Showing papers by "Qiang He published in 2018"

Optimal Edge User Allocation in Edge Computing with Variable Sized Vector Bin Packing

Abstract: In mobile edge computing, edge servers are geographically distributed around base stations placed near end-users to provide highly accessible and efficient computing capacities and services. In the mobile edge computing environment, a service provider can deploy its service on hired edge servers to reduce end-to-end service delays experienced by its end-users allocated to those edge servers. An optimal deployment must maximize the number of allocated end-users and minimize the number of hired edge servers while ensuring the required quality of service for end-users. In this paper, we model the edge user allocation (EUA) problem as a bin packing problem, and introduce a novel, optimal approach to solving the EUA problem based on the Lexicographic Goal Programming technique. We have conducted three series of experiments to evaluate the proposed approach against two representative baseline approaches. Experimental results show that our approach significantly outperforms the other two approaches.

Secondary foundation species enhance biodiversity.

Abstract: It has long been recognized that primary foundation species (FS), such as trees and seagrasses, enhance biodiversity. Among the species facilitated are secondary FS, including mistletoes and epiphytes. Case studies have demonstrated that secondary FS can further modify habitat-associated organisms (‘inhabitants’), but their net effects remain unknown. Here we assess how inhabitants, globally, are affected by secondary FS. We extracted and calculated 2,187 abundance and 397 richness Hedges’ g effect sizes from 91 and 50 publications, respectively. A weighted meta-analysis revealed that secondary FS significantly enhanced the abundance and richness of inhabitants compared to the primary FS alone. This indirect facilitation arising through sequential habitat formation was consistent across environmental and experimental conditions. Complementary unweighted analyses on log response ratios revealed that the magnitude of these effects was similar to the global average strength of direct facilitation from primary foundation species and greater than the average strength of trophic cascades, a widely recognized type of indirect facilitation arising through sequential consumption. The finding that secondary FS enhance the abundance and richness of inhabitants has important implications for understanding the mechanisms that regulate biodiversity. Integrating secondary FS into conservation practice will improve our ability to protect biodiversity and ecosystem function. Secondary foundation species, such as epiphytes, form structurally complex habitats on primary foundation species. A meta-analysis shows that they significantly enhance the abundance and richness of inhabitants compared to primary foundation species alone.

Service recommendation based on quotient space granularity analysis and covering algorithm on Spark

Abstract: The rapid growth of Web services has made it a challenge for users to find appropriate Web services because it is very difficult for traditional Web service recommendation approaches to process the large amount of service-relevant data. To address this issue, this paper proposes CA-QGS (Covering Algorithm based on Quotient space Granularity analysis on Spark), a scalable approach for accurate Web service recommendation in large-scale scenarios. CA-QGS first clusters users and Web services based on users’ past quality experiences on co-invoked Web services. It then performs granularity analysis on the clustering results to identify users and Web services that are similar to the target user and Web service, and employs the collaborate filtering technique to predict the quality of the target Web service for the target user. This way, appropriate Web services can finally be recommended to the target user. To increase the efficiency of CA-QGS, we parallelize CA-QGS on Spark. Extensive experiments show that CA-QGS outperforms existing approaches in both recommendation accuracy and efficiency.

Physical Stress, Consumer Control, and New Theory in Ecology

Abstract: Consumer–prey interactions form the foundation of food webs and are affected by the physical environment. Multiple foundational theories in ecology [e.g., the environmental stress model (ESM), the stress–gradient hypothesis (SGH), and ecosystem resilience theory] assume increased physical stress dampens top-down control of prey. In the large majority of empirical studies, however, physical stress either does not affect or amplifies consumer control. Additive and synergistic impacts of physical stress on consumer control appear more common, for example, for herbivory versus predation, and for warm- versus cold-blooded consumers. Predictability in how physical stress affects consumer control, however, remains largely unknown. We expand classical theories in ecology so that their assumption about physical stress–consumer control relationships can be inclusive of what primarily occurs in nature.

Formulating Criticality-Based Cost-Effective Fault Tolerance Strategies for Multi-Tenant Service-Based Systems

Abstract: The proliferation of cloud computing has fueled the rapid growth of multi-tenant service-based systems (SBSs), which serve multiple tenants simultaneously by composing existing services in the form of business processes. In a distributed and volatile operating environment, runtime anomalies may occur to the component services of an SBS and cause end-to-end quality violations. Engineering multi-tenant SBSs that can quickly handle runtime anomalies cost effectively has become a significant challenge. Different approaches have been proposed to formulate fault tolerance strategies for engineering SBSs. However, none of the existing approaches has sufficiently considered the service criticality based on multi-tenancy where multiple tenants share the same SBS instance with different multi-dimensional quality preferences. In this paper, we propose Criticality-based Fault Tolerance for Multi-Tenant SBSs (CFT4MTS), a novel approach that formulates cost-effective fault tolerance strategies for multi-tenant SBSs by providing redundancy for the critical component services. First, the criticality of each component service is evaluated based on its multi-dimensional quality and multiple tenants sharing the component service with differentiated quality preferences. Then, the fault tolerance problem is modelled as an Integer Programming problem to identify the optimal fault tolerance strategy. The experimental results show that, compared with three existing representative approaches, CFT4MTS can alleviate degradation in the quality of multi-tenant SBSs in a much more effective and efficient way.

Multi-Objective Optimization for Location Prediction of Mobile Devices in Sensor-Based Applications

Abstract: A mobile ad hoc network (MANET) can be constructed when a group of mobile users need to communicate temporarily in an ad hoc manner. It allows mobile services to be shared through device-to-device links and composed by combining a set of services together to create a complex, value-added, and cross-organizational business application. Nevertheless, various challenges, especially the reliability and quality-of-service of such a MANET-based mobile service composition, are yet to be properly tackled. Most studies and related composition strategies assume that mobile users are fully stable and constantly available. However, this is not realistic in most real-world scenarios where mobile users are mobile. The mobility of mobile users impact the reliability of corresponding mobile services and consequently impact the success rate of mobile service compositions. In this paper, we propose a reliability-aware mobile service composition approach based on prediction of mobile users’ positions. We model the composition problem as a multi-objective optimization problem and develop an evolutionary multi-objective optimization-based algorithm to solve it. Extensive case studies are performed based on a real-world mobile users’ trajectory data set and show that our proposed approach significantly outperforms traditional ones in terms of composition success rate.

The importance of an underestimated grazer under climate change: how crab density, consumer competition, and physical stress affect salt marsh resilience

Abstract: Climate change and consumer outbreaks are driving ecosystem collapse worldwide. Although much research has demonstrated that these factors can interact, how heterogeneity in top–down control intensity and physical forcing modulates ecosystem resilience to climate stress remains poorly understood. Here, we explore whether the nocturnal herbivorous crab Sesarma reticulatum can control spatially dominant cordgrass (Spartina alterniflora) growth and how its top–down effects vary with crab density, drought stress, and large-scale disturbance in southeastern US salt marshes. In multiple field experiments and surveys, we show that Sesarma depresses cordgrass growth and that its effects increase in a saturating manner with increasing crab density, such that the highest naturally occurring densities of this consumer can trigger local cordgrass die-off. This top–down effect of Sesarma is similar in magnitude to what is thought to be the dominant grazer in the system, the marsh periwinkle snail Littoraria irrorata. In a drought stress by Sesarma density experiment, we further show that salinity stress and intensive crab herbivory additively suppress cordgrass drought resistance. After drought subsides, surveys and experiments reveal that Sesarma also stifles cordgrass re-growth into existing die-off areas. Together, these results show that multiple grazers powerfully regulate the productivity and drought resilience of these intertidal grasslands and that heterogeneity in physical stress and consumer density can dictate when and where top–down forcing is important. More generally, this work provides a rare, experimental demonstration of the critical role top–down control can play across the initiation and recovery stages of ecosystem die-off.

Biogeography of salt marsh plant zonation on the Pacific coast of South America

Abstract: Aim The aim of this study was to investigate the biogeography of plant zonation in salt marshes on the Pacific coast of South America; to examine whether salt marsh plant zonation varies with latitude; and to explore the relative importance of climatic, tidal, edaphic and disturbance factors in explaining large-scale variation in salt marsh plant community structure. Location A 2,000-km latitudinal gradient on the Pacific coast in Chile, with a climate shift from hyper-arid at low to hyper-humid at high latitudes. Methods Plant zonation was quantified in field surveys of ten marshes. Climate, tidal regimes, edaphic factors and disturbances (tsunami and rainfall floods) were determined. We used multivariate analyses to explore their relative importance in explaining large-scale variation in salt marsh plant community structure. Results Across latitude, marshes were dominated by distinct plant communities in different climate regions, especially at the extreme dry and wet latitudes. Intertidal plant species zonation was present in hyper-arid and semi-arid climates, but not in arid, humid and hyper-humid climates. Latitudinal variation in low-marsh plant communities (regularly flooded at high tide) was largely a function of precipitation, while at high marshes (never flooded at high tide) latitudinal variation was explained with precipitation, temperature, tidal cycles, soil salinity and disturbances. Main conclusions Salt marshes on the Pacific coast of South America belong to Dry Coast and Temperate biogeographic types. Salt marsh plant zonation varies across latitude, and is explained by climatic, tidal, edaphic and disturbance factors. These patterns appear to be mechanistically explained by extrapolating experimentally generated community assembly models and have implications for predicting responses to climate change.

Characteristics of Cadmium Sorption by Heat-Activated Red Mud in Aqueous Solution

Abstract: Red mud as a waste material is produced in large quantities by the aluminum industry. Heat activation has been used to enhance sorption capacity of red mud for its beneficial reuse as an effective sorbent. In this study, heat-activated red mud (HARM) was investigated for its Cd(II) sorption capacity under various process conditions (Cd concentration, pH and contact time) using response surface methodology (RSM). Analysis with RSM identified pH as the most important process parameter. The positive correlation between higher pH and greater Cd(II) sorption was likely due to: (i) decreased proton competition with Cd(II) for sorption sites at higher pH; (ii) enhanced sorption via ion exchange by monovalent Cd species from hydrolysis at higher pH; and (iii) improved thermodynamics of sorption at higher pH as protons are being released as products. Further analysis indicated the sorption process was thermodynamically favorable with a negative change in Gibbs free energy. Additionally, the sorption process exhibited a positive change in enthalpy, indicative of endothermic nature of sorption; this is consistent with sorption increase at higher temperature. These findings provide needed insight into the mechanisms underlying Cd(II) sorption by HARM for more effective applications of heat-activated red mud as sorbents for Cd(II) removal.

Herbivory enhances the resistance of mangrove forest to cordgrass invasion.

Abstract: The biotic resistance hypothesis proposes that biotic interactions, such as competition and herbivory, resist the establishment and spread of non-native species. The relative and interactive role of competition and herbivory in resisting plant invasions, however, remains poorly understood. We investigated the interactive role of competition and herbivory (by the native rodent Rattus losea) in resisting Spartina alterniflora (cordgrass) invasions into mangrove forests. In southern China, although exotic cordgrass numerically dominates intertidal mudflats and open gaps in mangrove forests, intact forests appear to be highly resistant to cordgrass invasion. A field transplant and rodent exclusion experiment showed that while the impact of rodent grazing on cordgrass was weak on mangrove forest edges and open mudflats, rodent grazing strongly suppressed cordgrass in mangrove understory habitats. A greenhouse experiment confirmed a synergistic interaction between grazing and light availability (a proxy for mangrove shading and light competition) in suppressing cordgrass establishment, with the strongest impacts of grazing in low light conditions that likely weakened cordgrass to survive and resprout. When both were present, as in mangrove understory habitats, grazing and low light acted in concert to eliminate cordgrass establishment, resulting in resistance of mangrove forests to cordgrass invasion. Our results reveal that grazing by native herbivores can enhance the resistance of mangrove forests to cordgrass invasion in southern China, and suggest that investigating multifactor interactions may be critical to understanding community resistance to exotic invasions.

A Survey of Current End-User Data Analytics Tool Support

Abstract: There is a large growth in interest in big data analytics to discover unknown patterns and insights. A major challenge in this domain is the need to combine domain knowledge – what the data means (semantics) and what it is used for – with data analytics and visualization techniques to mine and communicate important information from huge volumes of raw data. Many data analytics tools have been developed for both research and practice to assist in specifying, integrating and deploying data analytics and visualization applications. However, delivering such big data analytics application requires a capable team with different skillsets including data scientists, software engineers and domain experts. Such teams and skillset usually take a long time to build and have high running costs. An alternative is to provide domain experts and data scientists with tools they can use to do the exploration and analysis directly with less technical skills required. We present an overview and analysis of several current approaches to supporting the data analytics for endusers, identifying key strengths, weaknesses and opportunities for future research.

Enhanced Adaptive Cloudlet Placement Approach for Mobile Application on Spark

Abstract: The applications of mobile devices are increasingly becoming computationally intensive while the computing capability of the user’s mobile device is limited Traditional approaches offload the tasks of mobile applications to the remote cloud However, the rapid growth of mobile devices has made it a challenge for the remote cloud to provide computing and storage capacities with low communication delays due to the fact that the remote cloud is geographically far away from mobile devices Reducing the completion time of applications in mobile devices through the technical expending mobile cloudlets which are moving collocated with Access Points (APs) is necessary To address the above issues, this paper proposes EACP-CA (Enhanced Adaptive Cloudlets Placement approach based on Covering Algorithm), an enhanced adaptive cloudlet placement approach for mobile applications in a given network area We apply the CA (Covering Algorithm) to adaptively cluster the mobile devices based on their geographical locations, the aggregation regions of the mobile devices are identified, and the cloudlet destination locations are also confirmed according to the clustering centers In addition, we can also obtain the traces between the original and destination locations of these mobile cloudlets To increase the efficiency, we parallelize CA on Spark Extensive experiments show that the proposed approach outperforms the existing approach in both effectiveness and efficiency

IIMOF: An Iterative Framework to Settle Influence Maximization for Opinion Formation in Social Networks

Abstract: Influence maximization for opinion formation (IMOF) in social networks is an important problem, which is used to determine some initial nodes and propagate the most ideal opinions to the whole network. The existing researches focus on improving the opinion formation models to compute the opinion of each node. However, little work has been done to describe the IMOF process mathematically, and the current researches cannot provide an effective mechanism to deal with the IMOF. In this paper, the IMOF is formulated mathematically and solved by an iterative framework. At first, we describe the IMOF as a constrained optimization problem. Then, based on node influence and neighbor coordination, the weighted coordination model is proposed to compute the opinions of network nodes with the change of iterations. In particular, in order to determine top- $k$ influential nodes (i.e., seed nodes), an iterative framework for the IMOF, called IIMOF is presented. Based on the framework, the score and rank of each node by Iterative 2-hop algorithm, i.e., SRI2 is proposed to compute the influence score of each node. Based on small in-degree and high out-degree, one-hop measure is proposed to better reflect the rank of all initial nodes. We also prove that IIMOF converges to a stable order set within the finite iterations. The simulation results show that IIMOF has superior average opinions than the comparison algorithms.

A Fluctuation-Aware Approach for Predictive Web Service Composition

Abstract: Service-Oriented paradigm enables the composition of loosely coupled services provided with varying nonfunctional properties in terms of Quality-of-Service (QoS). Given a composition template, finding the set of appropriate services that guarantees users' functional requirements under given QoS constraints has been widely acknowledged to be a challenge. The problem of optimal service selection and composition is usually addressed by considering timeinvariant, stochastic, or bounded QoS of candidate services in their QoS analysis models and scheduling algorithms. Our proposed work, instead, considers time-varying QoS of services and leverages its run-time fluctuations for generating dynamic and predictive service composition schedules, by using an ARIMA-based time-series prediction model and genetic algorithms. Extensive case studies based on multiple service composition templates and real-world QoS datasets clearly suggest that our proposed method outperform traditional ones in terms of response time and throughput.

Density Biased Sampling with Locality Sensitive Hashing for Outlier Detection

Abstract: Outlier or anomaly detection is one of the major challenges in big data analytics since unusual but insightful patterns are often hidden in massive data sets such as sensing data and social networks. Sampling techniques have been a focus for outlier detection to address scalability on big data. The recent study has shown uniform random sampling with ensemble can boost outlier detection performance. However, uniform sampling assumes that all points are of equal importance, which usually fails to hold for outlier detection because some points are more sensitive to sampling than others. Thus, it is necessary and promising to utilise the density information of points to reflect their importance for sampling based detection. In this paper, we formally investigate density biased sampling for outlier detection, and propose a novel density biased sampling approach. To attain scalable density estimation, we use Locality Sensitive Hashing (LSH) for counting the nearest neighbours of a point. Extensive experiments on both synthetic and real-world data sets show that our approach significantly outperforms existing outlier detection methods based on uniform sampling.

Assessing the Runoff Reduction Potential of Highway Swales and WinSLAMM as a Predictive Tool

Abstract: Across the United States, the impacts of stormwater runoff are being managed through the National Pollutant Discharge Elimination System (NPDES) in an effort to restore and/or maintain the quality of surface waters. State transportation authorities fall within this regulatory framework, being tasked with managing runoff leaving their impervious surfaces. Opportunely, the highway environment also has substantial amounts of green space that may be leveraged for this purpose. However, there are questions as to how much runoff reduction is provided by these spaces, a question that may have a dramatic impact on stormwater management strategies across the country. A highway median swale, located on Asheville Highway, Knoxville, Tennessee, was monitored for hydrology over an 11-month period. The total catchment was 0.64 ha, with 0.26 ha of roadway draining to 0.38 ha of a vegetated median. The results of this study indicated that 87.2% of runoff volume was sequestered by the swale. The Source Loading and Management Model for Windows (WinSLAMM) was used to model the swale runoff reduction performance to determine how well this model may perform in such an application. To calibrate the model, adjustments were made to measured on-site infiltration rates, which was identified as a sensitive parameter in the model that also had substantial measurement uncertainty in the field. The calibrated model performed reasonably with a Nash Sutcliffe Efficiency of 0.46. WinSLAMM proved to be a beneficial resource to assess green space performance; however, the sensitivity of the infiltration parameter suggests that field measurements of this characteristic may be needed to achieve accurate results.

Self-adaptive k-means based on a covering algorithm

Abstract: The -means algorithm is one of the ten classic algorithms in the area of data mining and has been studied by researchers in numerous fields for a long time. However, the value of the clustering number in the -means algorithm is not always easy to be determined, and the selection of the initial centers is vulnerable to outliers. This paper proposes an improved -means clustering algorithm called the covering -means algorithm (C- -means). The C- -means algorithm can not only acquire efficient and accurate clustering results but also self-adaptively provide a reasonable numbers of clusters based on the data features. It includes two phases: the initialization of the covering algorithm (CA) and the Lloyd iteration of the -means. The first phase executes the CA. CA self-organizes and recognizes the number of clusters based on the similarities in the data, and it requires neither the number of clusters to be prespecified nor the initial centers to be manually selected. Therefore, it has a “blind” feature, that is, is not preselected. The second phase performs the Lloyd iteration based on the results of the first phase. The C- -means algorithm combines the advantages of CA and -means. Experiments are carried out on the Spark platform, and the results verify the good scalability of the C- -means algorithm. This algorithm can effectively solve the problem of large-scale data clustering. Extensive experiments on real data sets show that the accuracy and efficiency of the C- -means algorithm outperforms the existing algorithms under both sequential and parallel conditions.

Collaborative Workflow Scheduling over MANET, a User Position Prediction-Based Approach

Abstract: The explosive increase of mobile devices and advanced communication technologies prompt the emergence of mobile computing. In this paradigm, mobile users’ idle resources can be shared as service through device-to-device links to other users. Some complex workflow-based mobile applications are therefor no longer need to be offloaded to remote cloud, on the contrary, they can be solved locally with the help of other devices in a collaborative way. Nevertheless, various challenges, especially the reliability and quality-of-service of such a collaborative workflow scheduling problem, are yet to be properly tackled. Most studies and related scheduling strategies assume that mobile users are fully stable and with constantly available. However, this is not realistic in most real-world scenarios where mobile users are mobile most of time. The mobility of mobile users impact the reliability of corresponding shared resources and consequently impact the success rate of workflows. In this paper, we propose a reliability-aware mobile workflow scheduling approach based on prediction of mobile users’ positions. We model the scheduling problem as a multi-objective optimization problem and develop an evolutionary multi-objective optimization based algorithm to solve it. Extensive case studies are performed based on a real-world mobile users’ trajectory dataset and show that our proposed approach significantly outperforms traditional approaches in term of workflow success rate.

Effect of planting and fertilization on lead partitioning in dredged sediment

Abstract: Dredging has been practiced to remove sediment impacted by persistent contaminants, such as heavy metals. Of these metals, lead (Pb) is of particular concern due to its toxicity. Therefore, dredged sediment containing Pb requires further mitigation. One method for Pb mitigation is phytoremediation of dredged sediment. In this study, the partitioning of Pb in sediment during phytoremediation by willow (Salix integra) was assessed. The results showed that, in general, the bioavailable forms of Pb declined with increased application of the standard Hoagland nutrient solution, which appeared to enhance the Fe–Mn oxide fraction and residual inert fraction. In contrast, the addition of excess phosphorus decreased the bioavailable fractions of Pb. However, the bioavailable fractions of Pb increased with additional potassium addition. Planting Salix integra was shown to promote the stabilization of Pb in sediment and led to a transformation from bioavailable forms to non-bioavailable forms. The results suggest that planting Salix integra can remediate Pb-contaminated dredged sediment via Pb immobilization by the roots. During this process, the application of Hoagland nutrient solution and the application of nutrient solutions with excess phosphorus not only promote root growth of Salix integra which would reduce Pb bioavailability, but also further enhance the immobilization of Pb in contaminated sediment, likely through the formation of Pb-containing compounds with low bioavailability.

Cast-in-place geopolymer pile with heating system

Abstract: Methods of preparing cast-in-place geopolymer piles using electrical heating wire (130) to provide heat to cure the piles are described. The heating wire (130) can be associated with a reinforcement cage (120) inserted in the pile shaft. Rod-shaped heating units comprising electrical heating wire (130) can be inserted into a pile shaft and can be reusable. Geopolymer piles with high compressive strength can be prepared from mixtures of class F fly ash and aqueous sodium hydroxide by heating the piles with the heating wire to a stable curing temperature for at least about 24 hours.