Channel Fragmentation in Dynamic Spectrum Access Systems - a Theoretical Study

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

Dynamic water allocation policies improve the global efficiency of storage systems

Water impoundment by dams strongly affects the river natural flow regime, its attributes and the related ecosystem biodiversity. Fostering the sustainability of water uses e.g., hydropower systems thus implies searching for innovative operational policies able to generate Dynamic Environmental Flows (DEF) that mimic natural flow variability. The objective of this study is to propose a Direct Policy Search (DPS) framework based on defining dynamic flow release rules to improve the global efficiency of storage systems. The water allocation policies proposed for dammed systems are an extension of previously developed flow redistribution rules for small hydropower plants by Razurel et al. (2016). The mathematical form of the Fermi-Dirac statistical distribution applied to lake equations for the stored water in the dam is used to formulate non-proportional redistribution rules that partition the flow for energy production and environmental use. While energy production is computed from technical data, riverine ecological benefits associated with DEF are computed by integrating the Weighted Usable Area (WUA) for fishes with Richter's hydrological indicators. Then, multiobjective evolutionary algorithms (MOEAs) are applied to build ecological versus economic efficiency plot and locate its (Pareto) frontier. This study benchmarks two MOEAs (NSGA II and Borg MOEA) and compares their efficiency in terms of the quality of Pareto's frontier and computational cost. A detailed analysis of dam characteristics is performed to examine their impact on the global system efficiency and choice of the best redistribution rule. Finally, it is found that non-proportional flow releases can statistically improve the global efficiency, specifically the ecological one, of the hydropower system when compared to constant minimal flows. (C) 2017 Elsevier Ltd. All rights reserved

StoRM: A Manager for Storage Resource in Grid

Nowadays, data intensive applications demand high-performance and large-storage systems capable of serving up to various Petabytes of storage space. Therefore, common solutions adopted in data centres include Storage Area Networks (SAN) and cluster parallel file systems, such as GPFS from IBM and Lustre from Sun Microsystems. In order to make these storage system solutions available in modern Data Grid architectures, standard interfaces are needed. The Grid Storage Resource Manager (SRM) interface is one of these standard interfaces. Grid storage services implementing the SRM standard provide common capabilities and advanced functionality such as dynamic space allocation and file management on shared storage systems. In this paper, we describe StoRM (STOrage Resource Manager). StoRM is a flexible and high-performing implementation of the standard SRM interface version 2.2. The software architecture of StoRM allows for an easy integration to different underlying storage systems via a plug-in mechanism. In particular, StoRM takes advantage from storage systems based on cluster file systems. Currently, StoRM is installed and used in production in various data centres, including the WLCG Italian Tier-1. In addition, Economics and Financial communities, as represented by the EGRID Project, adopt StoRM in production as well

Power-Aware Memory Allocation for Embedded Data-Intensive Signal Processing Applications

Many signal processing systems, particularly in the multimedia and telecommunication domains, are synthesized to execute data-intensive applications: their cost related aspects ­ namely power consumption and chip area ­ are heavily influenced, if not dominated, by the data access and storage aspects. This chapter presents a power-aware memory allocation methodology. Starting from the high-level behavioral specification of a given application, this framework performs the assignment of of the multidimensional signals to the memory layers ­ the on-chip scratch-pad memory and the off-chip main memory ­ the goal being the reduction of the dynamic energy consumption in the memory subsystem. Based on the assignment results, the framework subsequently performs the mapping of signals into the memory layers such that the overall amount of data storage be reduced. This software system yields a complete allocation solution: the exact storage amount on each memory layer, the mapping functions that determine the exact locations for any array element (scalar signal) in the specification, and, in addition, an estimation of the dynamic energy consumption in the memory subsystem

PRADA: Predictable Allocations by Deferred Actions

Modern hard real-time systems still employ static memory management. However, dynamic storage allocation (DSA) can improve the flexibility and readability of programs as well as drastically shorten their development times. But allocators introduce unpredictability that makes deriving tight bounds on an application\u27s worst-case execution time even more challenging. Especially their statically unpredictable influence on the cache, paired with zero knowledge about the cache set mapping of dynamically allocated objects leads to prohibitively large overestimations of execution times when dynamic memory allocation is employed. Recently, a cache-aware memory allocator, called CAMA, was proposed that gives strong guarantees about its cache influence and the cache set mapping of allocated objects. CAMA itself is rather complex due to its cache-aware implementations of split and merge operations. This paper proposes PRADA, a lighter but less general dynamic memory allocator with equally strong guarantees about its influence on the cache. We compare the memory consumption of PRADA and CAMA for a small set of real-time applications as well as synthetical (de-) allocation sequences to investigate whether a simpler approach to cache awareness is still sufficient for the current generation of real-time applications

Garbage collection can be made real-time and verifiable

An efficient means of memory reclamation (also known as Garbage Collection) is essential for Machine Intelligence applications where dynamic storage allocation is desired or required. Solutions for real-time systems must introduce very small processing overhead and must also provide for the verification of the software in order to meet the application time budgets and to verify the correctness of the software. Garbage Collection (GC) techniques are proposed for symbolic processing systems which may simultaneously meet both real-time requirements and verification requirements. The proposed memory reclamation technique takes advantage of the strong points of both the earlier Mark and Sweep technique and the more recent Copy Collection approaches. At least one practical implementation of these new GC techniques has already been developed and tested on a very-high performance symbolic computing system. Complete GC processing of all generated garbage has been demonstrated to require as little as a few milliseconds to perform. This speed enables the effective operation of the GC function as either a background task or as an actual part of the application task itself