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The set covering problem (SCP) is a fundamental NP-hard problem in computer science and has a broad range of important real-world applications. In practice, SCP instances transformed from real-world applications would be of large scale, so it is of significant importance to design effective heuristic algorithms, especially local search ones. However, there exist only few research works on developing local search algorithms for solving SCP. In this article, we propose a new local search algorithm for solving SCP, dubbed NuSC. In particular, NuSC introduces a new combined scoring function for subset selection, which combines different subset properties in an effective way and helps NuSC find more optimized solutions. Besides, NuSC incorporates a dynamic weighting scheme for elements, a tabu search strategy, and a novelty selection mechanism to further enhance its practical performance. In order to study the effectiveness and robustness of our proposed NuSC algorithm, we conduct extensive experiments to compare NuSC against many state-of-the-art competitors on various types of SCP instances. Our experimental results demonstrate that NuSC significantly outperforms its competitors on the majority of instances, indicating the superiority of NuSC. Also, our empirical evaluations confirm the effectiveness of each algorithmic technique underlying NuSC.

The semi-arid region of northern China is highly sensitive to environmental changes, especially the Hulunbuir Grassland, which has an essential ecological status and a fragile environment. This study focused on the NDVI characteristics of three different ecosystems and their dominant influencing factors. It proposed a method to show the immediate effects of factors influencing NDVI on a statistical level. The results showed that: (1) NDVI of floodplain wetland > NDVI of meadow > NDVI of sand ribbon. There were obvious differences among the three ecosystems, and the spatial distribution of NDVI was consistent with altitude. (2) The main explanatory factors were the phenological period, humidity, temperature, accumulated precipitation, runoff, and evaporation, which accounted for 68.8% of the total explanation. (3) Phenological period, humidity, and precipitation were positively correlated with NDVI. Temperature and evaporation had a positive effect on NDVI within a certain range. This study revealed the differences in environmental factors in different ecosystems, enriched the theory of NDVI influencing factors, and provided a scientific basis for future NDVI research and regional ecological conservation.

Desertification is one of the major challenges facing humanity. However, there is no effective way to effectively control desertification. China is experiencing serious desertification, and to alleviate this trend, China has implemented a series of major ecological projects since 1978 and has achieved good results. Remote sensing can record changes in surface processes over a long period of time and can play an active role in desertification monitoring and governance. However, there is no systematic remote sensing monitoring method for desertification. In response to this problem, this paper takes a demonstration and reconstruction project of the United Nations desertification in the Kubuqi Desert in China as an example. By using the 30-year time series Landsat data, we propose a new remote sensing monitoring method for the impact of ecological restoration engineering of desert to monitor the spatial and temporal evolution pattern of the Kubuqi Desert in the past 30 years and the impact of the human ecological reconstruction project on the location of land use types in the Kubuqi Desert. In addition, the method analyzes the impact of the Kubuqi Desert Reconstruction Project on the local ecological environment changes from time and space perspectives. The results show the following: (1) During 1991–2017, the Kubuqi Desert Reconstruction Project improved the cultivated land, afforestation, and the Yellow River Drainage to control the sand and in conjunction with other renovation projects improved the ecological environment. The area of the sandy land decreased from 45.76% to 28.66%, while the areas of the waters wetland and artificial forest (shrub), urban construction land, and agricultural land increased; (2) There are obvious time and space differences in the transformation, and the vegetation cover increases as a whole. The obvious change areas are mainly distributed in the riverbank irrigation area of the Yellow River section of the Kubuqi Desert and the torrents such as the Maobula Kong, Buer Taigou and Heilaigou and other mountain gully areas. The relevant experience of the Kubuqi Desert Reconstruction Project in China has important reference significance for world desertification transformation.

Continued construction of reservoirs around the world promotes socio-economic development and severely affects the ecological and hydrological processes of rivers and floodplain wetlands. In this study, the Yimin River in Inner Mongolia, China, was taken as the research area. The water inundation guarantee rate (WIGR) was used as the model basis to characterize the inundation probability of the floodplain area. The comparative study of the remote sensing data of the 38 periods before the construction of the reservoir, and the 14 periods after the construction of the reservoir, shows that: due to the impact of the reservoir construction, the submerged area of the water body (WIGR greater than zero) decreased from 49.03 km2 to 39.32 km2, a total reduction of 9.71 km2; the very low water inundation guarantee rate area (WIGR value of 0–20%) was the most affected, with a decrease of 12.14 km2, while the area of other areas with a WIGR value greater than 20% increased by 3.43 km2. In addition, the affected floodplain wetlands have significant spatial distribution characteristics: first, they are mainly distributed in the high-curvature river reach areas, and in this study 77.96% of the three high-curvature river reach areas accounted for 26.04% of the area, with area loss in very low WIGR areas; the second is that it is distributed far from the outside of the river channel, while the WIGR value in some areas near the river channel is increased. This study provides a technical reference for downstream wetland protection based on the WIGR model.

Various mobile devices have been used to collect, store and transmit tremendous trajectory data, and it is known that raw trajectory data seriously wastes the storage, network bandwidth and computing resource. To attack this issue, one-pass line simplification ($$\textsf {LS} $$) algorithms have been developed, by compressing data points in a trajectory to a set of continuous line segments. However, these algorithms adopt the perpendicular Euclidean distance, and none of them uses the synchronous Euclidean distance ($$\textsf {SED} $$), and cannot support spatiotemporal queries. To do this, we develop two one-pass error bounded trajectory simplification algorithms ($$\textsf {CISED} $$-$$\textsf {S} $$ and $$\textsf {CISED} $$-$$\textsf {W} $$) using $$\textsf {SED} $$, based on a novel spatiotemporal cone intersection technique. Using four real-life trajectory datasets, we experimentally show that our approaches are both efficient and effective. In terms of running time, algorithms $$\textsf {CISED} $$-$$\textsf {S} $$ and $$\textsf {CISED} $$-$$\textsf {W} $$ are on average 3 times faster than $$\textsf {SQUISH} $$-$$\textsf {E} $$ (the fastest existing $$\textsf {LS} $$ algorithm using $$\textsf {SED} $$). In terms of compression ratios, $$\textsf {CISED} $$-$$\textsf {S} $$ is close to and $$\textsf {CISED} $$-$$\textsf {W} $$ is on average $$19.6\%$$ better than $$\textsf {DPSED} $$ (the existing sub-optimal $$\textsf {LS} $$ algorithm using $$\textsf {SED} $$ and having the best compression ratios), and they are $$21.1\%$$ and $$42.4\%$$ better than $$\textsf {SQUISH} $$-$$\textsf {E} $$ on average, respectively.

The fabrication and hydroxylation of surface defects on multi-walled carbon nanotubes (MWCNTs) are performed through a single or combined method involving hydrothermal, ultrasonic and ball-milling treatments. These treatments cannot change the tubular morphology of the graphite structure in MWCNTs but they can alter the surface defect structure on MWCNTs. Surface defects on MWCNTs can promote the hydroxylation of the MWCNTs to produce hydroxyl groups. However, these defects are difficult to hydroxylate when occupied by other functional groups, such as carboxyl groups, even though the hydroxylation processes are the same. After hydroxylation, the MWCNTs with surface defects can be employed as metal-free catalysts for the oxidative dehydrogenation of ethylbenzene using CO2 as soft oxidants. The inherent disorder or artificial skeleton defects on MWCNTs cannot directly act as catalytic active sites, but they can be hydroxylated to enhance the catalytic activity of the MWCNTs. For the same type of MWCNT defects formed after hydroxylation, the catalytic activity is closely related to the defect density. If the disorder or skeleton defects cause a higher defect density on the hydroxylated MWCNTs, the MWCNTs will exhibit a higher catalytic activity. Due to the different structures and positions of the MWCNT defect carbons, the artificial skeleton defects on MWCNTs more efficiently promote the hydroxylation of MWCNTs compared to the disorder defects, decreasing their defect density and enhancing their catalytic activity. Therefore, MWCNTs-BOH36 with skeleton defects has a catalytic activity that is twice as high as that of MWCNTs-CS-120 with disorder defects.

This paper studies scaling out/in to cope with load surges. Given a graph G that is vertex-partitioned and distributed across n processors, it is to add (resp. remove) k processors and re-distribute G across n + k (resp. n - k ) processors such that the load among the processors is balanced, and its replication factor and migration cost are minimized. We show that this tri-criteria optimization problem is intractable, even when k is a constant and when either load balancing or minimum migration is not required. Nonetheless, we propose two parallel solutions to dynamic scaling. One consists of approximation algorithms by extending consistent hashing. Given a load balancing factor above a lower bound, the algorithms guarantee provable bounds on both replication factor and migration cost. The other is a generic scaling scheme. Given any existing vertex-partitioner VP of users' choice, it adaptively scales VP in and out such that it incurs minimum migration cost, and ensures balance and replication factors within a bound relative to that of VP. Using real-life and synthetic graphs, we experimentally verify the efficiency, effectiveness and scalability of the solutions.

Striking a balance between improved cluster utilization and guaranteed application QoS is a long-standing research problem in cluster resource management. The majority of current solutions require a large number of sandboxed experimentation for different workload combinations and leverage them to predict possible interference for incoming workloads. This results in non-negligible time complexity that severely restricts its applicability to complex workload co-locations. The nature of pure offline profiling may also lead to model aging problem that drastically degrades the model precision. In this paper, we present Perph, a runtime agent on a per node basis, which decouples ML-based performance prediction and resource inference from centralized scheduler. We exploit the sensitivity of long-running applications to multi-resources for establishing a relationship between resource allocation and consequential performance. We use Online Gradient Boost Regression Tree (OGBRT) to enable the continuous model evolution. Once performance degradation is detected, resource inference is conducted to work out a proper slice of resources that will be reallocated to recover the target performance. The integration with Node Manager (NM) of Apache YARN shows that the throughput of Kafka data-streaming application is 2.0x and 1.82x times that of isolation execution schemes in native YARN and pure cgroup cpu subsystem. In TPC-C benchmarking, the throughput can also be improved by 35% and 23% respectively against YARN native and cgroup cpu subsystem.

Graph neural networks (GNN) have achieved state-of-the-art performance on various industrial tasks. However, the poor efficiency of GNN inference and frequent Out-of-Memory (OOM) problem limit the successful application of GNN on edge computing platforms. To tackle these problems, a feature decomposition approach is proposed for memory efficiency optimization of GNN inference. The proposed approach could achieve outstanding optimization on various GNN models, covering a wide range of datasets, which speeds up the inference by up to 3×. Furthermore, the proposed feature decomposition could significantly reduce the peak memory usage (up to 5× in memory efficiency improvement) and mitigate OOM problems during GNN inference.

Recently Cyber-Physical-Human Systems (CPHS) have been attracted much attention. Unlike existing computing paradigms such as Cloud Computing and Edge Computing, CPHS usually comprise many types of hybrid resources such as software and hardware in the cloud layer, network devices and edge servers in the network layer, as well as the physical devices and human participation. Such hybrid resource makes it challenging to efficient resource scheduling for CPHS. Previous studies on CPHS often focus on improving scheduling efficiency for specific application scenarios and thus are limited for other CPHS. In this work, we present a unified hybrid resource scheduling framework for CPHS, including a hybrid resource model for multiple types of resource providers and participants in CPHS, a hybrid resource orchestration tool that can describe our hybrid resource model as the resource requirements, and a scheduling mechanism to satisfy the resource requirements especially by considering the resource constraints and crosslayer scheduling in CPHS. We perform the case study of typical CPHS scenarios and show that our hybrid resource model and scheduling framework is effective, and provide a new view for improving the resource provision efficiency in CPHS.

Balancing resource utilization and application QoS is a long-standing research topic in cluster resource management. Big data YARN clusters need to co-schedule diverse workloads on shared resources including batch processing jobs, streaming jobs, and other long-running applications such as web services, database services, etc. Current resource managers are only responsible for resource allocation among applications/jobs but completely unaware of runtime QoS requirements of interactive and latency-sensitive applications. Prior works to maximize the QoS of monolithic applications ignore inherent dependencies and temporal-spatio performance variability of components, characteristics of distributed applications primarily driven by microservices. In this paper, we present Toposch, a new resource management system to adaptively co-locate batch tasks and microservices by harvesting runtime latency. In particular, Toposch tracks full footprints of every request across microservices over time. A latency graph is periodically generated for identifying victim microservices through an end-to-end latency critical path analysis. We then exploit per-microservice and per-node risk assessment to gauge the visible resources to the capacity scheduler in YARN. Execution of batch tasks are adaptively throttled or delayed, thereby avoiding latency increase due to node over-saturation. TOPOSCH is integrated with YARN and experiments show that the latency of DLRAs can be reduced by up to 39.8% against the default capacity scheduling in YARN.

Scholarly search systems greatly aid the deep understanding of scholarly data and facilitate the research activities of scholars for scientific studies. Though a number of such systems have been developed, most of them either support rankings of limited search of entities or provide only basic ranking metrics. These existing systems also mainly adopt RDBMSs as their storage such that the linked feature of scholarly data is not fully exploited. In this study, we design and develop a novel scholarly search system Athena. (1) It supports four types of scholarly entity searches: articles, authors, venues and affiliations, and is equipped with five ranking metrics, including three traditional metrics and two comprehensive importance ranking metrics. (2) It also provides profiling of scholarly entities. (3) It further utilizes a graph storage to directly leverage the linked feature for speeding up the processing of complex queries. We demonstrate the advantages of Athena at scholarly search, profiling, graph storage and ranking quality.

Ag-CeO2 nanoassemblys are prepared with a simple melamine-induced thermal decomposition method. Ag-CeO2 nanoassemblys have a structure composed of Ag nanoparticles and CeO2 nanoparticles. For Ag-CeO2 nanoassemblys, Ag introduction can partially or completely activate lattice oxygen species of CeO2 nanoparticles and however, the ease of Ag-CeO2 interface oxygen activation is strongly influenced by their pyrolysis temperature. Ag-CeO2 nanoassemblys prepared at 350 °C have only partial activation of CeO2 surface oxygen. If the calcination temperature increases to 400 °C, Ag-CeO2 nanoassemblys are fully activated to form more reducible interfacial lattice oxygen and to promote their low-temperature reductivity. Although excessive calcination temperature (500 °C) induces full activation of Ag-CeO2 interface lattice oxygen, Ag-CeO2 nanoassemblys will construct a new Ag-CeO2 interface that is relative difficult to reduce, causing a less amount of reducible interface lattice oxygen provided at higher temperature. Ag-CeO2 nanoassemblys are investigated to reveal the relationship of Ag-CeO2 interfacial oxygen activation and CO catalytic oxidation performance. Ag-CeO2 nanoassemblys with more interfacial lattice oxygen provided at lower temperature are found to have higher catalytic activity than that catalyst with the partially or excessively activated interfacial oxygen.

Automaton or table-based multi-pattern matching methods have been widely used in cloud services, i.e., virtual Firewall service, virtual IDS service, etc. In cloud, a large scale of patterns in such services are frequently updated causing by users’ joining or quitting and adjustment of security and management policies. Therefore, how to quickly and accurately update the Automaton and Table becomes an important issue. In this paper, we propose Eagle+, an incremental approach for updating the matching Automaton and Table whilst avoiding recalculating the whole patterns after each change. In Eagle+, we attain efficiency by computing only the latest update set of patterns when updating the Automaton and Table. Moreover, Eagle+ achieves accurately local updating based on three atomic operations, adding, updating and deleting, each of which modifies values on classical Aho–Corasick (AC) automaton, Set Backward Oracle Matching (SBOM) automaton and Wu–Manber (WM) table. Compared with existing pattern updating methods, Eagle+ reduces the computation complexity from O ( n 2 ) to O ( n ) . The experimental results show that Eagle+ can save nearly 72%–92% of the time consumption in updating automatons and perform 100X faster in WM table.

The cloud operating system (cloud OS) is used for managing the cloud resources such that they can be used effectively and efficiently. And also it is the duty of cloud OS to provide convenient interface for users and applications. However, these two goals are often conflicting because convenient abstraction usually needs more computing resources. Thus, the cloud OS has its own characteristics of resource management and task scheduling for supporting various kinds of cloud applications. The evolution of cloud OS is in fact driven by these two often conflicting goals and finding the right tradeoff between them makes each phase of the evolution happen. In this paper, we have investigated the ways of cloud OS evolution from three different aspects: enabling technology evolution, OS architecture evolution and cloud ecosystem evolution. We show that finding the appropriate APIs (application programming interfaces) is critical for the next phase of cloud OS evolution. Convenient interfaces need to be provided without scarifying efficiency when APIs are chosen. We present an API-driven cloud OS practice, showing the great capability of APIs for developing a better cloud OS and helping build and run the cloud ecosystem healthily.

Cluster administrators are facing great pressures to improve cluster utilization through workload co-location. Guaranteeing performance of long-running applications (LRAs), however, is far from settled as unpredictable interference across applications is catastrophic to QoS [2]. Current solutions such as [1] usually employ sandboxed and offline profiling for different workload combinations and leverage them to predict incoming interference. However, the time complexity restricts the applicability to complex co-locations. Hence, this issue entails a new framework to harness runtime performance and mitigate the time cost with machine intelligence: i) It is desirable to explore a quantitative relationship between allocated resource and consequent workload performance, not relying on analyzing interference derived from different workload combinations. The majority of works, however, depend on offline profiling and training which may lead to model aging problem. Moreover, multi-resource dimensions (e.g., LLC contention) that are not completely included by existing works but have impact on performance interference need to be considered [3]. ii) Workload co-location also necessitates fine-grained isolation and access control mechanism. Once performance degradation is detected, dynamic resource adjustment will be enforced and application will be assigned an access to specific slices of each resources. Inferring a "just enough" amount of resource adjustment ensures the application performance can be secured whilst improving cluster utilization. We present Perphon, a runtime agent on a per node basis, that decouples ML-based performance prediction and resource inference from centralized scheduler. Figure 1 outlines the proposed architecture. We initially exploit sensitivity of applications to multi-resources to establish performance prediction. To achieve this, Metric Monitor aggregates application fingerprint and system-level performance metrics including CPU, memory, Last Level Cache (LLC), memory bandwidth (MBW) and number of running threads, etc. They are enabled by Intel-RDT and precisely obtained from resource group manager. Perphon employs an Online Gradient Boost Regression Tree (OGBRT) approach to resolve model aging problem. Res-Perf Model warms up via offline learning that merely relies on a small volume of profiling in the early stage, but evolves with arrival of workloads. Consequently, parameters will be automatically updated and synchronized among agents. Anomaly Detector can timely pinpoint a performance degradation via LSTM time-series analysis and determine when and which application need to be re-allocated resources. Once abnormal performance counter or load is detected, Resource Inferer conducts a gradient ascend based inference to work out a proper slice of resources, towards dynamically recovering targeted performance. Upon receiving an updated re-allocation, Access Controller re-assigns a specific portion of the node resources to the affected application. Eventually, Isolation Executor enforces resource manipulation and ensures performance isolation across applications. Specifically, we use cgroup cpuset and memory subsystem to control usage of CPU and memory while leveraging Intel-RDT technology to underpin the manipulation of LLC and MBW. For fine-granularity management, we create different groups for LRA and batch jobs when the agent starts. Our prototype integration with Node Manager of Apache YARN shows that throughput of Kafka data-streaming application in Perphon is 2.0x and 1.82x times that of isolation execution schemes in native YARN and pure cgroup cpu subsystem.

Over a decade, cloud and subsequent joint cloud computing has been evolving into one of biggest disruptive technologies in modern digital age. The rapidly maturing cloud service and system management still heavily relies on virtualization which underpins Infrastructure as a Service (IaaS) to offer on-demand and low-cost computing services. Nevertheless datacenters still suffer from low utilization and resource imbalance. IaaS systems and their workloads, as legacy estates, are intricate to be migrated or re-planned, thereby increasing the complexity of utilization improvement. Arguably workload co-location of long-running applications encapsulated in virtual machines and latency-insensitive batch jobs is an alternative to improve overall resource utilization. However, guaranteeing the quality of long-running services is still challenging. In this context, we proposed an isolation-based cluster resource sharing system Shaready to enable workload co-residences. By means of global resource quota configuration and multi-resource isolation, long-running services in virtual machines can be prioritized with maximized resource provisioning. We implemented and validated it based on Openstack and Yarn clusters, and experiments demonstrate that system CPU and memory utilization can be improved by roughly 50% and 16.67% respectively on average with at most 7% performance degradation.

Procrastination is a prevalent form of self-control failure. As it often concerns with the individual's ability to meet the deadline, an efficient time management is crucial for overcoming it. Though a considerable amount of work in behavioral economics provides useful insights, there is not a computational way to guide us how to obtain an appropriate schedule for all the things to be done, especially when the relationship of the deadlines is intrinsic. In this paper, we first propose the Procrastination-aware Scheduling Problem (PSP) to model an appropriate schedule. A bipartite graph formulation is then developed to further illustrate the concepts. We find the PSP is NP-hard in the strong sense and design an approximation algorithm. In addition, we note the significance of the PSP under the online scenario (called OnlinePSP). Finally, we verify the effectiveness and efficiency of the proposed algorithms through extensive experiments on real datasets.

Nowadays, mailing lists are widely used in team work for discussion and consultation. Identifying important emails in mailing list discussions could significantly benefit content summary and opinion leader recognition. However, previous studies only focus on the importance evaluation methods regarding personal emails, and there is no consensus on the definition of important emails. Therefore, in this paper we consider the characteristics of mailing lists and study how to evaluate email importance in mailing list discussions. Our contribution mainly includes the following aspects. First, we propose ER-Match, an email conversation thread reconstruction algorithm that takes nested quotation relationships into consideration while constructing the email relationship network. Based on the email relationship network, we formulate the importance of emails in mailing list discussions. Second, we propose a feature-rich learning method to predict the importance of new emails. Furthermore, we characterize various factors affecting email importance in mailing list discussions. Experiments with publicly available mailing lists show that our prediction model outperforms baselines with large gains.

Water samples at both surface and bottom layers were taken from 102 sites in Lake Taihu to study the fluorescent components and spatial patterns of chromophoric dissolved organic matters (CDOM). Three-dimensional excitation-emission matrix data obtained from the samples were analyzed by parallel factor approach in which four humic-like and two protein-like fluorescent components (named C1–C6) were identified. The results showed that fluorescence intensities were higher in the northern and western lake regions, and notable declines of fluorescence maxima (F max) were observed from the northwest to the center and then to the southeast of the lake. Calculated biological index (BIX) values ranged from 0.88 to 1.44 and humification index (HIX) values from 0.64 to 3.37 for all the samples. The spatial variations of BIX and HIX values suggested stronger allochthonous CDOM characteristics in Zhushan Bay and the western area and autochthonous characteristics in the southern and eastern areas. Vertically, the average F max value of the surface samples was about 6 % less than that of the bottom samples, but noticeable variations existed among different sampling sites and components. These distribution characteristics of CDOM were mainly attributed to the spatial heterogeneity of sources and wind-induced transportation process. Interestingly, the C6 component (Exmax/Emmax = 250/455 nm) seemed to be unique in samples from Zhushan Bay and probably resulted from the discharge of the Taige River. Therefore, it could be used as an indicator of point-source discharge and a tracer to study the fate of CDOM in the lake.

The increasing availability of folksonomy data makes them vital for user profiling approaches to precisely detect user preferences and better understand user interests, so as to render some personalized recommendation or retrieval results. This paper presents a rigorous probabilistic framework to discover user preference from folksonomy data. Furthermore, we incorporate three models into the framework with the corresponding inference methods, expectation-maximization or Gibbs sampling algorithms. The user preference is expressed through topical conditional distributions. Moreover, to demonstrate the versatility of our framework, a recommendation method is introduced to show the possible usage of our framework and evaluate the applicability of the engaged models. The experimental results show that, with the help of the proposed framework, the user preference can be effectively discovered.

OS-level virtualization is often used for server consolidation in data centers because of its high efficiency. However, the sharing of storage stack services among the colocated containers incurs contention on shared kernel data structures and locks within I/O stack, leading to severe performance degradation on manycore platforms incorporating fast storage technologies (e.g., SSDs based on nonvolatile memories). This article presents MultiLanes, a virtualized storage system for OS-level virtualization on manycores. MultiLanes builds an isolated I/O stack on top of a virtualized storage device for each container to eliminate contention on kernel data structures and locks between them, thus scaling them to manycores. Meanwhile, we propose a set of techniques to tune the overhead induced by storage-device virtualization to be negligible, and to scale the virtualized devices to manycores on the host, which itself scales poorly. To reduce the contention within each single container, we further propose SFS, which runs multiple file-system instances through the proposed virtualized storage devices, distributes all files under each directory among the underlying file-system instances, then stacks a unified namespace on top of them. The evaluation of our prototype system built for Linux container (LXC) on a 32-core machine with both a RAM disk and a modern flash-based SSD demonstrates that MultiLanes scales much better than Linux in micro- and macro-benchmarks, bringing significant performance improvements, and that MultiLanes with SFS can further reduce the contention within each single container.

Co3O4/N-doped graphene is synthesized with a one-pot and low-cost thermal decomposition in air. N-doped graphene improves their reductivity and their low-temperature catalytic performance of CO oxidation. After the treatment with dry reaction gases, the treated composites show the changed chemical structure of nitrogen species from N (C)3 structure to C N C structure. After the treatment with wet reaction gases, nitrogen species on N-doped graphene are decomposed and the anchored sites of Co3O4 nanoparticles are converted into oxygenated groups. The nitrogen sites of N (C)3 structure are beneficial to promote the reversible conversion between the reduced Co2+ ions and active Co3+ ions on Co3O4 nanoparticles at low temperature, but Co3+ ions can irreversibly be converted into the reduced Co2+ ions on Co3O4 nanoparticles anchored at the nitrogen sites of C N C structure and it is more difficult to reduce Co3O4 nanoparticles anchored at oxygenated groups from the converted nitrogen species. Since CO oxidation is promoted by the reversible conversion between Co2+ ions and Co3+ ions on Co3O4 nanoparticles, Co3O4/N-doped graphene has low-temperature catalytic activity, but the composites treated with dry reaction gases require higher temperature to show catalytic activity and the composites treated with wet reaction gases show lower catalytic performance.

Surface structures of one-dimensional CeO2 nanocrystals are tuned with different cationic ions in a facile and mild hydrothermal system with formate and carbonate. The synthetic key is to control the formation of anisotropic CeO2 precursors with the anion structure of formate and carbonate. The diluted concentration of Na+ ions with NH4+ ions can induce the formation of the CeO2 nanotube (NT) precursors, and yet Na+ ions can promote the production of the CeO2 nanorod (NR) precursors. CeO2 nanocrystals are applied in CO oxidation and show a very important effect of their surface structure on their catalytic performance. CeO2 NTs have rich cerium surface with the higher concentration of Ce3+ ions and can accelerate the mobility of more surface oxygen species than CeO2 NRs with difficultly reduced rich oxygen surface. However, after silver loading, CeO2 NRs can show the higher stability of CeO2 particles, more easily activated surface of CeO2 NRs with more Ce3+ ions, the promoted production of metallic Ag nanoparticles and higher mobility of more surface oxygen species on CeO2 surface. Therefore, CeO2 NTs have a higher catalytic activity of CO oxidation and conversely, after loading Ag, Ag/CeO2 NRs show the higher catalytic performance.

A long-standing challenge in cluster scheduling is to achieve a high degree of utilization of heterogeneous resources in a cluster. In practice there exists a substantial disparity between perceived and actual resource utilization. A scheduler might regard a cluster as fully utilized if a large resource request queue is present, but the actual resource utilization of the cluster can be in fact very low. This disparity results in the formation of idle resources, leading to inefficient resource usage and incurring high operational costs and an inability to provision services. In this paper we present a new cluster scheduling system, ROSE, that is based on a multi-layered scheduling architecture with an ability to over-subscribe idle resources to accommodate unfulfilled resource requests. ROSE books idle resources in a speculative manner: instead of waiting for resource allocation to be confirmed by the centralized scheduler, it requests intelligently to launch tasks within machines according to their suitability to oversubscribe resources. A threshold control with timely task rescheduling ensures fully-utilized cluster resources without generating potential task stragglers. Experimental results show that ROSE can almost double the average CPU utilization, from 36.37% to 65.10%, compared with a centralized scheduling scheme, and reduce the workload makespan by 30.11%, with an 8.23% disk utilization improvement over other scheduling strategies.

Ranking query independent scholarly articles is a practical and difficult task, due to the heterogeneous, evolving and dynamic nature of entities involved in scholarly articles. To do this, we first propose a scholarly article ranking model by assembling the importance of involved entities (i.e., articles, venues and authors) such that the importance is a combination of prestige and popularity to capture the evolving nature of entities. To compute the prestige of articles and venues, we propose a novel Time-Weighted PageRank that extends traditional PageRank with a time decaying factor. We then develop a batch algorithm for scholarly article ranking, in which we propose a block-wise method for Time-Weighted PageRank in terms of an analysis of the citation characteristics of scholarly articles. We further develop an incremental algorithm for dynamic scholarly article ranking, which partitions graphs into affected and unaffected areas, and employs different updating strategies for nodes in different areas. Using real-life data, we finally conduct an extensive experimental study, and show that our approach is both effective and efficient for ranking scholarly articles.

This article studies discovery of Graph Functional Dependencies (GFDs), a class of functional dependencies defined on graphs. We investigate the fixed-parameter tractability of three fundamental problems related to GFD discovery. We show that the implication and satisfiability problems are fixed-parameter tractable, but the validation problem is co-W[1]-hard in general. We introduce notions of reduced GFDs and their topological support, and formalize the discovery problem for GFDs. We develop algorithms for discovering GFDs and computing their covers. Moreover, we show that GFD discovery is feasible over large-scale graphs, by providing parallel scalable algorithms that guarantee to reduce running time when more processors are used. Using real-life and synthetic data, we experimentally verify the effectiveness and scalability of the algorithms.

The variety among massive users makes it difficult to model their retweeting activities. Obviously, it is not suitable to cover the overall users by a single model. Meanwhile, building one model per user is not practical. To this end, this paper presents a novel solution, of which the principle is to model the retweeting behavior over user groups. Our system, GruBa, consists of three key components for extracting user based features, clustering users into groups, and modeling upon each group. Particularly, we look into the user interest from different perspectives including long-term/short-term interests and explicit/implicit interests. We have evaluated the performance of GruBa using datasets of real-world social networking applications, showcasing its benefits.

Path planning is an optimization problem that is crucial for robot or UCAV. Among the optimization approaches, we focus in this paper on a new hybrid modified Rapidly Exploring Random Tree algorithm (RRTs) and Differential Evolution (DE), for solving the optimization path planning problem to generate a fast and optimal 3D collision-free path under complex environment. We demonstrate the proposed algorithm performance through comparative analysis with Improved Bat algorithm (IBA). The results demonstrated the robustness and effectiveness of the proposed algorithm for generating an optimal free collision path in a short time, which is suitable for the UCAV applications.

Container technology has been prevalent and widely-adopted in production environment considering the huge benefits to application packing, deploying and management. However, the deployment process is relatively slow by using conventional approaches. In large-scale concurrent deployments, resource contentions on the central image repository would aggravate such situation. In fact, it is observable that the image pulling operation is mainly responsible for the degraded performance. To this end, we propose Cider — a novel deployment system to enable rapid container deployment in a high concurrent and scalable manner at scale. Firstly, on-demand image data loading is proposed by altering the local Docker storage of worker nodes into all-nodes-sharing network storage. Also, the local copy-on-write layer for containers can ensure Cider to achieve the scalability whilst improving the cost-effectiveness during the holistic deployment. Experimental results reveal that Cider can shorten the overall deployment time by 85% and 62% on average when deploying one container and 100 concurrent containers respectively.