Opportunistic Resource Research Articles

Saline-alkaline conditions altered Bolboschoenus planiculmis carbon and nitrogen allocation tradeoffs

Soil salinization is an important factor that limits global agricultural production, specifically limiting the effectiveness of nitrogen-carbon resources and inhibiting plant growth. However, previous observations have focused on resource allocation, and there is little information about the coordination of carbon-nitrogen acquisition, allocation, and regulatory processes. We performed glasshouse pot experiments under different saline-alkaline conditions, and we measured 66 above- and belowground functional traits of Bolboschoenus planiculmis, to examine carbon-nitrogen resource acquisition and allocation strategies and their driving processes. Saline-alkaline conditions shifted B. planiculmis root-leaf functional traits toward a more acquisitive phenotype. Under low saline-alkaline conditions, although the root-leaf economic strategy inhibited resource acquisition efficiency, the opportunistic carbon-nitrogen capture and allocation strategy contributed to the maintenance of normal growth. However, highly saline-alkaline conditions led to the early enrichment of carbon-nitrogen resources in the corm. Additionally, saline-alkaline conditions altered the importance of physiological and biochemical processes in the carbon and nitrogen allocation regulatory network. In summary, B. planiculmis uses an opportunistic resource acquisition strategy under saline-alkaline conditions and a salt-avoidance allocation strategy under highly saline-alkaline conditions. This approach enables the maintenance of growth dominance under saline-alkaline conditions via gradient resource utilization.

Sequence Labelling with 2 Level Segregation (SL2LS): A framework to extract COVID-19 vaccine adverse drug reactions from Twitter data

Social media data is an opportunistic resource for post-marketing surveillance of COVID-19 (Coronavirus disease of 2019) vaccines. However, the usage of the term Adverse Drug Reaction (ADR) in different contexts, which do not refer to adverse reactions to given vaccines, poses a great challenge for existing ADR extraction techniques. In the present work, a framework titled “Sequence Labelling with 2 Level Segregation” (SL2LS) has been proposed to extract adverse reactions of COVID-19 vaccines from Twitter data. It handles the mentioned challenge by deploying a Bidirectional Long Short-Term Memory (Bi-LSTM) based two-level classification model to segregate the tweets containing ADR terms that represent adverse reactions of vaccines from the tweets that comprise ADR terms which are not referring to adverse reactions of vaccines based on the context of ADR term usage and then employ domain-specific pre-trained Bidirectional Encoder Representations from Transformers for Biomedical Text Mining (BioBERT) sequence labelling model for extracting vaccine linked ADRs from segregated tweets. The proposed framework has been applied to Covaxin and Covishield COVID-19 vaccine data collected from Twitter and annotated with the help of a domain expert. It achieved the highest precision, recall and F1-score of 95.46%, 93.2% and 94.33%, respectively, in comparison to other state-of-the-art techniques. The validation of detected top 20 frequent ADRs of the two vaccines with official surveillance reports resulted in 19 verified ADRs for Covaxin and all 20 verified ADRs for Covishield and one unverified ADR “Blood Clot” for Covaxin which is a valuable lead for medical experts for further exploration. Further analysis shows that the two vaccines share some common ADRs viz. “Discomfort”, “Fever”, “Pain”, “Headache” and “Fatigue” and “Discomfort” and “Fever” are the most frequent ADRs associated with Covaxin and Covishield vaccines, respectively.

AUDITOR: Accounting for opportunistic resources

The increasing computational demand in High Energy Physics (HEP) as well as increasing concerns about energy efficiency in highperformance/high-throughput computing are driving forces in the search for more efficient ways to utilise available resources. Since avoiding idle resources is key in achieving high efficiency, an appropriate measure is sharing of idle resources of underutilised sites with fully occupied sites. The software COBalD/TARDIS can automatically, transparently, and dynamically (dis)integrate such resources in an opportunistic manner. Sharing resources however also requires accounting. In this work we introduce AUDITOR (AccoUnting DatahandlIng Toolbox for Opportunistic Resources), a flexible and extensible accounting system that is able to cover a wide range of use cases and infrastructures. AUDITOR gathers accounting data via so-called collectors which are designed to monitor batch systems, COBalD/TARDIS, cloud schedulers, or other sources of information. The data is stored in a database and provided to so-called plugins, which act based on accounting records. An action could for instance be creating a bill of utilised resources, computing the CO2 footprint, adjusting parameters of a service, or forwarding accounting information to other accounting systems. Depending on the use case, a suitable collector and plugin can be chosen from a growing ecosystem of collectors and plugins. Libraries for interacting with AUDITOR are provided to facilitate the development of collectors and plugins by the community.

Prototyping a ROOT-based distributed analysis workflow for HL-LHC: The CMS use case

The challenges expected for the next era of the Large Hadron Collider (LHC), both in terms of storage and computing resources, provide LHC experiments with a strong motivation for evaluating ways of rethinking their computing models at many levels. Great efforts have been put into optimizing the computing resource utilization for the data analysis, which leads both to lower hardware requirements and faster turnaround for physics analyses. In this scenario, the Compact Muon Solenoid (CMS) collaboration is involved in several activities aimed at benchmarking different solutions for running High Energy Physics (HEP) analysis workflows. A promising solution is evolving software towards more user-friendly approaches featuring a declarative programming model and interactive workflows. The computing infrastructure should keep up with this trend by offering on the one side modern interfaces, and on the other side hiding the complexity of the underlying environment, while efficiently leveraging the already deployed grid infrastructure and scaling toward opportunistic resources like public cloud or HPC centers. This article presents the first example of using the ROOT RDataFrame technology to exploit such next-generation approaches for a production-grade CMS physics analysis. A new analysis facility is created to offer users a modern interactive web interface based on JupyterLab that can leverage HTCondor-based grid resources on different geographical sites. The physics analysis is converted from a legacy iterative approach to the modern declarative approach offered by RDataFrame and distributed over multiple computing nodes. The new scenario offers not only an overall improved programming experience, but also an order of magnitude speedup increase with respect to the previous approach.

Modeling Opportunistic Resource Fair Scheduling Efficiently for Multi-Beam 5G NR

Modeling Opportunistic Resource Fair Scheduling Efficiently for Multi-Beam 5G NR

The ephemeral record: The role of opportunistic animal resources in the archaeology of Pampa and Patagonia

The acquisition of opportunistic animal resources by hunter-gatherers-such as scavenged carcasses-is a well-known subsistence strategy. It is frequently mentioned in the context of the history of early human evolution, but not regularly considered among the strategies utilized by more recent foragers of the Southern Cone of South America. Historical and ethnographic information presented here suggests that the use of opportunistic animal resources was a strategy used under a number of conditions but only partially documented in the archaeological literature. We also present archaeological evidence from four sites-Guardia del Río, Paso Otero 1, Ponsonby, and Myren-in different Pampean and Patagonian settings where relevant guanaco (Lama guanicoe) bone assemblages were recovered. These sites record minimal anthropic involvement, basically cut marks on some guanaco bones accompanied by few stone tools, that we interpret as evidence for access and use of water logged immobilized or recently dead animals. Archaeological evidence for the use of these scavenging strategies is difficult to obtain at large archaeological sites that usually result from multiple occupations, since the distinction between the acquisition of actively pursued versus opportunistic animal resources is not straightforward. One conclusion from our review is that the best places to find and recognize this evidence are archaeological sites resulting from ephemeral occupations. The inclusion of these sites gives us access to crucial and rarely documented evidence for the long-term survival of hunter-gatherers.

Data Handling Optimization in Russian Data Lake Prototype

CERN experiments are preparing for the HL-LHC era, which will bring an unprecedented volume of scientific data. These data will need to be stored and processed by thousands of physicists, but expected resource growth is nowhere near the extrapolated requirements of existing models, in terms of both storage volume and compute power. Opportunistic CPU resources such as HPCs and university clusters can provide extra CPU cycles, but there is no opportunistic storage. In this article, we will present the main architectural ideas, deployment details, and test results, with emphasis on our research to build a prototype of a distributed data processing and storage system with a focus on optimizing the efficiency of resources by reducing overhead costs for accessing the data. The described prototype was built using the geographically distributed WLCG sites and university clusters in Russia.

Opportunistic admission and resource allocation for slicing enhanced IoT networks

Network slicing is envisioned as one of the key techniques to meet the extremely diversified service requirements of the Internet of Things (IoT) as it provides an enhanced user experience and elastic resource configuration. In the context of slicing enhanced IoT networks, both the Service Provider (SP) and Infrastructure Provider (InP) face challenges of ensuring efficient slice construction and high profit in dynamic environments. These challenges arise from randomly generated and departed slice requests from end-users, uncertain resource availability, and multi-dimensional resource allocation. Admission and resource allocation for distinct demands of slice requests are the key issues in addressing these challenges and should be handled effectively in dynamic environments. To this end, we propose an Opportunistic Admission and Resource allocation (OAR) policy to deal with the issues of random slicing requests, uncertain resource availability, and heterogeneous multi-resources. The key idea of OAR is to allow the SP to decide whether to accept slice requests immediately or defer them according to the load and price of resources. To cope with the random slice requests and uncertain resource availability, we formulated this issue as a Markov Decision Process (MDP) to obtain the optimal admission policy, with the aim of maximizing the system reward. Furthermore, the buyer-seller game theory approach was adopted to realize the optimal resource allocation, while motivating each SP and InP to maximize their rewards. Our numerical results show that the proposed OAR policy can make reasonable decisions effectively and steadily, and outperforms the baseline schemes in terms of the system reward.

Long‐term minnow community trait shifts and metacommunity dynamics in a geomorphically unstable river

AbstractEcological disturbances may affect both habitat connectivity and heterogeneity, changing the dominant metacommunity processes affecting local communities. Altered fluvial sediment dynamics in rivers experiencing headcutting and accelerated erosion may foster morphologically unstable reaches in which supplies of habitat‐forming materials are spatially and temporally unpredictable. We hypothesise such environments will select for species with traits which promote dispersal or rapid resource use. We tested this hypothesis using historical and recent datasets from a biologically and functionally diverse taxonomical group, minnows (Leuciscidae and Cyprinidae) in the Bayou Pierre, Mississippi. Local communities decreased in both α and β‐diversity over time. Spatial autocorrelation of communities was consistently high through time, and more strongly related to regional community structure than local habitat variables in contemporary data. Communities shifted towards species with larger body sizes, less parental care, shorter age at maturity and higher fecundity. Communities also shifted towards more generalist species with lower potential trophic positions, and keystone species declined. Our data support the hypothesis of community‐level shifts towards species with dispersal trait syndromes and opportunistic resource utilisation. These shifts have led to a decrease in ecological complexity and overall homogenisation of minnow communities in the geomorphically evolving Bayou Pierre mainstem. The Bayou Pierre presents a unique window into ecological dynamics in a watershed characterised by altered sediment dynamics and sediment loss, in contrast to the large body of literature examining systems with increased sedimentation. These insights demonstrate the importance of studying the full range of fluvial sediment dynamics, including how broader processes of loss and transport may affect long‐term habitat stability, persistent trait syndromes and metacommunity dynamics.

Psyllid honeydew as a Bombus food source in the boreal landscape

Abstract Bumblebees are opportunistic resource consumers and have been known to visit extrafloral nectaries and harvest aphid and scale honeydew. We report the first observation of bumblebees foraging psyllid honeydew in a tritrophic system in the boreal forest of Yukon Territory, Canada. In this system, a willow (Salix alaxensis) develops bizarrely enlarged petiole bases which act as nursery cavities for large numbers of psyllid immatures of Cacopsylla macleani (Homoptera: Psyllidae), with later instars dispersing over the underside of the leaf. These immatures produced abundant honeydew which was vigorously and persistently foraged by several bumblebees of multiple species (we identified Bombus vancouverensis, Bombus melanopygus and Bombus mckayi). Ants and vespid wasps were also present but in lower numbers. Salix alaxensis and Salix‐feeding psyllids are common throughout arctic and boreal regions, so honeydew production is potentially a supplementary food source for Bombus when floral nectar flows are limited due to low temperatures or drought.

Energy-efficient opportunistic multi-carrier NOMA-based resource allocation for beyond 5G (B5G) networks

The interplay between the non-orthogonal multiple access (NOMA) and the opportunistic cognitive radio (CR)-based orthogonal frequency multiple access (OFDMA) has been recently realized as a promising paradigm to support the unprecedented massive connectivity demands of future beyond fifth-generation (B5G) wireless communication systems. In such systems, which are called multi-carrier NOMA CR-based systems, each licensed band reserved for primary users can be opportunistically utilized based on power-domain NOMA to serve a group of secondary users simultaneously. An important challenge in this domain is how to provide energy-efficient resource allocation techniques that attempt to strike a balance between the total throughput (i.e., the achieved sum-rate) and the power required to achieve that rate while satisfying network QoS demands and being aware of the unique characteristics of the CR operating environment. In this paper, we propose an energy-efficient resource allocation technique for multi-carrier NOMA CR-based systems, which aims at maximizing the overall energy efficiency (EE) of the system under a set of CR and NOMA constraints. The EE maximization problem is shown to be a fractional non-convex optimization, which is, in general, hard to optimize. To deal with the fractional and the non-convexity nature of the formulated EE maximization problem, we exploit the Dinkelbach’s algorithm to transfer the EE problem to a parameterized optimization problem. Then we use an iterative optimization approach to obtain the solution for the EE maximization problem. Simulation results reveal that this EE maximization-based resource allocation technique outperforms the existing resource allocation techniques in terms of the overall EE of the system while striking a good balance between the sum-rate and the transmit power consumption.

Attaining the recesses of the cognitive space.

Existing neuropsychological tests of executive function often manifest a difficulty pinpointing cognitive deficits when these are intermittent and come in the form of omissions. We discuss the hypothesis that two partially interrelated reasons for this failure stem from relative inability of neuropsychological tests to explore the cognitive space and to explicitly take into account strategic and opportunistic resource allocation decisions, and to address the temporal aspects of both behaviour and task-related brain function in data analysis. Criteria for tasks suitable for neuropsychological assessment of executive function, as well as appropriate ways to analyse and interpret observed behavioural data are suggested. It is proposed that experimental tasks should be devised which emphasize typical rather than optimal performance, and that analyses should quantify path-dependent fluctuations in performance levels rather than averaged behaviour. Some implications for experimental neuropsychology are illustrated for the case of planning and problem-solving abilities and with particular reference to cognitive impairment in closed-head injury.

Exploiting Heterogeneity for Opportunistic Resource Scaling in Cloud-Hosted Applications

Cloud consumers have access to an increasingly diverse range of resource and contract options, but lack appropriate resource scaling solutions that can exploit this to minimize the cost of their cloud-hosted applications. Traditional approaches tend to use homogeneous resources and horizontal scaling to handle workload fluctuations and do not leverage resource and contract heterogeneity to optimize cloud costs. In this paper, we propose a novel opportunistic resource scaling approach that exploits both resource and contract heterogeneity to achieve cost-effective resource allocations. We model resource allocation as an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unbounded knapsack problem</i> , and resource scaling as an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">one-step ahead resource allocation problem</i> . Based on these models, we propose two scaling strategies: (a) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">delta capacity optimization</i> , which focuses on optimizing costs for the difference between existing resource allocation and the required capacity based on the forecast workload, and (b) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">full capacity optimization</i> , which focuses on optimizing costs for resource capacity corresponding to the forecast workload. We evaluate both strategies using two real world workload datasets, and compare them against three different scaling strategies. The results show that our proposed approach, particularly full capacity optimization, outperforms all of them and offers in excess of 70 percent cost savings compared to the traditional scaling approach.

The Impact of Community Social Capital on Asymmetric Behavior of Cost

Objective: The purpose of this research is investigating the effect of community social capital on asymmetric behavior of cost. Social capital refers to features of social organization such as norms and networks that simplify cooperation and harmony for mutual benefit. Community social capital captures the strength of cooperative norms and the density of social networks in a region. Cost stickiness is a behavioral property of cost that is substantially based on managerial decisions. In this case of cost behavior, managers are quick to expand resources when demand is increasing, but for a variety of reasons choose to stick with unutilized capacity when sales are decreasing. As such, community social capital is a socio-economic factor that might affect managerial resource adjustment decisions via different channels. Methods: The method of the present research is descriptive-correlation because in this study the principle is to make hypotheses, test them, explain the relationships between phenomena and also analyze the dependent variable based on independent variables.For this purpose, we have collected the financial information related to 191 companies listed in Tehran Stock Exchange during the period of 2012 to 2018 and measure the community social capital index by different provinces of the country. In order to measure the community social capital index, we used the components of participation rate in elections, labor force participation rate, density of social networks and density of non-profit organizations by quarterly method. We also use multiple linear regression patterns to test research hypotheses. Results: Research findings indicate that, there is a significant relationship between community social capital and the degree of stickiness in selling, general and administrative costs. In this regard community social capital significantly increases the degree of stickiness in selling, general and administrative costs. The findings also show that, there is no significant relationship between community social capital and the degree of stickiness in the total operating costs and cost of goods sold. Hence, we observe an issue related to the personal motivations of managers and their opportunistic behaviors. Conclusion: Based on the findings of the study, it can be argued thatcommunity social capital restrains managers from taking opportunistic resource adjustment decisions that would reduce costs stickiness. And this leads to an increase in the degree of costs stickiness. This article confirms the important role of managerial opinion in cost behavior and how local environmental factors show differences in firm’s cost behavior.

TSCO: Trust-based secure and cooperative opportunistic resource utilization networks

An opportunistic resource utilization network (denoted as Oppnet) is a type of network that enables its node to cooperate in an opportunistic and ad hoc manner. Cooperation in oppnet includes sharing node computational resources, rather than only opportunistic communication in the traditional opportunistic networks. The key idea of oppnet is its ability to grow from seed node (initiator) to expanded oppnet via joining more nodes until getting the desired resources in a timely manner. This expansion raises the security risk of joining malicious nodes and threatens the oppnet including nodes and data. Besides, uncontrolled oppnet expansion might allow joining nodes with low computational capabilities or limited resources that have delayed responses, and hence affect the performance of oppnet. This study aims to mitigate these two problems by proposing a new oppnet paradigm (an enhanced version of original oppnet) called Trust-based Secure Cooperative Oppnet (TSCO) that utilizing a trust as a foundation for security and cooperation. Trust foundation is the major aspect of TSCO, which relies mainly on establishing trust value for each oppnet node and updating these values based on cooperation experience with those nodes. In TSCO, oppnet nodes have classified based on their trust values in which the highest trust value has the priority to join oppnet, if it exists within the specific range. Otherwise, the second-highest trust value takes priority, and so on. To test the feasibility of TSCO in realistic systems, a framework of using TSCO as an oppnet expansion control regarding security and cooperation is simulated. The experimental results show that TSCO has a better performance in terms of secure, success expansion, and cooperation. The success rate of TSCO is higher than the success rate of the original oppnet (our experimental baseline). In addition, regarding the time of satisfying oppnet tasks, TSCO shows less average latency than the baseline.

New YARN Non-Exclusive Resource Management Scheme through Opportunistic Idle Resource Assignment

Efficiently managing resources and improving throughput in a large-scale cluster has become a crucial problem with the explosion of data processing applications in recent years. Hadoop YARN and Mesos, as two universal resource management platforms, have been widely adopted in the commodity cluster for co-deploying multiple data processing frameworks, such as Hadoop MapReduce and Apache Spark. However, in the existing resource management, a certain amount of resources are exclusively allocated to a running task and can only be re-assigned after that task is completed. This exclusive mode unfortunately leads to a potential problem that may under-utilize the cluster resources and degrade system performance. To address this issue, we propose a novel opportunistic and efficient resource allocation scheme, named OpERA, which breaks the barriers among the encapsulated resource containers by leveraging the knowledge of actual runtime resource utilizations to re-assign opportunistic available resources to the pending tasks. OpERA avoids incurring severe performance interference to active tasks by further using two approaches to efficiently balances the starvations of reserved tasks and normal queued tasks. We implement and evaluate OpERA in Hadoop YARN v2.5. Our experimental results show that OpERA significantly reduces the average job execution time and increases the resource (CPU and memory) utilizations.

Transparent Integration of Opportunistic Resources into the WLCG Compute Infrastructure

The inclusion of opportunistic resources, for example from High Performance Computing (HPC) centers or cloud providers, is an important contribution to bridging the gap between existing resources and future needs by the LHC collaborations, especially for the HL-LHC era. However, the integration of these resources poses new challenges and often needs to happen in a highly dynamic manner. To enable an effective and lightweight integration of these resources, the tools COBalD and TARDIS are developed at KIT. In this contribution we report on the infrastructure we use to dynamically offer opportunistic resources to collaborations in the World Wide LHC Computing Grid (WLCG). The core components are COBalD/TARDIS, HTCondor, CVMFS and modern virtualization technology. The challenging task of managing the opportunistic resources is performed by COBalD/TARDIS. We showcase the challenges, employed solutions and experiences gained with the provisioning of opportunistic resources from several resource providers like university clusters, HPC centers and cloud setups in a multi VO environment. This work can serve as a blueprint for approaching the provisioning of resources from other resource providers.

Reaching new peaks for the future of the CMS HTCondor Global Pool

The CMS experiment at CERN employs a distributed computing infrastructure to satisfy its data processing and simulation needs. The CMS Submission Infrastructure team manages a dynamic HTCondor pool, aggregating mainly Grid clusters worldwide, but also HPC, Cloud and opportunistic resources. This CMS Global Pool, which currently involves over 70 computing sites worldwide and peaks at 350k CPU cores, is employed to successfully manage the simultaneous execution of up to 150k tasks. While the present infrastructure is sufficient to harness the current computing power scales, CMS latest estimates predict a noticeable expansion in the amount of CPU that will be required in order to cope with the massive data increase of the High-Luminosity LHC (HL-LHC) era, planned to start in 2027. This contribution presents the latest results of the CMS Submission Infrastructure team in exploring and expanding the scalability reach of our Global Pool, in order to preventively detect and overcome any barriers in relation to the HL-LHC goals, while maintaining high effciency in our workload scheduling and resource utilization.

Harnessing HPC resources for CMS jobs using a Virtual Private Network

The processing needs for the High Luminosity (HL) upgrade for the LHC require the CMS collaboration to harness the computational power available on non-CMS resources, such as High-Performance Computing centers (HPCs). These sites often limit the external network connectivity of their computational nodes. In this paper we describe a strategy in which all network connections of CMS jobs inside a facility are routed to a single point of external network connectivity using a Virtual Private Network (VPN) server by creating virtual network interfaces in the computational nodes. We show that when the computational nodes and the host running the VPN server have the namespaces capability enabled, the setup can run entirely on user space with no other root permissions required. The VPN server host may be a privileged node inside the facility configured for outside network access, or an external service that the nodes are allowed to contact. When namespaces are not enabled at the client side, then the setup falls back to using a SOCKS server instead of virtual network interfaces. We demonstrate the strategy by executing CMS Monte Carlo production requests on opportunistic non-CMS resources at the University of Notre Dame. For these jobs, cvmfs support is tested via fusermount (cvmfsexec), and the native fuse module.

Seamless integration of commercial Clouds with ATLAS Distributed Computing

The CERN ATLAS Experiment successfully uses a worldwide distributed computing Grid infrastructure to support its physics programme at the Large Hadron Collider (LHC). The Grid workflow system PanDA routinely manages up to 700,000 concurrently running production and analysis jobs to process simulation and detector data. In total more than 500 PB of data are distributed over more than 150 sites in the WLCG and handled by the ATLAS data management system Rucio. To prepare for the ever growing data rate in future LHC runs new developments are underway to embrace industry accepted protocols and technologies, and utilize opportunistic resources in a standard way. This paper reviews how the Google and Amazon Cloud computing services have been seamlessly integrated as a Grid site within PanDA and Rucio. Performance and brief cost evaluations will be discussed. Such setups could offer advanced Cloud tool-sets and provide added value for analysis facilities that are under discussions for LHC Run-4.