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As the blockchain technology grows, the scale of the network is becoming huger and huger, which brings us a troublesome question: how to allocate our data into each computer of a blockchain network? For example, we can store our data from one computer, until the memory space filled, and then switch to another, spending a lot of time searching in sole computer. Or we can only put a small amount of data into one computer, resulting in a huge network and plenty of transmission time. In this paper, we compute the cost of all the allocations according to the mathematical model. By studying the formula, we get the solution of balancing the search time and the space occupancy by users' input. Based on the result, we design a data allocation strategy that can decide how many data allocated to an individual computer.

In this paper, as integral part for our previous efforts, a heuristic model is proposed. The basic objective of this model is to partition data vertically in Distributed Database System (DDBS). Driven by the running queries along with using hierarchical clustering algorithm, the presented model pursues to find a method by which an efficient data fragmentation and allocation is set to be made. The prime focus of this paper is oriented towards shedding the light on previous studies as well as presenting greedy based transmission costs-reducing data allocation algorithm. Data replication is also investigated to produce a comprehensive solution. Moreover, the site clustering has been integrated into this work. Moreover, data replication is carefully considered in such a way that cost is to be essentially minimized.

Domain Wall Memory (DWM), a recently developed spin-based non-volatile memory technology, inherently offers unprecedented benefits in density by storing multiple bits in the domains of a ferromagnetic nanowire, which logically resembles a bit-serial tape. However, this structure also leads to a unique challenge that the bits must be sequentially accessed by performing \shift" operations, resulting in variable and potential higher access latencies. In this paper, we propose a hardware and software co-optimize approach to improve area efficiency and performance for DWM in application-specific embedded systems. For an application-specific embedded system, this technique can obtain a DWM which consists of both micro-cell DWM and macro-cell DWM with minimal area size. Meanwhile, instruction schedule and data allocation with minimal memory access overhead are generated. Experimental results show that the proposed method can minimize the DWM area size while satisfying a system performance constraint.

The literature on scratchpad memories (SPMs) seems to indicate that the use of dynamic overlaying supersedes static, non-overlay-based (NOB) allocation. Although overlay-based (OVB) techniques operating at source-level code might benefit from multiple hot spots for higher energy savings, they cannot exploit libraries. When operating on binaries, OVB approaches lead to smaller savings, often require dedicated hardware, and sometimes prevent data allocation. Besides, all saving reports published so far ignore that, in cache-based systems, caches are likely to be optimized prior to SPM allocation. We show experimental evidence that, when handling binaries, NOB memory savings (15% to 33% on average) are as good as or better than OVB's. Since our savings (as opposed to related work) were measured after cache tuning -- when there is less room for optimization, our results encourage the use of simpler NOB methods to build library aware allocators that cannot depend on dedicated hardware. We also show that, given the capacity Ct of the equivalent pretuned cache, the optimal SPM size lies in [Ct/2, Ct] for 85% of the evaluated programs. Finally, we show counter-intuitive evidence that, even for cache-based architectures containing small SPMs, procedures should be preferred for allocation instead of basic blocks.

Solid-state disks (SSDs) are becoming widely used in personal computers and are expected to replace a great portion of magnetic disks in servers and supercomputers. Although many high-speed SSDs are present in the market, both the design of hardware architecture and the details of the flash translation layer (FTL) are not well known. Meanwhile, in the systems requiring high-end storages, specially tuned SSDs can perform better than the generic ones, because the applications in such environment are usually fixed.Based on the architectural design of our reconfigurable high-performance SSD prototype and by using a trace-driven simulator, we explore the key factors and tradeoffs that must be considered when designing a customized FTL. FTL related issues, such as data allocation, cleaning, and wear leveling, are analyzed in detail presenting suitable design decisions for different workload characteristics. The experimental result shows that the figures for the performance metrics will vary from several percent to more than tens of times among each other depending on the decision made for designing each FTL functionality.

Broadcast in the wireless environment has drawn much attention because it is capable of sending data to clients regardless of the number of clients. Most previous researches have aimed at obtaining an independent data item in a minimum time. However, the access probability of each data item based on query request probability has not been dealt with although a few studies have reported on allocating dependent data in different time and channels. This paper proposes a new method of allocating data items and simultaneously reducing average response time required in receiving all the dependent data items in a query. Our performance analysis shows that our proposed method gives better average response time over the existing methods.

In this paper, We discuss the server level data allocation problems in the distributed Video-on-Demand systems. We proposed two data allocation algorithms, Bandwidth Weighted Partition (BWP) algorithm and Popularity Based (PB) algorithm, based on the bandwidth and storage capacity limits of the distributed multimedia servers. We compare those two algorithms with the traditional Round Robin (RR) algorithm. The analysis and simulation studies show that PB algorithm is a simple and practical video data allocation algorithm for the distributed video servers. It provides near optimal system performance in any system condition.