Storage Budgets Research Articles

Storage Fit Learning with Feature Evolvable Streams

Feature evolvable learning has been widely studied in recent years where old features will vanish and new features will emerge when learning with streams. Conventional methods usually assume that a label will be revealed after prediction at each time step. However, in practice, this assumption may not hold whereas no label will be given at most time steps. A good solution is to leverage the technique of manifold regularization to utilize the previous similar data to assist the refinement of the online model. Nevertheless, this approach needs to store all previous data which is impossible in learning with streams that arrive sequentially in large volume. Thus we need a buffer to store part of them. Considering that different devices may have different storage budgets, the learning approaches should be flexible subject to the storage budget limit. In this paper, we propose a new setting: Storage-Fit Feature-Evolvable streaming Learning (SF2EL) which incorporates the issue of rarely-provided labels into feature evolution. Our framework is able to fit its behavior for different storage budgets when learning with feature evolvable streams with unlabeled data. Besides, both theoretical and empirical results validate that our approach can preserve the merit of the original feature evolvable learning i.e., can always track the best baseline and thus perform well at any time step.

Maximum Profit of Real-Time IoT Content Retrieval by Joint Content Placement and Storage Allocation in C-RANs

In Cloud Radio Access Networks (C-RANs), the performance of real-time Internet of Things (IoT) content retrieval is improved by placing/requesting the contents into/from remote radio heads (RRHs) and baseband units (BBUs). In the previous studies for the problem in jointly placing user contents and allocating storage allocation of RRHs and BBUs, a strategy of minimizing system resource consumption or transmission delay was used. In this article, we adopt a distinct strategy to maximize the profit of content retrieval services under the constraints of meeting the real-time requirements of users and the limited system resources in C-RANs. The problem is formulated as integer linear programming (ILP). Then, an algorithm for solving the ILP is proposed, and it can provide an approximate solution close to the optimal one with a bounded factor. In the simulation conducted, the results verified the above claims. Further, another algorithm is proposed to effectively expand the storage budgets in C-RANs. By controlling a bounded factor, not only the system performance is guaranteed, but also the upper limit of the expanded storage budget is provided. The smaller the bounding factor, the stricter the performance guarantee, but the expected storage budget will increase.

Joint Content Placement and Storage Allocation in C-RANs for IoT Sensing Service

The Internet of Things (IoT) sensing service allows systems and users to monitor environment states by transmitting the content sensed by a variety of sensors. Owing to billions of sensors and devices deployed in the IoT system, a huge amount of data (big data) are generated, thus injecting tremendous traffic into the network. Cloud radio access network (C-RAN) is a promising wireless network architecture to accommodate the fast growing IoT traffic and improve the performance of IoT services. Caching in C-RAN, which brings content to the edges, not only alleviates the network traffic, thus improving the end-to-end user quality of service (QoS), but also avoids activating the sensors too frequently, thus reducing their energy consumption. The content placement problem determines what and where to cache in C-RAN. However, the caching performance is highly related to the caching storages. The storage allocation problem determines the storage capacities of network entities. In this paper, we jointly optimize the storage allocation problem and content placement problem in a hierarchical cache-enabled C-RAN architecture for IoT sensing service. We formulate the joint problem as an integer linear programming (ILP) model with the objective to minimize the total network traffic cost. The storage allocation problem and content placement problem are constrained by caching storage budgets and cache capacities, respectively. Two heuristic algorithms are proposed in order to reduce the computational complexity of ILP. Extensive simulations have been conducted to demonstrate that the performances of our proposed algorithms approximate the optimal solutions.

Mosaic surface storages of a small boreal catchment

AbstractRecent studies have suggested that the hydrologic connectivity of northern headwater catchments is likely controlled by antecedent moisture conditions and land cover patterns. A water storage model (EWS), based on water levels (WLs), specific yield (Sy) and surface elevation (SE) changes, was compared with a basic water budget of a small, boreal, patterned fen (13 ha) during the ice‐free period. Results showed that the EWS model reproduced well storage variations derived from the water budget. These results suggest that storage variations can be properly represented by the fluctuations of WLs when we consider the heterogeneous soil properties. However, storage deviations occurred at the daily scale and could be explained by a lack of information on water retention in unsaturated layers, canopy interceptions and preferential flows. Despite the significant impact of SE changes on the different peatland cover storage budgets (strings and lawns), using Sy mean values had a low impact on storage estimations. This can be explained by the large proportion of pools and high WLs throughout the fen. At the fen scale, high storage in the pools seemed to reduce the Sy difference between strings and lawns. The results of this study provide new insights about the complex hydrological behaviour of northern catchments and allow for conceiving new hydrological modelling perspectives. Copyright © 2014 John Wiley & Sons, Ltd.

Streaming Solutions for Fine-Grained Network Traffic Measurements and Analysis

Online network traffic measurements and analysis is critical for detecting and preventing any real-time anomalies in the network. We propose, implement, and evaluate an online, adaptive measurement platform, which utilizes real-time traffic analysis results to refine subsequent traffic measurements. Central to our solution is the concept of Multi-Resolution Tiling (MRT), a heuristic approach that performs sequential analysis of traffic data to zoom into traffic subregions of interest. However, MRT is sensitive to transient traffic spikes. In this paper, we propose three novel traffic streaming algorithms that overcome the limitations of MRT and can cater to varying degrees of computational and storage budgets, detection latency, and accuracy of query response. We evaluate our streaming algorithms on a highly parallel and programmable hardware as well as a traditional software-based platforms. The algorithms demonstrate significant accuracy improvement over MRT in detecting anomalies consisting of synthetic hard-to-track elephant flows and global icebergs. Our proposed algorithms maintain the worst-case complexities of the MRT while incurring only a moderate increase in average resource utilization.

Historical storage budgets of organic carbon, nutrient and contaminant elements in saltmarsh sediments: Biogeochemical context for managed realignment, Humber Estuary, UK

Biogeochemical data from Welwick marsh (Humber Estuary, UK), an actively accreting saltmarsh, provides a decadal–centennial-scale natural analogue for likely future biogeochemical storage effects of managed realignment sites accreting either intertidal muds or saltmarsh. Marsh topographic profiles and progradation history from aerial photographs were combined with 137Cs and niobium contamination history to establish and verify chronology and sediment mass accumulation. These data, combined with down-core measurements of particulate organic carbon (C org), organic nitrogen (N org), particle reactive phosphorus and selected contaminant metal (Zn, Pb, Cu, As and Nb) contents were then used to calculate sediment and chemical storage terms and to quantify changes in these over time. These data are used to help predict likely future biogeochemical storage changes at managed realignment sites in the estuary. The net effect of returning some 26 km 2 of reclaimed land to intertidal environments now (about 25% of the maximum possible realignment storage identified for the estuary) could result in the storage of some 40,000 tonnes a − 1 of sediment which would also bury about 800 tonnes a − 1 of C org and 40 tonnes a − 1 of N org. Particulate contaminant P burial would be around 25 tonnes a − 1 along with ~ 6 tonnes a − 1 contaminant Zn, 3 tonnes a − 1 contaminant Pb, and ~ 1 tonnes a − 1 contaminant As and Cu. The study also shows that reclamation activities in the outer estuary since the mid-1700s has prevented, in total, the deposition of about 10 million tonnes of sediment, along with 320,000 tonnes of C org and 16,000 tonnes of N org. The study provides a mid-1990s baseline against which future measurements at the site can determine changes in burial fluxes and improvement or deterioration in contaminant metal contents of the sediments. The data are directly relevant for local managed realignment sites but also broadly indicative for sites generally on the European North Sea Coast.

Charcoal and carbon storage in forest soils of the Rocky Mountain West

Charcoal represents a super-passive form of carbon (C) that is generated during fire events and is one of the few legacies of fire recorded in the soil profile; however, the importance of this material as a form of C storage has received only limited scientific attention. Here, we review the formation of charcoal in temperate and boreal forest ecosystems, discuss some of its desirable properties, and estimate the potential contribution charcoal to long-term C sequestration in forest ecosystems. Charcoal deposition over the course of several mil lennia probably accounts for a substantial proportion of the total soil C pool in fire-maintained forest ecosystems. Forest management processes that interfere with natural fire processes eliminate the formation of this passive form of C. We recommend that charcoal be considered in C storage budgets and modeling of forest ecosystems, especially in light of climate change and increasing occurrence of wildfire.