Delay Neural Networkto forecast the future values of the energy features in the community. Then, these forecasts are used by astochastic Model Predictive Control to optimize the community operations with a proper control strategy ofBattery Energy Storage System. The results of the predictions performed on a public dataset with a predictionhorizon of 24 h return a Mean Absolute Error of 1.60 kW, 2.15 kW, and 0.30 kW for photovoltaic generation,total energy consumption, and common services, respectively. The model predictive control fed with suchpredictions generates maximum income compared to the competitors. The total income is increased by 18.72%compared to utilizing the same management system without exploiting predictions from a forecasting method.

Construction and application of LHAASO data processing platform

Purpose The LHAASO project collects trillions of cosmic ray events every year,generating about 10 PB of raw data annually,which brings big challenges for data processing platform.Method The LHAASO data processing platform is built to handle such a large amount of data,which is composed of some subsystems such as data transfer,data storage,high throughput computing and metadata management.Results and conclusions The platform was under construction since 2018 and has been working well since 2021.In this paper,the details of the design,implementation and performance of the data processing platform are presented.

Garbage Collection in Uncoordinated Checkpointing Algorithms

In this papert the hard problem of the thorough garbage collection in uncoordinated Checkpointing algorithms is studied. After introduction of the traditional garbage collecting scheme, with which only obsolete checkpoints can be discarded, it is shown that this kind of traditional method may fail to discard any checkpoint in some special cases, and it is necessary and urgent to find a thorough garbage collecting method, with which all the checkpoints useless for any future rollback-recovery including the obsolete ones can be discarded. Then, the Thorough Garbage Collection Theorem is proposed and proved, which ensures the feasibility of the thorough garbage collection, and gives the method to calculate the set of the useful checkpoints as well.

Ocean energy applications for coastal communities with artificial intelligence–a state-of-the-art review

Ocean energy plays essential roles in reducing carbon emission and transforming towards carbon neutrality, with cleaner power production, whereas the vertical cascade ocean energy systems with spatiotemporal power supply characteristics might lead to fluctuated power frequency, disruptive disturbance and grid shock. Hybrid renewable energy dispatch, coordinated demand-side management, and electrical energy storages for grid ancillary services provision with different response time-durations are effective solutions to integrate ocean energy with stable and grid-friendly operation. This study is to review advanced ocean energy converters with thermodynamic, hydrodynamic, aerodynamic, and mechanical principles. Power supply characteristics from multi-diversified ocean energy resources are analysed, with intermittency, fluctuation, and spatiotemporal uneven distribution. Hybrid ocean energy storages with synergies are reviewed to overcome the intermittency and provide grid ancillary services, including pumped hydroelectric energy storage, ocean compressed air energy storage, and ocean hydrogen-based storage in different response time durations. Applications of diversified ocean energy systems for coastal residential communities are reviewed, with energy management and controls, collaboration on multi-carrier energy networks. Furthermore, application of artificial intelligence is reviewed for sustainable and smart ocean energy systems. Results indicated that, effective strategies for stable and gridfriendly operations mainly include complementary hybrid renewable system integrations, synergies on hybrid thermal/electrical storages, and collaboration on multi-carrier energy networks. Furthermore, depending on the geographical location, flexible on-shore and off-shore installation of transformers can provide large-scale ocean energy system integrations for long-distance transmission, with low transmission losses, low resistive losses, and simple system configuration. Research results can provide a heuristic overview on ocean energy integration in smart energy systems, providing alternatives for solar and wind energy resources and paving path for the carbonneutrality transition.