Resource Requests Research Articles

Crosstalk between vrille transcripts, proteins, and regulatory elements controlling circadian rhythms and development in Drosophila.

SummaryThe vrille (vri) gene encodes a transcriptional repressor required for Drosophila development as well as circadian behavior in adults. Alternate first exons produce vri transcripts predicted to produce a short VRI isoform during development and long VRI in adults. A vri mutant (vriΔ679) lacking long VRI transcripts is viable, confirming that short VRI is sufficient for developmental functions, yet behavioral rhythms in vriΔ679 flies persist, showing that short VRI is sufficient for clock output. E-box regulatory elements that drive rhythmic long VRI transcript expression are required for developmental expression of short VRI transcripts. Surprisingly, long VRI transcripts primarily produce short VRI in adults, apparently due to a poor Kozak sequence context, demonstrating that short VRI drives circadian behavior. Thus, E-box-driven long VRI transcripts primarily control circadian rhythms via short VRI, whereas the same E-boxes drive short VRI transcripts that control developmental functions using short VRI.

Let’s Share: A Game-Theoretic Framework for Resource Sharing in Mobile Edge Clouds

Mobile edge computing seeks to provide resources to different delay-sensitive applications. This is a challenging problem as an edge cloud-service provider may not have sufficient resources to satisfy all resource requests. Furthermore, allocating available resources optimally to different applications is also challenging. Resource sharing among different edge cloud-service providers can address the aforementioned limitation as certain service providers may have resources available that can be “rented” by other service providers. However, edge cloud service providers can have different objectives or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">utilities</i> . Therefore, there is a need for an efficient and effective mechanism to share resources among service providers, while considering the different objectives of various providers. We model resource sharing as a multi-objective optimization problem and present a solution framework based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cooperative Game Theory</i> (CGT). We consider the strategy where each service provider allocates resources to its native applications first and shares the remaining resources with applications from other service providers. We prove that for a monotonic, non-decreasing utility function, the game is canonical and convex. Hence, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">core</i> is not empty and the grand coalition is stable. We propose two algorithms, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Game-theoretic Pareto optimal allocation</i> (GPOA) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Polyandrous-Polygamous Matching based Pareto Optimal Allocation</i> (PPMPOA) that provide allocations from the core. Hence the obtained allocations are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pareto</i> optimal and the grand coalition of all the service providers is stable. Experimental results confirm that our proposed resource sharing framework improves utilities of edge cloud-service providers and application request satisfaction.

HeteroYARN: A Heterogeneous FPGA-Accelerated Architecture Based on YARN

In recent years, the heterogeneous distributed platform integrating with FPGAs to accelerate computation tasks has been widely studied to deal with the deluge of data. However, most of current works suffer from poor universality and low resource utilization that run specific algorithms with the highly customized structure. Moreover, there are still many challenges, such as data curation, task scheduling, and resource management, which further limit the scalability of a CPU-FPGA distributed platform. In this paper, we present HeteroYARN, an FPGA-accelerated heterogeneous architecture based on YARN platform, which provides resource management and programming support for computing-intensive applications using FPGAs. In particular, the HeteroYARN abstracts FPGA accelerators as general resources and provides programming APIs to utilize those accelerators easily. Our HeteroYARN simplifies the request and usage of FPGA resources to enhance the efficiency of the heterogeneous framework while maintaining previous workflow unchanged. Experimental results using two representative algorithms, K-means and Naive Bayes classifier, which are accelerated by FPGAs, demonstrate the usability of the HeteroYARN framework and show performance speedup improvement by 7.5x (K-means) and 2.3x (Naive Bayes) respectively compared to conventional CPU-only applications provided by Mahout.

A Virtualized Target Queuing Balanced Model Using Vmax/M/G-Load(Min)/Cache-P2λM for Reducing Queuing Time Expectation of Customer Delay Tolerance

Queuing theory is a formal mathematical model of late response arrival of customer waiting in various access services, accepting processes management as a whole field. Often multiple server sorting can be used to estimate the number of servers and service rates be high due too lacking of time, with an average delay sequence in the analysis. Using these sequence models to read more than one sequence during execution approximates system sorting performance. To resolve this problem we modulated the distributed process into Virtualized Target Queuing Balanced Model (VTQBM) using Vmax/M/G-Load (Min)/Cache-P2λM for reducing queuing time expectation of customer delay tolerance. The Virtual Resource queuing System is optimized with fork-join queue by creating single to multimodal service performance using target request load balancer in a shared distribution mode. Sorting the request buffer cache is created based on Customer Request Arrival Rate (CRAR) and resource output preferred to check with target rule waiting in the Load balancer. To improve the use of model complex virtual resource sharing shared with customer arrival to Cache Target Load Allocator (CTLA) on queuing distributed resources, This fork joiner target the service performance to reduce the customer waiting time in queuing and produce outputs can achieve a different parameter input transformation on queuing theory.

Imaging Acute Metabolic Changes in Patients with Mild Traumatic Brain Injury Using Hyperpolarized [1-13C]Pyruvate.

SummaryTraumatic brain injury (TBI) involves complex secondary injury processes following the primary injury. The secondary injury is often associated with rapid metabolic shifts and impaired brain function immediately after the initial tissue damage. Magnetic resonance spectroscopic imaging (MRSI) coupled with hyperpolarization of 13C-labeled substrates provides a unique opportunity to map the metabolic changes in the brain after traumatic injury in real-time without invasive procedures. In this report, we investigated two patients with acute mild TBI (Glasgow coma scale 15) but no anatomical brain injury or hemorrhage. Patients were imaged with hyperpolarized [1-13C]pyruvate MRSI 1 or 6 days after head trauma. Both patients showed significantly reduced bicarbonate (HCO3–) production, and one showed hyperintense lactate production at the injured sites. This study reports the feasibility of imaging altered metabolism using hyperpolarized pyruvate in patients with TBI, demonstrating the translatability and sensitivity of the technology to cerebral metabolic changes after mild TBI.

Fshr: a fish sex-determining locus shows variable incomplete penetrance across flathead grey mullet populations.

SummaryWhole-genome sequencing data were produced from a single flathead grey mullet female and assembled into a draft genome sequence, whereas publicly available sequence data were used to obtain a male draft sequence. Two pools, each consisting of 60 unrelated individuals, respectively, of male and female fish were analyzed using Pool-Sequencing. Mapping and analysis of Pool-Seq data against the draft genome(s) revealed >30 loci potentially associated with sex, the most promising locus of which, encoding the follicle-stimulating hormone receptor (fshr) and harboring two missense variants, was genotyped on 245 fish from four Mediterranean populations. Genotype data showed that fshr represents a previously unknown sex-determining locus, although the incomplete association pattern between fshr genotype and sex-phenotype, the variability of such pattern across different populations, and the presence of other candidate loci reveal that a greater complexity underlies sex determination in the flathead grey mullet.

Data Object Exchange (DOEx) as a Method to Facilitate Intraorganizational Collaboration by Managed Data Sharing: Viewpoint.

BackgroundTo help reduce expenses, shorten timelines, and improve the quality of final deliverables, the Veterans Health Administration (VA) and other health care systems promote sharing of expertise among informatics user groups. Traditional barriers to time-efficient sharing of expertise include difficulties in finding potential collaborators and availability of a mechanism to share expertise.ObjectiveWe aim to describe how the VA shares expertise among its informatics groups by describing a custom-built tool, the Data Object Exchange (DOEx), along with statistics on its usage.MethodsA centrally managed web application was developed in the VA to share informatics expertise using database objects. Visitors to the site can view a catalog of objects published by other informatics user groups. Requests for subscription and publication made through the site are routed to database administrators, who then actualize the resource requests through modifications of database object permissions.ResultsAs of April 2019, the DOEx enabled the publication of 707 database objects to 1202 VA subscribers from 758 workgroups. Overall, over 10,000 requests are made each year regarding permissions on these shared database objects, involving diverse information. Common “flavors” of shared data include disease-specific study populations (eg, patients with asthma), common data definitions (eg, hemoglobin laboratory results), and results of complex analyses (eg, models of anticipated resource utilization). Shared database objects also enable construction of community-built data pipelines.ConclusionsTo increase the efficiency of informatics user groups, a method was developed to facilitate intraorganizational collaboration by managed data sharing. The advantages of this system include (1) reduced duplication of work (thereby reducing expenses and shortening timelines) and (2) higher quality of work based on simplifying the adoption of specialized knowledge among groups.

Can You Hear Me Now? Audio and Visual Interactions That Change App Choices.

Android and iOS mobile operating systems use permissions to enable phone owners to manage access to their device's resources. Both systems provide resource access dialogues at first use and per-resource controls. Android continues to offer permission manifests in the Android PlayStore for older apps but is transitioning away from this. Neither manifests nor first-use dialogues enable people to easily compare apps based on resource requests, and the corresponding privacy and security risks. Without the ability to compare resource requests when choosing an app, customers cannot select those apps that request fewer resources. Unnecessary and excessive permission requests, overuse of resources, information exfiltration, and risky apps are endemic. To address this issue we built upon past work in warning science and risk communication to design multimedia indicators to communicate the aggregate privacy and security risk associated with an app. Specifically, we provided participants with a privacy rating using the familiar padlock icon and used audio notifications to either warn or reinforce user choices. We empirically tested participants' app decisions with these padlock icons and audio notifications. The results showed that people with both visual cues and audio feedback are more likely to make app choices that are inversely correlated with the resources requested by the app. Those with neither indicators made decisions reflecting only app rating, while decisions made by those with either the audio or the visual indicators are sometimes inversely correlated with resource requests. This illustrates that simple clear communication about apps' aggregate risk, as opposed to atomic resource requests, changes participants' app selections potentially mitigating the state of information overuse and potential abuse. Additionally, neither the visual indicator nor the audio feedback affected the time required for participants to make a decision.

Pyruvate Kinase M2 Coordinates Metabolism Switch between Glycolysis and Glutaminolysis in Cancer Cells.

SummaryCancer cells alter their nutrition metabolism to cope the stressful environment. One important metabolism adjustment is that cancer cells activate glutaminolysis in response to the reduced carbon from glucose entering into the TCA cycle due to inactivation of several enzymes in glycolysis. An important question is how the cancer cells coordinate the changes of glycolysis and glutaminolysis. In this report, we demonstrate that the pyruvate kinase inactive dimer PKM2 facilitates activation of glutaminolysis. Our experiments show that growth stimulations promote PKM2 dimer. The dimer PKM2 plays a role in regulation of glutaminolysis by upregulation of mitochondrial glutaminase I (GLS-1). PKM2 dimer regulates the GLS-1 expression by controlling internal ribosome entry site (IRES)-dependent c-myc translation. Growth stimulations promote PKM2 interacting with c-myc IRES-RNA, thus facilitating c-myc IRES-dependent translation. Our study reveals an important linker that coordinates the metabolism adjustment in cancer cells.

High-severity wildfire potential – associating meteorology, climate, resource demand and wildfire activity with preparedness levels

National and regional preparedness level (PL) designations support decisions about wildfire risk management. Such decisions occur across the fire season and influence pre-positioning of resources in areas of greatest fire potential, recall of personnel from off-duty status, requests for back-up resources from other areas, responses to requests to share resources with other regions during fire events, and decisions about fuel treatment and risk reduction, such as prescribed burning. In this paper, we assess the association between PLs assigned at national and regional (Northwest) scales and a set of predictors including meteorological and climate variables, wildfire activity and the mobilisation and allocation levels of fire suppression resources. To better understand the implicit weighting applied to these factors in setting PLs, we discern the qualitative and quantitative factors associated with PL designations by statistical analysis of the historical record of PLs across a range of conditions. Our analysis constitutes an important step towards efforts to forecast PLs and to support the future projection and anticipation of firefighting resource demand, thereby aiding wildfire risk management, planning and preparedness.

Finding K Shortest Paths in a Network Using Genetic Algorithm

With the advent of new applications, different service needs come up. These needs could be in the form of reliability in delivering data, capacity amount in a particular range and certain amount of permissible delay. In order to provide high Quality of service to Networks, it is essential to provide a path between a given source and multiple destinations which satisfy certain constraints. For a domain catering to high QoS, there is a request of resources with certain constraints by all the applications. Speed and Scalability which are not flexible in terms of Network size and Topology are the basic issues to be considered here. Multimedia applications in general make use of k shortest paths whenever communication is to be carried out between a single source and one or more than one destination. In this paper, a genetic algorithm is used, which helps in determination of k shortest paths from a source node to more than one destination node, with bandwidth constraint. The algorithm makes use of the connection matrix as well as link bandwidth for determination of k shortest paths. The significance of using K shortest paths in a network is to increase Throughput and Packet delivery ratio.

Lifetime-Priority-Driven Resource Allocation for WNV-Based Internet of Things

Resources allocation efficiently of wireless network virtualization (WNV) in Internet of Things (IoT) has become a key challenge owing to the conflict between the limited capacity of physical resources and the massive virtual resources requests. Focusing on time-frequency resource allocation in WNV-based IoT, this article proposes a dynamic resource allocation algorithm based on the lifetime priority (LP-DRA) of virtual network requests (VNRs). First, LP-DRA investigates the continuity of physical time-frequency resource blocks by the Karnaugh map approach, then calculates the reallocation impact factor (RIF) of existing virtual networks (VNs), and reallocates the time-frequency resources to the VN with the largest RIF. Finally, the resources are preferentially allocated to the VNs with shorter lifetimes. The performance of LP-DRA is compared with the static resource allocation algorithm and the greedy dynamic resource allocation algorithm. The simulation results show that LP-DRA can utilize physical resources more effectively, improve the acceptance rate of VNRs, and increase the revenue of the physical network.

Functions of MnOx in NaCl Aqueous Solution for Artificial Photosynthesis.

SummaryPhotoelectrochemical water splitting has been intensively investigated as artificial photosynthesis technology to convert solar energy into chemical energy. The use of seawater and salted water has advantages for minimum environmental burden; however, the oxidation of Cl− ion to hypochlorous acid (HClO), which has toxicity and heavy corrosiveness, should occur at the anode, along with the oxygen evolution. Here, O2 and HClO production in aqueous solution containing Cl− on photoanodes modified with various metal oxides was investigated. The modification of MnOx resulted in the promotion of the O2 evolution reaction (OER) specifically without HClO production over a wide range of conditions. The results will contribute not only to the practical application of artificial photosynthesis using salted water but also to the elucidation of substantial function of manganese as the element for OER center in natural photosynthesis.

Cognitive Balance for Fog Computing Resource in Internet of Things: An Edge Learning Approach

Currently, the highly dynamic fog computing resource requirements introduced by the diverse services of the Internet of Things (IoT) result in an imbalance between computing resource providers and consumers. However, current computing resource scheduling schemes cannot cognize the dynamic resources available and do not possess decision-making or management capabilities, which leads to inefficient use of computing resources and a decreased quality of service (QoS). Balancing computing resources cognitively at the IoT edge remains unresolved. In this paper, a cognition-centric fog computing resource balancing (CFCRB) scheme is proposed for edge intelligence-enabled IoT. First, we propose a cognitive balance architecture with a cognition plane, which includes service demand monitoring, policy processing and knowledge storage of cognitive fog resources. Second, we propose the fog functions structure with sensing, interaction and learning functionalities, realizing the knowledge-based proactive discovery and dynamic orchestration of resource sharing nodes. Finally, a distributed edge learning algorithm is proposed to construct knowledge of the balance between computing resource helpers and requesters in cognitive fogs, which is further proved with mathematics. The simulation results indicate the efficiency of the proposed scheme.

You Calculate and I Provision: A DRL-Assisted Service Framework to Realize Distributed and Tenant-Driven Virtual Network Slicing

This article studies the problem of virtual network (VNT) slicing in datacenter interconnections (DCIs), and proposes a novel service framework to better balance the tradeoff between cost-effectiveness, and time-efficiency. Our idea is to partition a DCI into non-overlapped subgraphs, divide the VNT slicing in each subgraph into four collaborative steps, and get tenants involved in the calculation of virtual network embedding (VNE) schemes. With our proposal, an agent of infrastructure provider (InP) leverages deep reinforcement learning (DRL) to price, and advertise the substrate resources in one subgraph, motivates tenants to request resources in a load-balanced manner, and accepts VNE schemes from the tenants to avoid resource conflicts. Meanwhile, the tenants' task is to compute their own VNE schemes independently, and distributedly according to the resource information (i.e., the available resources, and their prices) advertised by the agent. We first design the DRL model based on the deep deterministic policy gradient (DDPG), and develop a VNT compression method based on auto-encoder (AE) to generalize the DRL's operation. Then, we study how to resolve resource conflicts among the distributedly-calculated VNE schemes, build a conflict graph (CG) to transform the VNE selection into finding the maximum weighted independent set (MWIS) in the CG, and design a polynomial-time approximation algorithm to solve the problem. Extensive simulations confirm that compared with the centralized service framework relying solely on the InP for VNE calculation, our proposed DRL-assisted distributed framework provisions VNT requests with significantly shorter computation time, and comparable blocking performance.

Computational Study on Strategyproofness of Resource Matching in Crowdsourced Manufacturing

The need for a sustainable society has grown rapidly. This trend requires new production system concepts following an era of mass customization. As one of these new concepts, “crowdsourced manufacturing” has attracted noticeable attention. In such systems, each participant shares their manufacturing resources for ecosystem co-prosperity, providing new value for the next society. To realize such a concept, it is important to (1) match resource requests and resource offers so as to achieve high efficiency, and (2) induce participants to act in a fair way. Previously, some studies showed production efficiency improvements. Nevertheless, relatively few studies have been conducted on induction mechanisms. The purpose of this study is to develop induction mechanisms for participants. Concerning induction mechanisms, we focus on two viewpoints: (a) matching stability, and (b) “strategyproofness.” These viewpoints are well-known concepts in the market design research field. We previously proposed a resource matching stability analysis method and mechanism for inducing participants to accept matching plans. Formally, a matching method is “strategyproof” when it is a dominant strategy for all participants to submit their true information. However, it is hard to satisfy this condition. Practically, it would be useful to evaluate the strength of an induction, even if the matching method is not strategyproof. In this study, we propose indices for showing the strength of induction (“strength of strategyproofness”). Subsequently, we evaluate matching methods, and show that participants will state false information to maximize their profit in a system with resource matching methods for the profit maximization of the entire system. As the resource providers, they can obtain greater profit by submitting false information regarding resource usage fees. Then, the profits of the resource requesters are unfairly impaired. Furthermore, we propose a new resource matching method, inspired from the “nucleolus” concept in cooperative game theory. The proposed method reduces the maximum dissatisfaction (i.e., profit loss) of resource requesters and resource providers, based on profit sharing. The computational results show that the proposed method induces participants to submit true information, while maintaining high production efficiency.

Open to offers, but resisting requests: How the framing of anchors affects motivation and negotiated outcomes.

Abundant research has established that first proposals can anchor negotiations and lead to a first-mover advantage. The current research developed and tested a motivated anchor adjustment hypothesis that integrates the literatures on framing and anchoring and highlights how anchoring in negotiations differs in significant ways from standard decision-making contexts. Our research begins with the premise that first proposals can be framed as either an offer of resources (e.g., I am offering my A for your B) that highlights gains versus a request for resources (e.g., I am requesting your B for my A) that highlights losses to a responder. We propose that this framing would affect the concession aversion of responders and ultimately the negotiated outcomes. We predicted that when a first proposal is framed as an offer, the well-documented anchoring and first-mover advantage effect would emerge because offers do not create high levels of concession aversion. In contrast, because requests highlight what the responder has to give up, we predicted that opening requests would produce concession aversion and eliminate and even reverse the first-mover advantage. Across 5 experiments, the classic first-mover advantage in negotiations was moderated by the framing of proposals because anchor framing affected concession aversion. The studies highlight how motivational forces (i.e., concession aversion) play an important role in producing anchoring effects, which has been predominantly viewed through a purely cognitive lens. Overall, the findings highlight when and how motivational processes play a key role in both judgmental heuristics and mixed-motive decision-making. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Hierarchical Combinatorial Auction in Computing Resource Allocation for Mobile Blockchain

The mobile blockchain has been recognized as an emerging solution to address the security and privacy issues in a mobile application system. The mining process in mobile blockchain requires high computing resources which could overwhelm that which mobile devices can offer. In this case, mobile edge computing servers (MESs) can be involved to offer computing services to miners in mobile blockchain. Note that the resources of MESs are also limited; MESs could further request resources from the cloud computing server (CCS). Accordingly, the issue of hierarchical computing resource allocation arises. In this paper, we first consider a simple case with single-seller multiple buyers and a hierarchical single-seller multibuyer combinatorial auction model is proposed to solve this problem, based on which efficient and truthful frameworks are provided. We then extend the model to consider multiple CCSPs and propose a hierarchical multiple-seller multiple-buyer combinatorial auction model. For both models, the winner determination problems are formulated and computationally tractable algorithms are proposed. Also, pricing schemes are proposed to ensure the property of incentive compatibility and individual rationality. Finally, we evaluate the proposed schemes via simulations.

A request allocation model for processing data in federated cloud computing

PurposeAs the storage and processing requirement of digital information is increasing on the cloud, it is very difficult for the single cloud provider (CP) to meet the resource requirement. Multiple providers form a federation for the execution of users’ requests. For the federated cloud, this paper aims to address the issue distribution of users’ request for resources and revenue among the providers by offering fair and stable distribution models for the federated cloud.Design/methodology/approachThis paper uses cooperative game (CG)-theoretical models, i.e. Shapley–Shubik power index (SSPI) and Banzhaf power index (BPI) for distribution. Performance is analysed using variance and monotonicity using a case study.FindingsNumerical analysis is done using two scenarios. Monotonicity is evaluated. Results show that SSPI performs better as compared to BPI in terms of fairness accuracy and the framework provide the fair distribution of revenue among providers in the federated cloud.Research limitations/implicationsThe proposed framework works efficiently under the specific defined conditions.Social implicationsPaper provides the fair distribution. It assist the centralised cloud exchange in managing the users’ request in such a way every CPs, in the federated cloud will get an equal chance of serving the users’ request. The framework also provides the stable federation. Proposed work provides less rejection rate of users’ request. Finally, it assists the providers in increasing their profits in the federation.Originality/valueThis paper presents a CG theoretic-based framework for the distribution of resources required and revenue. The framework analysed the performance of distribution models by considering the variance and monotonicity for multiple users’ requests.

Water balance assessment using remote sensing, Wet-Spass model, CN-SCS, and GIS for water resources management in Saïss Plain (Morocco)

Saiss plain contains an important aquifer. This complex groundwater system plays an important socio-economic role. Managing the groundwater resources requests a good understanding of resources availability and human needs satisfaction. These two components are estimated to get annual water balance of Saiss aquifer. Resources availability depends on meteorological parameters (rainfall, wind speed, temperature, etc.), soil types, topography, water table, and land use. Previous studies do not consider all of these parameters in assessing the water resources availability in this region. In this study, we have identified the relationship between all Saiss hydro-meteorological parameters and we have organized all data in Geographic Information System (GIS) to assess inputs of the water balance. Meteorological and hydrological data and landuse/landcover maps obtained from Landsat imageries classification were used in Wet-Spass (water and energy transfer between soil, plants, and atmosphere under quasi-steady state) model and Soil Conservation Service Curve Number (CN-SCS) method to assess annual runoff, potential evapotranspiration, interception, actual evapotranspiration, and natural recharge of shallow aquifer of Saiss plain. The results of the water balance calculation indicate significant fluctuations in the aquifer recharge: 280 Mm3 (1987) and 418 Mm3 (2018). The annual direct runoff increased from 344 Mm3 (1987) to 638 Mm3 (2018).