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Abstract: We study the single-device Dynamic Storage Allocation (DSA) problem and the multi-device Balancing DSA problem in this paper. The goal is to dynamically allocate the job into memory to minimize the usage of space without concurrency. The SRF problem is just a variant of the DSA problem. Our results are as follows. [•] The NP-completeness for the 2-SRF problem, 3-DSA problem, and DSA problem for jobs with agreeable deadlines. [•] An improved 3-competitive algorithm for jobs with agreeable deadlines on single-device DSA problems. A 4-competitive algorithm for jobs with agreeable deadlines on multi-device Balancing DSA problems. [•] Lower bounds for jobs with agreeable deadlines: any non-clairvoyant algorithm cannot be -competitive and any clairvoyant algorithm cannot be -competitive. [•] The first -competitive algorithm for general jobs on multi-device Balancing DSA problems without any assumption. [Copyright &y& Elsevier]

We consider a processor-sharing storage allocation model, which has m primary holding spaces and infinitely many secondary ones, and a single processor servicing the stored items. All of the spaces are numbered and ordered. An arriving customer takes the lowest available space. Dynamic storage allocation and the fragmentation of computer memory are well-known applications of this model. We define the traffic intensity ρ to be λ/μ, where λ is the customers' arrival rate and μ is the service rate of the processor. We study the joint probability distribution of the numbers of occupied primary and secondary spaces. We study the problem in two asymptotic limits: (1) m → ∞ with a fixed ρ < 1, and (2) ρ ↑ 1, m → ∞ with m(1-ρ) = O(1). The asymptotics yield insight into how many secondary spaces tend to be needed, and into the sample paths leading to the occupation of the two types of spaces. We show that the asymptotics lead to accurate numerical approximations. [ABSTRACT FROM AUTHOR]

This study investigates the effects of transmission losses, constraints and increased renewable energy penetration on planning energy storage allocation and investment. By modifying a DC optimal power flow model using a linearised approximation for ohmic losses, the authors were able to understand which network characteristic or inhibitor drives the most change in expanding utility-scale storage. Four different storage technologies were explored: compressed air energy storage, pumped hydro storage, lithium-ion battery and fly wheel. Each had different charging, capacity and cost characteristics. The results of the storage allocation trials revealed that network congestion was a more influential network inhibitor than were line losses. Losses only had substantial effects on a free-flowing network but produced marginal changes in allocation in congested ones. The conclusion of the investment trials revealed two things: (i) storage investment is not significantly affected by transmission constraints so long as renewable generation stays constant and relatively low,(ii) more flexible technologies like flywheels are favoured at lower volumes of renewable penetration for their load balancing capabilities while cheaper technologies are best as the volume of renewable power generated increases. [ABSTRACT FROM AUTHOR]

We consider a processor-sharing storage allocation model, which has m primary holding spaces and infinitely many secondary ones, and a single processor servicing the stored items (customers). An arriving customer takes a primary space, if one is available. We define the traffic intensity ρ to be λ/ μ where λ is the customers’ arrival rate and μ is the service rate of the processor. We study the joint probability distribution of the numbers of occupied primary and secondary spaces. For 0< ρ<1, we obtain the exact solutions for m=1 and m=2. For arbitrary m we study the problem in the asymptotic limit ρ↑1 with m fixed. We also give the tail of the distribution for a fixed ρ<1 and any m. [ABSTRACT FROM AUTHOR]

The design and scalability of a nano-electro- mechanical memory (NEMory) cell are investigated via analytical modeling and finite element analysis (FEA) simulation. Proportionate scaling of all the cell dimensions provides for improved storage density together with low operating voltages and fast program/erase times. From FEA simulation, a 75-nm-long aluminum cantilever-beam NEMory cell is expected to have sub-1-ns erase and program times for sub-1-V operation. Because there are practical limits to beam and air-gap thickness scaling, it will be difficult to achieve low-voltage operation for very short beams (Lbeam, < 50 nm), unless a beam material with a low Young's modulus is used. Fracture strain imposes a fundamental limit for beam-length scaling. Thus, a high fracture-strain beam material is desirable to extend NEMory scalability. [ABSTRACT FROM AUTHOR]

Most of currently deployed Grid systems employ hierarchical or centralized approaches to simplify system management. However, the approaches cannot satisfy the requirements of complex Grid applications which involve hundreds or thousands of geographically distributed nodes. This paper proposes a Dynamic and Scalable Storage Management (DSSM) architecture for Grid Oriented Storage (GOS) devices. Since large-scale data intensive applications frequently involve a high degree of data access locality, the DSSM divides GOS nodes into multiple geographically distributed domains to facilitate the locality and simplify the intra-domain storage management. Dynamic GOS agents selected from the domains are organized as a virtual agent domain in a Peer-to-Peer (P2P) manner to coordinate multiple domains. As only the domain agents participate in the inter-domain communication, system wide information dissemination can be done far more efficiently than flat flooding. Grid service based storage resources are adopted to stack simple modular service piece by piece as demand grows. The decentralized architecture of DSSM avoids the hierarchical or centralized approaches of traditional Grid architectures, eliminates large-scale flat flooding of unstructured P2P systems, and provides an interoperable, seamless, and infinite storage pool in a Grid environment. The DSSM architecture is validated by a proof-of-concept prototype system. [Copyright &y& Elsevier]

Dynamic storage allocation is the problem of packing given axis-aligned rectangles into a horizontal strip of minimum height by sliding the rectangles vertically but not horizontally. Where L = LOAD is the maximum sum of heights of rectangles that intersect any vertical line and OPT is the minimum height of the enclosing strip, it is obvious that OPT ≥ LOAD; previous work showed that OPT < 3 LOAD. We continue the study of the relationship between OPT and LOAD, proving that OPT = L + O((hmax/L)1/7)L, where hmax is the maximum job height. Conversely, we prove that for any &epsiv; > 0, there exists a c > 0 such that for all sufficiently large integers hmax, there is a dynamic storage allocation instance with maximum job height hmax, maximum load at most L, and OPT ≥ L + c(hmax/L)1/2+&epsiv;L, for infinitely many integers L. En route, we construct several new polynomial-time approximation algorithms for dynamic storage allocation, including a (2 + &epsiv;)-approximation algorithm for the general case and polynomial-time approximation schemes for several nafliral special cases. [ABSTRACT FROM AUTHOR]

It is demonstrated how dynamic storage allocation algorithms can be analyzed in terms of finite-size scaling. The method is illustrated in the three simple cases of the first-fit, next-fit and best-fit algorithms, and the system works at full capacity. The analysis is done from two different points of view—running speed and employed memory. In both cases, and for all algorithms, it is shown that a simple scaling function exists and the relevant exponents are calculated. The method can be applied on similar problems as well. [Copyright &y& Elsevier]

An efficient dynamic storage allocator is important for time-critical parallel programs. In this paper, we present a fast and simple parallel allocator for fixed size blocks on shared-memory multiprocessors. We show both theoretically and empirically that the allocator incurs very low lock contention. The allocator is tested with parallel simulation applications with frequent allocation and release requests. The results confirm that our allocator is indeed very efficient. [ABSTRACT FROM AUTHOR]

Provides information on a study which described fusion-based register allocation in detail and compared it with other approaches to register allocation. Phases identified by the framework of the register allocation computer filing program; Aspects of graph reconstruction addressed by the program; Placement of a shuffle code.

The growing demands of large-capacity flash-based storages have facilitated the downscaling process of NAND flash memory. However, the downscaling of traditional planar floating-gate flash memory faces several challenges. Therefore, new NAND flash technologies have been explored to provide larger capacity with low cost. Among these new technologies, the 3-D charge-trap flash is regarded as one of the most promising candidates. The 3-D charge-trap flash is composed of several gate-stack layers and vertical cylindrical channels to provide high-density and low cell-to-cell interference. Owing to the cylindrical geometry of vertical channels, the access performance of each page in one block is distinctive, and this situation is exacerbated in the 3-D charge-trap flash with the fast-growing number of gate-stack layers. In this paper, a progressive performance boosting strategy is proposed to boost the performance of 3-D charge-trap flash by utilizing its asymmetric page access speed feature. A series of experiments was conducted to demonstrate the capability of the proposed strategy on improving the access performance of 3-D charge-trap flash. [ABSTRACT FROM AUTHOR]

Recall decreases across a series of subspan immediate-recall trials but rebounds if the semantic category of the words is changed, an example of release from proactive interference (RPI). The size of the rebound depends on the semantic categories used and ranges from 0% to 95%. We used a corpus of novels to create vectors representing the meaning of about 40,000 words using the BEAGLE algorithm. The distance between categories and spread within categories jointly predicted the size of the RPI. We used a holographic model for recall equipped with a lexicon of BEAGLE vectors representing the meaning of words. The model captured RPI using a hologram as an interface to bridge information from episodic and semantic memory; it is the first account of RPI to capture release at the level of individual words in categorized lists. [ABSTRACT FROM AUTHOR]

Purpose The contemporary e-tailing marketplace insists that distribution centers are playing the roles of both wholesalers and retailers which require different storage-handling load sizes due to different product variants. To fulfill piecewise retail orders, a separate small size-fast pick area is design called “forward buffer” wherein pallets are allocated from reserve area. Due to non-uniform pallets, the static allocation policy diminishes forward space utilization and also, more than practically required buffer size has been identified as wastage. Thus, dynamic storage allocation policy is required to design for reducing storage wastage and improving throughput considering non-uniform unit load sizes. The purpose of this paper is to model such policy and develop an e-decision support system assisting enterprise practitioners with real-time decision making.Design/methodology/approach The research method is developed as a dynamic storage allocation policy and mathematical modeled as knapsack-based heuristics. The execution procedure of policy is explained as an example and tested with case-specific data. The developed model is implemented as a web-based support system and tested with rational data instances, as well as overcoming prejudices against single case findings.Findings The provided model considers variable size storage-handling unit loads and recommends number of pallets allocations in forward area reducing storage wastes. The algorithm searches and suggests the “just-right” amount of allocations for each product balancing existing forward capacity. It also helps to determine “lean buffer” size for forward area ensuring desired throughput. Sensitivity and buffer performance analysis is carried out for Poisson distributed data sets followed by research synthesis.Practical implications Warehouse practitioners can use this model ensuring a desired throughput level with least forward storage wastages. The model driven e-decision support system (DSS) helps for effective real-time decision making under complicated business scenarios wherein products are having different physical dimensions. It assists the researchers who would like to explore the emerging field of “lean” adoption in enterprise information and retail-distribution management.Originality/value The paper provides an inventive approach endorsing lean thinking in storage allocation policy design for a forward-reserve model. Also, the developed methodology incorporating features of e-DSS along with quantitative modeling is an inimitable research contribution justifying rational data support. [ABSTRACT FROM AUTHOR]

Presents a letter to the editor about the asymptotic optimality of first-fit storage allocation, published in the February 1985 issue of the periodical "IEEE Transactions on Software Engineering".

This paper discusses the problem of allocating storage for the activation records of procedure calls within a system of parallel processes. A compile time storage allocation scheme is given, which determines the relative address within the memory segment of a process for the activation records of all procedures called by the process. This facilitates the generation of an efficient run-time code. The allocation scheme applies to systems in which data and procedures can be shared among several processes. However, recursive procedure calls are not supported. [ABSTRACT FROM AUTHOR]

As the era of big data is coming, more and more users choose to store data in the cloud. Cloud storage provides users with flexible, dynamic and cost effective data storage service. However, the new paradigm of service introduces new security challenges such as users loss control of the remote data and they cannot ensure data integrity in the cloud. Moreover, supporting dynamic data updates is also a practical requirement of cloud storage. It is imperative to provide an efficient and secure dynamic auditing protocol to check the data integrity in the cloud. In this paper, we first analyze the dynamic performance of some prior works. In order to solve the inefficiency problem caused by the Merkle Hash Tree (MHT) in dynamic update, the Large Branching Tree (LBT) data structure was introduced in our Dynamic Provable Data Possession (DPDP) scheme. LBT structure simplifies the process of updates and supports updating several blocks synchronously, and reduces the auxiliary information during the challenge-respond process as well. Our scheme is able to efficiently support fully dynamic data updates and batch updates. Based on the LBT structure, we use bilinear algebraic maps to optimize the authenticate process. A signature scheme is used to authenticate both the value and the position of data blocks, which reduces computation overhead during the dynamic update phase. The security and performance analysis show that our DPDP scheme is provably secure and efficient. [ABSTRACT FROM AUTHOR]

Purpose The retail revolution swing from traditional distribution to e-tailing services and unprecedented increase in internet adoption insist practitioners to diversely plan warehousing strategies. More than practically required storage space has been identified as wastes, and also it does not improve performance. An organized framework integrating storage design policies, operational performance and customer value improvement for retail-distribution management is lacking. Therefore, the purpose of this paper is to develop broad guidelines to design the “just-right” amount of forward area, i.e., “lean buffer” answering the following questions: “What should be lean buffer size? How effective the forward area is? As per demand variations, which storage waste (SKU) should be allocated with how much storage space? What is the amount of storage waste (SW)? How smooth the material flow is in between reserve-forward area?” for storage allocation in cosmetics distribution centers.Design/methodology/approach After forecasting static storage allocation between two planning horizons, if a particular SKU is less or non-moving, then it will cause SW, as the occupied location can be utilized by other competing SKUs, and also it impedes material flow for an instance. A dynamically efficient and self-adaptive, knapsack instance based heuristics is developed in order to make effective storage utilization.Findings The existing state-of-the-art under study is supported with a distribution center case, and the study investigates the need of a model adopting lean management approach in storage allocation policies along with test results in LINGO. The sensitivity analysis describes the impact of varying demand and buffer size on performance. The results are compared with uniform and exponential distributed demands, and findings reveal that the proposed heuristics improves efficiency and reduce SWs in forward-reserve area.Originality/value The presented model demonstrates a novel thinking of lean adoption in designing storage allocation strategy and its performance measures while reducing wastes and improving customer value. Future research issues are highlighted, which may be of great help to the researchers who would like to explore the emerging field of lean adoption for sustainable retail and distribution operations. [ABSTRACT FROM AUTHOR]

The potential for dynamic storage to serve as a metric of basin behaviour was assessed using data from five drainage basins with headwaters on the thick sand and gravel deposits of the Oak Ridges Moraine in southern Ontario, Canada. Dynamic storage was directly correlated with the ratio of variability of δ2H in streamflow relative to that in precipitation. This ratio has previously been shown to be inversely related to basin mean transit time ( MTT), suggesting an inverse relationship between dynamic storage and MTT for the study basins. Dynamic storage was also directly correlated with interannual variability in stream runoff, baseflow and baseflow:runoff ratio, implying that basins with smaller dynamic storage have less interannual variability in their streamflow regimes. These preliminary results suggest that dynamic storage may serve as a readily derived and useful metric of basin behaviour for inter-basin comparisons. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We consider a map of the unit square which is not 1-1, such as the memory map studied in Góra (Statistical and deterministic dynamics of maps with memory, ). Memory maps are defined as follows: $$x_{n+1}=M_{\alpha }(x_{n-1},x_{n})=\tau (\alpha \cdot x_{n}+(1-\alpha )\cdot x_{n-1}),$$ where $$\tau $$ is a one-dimensional map on $$I=[0,1]$$ and $$0<\alpha <1$$ determines how much memory is being used. In this paper we let $$\tau $$ to be the symmetric tent map. To study the dynamics of $$M_\alpha $$ , we consider the two-dimensional map The map $$G_\alpha $$ for $$\alpha \in (0,3/4]$$ was studied in Góra (Statistical and deterministic dynamics of maps with memory, ). In this paper we prove that for $$\alpha \in (3/4,1)$$ the map $$G_\alpha $$ admits a singular Sinai-Ruelle-Bowen measure. We do this by applying Rychlik's results for the Lozi map. However, unlike the Lozi map, the maps $$G_\alpha $$ are not invertible which creates complications that we are able to overcome. [ABSTRACT FROM AUTHOR]