Dynamic Storage Allocation Research Articles

A Resource Allocation Algorithm for a History-Aware Frame Graph

We consider the problem of memory consumption by a real-time GPU-accelerated graphical application. A history of a resource is defined for a particular frame to be the final contents of such a resource at the end of the previous frame. When organizing a graphical application using a frame rendering graph approach, it makes sense to implement automatic serving of resource history read requests of nodes. In absence of history resource requests, allocating resources for a fixed frame graph is the classic problem of dynamic storage allocation (DSA). In this paper, we formulate a generalization of DSA that enables memory reuse for resources with history requests and provide a practical approximate algorithm for solving it.

Dynamic Picking and Storage Optimization of Robotic Picking Systems

With the development of e-commerce and the improvement of logistics requirements, more and more ‘parts-to-picker’ picking systems begin to replace the inefficient ‘picker-to-parts’ picking systems in various scenarios. As the mainstream ‘parts-to-picker’ system, the robotic mobile fulfillment system has been attracting much attention in recent years. In addition to the customer's changing requirements, the rapid response of the picking system to the order is particularly important. In the above context, to seek a breakthrough in the picking system's picking efficiency without increasing the cost of additional equipment, the storage allocation of the pods becomes very important. This article focuses on the dynamic storage allocation of robotic mobile fulfillment system, which has positive theoretical and practical significance. By analyzing the pod storage process of the robotic mobile fulfillment system, a dynamic pod storage allocation model suitable for the robotic mobile fulfillment system is established with the goal of minimizing the pod handling distance. Two dynamic pod storage allocation strategies are proposed for the system. By simulating the picking systems of different scales, the effectiveness of the dynamic storage allocation strategy is verified, which has a certain reference to the operation of the robotic mobile fulfillment system in practice.

From Spectrum Bonding to Contiguous-Resource Batching Task Scheduling

We formulate and analyze a generic task scheduling problem: a set of tasks need to be executed on a pool of continuous resource such as spectrum and memory, each requiring a certain amount of time and contiguous resource; some tasks can be executed simultaneously in batch by sharing the resource, while others requiring exclusive use of the resource. We seek an optimal resource allocation and the related scheduling policy maximizing the overall system utility. This problem, termed as the contiguous-resource batching task scheduling problem, arises in a variety of engineering fields, where communication and storage resources are potential bottlenecks and thus need to be carefully scheduled. Two motivating examples are the spectrum bonding problem in dynamic spectrum access systems and the dynamic storage allocation problem in computer systems. In this paper, we investigate both offline and online scheduling settings. We first establish the NP-hardness of the offline setting and the inapproximability of the online setting in its generic form. Given the theoretical performance limit, we then develop approximation algorithms with mathematically proven performance guarantee in terms of approximation and competitive ratios for the offline and online settings.

Asymptotic analysis of a storage allocation model with finite capacity: Marginal and conditional distributions

We consider a dynamic storage allocation model with a finite number of storage spaces. There are m primary spaces and R secondary spaces. Items arrive according to a Poisson process and an arriving item takes the lowest ranked available space. Each item occupies a storage space for an exponentially distributed amount of time. Letting N1 and N2 denote the numbers of occupied primary and secondary spaces, we examine the steady-state marginal distribution of N2 and derive various conditional limit laws for Prob[N1=k∣N2=r]. The joint process (N1,N2) behaves as a random walk in a lattice rectangle. We scale the storage capacities m and R to be large and study the problem asymptotically as the Poisson arrival rate λ becomes large. We previously derived detailed asymptotic results for the joint distribution Prob[N1=k,N2=r] and we use these results here to study the marginal and conditional distributions.

Dynamic Allocation of Carbon Storage and Nutrient-Dependent Exudation in a Revised Genome-Scale Model of Prochlorococcus.

Microbial life in the oceans impacts the entire marine ecosystem, global biogeochemistry and climate. The marine cyanobacterium Prochlorococcus, an abundant component of this ecosystem, releases a significant fraction of the carbon fixed through photosynthesis, but the amount, timing and molecular composition of released carbon are still poorly understood. These depend on several factors, including nutrient availability, light intensity and glycogen storage. Here we combine multiple computational approaches to provide insight into carbon storage and exudation in Prochlorococcus. First, with the aid of a new algorithm for recursive filling of metabolic gaps (ReFill), and through substantial manual curation, we extended an existing genome-scale metabolic model of Prochlorococcus MED4. In this revised model (iSO595), we decoupled glycogen biosynthesis/degradation from growth, thus enabling dynamic allocation of carbon storage. In contrast to standard implementations of flux balance modeling, we made use of forced influx of carbon and light into the cell, to recapitulate overflow metabolism due to the decoupling of photosynthesis and carbon fixation from growth during nutrient limitation. By using random sampling in the ensuing flux space, we found that storage of glycogen or exudation of organic acids are favored when the growth is nitrogen limited, while exudation of amino acids becomes more likely when phosphate is the limiting resource. We next used COMETS to simulate day-night cycles and found that the model displays dynamic glycogen allocation and exudation of organic acids. The switch from photosynthesis and glycogen storage to glycogen depletion is associated with a redistribution of fluxes from the Entner–Doudoroff to the Pentose Phosphate pathway. Finally, we show that specific gene knockouts in iSO595 exhibit dynamic anomalies compatible with experimental observations, further demonstrating the value of this model as a tool to probe the metabolic dynamic of Prochlorococcus.

Deep-Learning-Based Joint Optimization of Renewable Energy Storage and Routing in Vehicular Energy Network

Recent development in renewable energy-enabled electric vehicles (EVs) has posed challenges to the stability and efficiency of the vehicular energy network (VEN), which is a concrete implementation of Internet of Things (IoT) in energy and vehicular networks. In this article, we study a VEN with time-varying point-to-point traffic flow and adjustable energy storage capacity at stations. The goal is to jointly optimize the routing and dynamic storage allocation of renewable energy so as to maximize the efficiency of plant-to-station energy transferring. We first adopt a time-expanded topology graph to describe the scenario and model it as a maximum flow problem. Next, we incorporate routing in our methodology and derive a joint energy storage capacity and route planning method based on linear programming. Then, the problem is extended to a more general case, where the traffic pattern of the VEN is unknown. We apply the long short-term memory model, a deep learning method, to predict the traffic pattern and utilize the concept of reinforcement learning to iteratively improve the prediction accuracy. To evaluate the performance, we implement our method first on regular buses, then extend to EVs based on the real trace data from the PeMS system in California. The simulation results show that the joint optimization can achieve near-optimal performance and performs well even in case of high rate of missing traffic information, with the help of deep reinforcement learning.

Modelo de asignación dinámica de lugares de almacenamiento en centros Cross Docking

Este trabajo describe una metodología para la asignación de lugares de almacenamiento temporal en centros tipo Cross Docking, para lo cual se desarrolla un modelo cuyo principal aporte es la asignación dinámica de productos entrantes a estos lugares de almacenamiento. El modelo minimiza los costos asociados al inventario final de los productos y los costos asociados a las distancias recorridas por unidad de carga. Se logra, además, una reducción del tiempo de permanencia y una aceleración del flujo de los productos desde la recepción hasta el despacho. Como principal conclusión, se identifica la importancia de las asignaciones puerta a puerta, en la reducción de los costos.

Designing a lean storage allocation policy for non-uniform unit loads in a forward-reserve model

PurposeThe 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/approachThe 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.FindingsThe 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 implicationsWarehouse 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/valueThe 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.

Dynamic power allocation of battery-supercapacitor hybrid energy storage for standalone PV microgrid applications

Standalone photovoltaic-based microgrid with energy storage system could be a promising solution for powering up off-grid communities. One of the major issues that hinder the development of standalone microgrids is the poor service life of the batteries. To address this issue, hybrid energy storage systems (HESS) and novel power management strategies have been proposed by researchers to enhance the service life of battery bank. This paper presents a novel multi-level hybrid energy storage system topology and its associated power management strategy to mitigate the charge/discharge stress on battery. Matlab Simulink model of typical standalone PV microgrid with different HESS topologies are developed to evaluate the performance of the proposed system. Actual solar irradiance data with different weather conditions and estimated load profile based on site survey results are used to analyze the effectiveness of different HESS in mitigating stress on batteries. A comprehensive analysis and benchmarking is presented to compare the technical and financial viability of the proposed system with existing HESS solutions. Simulation results show that the proposed HESS can improve the life expectancy of the battery and reduce the operating cost of the standalone PV-battery microgrid.

Storage allocation scheme for virtual instances of cloud computing

Cloud computing delivers resources and services through virtual machines on a pay-as-you-go basis. The allocation of storage space to users is usually determined by means of open allocation mechanisms that cannot guarantee an efficient allocation. Current allocation mechanisms do not consider user requests when making provisioning decisions. In other words, they assume that the storage spaces are fixed. In this study, we propose an algorithm for allocating storage spaces based on the requests of users. We present a unified storage allocation scheme (USAS) for cloud computing. USAS is a dynamic storage allocation framework for unlimited, limited, and free users. Our proposed approach is based on a storage partitioning policy, and we have compared our proposed scheme with open storage scheme and fixed storage scheme with common partition. We show through simulation study that USAS dynamically allocates space for different user requirements for all traffic loads.

Recycling trash in cache

The disparity between processing and storage speeds can be bridged in part by reducing the traffic into and out of the slower memory components. Some recent studies reduce such traffic by determining dead data in cache, showing that a significant fraction of writes can be squashed before they make the trip toward slower memory. In this paper, we examine a technique for eliminating traffic in the other direction, specifically the traffic induced by dynamic storage allocation. We consider recycling dead storage in cache to satisfy a program's storage-allocation requests. We first evaluate the potential for recycling under favorable circumstances, where the associated logic can run at full speed with no impact on the cache's normal behavior. We then consider a more practical implementation, in which the associated logic executes independently from the cache's critical path. Here, the cache's performance is unfettered by recycling, but the operations necessary to determine dead storage and recycle such storage execute as time is available. Finally, we present the design and analysis of a hardware implementation that scales well with cache size without sacrificing too much performance.

Generalized dynamic storage allocation

Graph Theory Dynamic Storage Allocation is a problem concerned with storing items that each have weight and time restrictions. Approximate algorithms have been constructed through online coloring of interval graphs. We present a generalization that uses online coloring of tolerance graphs. We utilize online-with-representation algorithms on tolerance graphs, which are online algorithms in which the corresponding tolerance representation of a vertex is also presented. We find linear bounds for the online-with-representation chromatic number of various classes of tolerance graphs and apply these results to a generalization of Dynamic Storage Allocation, giving us a polynomial time approximation algorithm with linear performance ratio.

A performance analysis of channel fragmentation in dynamic spectrum access systems

Dynamic Spectrum Access systems offer temporarily available spectrum to opportunistic users capable of spreading transmissions over a number of non-contiguous subchannels. Such methods can be highly beneficial in terms of spectrum utilization, but excessive fragmentation degrades performance and hence off-sets the benefits. To get some insight into acceptable levels of fragmentation, we present experimental and analytical results derived from a mathematical model. According to the model, a system operates at capacity serving requests for bandwidth by assigning a collection of one or more gaps of unused bandwidth to each request as bandwidth becomes available. Our main result is a proof that, even if fragments can be arbitrarily small, the system remains stable in the sense that the average total number of fragments remains bounded. Within the class of dynamic fragmentation models, including models of dynamic storage allocation that have been around for many decades, this result appears to be the first of its kind. In addition, we provide extensive experimental results that describe behavior, at times unexpected, of fragmentation as parameter values are varied. Different scanning rules for searching gaps of available spectrum, all covered by the above stability result, are also studied. Our model applies to dynamic linked-list storage allocation, and provides a novel analysis in that domain. We prove that, interestingly, a version of the 50 % rule of the classical, non-fragmented allocation model holds for the new model as well. Overall, the paper provides insights into the behavior of practical fragmentation algorithms.

Single and multiple device DSA problems, complexities and online algorithms

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 ( 2 − ϵ ) -competitive and any clairvoyant algorithm cannot be ( 1.54 − ϵ ) -competitive. • The first O ( log L ) -competitive algorithm for general jobs on multi-device Balancing DSA problems without any assumption.

Max-coloring and online coloring with bandwidths on interval graphs

Given a graph G = ( V, E ) and positive integral vertex weights w : V → N , the max-coloring problem seeks to find a proper vertex coloring of G whose color classes C 1 , C 2 , …, C k , minimize ∑ i =1 k max v ∈ C i w ( v ). This problem, restricted to interval graphs, arises whenever there is a need to design dedicated memory managers that provide better performance than the general-purpose memory management of the operating system. Though this problem seems similar to the dynamic storage allocation problem, there are fundamental differences. We make a connection between max-coloring and online graph coloring and use this to devise a simple 2-approximation algorithm for max-coloring on interval graphs. We also show that a simple first-fit strategy, that is a natural choice for this problem, yields an 8-approximation algorithm. We show this result by proving that the first-fit algorithm for online coloring an interval graph G uses no more than 8 ċ χ( G ) colors, significantly improving the bound of 26 ċ χ( G ) by Kierstead and Qin [1995]. We also show that the max-coloring problem is NP-hard. The problem of online coloring of intervals with bandwidths is a simultaneous generalization of online interval coloring and online bin packing. The input is a set I of intervals, each interval i ∈ I having an associated bandwidth b ( i ) ∈ (0, 1]. We seek an online algorithm that produces a coloring of the intervals such that for any color c and any real r , the sum of the bandwidths of intervals containing r and colored c is at most 1. Motivated by resource allocation problems, Adamy and Erlebach [2003] consider this problem and present an algorithm that uses at most 195 times the number of colors used by an optimal offline algorithm. Using the new analysis of first-fit coloring of interval graphs, we show that the Adamy-Erlebach algorithm is 35-competitive. Finally, we generalize the Adamy-Erlebach algorithm to a class of algorithms and show that a different instance from this class is 30-competitive.

Storage allocation under processor sharing II: Further asymptotic results

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.

Channel fragmentation in dynamic spectrum access systems

Dynamic Spectrum Access systems exploit temporarily available spectrum ('white spaces') and can spread transmissions over a number of non-contiguous sub-channels. Such methods are highly beneficial in terms of spectrum utilization. However, excessive fragmentation degrades performance and hence off-sets the benefits. Thus, there is a need to study these processes so as to determine how to ensure acceptable levels of fragmentation. Hence, we present experimental and analytical results derived from a mathematical model. We model a system operating at capacity serving requests for bandwidth by assigning a collection of gaps (sub-channels) with no limitations on the fragment size. Our main theoretical result shows that even if fragments can be arbitrarily small, the system does not degrade with time. Namely, the average total number of fragments remains bounded. Within the very difficult class of dynamic fragmentation models (including models of storage fragmentation), this result appears to be the first of its kind. Extensive experimental results describe behavior, at times unexpected, of fragmentation under different algorithms. Our model also applies to dynamic linked-list storage allocation, and provides a novel analysis in that domain. We prove that, interestingly, the 50% rule of the classical (non-fragmented) allocation model carries over to our model. Overall, the paper provides insights into the potential behavior of practical fragmentation algorithms.

Simulating reachability using first-order logic with applications to verification of linked data structures

This paper shows how to harness existing theorem provers for first-order logic to automatically verify safety properties of imperative programs that perform dynamic storage allocation and destructive updating of pointer-valued structure fields. One of the main obstacles is specifying and proving the (absence) of reachability properties among dynamically allocated cells. The main technical contributions are methods for simulating reachability in a conservative way using first-order formulas--the formulas describe a superset of the set of program states that would be specified if one had a precise way to express reachability. These methods are employed for semi-automatic program verification (i.e., using programmer-supplied loop invariants) on programs such as mark-and-sweep garbage collection and destructive reversal of a singly linked list. (The mark-and-sweep example has been previously reported as being beyond the capabilities of ESC/Java.)

A constant-time dynamic storage allocator for real-time systems

Dynamic memory allocation has been used for decades. However, it has seldom been used in real-time systems since the worst case of spatial and temporal requirements for allocation and deallocation operations is either unbounded or bounded but with a very large bound. In this paper, a new allocator called TLSF (Two Level Segregated Fit) is presented. TLSF is designed and implemented to accommodate real-time constraints. The proposed allocator exhibits time-bounded behaviour, O(1), and maintains a very good execution time. This paper describes in detail the data structures and functions provided by TLSF. We also compare TLSF with a representative set of allocators regarding their temporal cost and fragmentation. Although the paper is mainly focused on timing analysis, a brief study and comparative analysis of fragmentation incurred by the allocators has been also included in order to provide a global view of the behaviour of the allocators. The temporal and spatial results showed that TLSF is also a fast allocator and produces a fragmentation close to that caused by the best existing allocators.

Implementation of a constant‐time dynamic storage allocator

AbstractThis paper describes the design criteria and implementation details of a dynamic storage allocator for real‐time systems. The main requirements that have to be considered when designing a new allocator are concerned with temporal and spatial constraints. The proposed algorithm, called TLSF (two‐level segregated fit), has an asymptotic constant cost, O(1), maintaining a fast response time (less than 200 processor instructions on a x86 processor) and a low level of memory usage (low fragmentation). TLSF uses two levels of segregated lists to arrange free memory blocks and an incomplete search policy. This policy is implemented with word‐size bitmaps and logical processor instructions. Therefore, TLSF can be categorized as a good‐fit allocator. The incomplete search policy is shown also to be a good policy in terms of fragmentation. The fragmentation caused by TLSF is slightly smaller (better) than that caused by best fit (which is one of the best allocators regarding memory fragmentation). In order to evaluate the proposed allocator, three analyses are presented in this paper. The first one is based on worst‐case scenarios. The second one provides a detailed consideration of the execution cost of the internal operations of the allocator and its fragmentation. The third analysis is a comparison with other well‐known allocators from the temporal (number of cycles and processor instructions) and spatial (fragmentation) points of view. In order to compare them, a task model has been presented. Copyright © 2007 John Wiley & Sons, Ltd.