Multiple Storage Devices Research Articles

Scheduling of Electricity Storage for Peak Shaving with Minimal Device Wear

In this work, we investigate scheduling problems for electrical energy storage systems and formulate an algorithm that finds an optimal solution with minimal charging cycles in the case of a single device. For the considered problems, the storage system is used to reduce the peaks of the production and consumption within (part of) the electricity distribution grid, while minimizing device wear. The presented mathematical model of the storage systems captures the general characteristic of electrical energy storage devices while omitting the details of the specific technology used to store the energy. In this way, the model can be applied to a wide range of settings. Within the model, the wear of the storage devices is modeled by either: (1) the total energy throughput; or (2) the number of switches between charging and discharging, the so-called charging cycles. For the first case, where the energy throughput determines the device wear, a linear programming formulation is given. For the case where charging cycles are considered, an NP-hardness proof is given for instances with multiple storage devices. Furthermore, several observations about the structure of the problem are given when considering a single device. Using these observations, we develop a polynomial time algorithm of low complexity that determines an optimal solution. Furthermore, the solutions produced by this algorithm also minimize the throughput, next to the charging cycles, of the device. Due to the low complexity, the algorithm can be applied in various decentralized smart grid applications within future electricity distribution grids.

AUTHENTICATED KEY EXCHANGE PROTOCOLS FOR PARALLEL NETWORK FILE SYSTEMS

We study the problem of key establishment for secure many-to-many communications. The problem is inspired by the proliferation of large-scale distributed file systems supporting parallel access to multiple storage devices. Our work focuses on the current Internet standard for such file systems, i.e. , parallel Network File System (pNFS), which makes use of Kerberos to establish parallel session keys between clients and storage devices. Our review of the existing Kerberos-based protocol shows that it has a number of limitations: (i) a metadata server facilitating key exchange between the clients and the storage devices has heavy workload that restricts the scalability of the protocol; (ii) the protocol does not provide forward secrecy; (iii) the metadata server generates itself all the session keys that are used between the clients and storage devices, and this inherently leads to key escrow. In this paper, we propose a variety of authenticated key exchange protocols that are designed to address the above issues. We show that our protocols are capable of reducing up to approximately 54% of the workload of the metadata server and concurrently supporting forward secrecy and escrow-freeness. All this requires only a small fraction of increased computation overhead at the client.

Authenticated Key Exchange Protocols for Parallel Network File Systems

We study the problem of key establishment for secure many-to-many communications. The problem is inspired by the proliferation of large-scale distributed file systems supporting parallel access to multiple storage devices. Our work focuses on the current Internet standard for such file systems, i.e., parallel Network File System (pNFS), which makes use of Kerberos to establish parallel session keys between clients and storage devices. Our review of the existing Kerberos-based protocol shows that it has a number of limitations: (i) a metadata server facilitating key exchange between the clients and the storage devices has heavy workload that restricts the scalability of the protocol; (ii) the protocol does not provide forward secrecy; (iii) the metadata server generates itself all the session keys that are used between the clients and storage devices, and this inherently leads to key escrow. In this paper, we propose a variety of authenticated key exchange protocols that are designed to address the above issues. We show that our protocols are capable of reducing up to approximately 54 percent of the workload of the metadata server and concurrently supporting forward secrecy and escrow-freeness. All this requires only a small fraction of increased computation overhead at the client.

Performance investigation of a hybrid renewable power generation and storage system using systemic power management models

Hybrid renewable power generation systems considering storage of energy and/or hydrogen involve considerable complexity as heterogeneous equipment need to be interconnected and activated or deactivated at different time instances. This work employs generic and flexible decision making models to develop complex power/hydrogen management strategies and investigate their performance on systems integrating multiple power generation and storage devices. These models significantly facilitate the analysis of both a standalone power generation and storage system forming a microgrid currently in operation as well as three interconnected microgrids of different functionalities considered in this work. These systems are used to investigate the performance of a large number of power management strategies developed using the proposed models.

Optimal Lexicographic Shaping of Aggregate Streaming Data

We investigate the problem of smoothing multiplexed network traffic when either a streaming server transmits data to multiple clients or a storage server accesses data from multiple storage devices or other servers. We introduce efficient algorithms for lexicographically optimally smoothing the aggregate bandwidth requirements over a shared network link. Possible applications include improvement in the bandwidth utilization of network links and reduction in the energy consumption of server hosts. In the data transmission problem, we consider the case in which the clients have different buffer capacities and unlimited bandwidth constraints or unlimited buffer capacities and different bandwidth constraints. For the data access problem, we handle the general case of a shared buffer capacity and individual network bandwidth constraints. Previous approaches for the data access problem handled either the case of only a single stream or did not compute the lexicographically optimal schedule. By provably minimizing the variance of the required aggregate bandwidth, lexicographically optimal smoothing makes the maximum resource requirements within the network more predictable and increases the useful resource utilization. It also improves fairness in sharing a network link among multiple users and makes new requests from future clients more likely to be successfully admitted without the need for rescheduling previously accepted traffic. With appropriate hardware and system support, data traffic smoothing can also reduce the energy consumption of the host processor and the communication links. Overall, we expect that efficient resource management at the network edges will better meet quality of service requirements without restricting the scalability of the system.

Hash-based labeling techniques for storage scaling

Scalable storage architectures allow for the addition or removal of storage devices to increase storage capacity and bandwidth or retire older devices. Assuming random placement of data objects across multiple storage devices of a storage pool, our optimization objective is to redistribute a minimum number of objects after scaling the pool. In addition, a uniform distribution, and hence a balanced load, should be ensured after redistribution. Moreover, the redistributed objects should be retrieved efficiently during the normal mode of operation: in one I/O access and with low complexity computation. To achieve this, we propose an algorithm called random disk labeling (RDL), based on double hashing, where storage can be added or removed without any increase in complexity. We compare RDL with other proposed techniques and demonstrate its effectiveness through experimentation.

A high-performance distributed parallel file system for data-intensive computations

One of the challenges brought by large-scale scientific applications is how to avoid remote storage access by collectively using sufficient local storage resources to hold huge amounts of data generated by the simulation while providing high-performance I/O. DPFS, a distributed parallel file system, is designed and implemented to address this problem. DPFS collects locally distributed and unused storage resources as a supplement to the internal storage of parallel computing systems to satisfy the storage capacity requirement of large-scale applications. In addition, like parallel file systems, DPFS provides striping mechanisms that divide a file into small pieces and distributes them across multiple storage devices for parallel data access. The unique feature of DPFS is that it provides three file levels with each file level corresponding to a file striping method. In addition to the traditional linear striping method, DPFS also provides a novel Multidimensional striping method that can solve performance problems of linear striping for many popular access patterns. Other issues such as load-balancing and user interface are also addressed in DPFS.

Characterizing Concurrency Control Performance for the PIOUS Parallel File System

Parallel file systems employ data declustering to increase I/O throughput. However, because a single read or write operation can generate data accesses on multiple independent storage devices, a concurrency control mechanism must be employed to retain familiar file access semantics. Concurrency control negates some of the performance benefits of data declustering by introducing additional file access overhead. This paper examines the performance characteristics of the transaction-based concurrency control mechanism implemented in the PIOUS parallel file system. Results demonstrate that linearizability of file access operations is provided without loss of scalability or stability.

An online video placement policy based on bandwidth to space ratio (BSR)

In a video-on-demand server, resource reservation is required to guarantee continuous delivery. Hence any given storage device (or a striping group treated as a single logical device) can serve only up to a fixed number of client access streams. Each storage device is also limited by the number of video files it can store. For the reasons of availability, incremental growth, and heterogeneity, there may be multiple storage devices in a video server environment. Hence, one or more copies of a particular video may be placed on different storage devices. Since the access rates to different videos are not uniform, there may be load imbalance among the devices. In this paper, we propose a dynamic placement policy (called the Bandwidth to Space Ratio (BSR) Policy) that creates and/or deletes replica of a video, and mixes hot and cold videos so as to make the best use of bandwidth and space of a storage device. The proposed policy is evaluated using a simulation study.

Algorithms for parallel memory, I: Two-level memories

We provide the first optimal algorithms in terms of the number of input/outputs (I/Os) required between internal memory and multiple secondary storage devices for the problems of sorting, FFT, matrix transposition, standard matrix multiplication, and related problems. Our two-level memory model is new and gives a realistic treatment of {\em parallel block transfer}, in which dureing a single I/O each of the $P$ secondary storage devices can simultaneously transfer a contiguous block of $B$ records. The model pertains to a large-scale uniprocessor system or parallel multiprocessor system with $P$ disks. In addition, the sorting, FFT, permutation network, and standard matrixmultiplication algorithms are typically optimal in terms of the amount of internal processing time. The difficulty in developing optimal algorithms is to cope with the partitioning of memory into $P$ separate physical devices. Our algorithms'' performance can be significantly better than those obtained by the well-known but nonoptimal technique of disk striping. Our optimal sorting algorithm is randomized, but practical; the probability of using more than $\ell$ times the optimal number of I/Os is exponentially small in $\ell$ (log $\ell$)log($M/B$), where $M$ is the internal memory size.