An Effective Path Selection Method in Multiple Care-of Addresses MIPv6 with Parallel Delay Measurement Technique

Abstract. In the Ubiquitous Society, there will be many types of mobile access network surrounding us and we can access the Internet anytime anywhere. At that time, mobile device can select several links from surrounded mobile access networks and access the Internet with multiple interfaces. We have already Mobile IPv6 protocol that supports mobility and try to extend to support multiple Care-of Addresses registration. But, we don't have any solution for selecting effective path. The effective path has many advantages such as reducing communication overhead. In this paper, we propose that effective path selection method in Multiple Care-of Addresses Mobile IPv6 environment with 'Parallel Delay Measurement' technique. With our technique, we can make down average packet delay

An Analytical Model-based Capacity Planning Approach for Building CSD-based Storage Systems

The data movement in large-scale computing facilities (from compute nodes to data nodes) is categorized as one of the major contributors to high cost and energy utilization. To tackle it, in-storage processing (ISP) within storage devices, such as Solid-State Drives (SSDs), has been explored actively. The introduction of computational storage drives (CSDs) enabled ISP within the same form factor as regular SSDs and made it easy to replace SSDs within traditional compute nodes. With CSDs, host systems can offload various operations such as search, filter, and count. However, commercialized CSDs have different hardware resources and performance characteristics. Thus, it requires careful consideration of hardware, performance, and workload characteristics for building a CSD-based storage system within a compute node. Therefore, storage architects are hesitant to build a storage system based on CSDs as there are no tools to determine the benefits of CSD-based compute nodes to meet the performance requirements compared to traditional nodes based on SSDs. In this work, we proposed an analytical model-based storage capacity planner called CSDPlan for system architects to build performance-effective CSD-based compute nodes. Our model takes into account the performance characteristics of the host system, targeted workloads, and hardware and performance characteristics of CSDs to be deployed and provides optimal configuration based on the number of CSDs for a compute node. Furthermore, CSDPlan estimates and reduces the total cost of ownership (TCO) for building a CSD-based compute node. To evaluate the efficacy of CSDPlan, we selected two commercially available CSDs and 4 representative big data analysis workloads

Power-Efficient Deep Neural Network Accelerator Minimizing Global Buffer Access without Data Transfer between Neighboring Multiplier—Accumulator Units

This paper presents a novel method for minimizing the power consumption of weight data movements required by a convolutional operation performed on a two-dimensional multiplier–accumulator (MAC) array of a deep neural-network accelerator. The proposed technique employs a local register file (LRF) at each MAC unit in a manner such that once weight pixels are read from the global buffer into the LRF, they are reused from the LRF as many times as desired instead of being repeatedly fetched from the global buffer in each convolutional operation. One of the most evident merits of the proposed method is that the procedure is completely free from the burden of data transfer between neighboring MAC units. It was found from our simulations that the proposed method provides a power saving of approximately 83.33% and 97.62% compared with the power savings recorded by the conventional methods, respectively, when the dimensions of the input data matrix and weight matrix are 128 × 128 and 5 × 5, respectively. The power savings increase as the dimensions of the input data matrix or weight matrix increase

Preemptive Zone Reset Design within Zoned Namespace SSD Firmware

Zoned Namespace (ZNS) SSDs address the disadvantages that come from supporting the block interface within conventional SSDs, granting more control over data management to host systems, while also relieving heavy duties from device firmware. However, with the removal of on-device garbage collection, host systems must explicitly send zone reset requests to free up storage space, which may incur multiple NAND block erase operations according to the configured zone size, resulting in increased tail latency. In this article, we propose a Preemptive Zone Reset scheduling design, which we implemented within the firmware of our ZNS SSD prototype, and compare it to an intuitive Zone Mapping Table method, which we consider as the state-of-the-art. The main idea is to service high priority foreground I/O requests while preempting block erase operations induced by zone resets. Our proposed approach, opposed to the baseline method, as much as halved tail latency for write-only workloads, and reduced read tail latency by up to 1.76 times in a mixed workload

Thin-film magnesium as a sacrificial and biodegradable material

This work reports the study of ebeam-deposited thin-film magnesium (Mg) as a sacrificial and a biodegradable material. We have tested etchants including diluted hydrochloric acid (HCl), saline, and culture medium. Both vertical etching method and channel undercut method are used to characterize the Mg etching properties. The initial results confirm that thin-film Mg is a promising dual sacrificial and biodegradable material. In addition, an etching model, which fits accurately the etching length vs. time over a wide range of HCl concentrations (0.02-1M) is developed. This model is based on diffusion and a combined first-and-second order chemical reaction mechanism

Graphene Monoxide Bilayer As a High-Performance on/off Switching Media for Nanoelectronics

The geometries and electronic characteristics of the graphene monoxide (GMO) bilayer are predicted via density functional theory (DFT) calculations. All the possible sequences of the GMO bilayer show the typical interlayer bonding characteristics of two-dimensional bilayer systems with a weak van der Waals interaction. The band gap energies of the GMO bilayers are predicted to be adequate for electronic device application, indicating slightly smaller energy gaps (0.418–0.448 eV) compared to the energy gap of the monolayer (0.536 eV). Above all, in light of the band gap engineering, the band gap of the GMO bilayer responds to the external electric field sensitively. As a result, a semiconductor-metal transition occurs at a small critical electric field (<i>E</i>_C = 0.22–0.30 V/Å). It is therefore confirmed that the GMO bilayer is a strong candidate for nanoelectronics

T6SS Accessory Proteins, Including DUF2169 Domain-Containing Protein and Pentapeptide Repeats Protein, Contribute to Bacterial Virulence in T6SS Group_5 of <i>Burkholderia glumae</i> BGR1

Burkholderia glumae are bacteria pathogenic to rice plants that cause a disease called bacterial panicle blight (BPB) in rice panicles. BPB, induced by B. glumae, causes enormous economic losses to the rice agricultural industry. B. glumae also causes bacterial disease in other crops because it has various virulence factors, such as toxins, proteases, lipases, extracellular polysaccharides, bacterial motility, and bacterial secretion systems. In particular, B. glumae BGR1 harbors type VI secretion system (T6SS) with functionally distinct roles: the prokaryotic targeting system and the eukaryotic targeting system. The functional activity of T6SS requires 13 core components and T6SS accessory proteins, such as adapters containing DUF2169, DUF4123, and DUF1795 domains. There are two genes, bglu_1g23320 and bglu_2g07420, encoding the DUF2169 domain-containing protein in the genome of B. glumae BGR1. bglu_2g07420 belongs to the gene cluster of T6SS group_5 in B. glumae BGR1, whereas bglu_1g23320 does not belong to any T6SS gene cluster in B. glumae BGR1. T6SS group_5 of B. glumae BGR1 is involved in bacterial virulence in rice plants. The DUF2169 domain-containing protein with a single domain can function by itself; however, Δu1g23320 showed no attenuated virulence in rice plants. In contrast, Δu2g07420DUF2169 and Δu2g07420PPR did exhibit attenuated virulence in rice plants. These results suggest that the pentapeptide repeats region of the C-terminal additional domain, as well as the DUF2169 domain, is required for complete functioning of the DUF2169 domain-containing protein encoded by bglu_2g07420. bglu_2g07410, which encodes the pentapeptide repeats protein, composed of only the pentapeptide repeats region, is located downstream of bglu_2g07420. Δu2g07410 also shows attenuated virulence in rice plants. This finding suggests that the pentapeptide repeats protein, encoded by bglu_2g07410, is involved in bacterial virulence. This study is the first report that the DUF2169 domain-containing protein and pentapeptide repeats protein are involved in bacterial virulence to the rice plants as T6SS accessory proteins, encoded in the gene cluster of the T6SS group_5

Data-efficient parameter identification of electrochemical lithium-ion battery model using deep Bayesian harmony search

Lithium-ion batteries have been used in many applications owing to their high energy density and rechargeability. It is very important to monitor the internal physical parameters of the lithium-ion battery for safe and efficient usage, because this can help estimate the state of the battery, develop battery aging models, and schedule optimal operation of batteries. Parameter optimization methods using an accurate electrochemical battery model are much less expensive than direct parameter measurement methods, such as post-mortem methods. Thus, many model-based parameter optimization methods have been developed so far. However, most of these methods are random search methods that are based on heuristic rules, which leads to data-inefficient parameter identification. This means that they require many time-consuming battery model simulation runs to identify optimal parameters. Herein, a novel learning-based method is proposed for data-efficient parameter identification of lithium-ion batteries. A deep Bayesian neural network is used to efficiently identify optimal parameters. The simulations and experimental data validation show that the proposed method requires much fewer battery model simulation runs to identify optimal parameters than existing methods such as genetic algorithms, particle swarm optimization, and the Levenberg-Marquardt algorithm. The parameter estimation error of the proposed method is about 10 times lower than that of the second-best algorithm.11Nsciescopu