A New Collaborative Scheduling Mechanism Based on Grading Mapping for Resource Balance in Distributed Object Cloud Storage System

Large-scale storage systems are crucial components in data-intensive applications such as search engine clusters, video-on-demand servers, sensor networks and grid computing. A storage server typically consists of a set of storage devices. In such systems, data layouts may need to be reconfigured over time for load balancing or in the event of system failure/upgrades. It is critical to migrate data to their target locations as quickly as possible to obtain the best performance. Most of the previous results on data migration assume that each storage node can perform only one data transfer at a time. A storage node, however, can typically handle multiple transfers simultaneously and this can reduce the total migration time significantly. Moreover, storage devices tend to have heterogeneous capabilities as devices may be added over time due to storage demand increase. In this paper, we consider the heterogeneous data migration problem, where we assume that each storage node v has different transfer constraint cv, which represents how many simultaneous transfers v can handle. We develop algorithms to minimize the data migration time. We show that it is possible to find an optimal migration schedule when all cvs are even. Furthermore, though the problem is NP-hard in general, we give an efficient algorithm that offers a rigorous (1 + o(1))-approximation guarantee.

We have proposed a distributed storage system which aims to utilize storage fragments distributed in network nodes without administrators' maintenance cost. Even the performance of storage devices are not uniform, autonomously data block migration helps to achieve enhancing the whole I/O performance of the storage system. However, we have considered all the storage devices to be reliable equally. On this assumption, migration of an important data block to unstable storage might cause degradation of the availability of the data block. In this paper, we propose a scheme to choose the destination of data block migration by evaluating the nodes' reliability along with the performance of storage devices. This scheme aims to improve system stability by preventing to locate a critical data onto an unstable storage device. The storage nodes which lack stability are also utilized for storing a data block which can be easily regenerated or duplicated. The results of the preliminary investigation show that a large number of warning logs from Machine Check Architecture may suggest a sign of unexpected system shutdown.

Data migration schemes are critical to balance the load in storage clusters for performance improvement. However, as NAND flash based SSDs are widely deployed in storage systems, extending the lifespan of SSD storage clusters becomes a new challenge for data migration. Prior approaches designed for HDD storage clusters, however, are inefficient due to excessive write amplification during data migration, which significantly decrease the lifespan of SSD storage clusters. To overcome this problem, we propose EDM, an endurance aware data migration scheme with careful data placement and movement to minimize the data migrated, so as to limit the worn-out of SSDs while improving the performance. Based on the observation that performance degradation is dominated by the wear speed of an SSD, which is affected by both the storage utilization and the write intensity, two complementary data migration policies are designed to explore the trade-offs among throughput, response time during migration, and lifetime of SSD storage clusters. Moreover, we design an SSD wear model and quantitatively calculate the amount of data migrated as well as the sources and destinations of the migration, so as to reduce the write amplification caused by migration. Results on a real storage cluster using real-world traces show that EDM performs favorably versus existing HDD based migration techniques, reducing cluster-wide aggregate erase count by up to 40%. In the meantime, it improves the performance by 25% on average compared to the baseline system which achieves almost the same effectiveness of performance improvement as previous migration techniques.

Increasing the global energy demand as well as the carbon dioxide (CO2) level in the atmosphere caused by burning fossil fuel as the main source of energy motivate the development of green energy technologies. However, the real activity improvement for these technologies is mainly impeded by intermittent nature of renewable energy technologies. One smart approach to overcome this issue is to store energy into the chemical bonds1–6 using inexpensive and efficient energy conversion and storage technologies. In these systems, energy can be converted or stored into chemical bonds as a form of fuels or electricity using renewable energy sources, e.g., solar and wind energy7–9. Despite recent progress, the development of these systems is advanced far more slowly due to the expensive and less efficient materials employed in these technologies. Here, I will present my aim to design, synthesis, and characterization of inexpensive earth-abundant transition metal dichalcogenide class of materials suitable for energy conversion and storage systems. I have tested the performance of this class of catalysts for the carbon dioxide (CO2) reduction reaction, oxygen reduction, oxygen evolution reactions. The results indicate (i) 100 times higher turn over frequency- per atom activity- far exceeding the performance of state-of-the-art catalysts for CO2 reduction reaction, and (ii) a highly efficient bi-functional catalyst for oxygen reduction and evolution reactions in the aprotic media compared to conventional noble metal catalysts used for the same application (e.g., Platinum and Gold). I also tested the performance of molybdenum disulfide nanoflakes (MoS2 NFs) -a versatile member of TMDCs- in lithium-air batteries known as a promising alternative to the conventional lithium-ion batteries. The results indicate that MoS2 NFs in the ionic liquid EMIM-BF4 electrolyte performs remarkably well in the actual air environment with a small discharge/charge potential gap as well as good stability and cyclability up to 550 cycles without any evidence of failure. I will discuss these and other results including the potential of our recent discovery to open a new route towards energy efficient, highly active and cost-effective energy storage and conversion systems to replace fossil fuels.

Incentive programs and ongoing reduction in costs are driving joint installation of solar PV panels and storage systems in residential households. There is a need for optimal investment decisions to reduce the electricity consumption costs of the households further. In this paper, we first develop analytical expression of storage investment decision and then of solar investment decision for a household which is under net metering billing mechanism with time of use pricing condition. Using real data of a residential household in Austin, TX, USA, we study how the investment decisions would provide benefit for a period of one year. Results show significant profit when using storage devices and solar panels optimally for the system. It is important to note that though our approach can help significantly to take investment decisions, the solution will still be sub-optimal for somebody who needs optimal investment jointly on both storage and solar systems.

Currently the QoS requirements for storage systems are usually presented in the form of service-level agreement (SLA) to bound I/O measures such as latency and throughput of I/O requests. However, SLA is not an effective performance interface for users to specify their required I/O service quality for two major reasons. First, for users it is difficult to determine appropriate latency and throughput bounds to ensure their required application performance without resource over-provisioning. Second, for storage system administrators it is a challenge to estimate a user’s real resource demand because the specified SLA measures are not consistently correlated with the user’s resource demand. This makes resource provisioning and scheduling less informative and can greatly reduce system efficiency. We propose the concept of reference storage system (RSS), which can be a storage system chosen by users and whose performance can be measured offline and mimicked online, as a performance interface between applications and storage servers. By designating an RSS to represent I/O performance requirement, a user can expect the performance received from a shared storage server servicing his I/O workload is not worse than the performance received from the RSS servicing the same workload. The storage system is responsible for implementing the RSS interface. The key enabling techniques are a machine learning model that derives request-specific performance requirements and an RSS-centric scheduling that efficiently allocates resource among requests from different users. The proposed scheme, named as YouChoose , supports the user-chosen performance interface through efficiently implementing and migrating virtual storage devices in a host storage system. Our evaluation based on trace-driven simulations shows that YouChoose can precisely implement the RSS performance interface, achieve a strong performance assurance and isolation, and improve the efficiency of a consolidated storage system consisting of different types of storage devices.

Overheating of Li-ion cells and battery packs is an ongoing technological challenge for electrochemical energy conversion and storage, including in electric vehicles. Immersion cooling is a promising thermal management technique to address these challenges. This work presents experimental and theoretical analysis of the thermal and electrochemical impact of immersion cooling of a small module of Li-ion cells. Significant reduction in both surface and core temperature due to immersion cooling is observed, consistent with theoretical and simulation models developed here. However, immersion cooling is also found to result in a small but non-negligible increase in capacity fade of the cells. A number of hypotheses are formed and systematically tested through a comparison of experimental measurements with theoretical modeling and simulations. Electrochemical Impedance Spectroscopy measurements indicate that the accelerated cell aging due to immersion cooling is likely to be due to enhanced lithium plating. Therefore, careful consideration of the impact of immersion cooling on long-term performance may be necessary. The results presented in this work quantify both thermal and electrochemical impacts of a promising thermal management technique for Li-ion cells. These results may be of relevance for design and optimization of electrochemical energy conversion and storage systems.

Design analysis and performance prediction of packed bed latent heat storage system employing machine learning models

This paper surveys previous distributed storage systems and related data redundancy and fault-tolerance schemes which are introduced to overcome the impact of host churn on data reliability. Furthermore, a hybrid storage system model is proposed which offers a reliable data storage service by integrating idle storage contributed by volatile peer nodes and stable and durable storage utilities. In order to ensure high availability and durability for this hybrid storage system, we explore four reliability improvement strategies, including File Replica Strategy, File Encoding Strategy, Replica Repair Strategy, and Stable-Volatile Strategy, as well as the combination of these four strategies. Extensive simulations based on real traces are performed, in which data availability, data durability, and storage overhead are evaluated. Simulation results show that compared with previous peer-to-peer storage systems, the proposed hybrid storage system could achieve a higher availability and durability with less storage consumption, due to proposed new strategies. Finally, taking into account storage and traffic cost, the tradeoffs between storage efficiency and reliability are discussed.

This paper analyzes the problems of load balance caused by data access in parallel file systems and gives an accurate way to estimate the load of Data Servers. On the base of this evaluation, the paper proposes an adaptive loading data migration (ALDM) strategy to balance the load of data servers. Experiment results show ALDM has little impact on system performance during data migration, and the performance improves about 10% after data migration completed. Furthermore, ALDM also has good adaptability to various loads in different storage systems.

Economic prospects for the application of new electric energy storage devices

—rigid or hard magnetic disks; —parallel disk arrays; —optical disks, which have several variants: read-only [such as CD-ROM and digital video disk (DVD)], write-once (WORM), and rewritable (magnetooptical or phase change); —magnetic tape, such as 4mm (DAT), 8mm, and digital linear tape (DLT); —autochangers, which combine storage devices with racks of tape cartridges or optical disk platters that can be moved to and from the drives by robotics. —Complete storage systems also include software to manage and orchestrate the storage components. Such software includes management and configuration utilities for storage devices, logical volume managers that tie multiple physical devices together as one, and file systems to arrange layout of data on storage devices. —Storage systems are a vital and growing market. Their primary components are magnetic and optical disk drives, magnetic tapes, and largecapacity robotic assemblies of drives and cartridges. Storage hardware sales in 1995 topped $40 billion, including more than 60,000 terabytes of hard disk storage. In recent years, the amount of storage sold has been almost doubling each year; in the near future it is expected to sustain an annual growth of about 60%. This enormous growth rate has been accompanied by a 50% per year decrease in the cost per byte of storage. These growth trends exceed those of the personal computer market. —The focus of this article is on secondary storage systems and primarily on magnetic disk drives, since these are

Agriculture constitutes a foundational pillar of the Indian economy, contributing nearly 18% to the national Gross Domestic Product (GDP) and ranking second globally in horticultural output. Beyond its economic significance, the sector underpins rural employment, food security, and a wide range of agro-based downstream industries. Despite these strengths, Indian agriculture continues to encounter critical bottlenecks—most notably, post-harvest losses in fruits, which are estimated to range between 6.02% and 15.05%. These losses are predominantly attributed to the lack of accessible and decentralized cold storage infrastructure. Maintaining optimal temperature and humidity levels throughout the cold chain is essential to curtail physicochemical degradation and suppress microbial growth, both of which substantially diminish the quality and shelf life of perishable produce. This study introduces a solar photovoltaic (PV)-driven micro cold storage (MCS) system, specifically engineered for seamless integration with electric vehicles (EVs) to effectively mitigate post-harvest losses in perishable agricultural commodities. The research undertakes a comprehensive performance evaluation of the proposed system, which employs a thermoelectric cooling mechanism powered entirely by solar energy. Emphasis is placed on assessing the system’s thermal, electrical, and microbial preservation capabilities under both static and dynamic operational conditions, highlighting its potential for sustainable and mobile cold chain applications in rural agricultural contexts. The system comprises a 100 Wp polycrystalline solar photovoltaic (PV) module, which supplies power to a 12 V/6A shunt-configured thermoelectric cooler with a 12 L storage capacity via a 12 V/8A solar charge controller. Functioning as an off-grid refrigeration unit, the system is supported by a 12 V/40Ah battery energy storage system. The experimental analysis focuses on assessing the shelf life of Vitis vinifera (grapes) over a one-week storage period by measuring physiological loss in weight (PLW) as the key parameter for evaluating storage efficiency. The refrigeration chamber maintains a controlled temperature range of + 2 °C to + 8 °C. Findings indicate a controlled weight reduction of up to 87.6% in refrigerated grapes compared to those stored under ambient conditions. Also, the system’s performance to maintain proper storage conditions during short-distance transportation (six hours) is evaluated to demonstrate effective farm-to-market connectivity through electric vehicle utilization. The study evaluates the electrical and thermal performance of a system for renewable energy-integrated electric vehicle applications. It also investigates the effectiveness of a solar-powered modified controlled storage (MCS) system in preventing microbial growth and maintaining agro-produce quality during storage and transport. The microbial load, including bacterial, fungal, and yeast populations, was quantified using colony-forming unit (CFU) counts per millilitre to evaluate the system’s efficacy in ensuring food safety. The findings underscore the environmental sustainability and practical applicability of the MCS system in the preservation of perishable agricultural produce. By enabling access to affordable, reliable, and renewable energy sources, the system directly contributes to the achievement of Sustainable Development Goal (SDG) 7, while simultaneously addressing food waste reduction and improving the efficiency and resilience of agro-supply chains.

This paper presents a hardware-based AES-256 XTS encryption system implemented on FPGA, providing a complete inline bridge between a storage controller and the storage device. Unlike prior works that focused only on AES core optimization, this design integrates the core into the full SATA protocol and evaluates end-to-end storage-path performance. The pipelined XTS-AES core enables high-throughput, real-time sector-level encryption with minimal performance impact. FPGA implementation offers flexibility in key sizes and encryption modes, supports algorithm updates through partial reconfiguration, and allows scalability to various storage systems, including NAS storage systems. The main contributions are: (i) proposing an FPGA-based inline encryption architecture with an AES-XTS core fully integrated into the SATA protocol; (ii) implementing and evaluating the encryption performance on a real storage system, demonstrating practical feasibility and transparency in real-time operations.

The performance gap between processor and storage device has continuously increased during the past few decades. The gap is further exacerbated recently because applications are becoming more data-intensive in both industry and academia. Traditional storage devices, such as hard disk drives (HDD), fail to keep up with the paces of this growth. A known solution is to use solid state drives (SSD) as fast storage. Due to high cost of SSD, data and supercomputing centers usually adopt a hybrid storage system, which consists of a combination of HDD and SSD I/O servers. However, hybrid I/O and storage systems have increased the complexity, making SSD often underutilized. The configuration and utilization of HDD/SSD hybrid systems is a lasting phenomenon. In this study, we propose a high performance hybrid parallel I/O and storage simulator, HPIS3. As a co-design tool, HPIS3 is capable of simulating a variety of parallel storage systems, especially under hybrid scenarios. The experimental results show that the lowest error rate is 2%, and the average is 11.98%.