Search

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.

With the development of smart phones and online to offline, spatial platforms, such as TaskRabbit, are getting famous and popular. Tasks on these platforms have three main characters: they are in real-time dynamic scenario, they are on the road networks, and some of them have multiple skills. However, existing studies do not take into account all these three things simultaneously. Therefore, an important issue of spatial crowdsourcing platforms is to assign workers to tasks according to their skills on road networks in a real-time scenario. In this paper, we first propose a practical problem, called online multi-skilled task assignment on road networks (OMTARN) problem, and prove that the OMTARN problem is NP-Hard and no online algorithms can achieve a constant competitive ratio on this problem. Then, we design a framework using batch-based algorithms, including fixed and dynamic batch-based algorithm, and we show that how the algorithms update the batch. After that, we use the hierarchically separated tree structure to accelerate our algorithms. Finally, we implement all the algorithms of the OMTARN problem and clarify their strengths and weaknesses by testing them on both synthetic and real datasets.

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.

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.

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.

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.

The incremental problem for a class Q of graph queries aims to compute, given a query Q in 'Q, graph G, output Q(G) and updates Δ G to G as input, changes Δ O to Q(G) such that Q(G ⊕ Δ G) = Q(G) ⊕ Δ O. It is called bounded if its cost can be expressed as a polynomial function in the sizes of Q, Δ G and Δ O. It is to reduce computations on possibly big G to small Δ G and Δ O. No matter how desirable, however, our first results are negative: for common graph queries such as graph traversal, connectivity, keyword search and pattern matching, their incremental problems are unbounded. In light of the negative results, we propose two characterizations for the effectiveness of incremental computation: (a) localizable, if its cost is decided by small neighbors of nodes in Δ G instead of the entire G; and (b) bounded relative to a batch algorithm T, if the cost is determined by the sizes of Δ G and changes to the affected area that is necessarily checked by T. We show that the incremental computations above are either localizable or relatively bounded, by providing corresponding incremental algorithms. That is, we can either reduce the incremental computations on big graphs to small data, or incrementalize batch algorithms by minimizing unnecessary recomputation. Using real-life graphs, we experimentally verify the effectiveness of our algorithms.

This position paper provides an overview of our recent advances in the study of big graphs, from theory to systems to applications. We introduce a theory of bounded evaluability, to query big graphs by accessing a bounded amount of the data. Based on this, we propose a framework to query big graphs with constrained resources. Beyond queries, we propose functional dependencies for graphs, to detect inconsistencies in knowledge bases and catch spams in social networks. As an example application of big graph analyses, we extend association rules from itemsets to graphs for social media marketing. We also identify open problems in connection with querying, cleaning and mining big graphs.

The Resource Description Framework (RDF) andSPARQL query language are gaining increasing popularity andacceptance. The ever-increasing RDF data has reached a billionscale of triples, resulting in the proliferation of distributed RDFstore systems within the Semantic Web community. However, theelasticity and performance issues are still far from settled inface of data volume explosion and workload spike. In addition, providers face great pressures to provision uninterrupted reliablestorage service whilst reducing the operational costs due to avariety of system failures. Therefore, how to efficiently realizesystem fault tolerance remains an intractable problem. In this paper, we introduce ScalaRDF, a distributed and elastic in-memoryRDF triple store to provision a fault-tolerant and scalable RDFstore and query mechanism. Specifically, we describe a consistenthashing protocol that optimizes the RDF data placement, dataoperations (especially for online RDF triple update operations)and achieves an autonomously elastic data re-distribution in theevent of cluster node joining or departing, avoiding the holisticoscillation of data storage. In addition, the data store is ableto realize a rapid and transparent failover through replicationmechanism which stores in-memory data replica in the next hashhop. The experiments demonstrate that query time and updatetime are reduced by 87% and 90% respectively compared to otherapproaches. For an 18G source dataset, the data redistributiontakes at most 60 seconds when system scales out and at most 100seconds for recovery when nodes undergo crash-stop failures.

Virtual machine live migration technology allows a running VM migrates from one physical host to another with no impact on users. As the scale of distributed computing gets larger and larger, hybrid cloud, which is integrated cloud service utilizing both private and public clouds to perform distinct functions within the same organization becomes a hotspot for both academia and industry. Thus, it will be vital to migrate virtual machines in hybrid clouds. The biggest issue which VM migrationinhybridcloudsfacestodayishowtotransferthelarge amount of storage data. Storage migration of virtual machine with existing mechanisms of memory migration has been studied for a long time. Unfortunately, no one can perform perfectly without modification. It is compelling to consider a newmigration model and optimization methods. In this paper, we analyze the advantage and disadvantage of classical methods, and put forward a method which combines the strengths of pre-copy and post-copy migration model and we also use disk working set to optimize virtual machines storage migration. We have developed and implemented our approach to the QEMU/KVM hypervisor and run a series of experiments. The presented technique shows a reduction of up 20.5% on average of the total transfer time for most of the selected scenario. Our approach can also reduce the migration downtime in most cases.

Software as a Service (SaaS) is a software delivery and business model widely used by Cloud computing. Instead of purchasing and maintaining a software suite permanently, customers only need to lease the software on-demand. The domain of high assurance distributed systems has focused greatly on the areas of fault tolerance and dependability. In a multi-tenant context, it is particularly important to store, manage and provision data services to customers in a highly efficient and dependable manner due to a large number of file operations involved in running such services. It is also desirable to allow a user group to share and cooperate (e.g., co-edit) on some specific data. In this paper we present a dependable data provisioning service in a multitenant Cloud environment. We describe a metadata management approach and leverage multiple replicated metadata caching to shorten the file access time, with the improved efficiency of data sharing. In order to reduce frequent data transmission and data access latency, we introduce a distributed cooperative disk cache mechanism that supports effective cache placement and pull-push cache synchronization. In addition, we use efficient component failover to enhance the service dependability whilst avoiding negative impact from system failures. Our experimental results show that our system can significantly reduce both unused data transmission and response latency. Specifically, over 50% network transmission and operational latency can be saved for random reads while 28.24% network traffic and 25% response latency can be reduced for random write operations. We believe that these findings are demonstrating positive results along the right direction of resolving storage-related challenges in a multitenant Cloud environment.

A muon facility—EMuS (Experimental Muon Source)—at China Spallation Neutron Source (CSNS) has been studied since 2007. CSNS, which is designed to deliver a proton beam power of 100 kW at Phase-I, and will serve multidisciplinary research based on neutron scattering techniques, has just completed construction, and is ready to open to general users from September 2018. As an additional platform to CSNS, EMuS aims to provide different muon beams for multiple applications, among which, magnetism study by μSR techniques is a core part. By using innovative designs, such as a long target in conical shape situating in superconducting capture solenoids and forward collection method, EMuS can provide very intense muon beams with a proton beam of 5 kW and 1.6 GeV, from surface muons, decay muons, and high momentum muons to slow muons. In this article, the design aspects of EMuS, including general design, target station, muon beamlines, and μSR spectrometer, as well as prospects for applications on magnetism studies, will be reviewed.

On one hand, compared with traditional relational and XML models, graphs have more expressive power and are widely used today. On the other hand, various applications of social computing trigger the pressing need of a new search paradigm. In this article, we argue that big graph search is the one filling this gap. To show this, we first introduce the application of graph search in various scenarios. We then formalize the graph search problem, and give an analysis of graph search from an evolutionary point of view, followed by the evidences from both the industry and academia. After that, we analyze the difficulties and challenges of big graph search. Finally, we present three classes of techniques towards big graph search: query techniques, data techniques and distributed computing techniques.