Shared Storage System Research Articles

Coordinated design of multi-stakeholder community energy systems and shared energy storage under uncertain supply and demand: A game theoretical approach

Shared energy storage plays an important role in achieving sustainable development of renewable-based community energy systems. In practice, the independent or disordered planning of community energy systems and shared storage systems can lead to suboptimal design without considering the complex interactions between neighboring energy systems. Therefore, a coordinated design approach for community energy systems and shared energy storage is proposed, and a pricing mechanism for storage sharing based on bounded rationality theory is developed. A Stackelberg game is introduced to enable consideration of storage sharing among energy systems at the design phase. The uncertainties of solar irradiation, wind speed, and electrical demand on both the supply and demand sides are also considered. Then, an uncertainty model is formed to optimize the system configurations, operation strategies, and storage sharing strategies of each stakeholder, simultaneously. Furthermore, the influence of the profit margin, storage investment cost, and carbon tax on the storage rental price and system design strategies are discussed. Illustrative examples highlight the feasibility and applicability of the shared storage pricing mechanism and coordinated design approach. This paper provides references for the government with respect to the problem of coordinated design for multi-stakeholder distributed energy systems and storages in a market-oriented environment.

An Economic Dispatch for a Shared Energy Storage System Using MILP Optimization: A Case Study of a Moroccan Microgrid

Energy storage systems are an effective solution to manage the intermittency of renewable energies, balance supply, and demand. Numerous studies recommend adopting a shared energy storage system (ESS) as opposed to multiple single ESSs because of their high prices and inefficiency. Thus, this study examines a shared storage system in a grid-connected microgrid. By modifying the power outputs of the energy resources, this work intends to implement an economic dispatch of a shared ESS in order to satisfy the power balance and lower the overall cost of electricity. In this context, an optimization problem was formulated and developed using a mixed-integer linear programming (MILP) model. Furthermore, a pilot project (the Solar Decathlon Africa Village) in the Green & Smart Building Park (GSBP), Benguerir, Morocco, was employed to evaluate and verify the proposed approach. Some comparable scenarios were therefore run in a MATLAB environment. The collected findings demonstrate the efficacy of the developed algorithm in terms of optimizing energy cost reductions and enhancing the integration of renewable resources into the Moroccan energy mix.

Shared energy storage system for prosumers in a community: Investment decision, economic operation, and benefits allocation under a cost-effective way

With the rapid development of distributed renewable energy, energy storage system plays an increasingly prominent role in ensuring efficient operation of power system in local communities. However, high investment cost and long payback period make it impossible for prosumers to own the storage system. In this context, considering the complementarity of power generation and consumption behavior among different prosumers, this paper proposes an energy storage sharing framework towards a community, to analyze the investment behavior for shared storage system at the design phase and energy interaction among participants at the operation phase. Therefore, under the sharing mode that the operator invests and virtualizes physical storage system to provide virtual storage services for prosumers, this paper first establishes a two-stage optimization model. Based on a new storage sharing contribution rate index and asymmetric Nash bargaining model, a profit-allocation mechanism is designed. Then, combined with alternating direction method of multipliers, to achieve a mutual benefit and win-win situation. Finally, the above model is simulated by three buildings' realistic data, and the effectiveness of shared energy storage system is analyzed by using different technical and economic performance indexes. Additionally, the influence of battery cost, carbon tax, electricity price, and the uncertainties of renewable energy output power and load on shared storage system planning are also discussed. Numerical results show that, compared with personal energy storage scenario, the proposed storage sharing mechanism can achieve 6.09% cost savings, the self-consumption rate and self-sufficiency rate of renewable energy respectively increase by 5.01% and 5.21%, and all financial evaluation indexes have improved. In short, this paper can give practical guidelines for investors and prosumers to reasonably plan and share energy storage system, and provide realistic references for the government to effectively implement the shared energy storage.

Multi-shuttle crane scheduling in automated storage and retrieval systems

We study a shared-storage automated storage and retrieval system (AS/RS) with one crane capable of carrying multiple unit loads. In a shared-storage system, items to be stored are not pre-assigned to dedicated shelf spaces. Therefore, given a set of storage and retrieval requests, the crane scheduling problem consists of deciding which requests are processed together in the same tour, determining the sequence in which the requests are processed, and assigning each storage request to an available slot on the shelf. We reformulate the problem as a special type of capacitated vehicle routing problem, which we use to close some open questions regarding the time complexity of related geometric routing problems. The reformulation allows us to tap into the rich and mature vehicle routing toolbox from the literature to propose a new exact solution approach. We show that this method is capable of solving large instances to optimality, outperforming previous methods from the literature. We use our new approach to derive multiple insights. Specifically, we show that system throughput can be predicted from the capacity of the crane via a simple rule. We also determine the optimal shape of a shelf and investigate the value of having knowledge of multiple requests when planning the crane schedule. Finally, we demonstrate that our approach can easily be extended to solve a whole family of multi-shuttle crane scheduling problems.

Analytical and simulation determination of order picking time in a low storage warehouse for shared storage systems

The aim of the research is comparison between average order picking times obtained using the analytical model and simulation methods for shared storage systems. We also compare the results obtained with the results obtained for dedicated storage. We assume the random and ABC-class storage (with within and across aisle storage policies). We select the locations by means of the TOPSIS method for two take-out strategies: quantity adjustment (QA) and priority of partial units (PPU). We determine the route by using s-shape and return heuristics. In most cases, the simulated average order picking times are shorter than the analytical ones. It results from not considering the criteria’ weights in calculation of the analytical order picking time. Also, the results for shared storage with QA strategy are in most cases better than for dedicated storage. This might imply an advantage of shared over dedicated storage, but needs further confirmation.

Implementation of Data Synchronization Mechanism in Virtual Desktop Infrastructure

There are more and more applications and services of Cloud technology. Whether the government, enterprises or organizations, even everyone may have requirements of constructing cloud systems. To build the virtual cloud infrastructure VMware vSphere which is the leader of the information industry on virtual platforms can let users use Virtual Desktop Infrastructure by their mobile device. This study integrated some management programs such as Activity Directory, VMware vCenter, View connection server, VMware vSphere and used the shared storage system of FreeNAS to provide the cloud virtual environment to users. With the simple interface to manage, it successfully provides administrators to build Virtual Desktop Infrastructure faster. Users can use their own device to control the virtual desktop in a mobile classroom by VMware Horizon View or browser. Users can also use a shared storage system to be the information exchange platform. If you can connect to the Internet, you can use your own virtual desktop anywhere. Finally, the complete construction implements the combination of several operating systems and improves their performance to save lots of hardware cost. It also meets the social focus of carbon reduction and environment protection to achieve sustainable use.

KRanTi: Blockchain‐based farmer's credit scheme for agriculture‐food supply chain

AbstractThe advent of agro‐industrialization has helped the agricultural food supply chain (AFSC) in introducing ease and innovation to other facets. In addition, Blockchain (BC) technology is revolutionizing each distributed data‐related application by adding authentication and trust among the system's stakeholders. Inspired by the above‐mentioned argument, this article proposes a system, KRanTi, to promote the supply chain of agricultural food by BC embedding across the 5G network. The 5G network upgrades the data connectivity by enhancing network infrastructure efficiency. In KRanTi, we present an efficient credit system that enables associated farmers to purchase the requisite agricultural raw product of higher quality without the pressure of instant payment. A score‐based farm‐food quality assurance system is then proposed, which guarantees optimal quality grading. Trust, transparency, and traceability are preserved through the implementation of Smart Contracts (SC) distributed across public Ethereum BC. The cost‐effective off‐line data storage service in AFSC is offered through the use of the Inter‐Planetary File System (IPFS), a shared storage system to hold all data from stakeholders. Results show that the efficiency of the proposed scheme KRanTi is improved compared with traditional schemes with metrics such as less than 1 millisecond latency with 99.99% reliability compared with LTE with 5G, scalability, packet failure ratio, BC performance, cost‐quality ratio, and data storage contrast.

Efficiency fairness tradeoff in battery sharing

The increasing presence of decentralized renewable generation in the power grid has motivated consumers to install batteries to save excess energy for future use. The high price of energy storage calls for a shared storage system, but careful battery management is required so that the battery is operated in a manner that is fair to all and as efficiently as possible. In this paper, we study the tradeoffs between efficiency and fairness in operating a shared battery. We develop a framework based on constrained Markov decision processes to study both regimes, namely, optimizing efficiency under a hard fairness constraint and optimizing fairness under hard efficiency constraint. Our results show that there are fundamental limits to efficiency under fairness and vice-versa, and, in general, the two cannot be achieved simultaneously. We characterize these fundamental limits via absolute bounds on these quantities, and via the notion of price of fairness that we introduce in this paper.

Impact of the Method of Criteria Normalisation on the Order Picking Route and Time

Purpose: The purpose of the article is the selection of the best criteria for the normalisation method in order to achieve the minimal order picking route and time. Design/Methodology/Approach: When a company utilizes warehouse with the shared storage system, every product can be stored in many, sometimes very distant from each other, locations. Locations were selected by the multiple-criteria decision-making technique – TOPSIS. The research was conducted by means of the simulation methods. Every location was described by three criteria (distance from the I/O point, degree of demand satisfaction and the number of other picked products in the proximity of the analyzed location), for which 37 combinations of weights and 18 normalization formulas were applied. For every combination of weights and normalization method 1000 orders were generated. Findings: The best results were obtained when high weight was assigned to the degree of demand satisfaction. It was hard to indicate unequivocally the best normalization method. However, quotient inversions generally yielded slightly worse results than standard scores and feature scaling. Practical Implications: Obtained results indicate that the presented approach can be useful in real warehouse management. It is a quite versatile method that can be adopted to various situations. Originality/value: Although the routing problem in order picking has already been widely discussed, the literature about the problem of selection of locations in shared storage is quite scarce. Therefore, the presented approach can serve as one method of selection of locations in the shared storage system.

REANA: A System for Reusable Research Data Analyses

The revalidation, reinterpretation and reuse of research data analyses requires having access to the original computing environment, the experimental datasets, the analysis software, and the computational workflow steps which were used by researchers to produce the original scientific results in the first place. REANA (Reusable Analyses) is a nascent platform enabling researchers to structure their research data analyses in view of enabling future reuse. The analysis is described by means of a YAML file that captures sufficient information about the analysis assets, parameters and processes. The REANA platform consists of a set of micro-services allowing to launch and monitor container-based computational workflow jobs on the cloud. The REANA user interface and the command-line client enables researchers to easily rerun analysis workflows with new input parameters. The REANA platform aims at supporting several container technologies (Docker), workflow engines (CWL, Yadage), shared storage systems (Ceph, EOS) and compute cloud infrastructures (Ku-bernetes/OpenStack, HTCondor) used by the community. REANA was developed with the particle physics use case in mind and profits from synergies with general reusable research data analysis patterns in other scientific disciplines, such as bioinformatics and life sciences.

CBase: Fast Virtual Machine storage data migration with a new data center structure

Live Virtual Machine (VM) migration within a data center is an important technology for cloud management, and has brought many benefits to both cloud providers and users. With the development of cloud computing, across-data-center VM migration is also desired. Normally, there is no shared storage system between data centers, hence the storage data (disk image) of a VM will be migrated to the destination data center as well. However, the slow network speed of the Internet and the comparatively large size of VM disk image make VM storage data migration become a bottleneck for live VM migration across data centers. In this paper, based on a detailed analysis of VM deployment models and the nature of VM image data, we design and implement a new migration system, called CBase. The key concept of CBase is a newly introduced central base image repository for reliable and efficient data sharing between VMs and data centers. With this central repository, further performance optimizations to VM storage data migration are made possible. Two migration mechanisms (data deduplication and Peer-to-Peer (P2P) file sharing) are utilized to accelerate base image migration, and a strategy is designed to elevate the synchronization of newly-written disk blocks. The results from an extensive experiment show that CBase significantly outperforms existing migration mechanisms under different conditions regarding total migration time and total network traffic. In particular, CBase with data deduplication is better than P2P file sharing for base image migration in our experimental environment.

Comparison of Several Linear Ordering Methods for Selection of Locations in Order‑picking by Means of the Simulation Methods

When a company uses a shared storage system, selection of locations during the order‑picking process is not an obvious task. Every location where the picked product is placed, can be described by means of several variables, such as: storage time, distance from the I/O point, degree of demand satisfaction, or the number of other picked products in the order. Therefore, the “attractiveness” of each location from the point of view of a certain order can be described by means of synthetic variable, on the basis of which a ranking is created. For each product, the decision‑maker selects the highest‑ranking locations and designates a route for the picker. In the article, by means of the simulation methods, results obtained by several classification methods will be compared. These methods are: Taxonomic Measure of Location’s Attractiveness (based on the Hellwig’s Composite Measure of Development), the TOPSIS method with the Euclidean and GDM distances and the Gen­eralised Distance Measure used as the composite measure of development.

Sharing electricity storage at the community level: An empirical analysis of potential business models and barriers

More and more households are installing residential electricity storage systems to increase the self-consumption of electricity they produced. Some governments have accelerated this development through specific financial support schemes to offset the costs, which still remain high. Compared to the use of single-household systems, the sharing of mid-scale electricity storage systems in neighborhoods could reduce the Levelized Costs of Storage (LCOS). However, a model for the shared usage of storage by multiple households has yet to emerge. We investigated eight demonstration projects in Germany and Western Australia with capacities between 100 and 1100 kW h with respect to potential business models and barriers in a cross-case study based on document analyses and expert interviews. We found that models relying on the transmission of electricity from individual rooftop photovoltaics to a shared storage system through the public grid are facing significant regulatory barriers. Removing these policy barriers would enable a more efficient use of electricity storage systems. By contrast, projects relying on a less regulated microgrid managed by the administration or strata entities of multi-household developments already seem promising under the current regulatory framework.

Dynamic Scheduling with Service Curve for QoS Guarantee of Large-Scale Cloud Storage

With the growing popularity of cloud storage, more and more diverse applications with diverse service level agreements (SLAs) are being accommodated into it. The quality of service (QoS) support for applications in a shared cloud storage becomes important. However, performance isolation, diverse performance requirements, especially harsh latency guarantees and high system utilization, are all challenging and desirable for QoS design. In this paper, we propose a service curve-based QoS algorithm to support latency guarantee applications, IOPS guarantee applications and best-effort applications at the same storage system, which not only provides a QoS guarantee for applications, but also pursues better system utilization. Three priority queues are exploited and different service curves are applied for different types of applications. I/O requests from different applications are scheduled and dispatched among the three queues according to their service curves and I/O urgency status, so that QoS requirements of all applications can be guaranteed on the shared storage system. Our experimental results show that our algorithm not only simultaneously guarantees the QoS targets of latency and throughput (IOPS), but also improves the utilization of storage resources.

LayerMover: Fast virtual machine migration over WAN with three-layer image structure

Live Virtual Machine (VM) migration across data centers is an important technology to facilitate cloud management and deepen the cooperation between cloud providers. Without the support of a shared storage system between data centers, migrating the storage data (i.e. virtual disk) of a VM becomes the bottleneck of live VM migration over Wide Area Network (WAN) due to the contradiction between the low bandwidth of the Internet and the comparatively large size of VM storage data. According to previous studies, many inter- and intra-VM duplicated blocks exist between VM disk images. Therefore, data deduplication is widely used for accelerating VM storage data migration. However, it must make a trade-off between computation cost and transmission benefit. Existing approaches are fragile as they explore only the static data feature of image files without much consideration on data semantics. They may adversely influence on migration performance when the benefit resulting from data deduplication cannot remedy its computation overhead. In this paper, we propose a new space-efficient VM image structure—three-layer structure. According to different functions and features, the data of a VM are separated into an Operating System (OS) layer, a Working Environment (WE) layer, and a User Data (UD) layer. Based on this structure, we design a novel VM migration system—LayerMover. It mainly focuses on improving migration performance through optimizing the data deduplication technique. Our experimental results show that three-layer image structure can improve data sharing between VMs, and the similarity ratio between WE images can reach 70%. The tests for LayerMover indicate that it can be significantly beneficial to VM migration across data centers, especially when multiple VMs which share base images are migrated.

Comparison of the TMAL and the TOPSIS methods in selection of locations in order-picking

There are two methods of storing products in a warehouse. First, there is a dedicated storage, for which each product can be stored in only one location and one location is dedicated for just one product. There is also a shared storage, in which each location can store any number of various products and each product can be stored in many, sometimes very distant locations. The advantage of the dedicated storage is that it is very simple to manage and the pickers can quite easily remember, where each product is stored. The disadvantage of such system is that the storing space is used much less efficiently. The shared storage uses the storage space much better, but causes that remembering, where each product is stored is impossible. Therefore, if a company utilises the shared storage system, it must use a specialised system of warehouse management. Such system must manage, where to place replenishment orders and from where products should be taken in order to complete the customers’ orders. The article compares the two multiple-criteria decision making techniques that will be used in order to select locations: the Taxonomical Measure of Location’s Attractiveness (TMAL), based on the Composite Measure of Development and the TOPSIS method. The results show that no method can be considered as better in general.

Extreme I/O on HPC for HEP using the Burst Buffer at NERSC

In recent years there has been increasing use of HPC facilities for HEP experiments. This has initially focussed on less I/O intensive workloads such as generator-level or detector simulation. We now demonstrate the efficient running of I/O-heavy analysis workloads on HPC facilities at NERSC, for the ATLAS and ALICE LHC collaborations as well as astronomical image analysis for DESI and BOSS.To do this we exploit a new 900 TB NVRAM-based storage system recently installed at NERSC, termed a Burst Buffer. This is a novel approach to HPC storage that builds on-demand filesystems on all-SSD hardware that is placed on the high-speed network of the new Cori supercomputer.We describe the hardware and software involved in this system, and give an overview of its capabilities, before focusing in detail on how the ATLAS, ALICE and astronomical workflows were adapted to work on this system. We describe these modifications and the resulting performance results, including comparisons to other filesystems. We demonstrate that we can meet the challenging I/O requirements of HEP experiments and scale to many thousands of cores accessing a single shared storage system.

AdaptScale: An adaptive data scaling controller for improving the multiple performance requirements in Clouds

Data scaling issue has become a bottleneck in multi-tenancy cloud environment. Fluctuated workloads bring challenges to current automatic data scaling strategies on meeting variable user performance requirements in a shared storage system. To this end, this paper develops an adaptive data scaling controller to meet multiple performance requirements in Clouds. The controller consists of three components: (1) a performance model, which determines whether the nodes are over-loaded; (2) a workload monitor and a predictor, which are responsible for collecting workload information and estimating the fluctuating trends, respectively; (3) a data scaling strategy generator, which enables the data scaling solution for over-loaded or under-loaded nodes. The numerical results show that the developed controller achieves the goal of automatic data scaling, which not only satisfies diversified performance requirements, but also reduces the execution time of MOVE operations with regards to system performance.

Cooperative Distributed Control for the Energy Scheduling of Smart Homes with Shared Energy Storage and Renewable Energy Source

This paper presents a distributed control technique for the energy scheduling of a group of interconnected smart city residential users. The proposed model aims at a simultaneous cost-optimal planning of users’ controllable appliances and of the shared storage system charge/discharge and renewable energy source. The distributed control algorithm is based on an iterative procedure combining parametric optimization with the block coordinate descent method. A realistic case study simulated in different scenarios demonstrates that the approach allows fully exploiting the potential of storage systems sharing to reduce individual users’ energy consumption costs and limit the peak average ratio of the energy profiles.

学術資源の全国的保存システムと共同保存図書館 (特集:リソース・シェアリング:資料利用のための協力)

学術資源の全国的保存システムと共同保存図書館 (特集:リソース・シェアリング:資料利用のための協力)