Event detection (ED) is a challenging task in the field of information extraction. Due to the monolingual text and rampant confusing triggers, traditional ED models suffer from semantic confusions in terms of polysemy and synonym, leading to severe detection mistakes. Such semantic confusions can be further exacerbated in a practical situation where scarce labeled data cannot provide sufficient semantic clues. To mitigate such bottleneck, we propose a multilingual prompt learning (MultiPLe) framework for few-shot event detection (FSED), including three components, i.e., a multilingual prompt, a hierarchical prototype and a quadruplet contrastive learning module. In detail, to ease the polysemy confusion, the multilingual prompt module develops the in-context semantics of triggers via the multilingual disambiguation and prior knowledge in pretrained language models. Then, the hierarchical prototype module is adopted to diminish the synonym confusion by connecting the captured inmost semantics of fuzzy triggers with labels at a fine granularity. Finally, we employ the quadruplet contrastive learning module to tackle the insufficient label representation and potential noise. Experiments on two public datasets show that MultiPLe outperforms the state-of-the-art baselines in weighted F1-score, presenting a maximum improvement of 13.63% for FSED.

Acronym disambiguation is the process of determining the correct expansion of an acronym in given context, which can assist many downstream natural language processing tasks. Typically, existing methods on this task will directly perform semantic comparisons between the candidate expansions and the original sentence, ignoring the relevance of contextual information to expansions. To solve this issue, this paper proposes a context‐aware acronym disambiguation method with Siamese BERT network (ContextAD). First, we combine each candidate expansion with corresponding acronym’s context to form a new sentence set. Then, the new and original sentences are input into a Siamese BERT network that can obtain the semantic similarity. The new sentences and the separate candidate expansions are input into the Siamese BERT network, respectively, along with the original sentences, which can obtain another semantic similarity. Finally, the two different semantic similarities are combined to determine the most suitable expansion. We quantify the improvement of our proposed ContextAD model against a state‐of‐the‐art baseline using the public dataset of the shared tasks of acronym disambiguation (AD) held under AAAI‐2021 workshop on SDU and show that it achieves a better performance based on the same BERT model.

With the widespread use of Internet of Things (IoT) devices and the arrival of the 5G era, edge computing has become an attractive paradigm to serve end-users and provide better QoS. Many efforts have been paid to provision some merging public network services at the network edge. We reveal that it is very common that specific users call for private and isolated edge services to preserve data privacy and enable other security intentions. However, it still remains open to fulfill such kind of mixed requests in edge computing. In this article, we propose a cooperative edge computing framework, i.e., HyEdge, to offer both public and private edge services systematically. To fully exploit the benefits of this novel framework, we define the problem of optimal request scheduling over a given placement solution of hybrid edge servers to minimize the response delay. This problem is further modeled as a mixed integer non-linear programming problem (MINLP), which is typically NP-hard. Accordingly, we propose the partition-based optimization method, which can efficiently solve this NP-hard problem via the problem decomposition and the branch and bound strategies. We finally conduct extensive evaluations with a real-world dataset to measure the performance of our method. The results indicate that the proposed method achieves elegant performance with low computation complexity.

Blockchain-powered edge computing (BEC) is a promising extension to strengthen the security and the trustworthiness among collaborative edge clouds for delivering computation-intensive and delay-sensitive services in the environments of IoT and 5G. A fundamental challenge is how to respond to the maximum number of IoT requests at the network edge instead of the remote cloud. Although some work has been done to consider service provisioning and request scheduling in collaborative edge clouds, they assume that a single service is used to respond to each request. This assumption, however, is not practical to meet the demand of emerging IoT applications. In reality, the request needs to call a set of services with a chain-based structure. To tackle this challenge, in this article, we first propose a chain-based service request model for emerging IoT applications and further study the joint service provisioning and request scheduling problem for chain-based service requests at the network edge. We characterize this problem as an integer linear programming (ILP) model and prove the NP-hardness of this joint optimization problem. Furthermore, we prove that the related problem is of approximate submodularity with an approximation ratio guarantee. Finally, a novel two-stage optimization (TSO) scheme is proposed, and the results of extensive experiments show the efficiency and the effectiveness of the TSO scheme.

The emergence of edge computing can effectively tackle the problem of large transmission delays caused by the long-distance between user devices and remote cloud servers. Users can offload tasks to the nearby edge servers to perform computations, so as to minimize the average task response time through effective task dispatching and scheduling methods. However: 1) in the task dispatching phase, the dynamic features of network conditions and server loads make it difficult for the offloaded tasks to select the optimal edge server and 2) in the task scheduling phase, each edge server may face a large number of offloading tasks to schedule, resulting in long average task response time, or even severe task starvation. In this article, we propose an online task dispatching and fair scheduling method OTDS to tackle the above two challenges, which combines online learning (OL) and deep reinforcement learning (DRL) techniques. Specifically, using an OL approach, OTDS performs real-time estimating of network conditions and server loads, and then dynamically assigns tasks to the optimal edge servers accordingly. Meanwhile, at each edge server, by combing the round-robin mechanism with DRL, OTDS is able to allocate appropriate resources to each task according to its time sensitivity and achieve high efficiency and fairness in task scheduling. Evaluation results show that our online method can dynamically allocate network resources and computing resources to those offloaded tasks according to their time-sensitive requirements. Thus, OTDS outperforms the existing methods in terms of the efficiency and fairness on task dispatching and scheduling by significantly reducing the average task response time.

The emergence of large-scale dynamic sets in networked and distributed applications attaches stringent requirements to approximate set representation. The existing data structures (including Bloom filter, Cuckoo filter, and their variants) preserve a tight dependency between the cells or buckets for an element and the lengths of the filters. This dependency, however, degrades the capacity elasticity, space efficiency and design flexibility of these data structures when representing dynamic sets. In this paper, we first propose the Index-Independent Cuckoo filter (I2CF), a probabilistic data structure that decouples the dependency between the length of the filter and the indices of buckets which store the information of elements. At its core, an I2CF maintains a consistent hash ring to assign buckets to the elements and generalizes the Cuckoo filter by providing optional${k}$candidate buckets to each element. By adding and removing buckets adaptively, I2CF supports the bucket-level capacity alteration for dynamic set representation. Moreover, in case of a sudden increase or decrease of set cardinality, we further organize multiple I2CFs as a Consistent Cuckoo filter (CCF) to provide the filter-level capacity elasticity. By adding untapped I2CFs or merging under-utilized I2CFs, CCF is capable of resizing its capacity instantly. The trace-driven experiments indicate that CCF outperforms its alternatives and realizes our design rationales for dynamic set representation simultaneously, at the cost of a little higher complexity.

Edge computing (EC) is a promising paradigm for providing ultra-low latency experience for IoT applications at the network edge, through pre-caching required services in fixed edge nodes. However, the supply-demand mismatch can arise while meeting the peak period of some specific service requests. The mismatch between capacity provision and user demands can be fatal to the delay-sensitive user requests of emerging IoT applications and will be further exacerbated due to the long service provisioning cycle. To tackle this problem, we propose the mobile-assisted edge computing framework to improve the QoS of fixed edge nodes by exploiting mobile edge nodes. Furthermore, we devise a CRI (Credible, Reciprocal, and Incentive) auction mechanism to stimulate mobile edge nodes to participate in the services for user requests. The advantages of our mobile-assisted edge computing framework include higher task completion rate, profit maximization, and computational efficiency. Meanwhile, the theoretical analysis and experimental results guarantee the desirable economic properties of our CRI auction mechanism.

Multi-party set reconciliation is a key component in distributed and networking systems. It naturally contains two dimensions, i.e., set representation and reconciliation protocol. However, existing sketch data structures are insufficient to satisfy the new needs brought by the multi-party scenario simultaneously, including space-efficiency, mergeability, and completeness. The current reconciliation protocols, on the other hand, fail to achieve the global optimization of communication cost. To this end, in this article, we propose the marked cuckoo filter (MCF), a data structure for representing set members. Grounded on MCF, we implement the MCFsyn protocol to reconcile multiple sets. MCFsyn aggregates and distributes sets information represented by MCFs along with an underlying minimum spanning tree among the participants. The participants then identify the different elements by traversing the overall MCF which contains the information of all elements in the union set. For the identified missing elements, MCFsyn helps the participants to choose the optimal senders to fetch with the minimum communication cost. Comprehensive evaluations indicate that MCFsyn significantly outperforms existing alternatives in terms of both reconciliation accuracy and communication cost.

Set reconciliation is a common and fundamental task in distributed systems. In many cases, given set A on $Host_A$ and set B on $Host_B$, applications need to identify those elements that appear in set A but not in set B, and vice versa. However, existing methods incur unsatisfactory space utilization and non-trivial false positives and false negatives. In this paper, we present a novel reconciliation method based on Cuckoo filter (CF). After exchanging the CFs each of which represents a set of elements, we query the local elements against the received CF to determine the elements that only belong to the local host and should be transmitted to the other host. The evaluation results indicate that the CF-based reconciliation method outperforms existing methods significantly.

The mission of set reconciliation (also called set synchronization) is to identify those elements which appear only in exactly one of two given sets. In this paper, we extend the set reconciliation problem into three design rationales: (i) multiset support; (ii) near 100% reconciliation accuracy; (iii) communication-friendly and time-saving. Prior reconciliation methods fail to realize the three rationales simultaneously. To this end, we redesign Trie and Fenwick Tree (FT), to near-accurately represent and reconcile two types of multisets that we refer to as unsorted and sorted multisets, respectively. Comprehensive evaluations are conducted to quantify the performance of our proposals. The trace-driven evaluations demonstrate that Trie and FT achieve near-accurate multiset reconciliation, with 4.31 and 2.96 times faster than the CBF-based method, respectively.

The emergence of large-scale dynamic sets in networking applications attaches stringent requirements to approximate set representation. The existing data structures (including Bloom filter, Cuckoo filter, and their variants) preserve a tight dependency between the cells or buckets for an element and the lengths of the filters. This dependency, however, degrades the capacity elasticity, space efficiency and design flexibility of these data structures when representing dynamic sets. In this paper, we first propose the Index-Independent Cuckoo filter (I2CF), a probabilistic data structure that decouples the dependency between the length of the filter and the indices of buckets which store the information of elements. At its core, an I2CF maintains a consistent hash ring to assign buckets to the elements and generalizes the Cuckoo filter by providing optional k candidate buckets to each element. By adding and removing buckets adaptively, I2CF supports the bucket-level capacity alteration for dynamic set representation. Moreover, in case of a sudden increase or decrease of set cardinality, we further organize multiple I2CFs as a Consistent Cuckoo filter (CCF) to provide the filter-level capacity elasticity. By adding untapped I2CFs or merging under-utilized I2CFs, CCF is capable of resizing its capacity instantly. The trace-driven experiments indicate that CCF outperforms its alternatives and realizes our design rationales for dynamic set representation simultaneously, at the cost of a little higher complexity.

The mission of set reconciliation (also called set synchronization) is to identify those elements which appear only in exactly one of two given sets. In this paper, we extend the set reconciliation problem into three design rationales: (i) multiset support; (ii) near 100 percent reconciliation accuracy; and (iii) communication-friendly and time-saving. These three rationales, if realized, will lead to unprecedented benefits for the set reconciliation paradigm. Generally, prior reconciliation methods are mainly designed for simple sets and thus remain inapplicable for multisets. Methods based on probabilistic data structures, e.g., the Counting Bloom Filter (CBF), support efficient representation, and multiplicity queries. Based on these probabilistic data structures, approximate multiset reconciliation can be enabled. However, they often cannot achieve a statisfying accuracy, due to potential hash collisions. The reconciliations enabled by logs or lists incur high time-complexity and communication overhead. Therefore, existing reconciliation methods, fail to realize the three rationales simultaneously. To this end, we redesign Trie and Fenwick Tree (FT), to near-accurately represent and reconcile two types of multisets that we refer to as unsorted and sorted multisets, respectively. Moreover, to further reduce the communication overhead during the reconciliation process, we design a partial transmission strategy when exchanging two Tries or FTs. Comprehensive evaluations are conducted to quantify the performance of our proposals. The trace-driven evaluations demonstrate that Trie and FT achieve near-accurate multiset reconciliation, with 4.31 and 2.96 times faster than the CBF-based method, respectively. The simulations based on synthetic datasets further indicate that our proposals outperform the CBF-based method in terms of accuracy and communication overhead at most time.

The topology of a network may change inevitably, due to dynamic behaviors of nodes and links, and failures of hardware and software. Many protocols and applications must be aware of the up-to-date topology of the underlying network. This triggers the topology calibration problem, which means to deduce those different nodes and links between two topologies. The Bloom filter and its variants are efficient to represent and calibrate two general sets. They, however, fail to represent all links and nodes in a topology simultaneously, and thus remain inapplicable to the topology calibration problem. In this paper, we design the graph filter, a novel space-efficient data structure to record not only the node set but also the link set of any given topology. Accordingly, given two topologies we aim to represent them via two respective graph filters, and thereafter deduce those different links in an invertible manner. To this end, we design three essential operations for graph filter, i.e., encoding, subtracting and decoding. Although such operations are sufficient to solve the topology calibration problem, two challenging issues still remain open. First, the XOR traps which occur with low probability at the encoding stage may result in a few miscalculations at the decoding stage. Thus, we propose another augmented decoding algorithm to lessen the impact of XOR traps via terminating illegal decodings. Second, several different links may form cycles in the worst case; hence, we further design a cycle destruction algorithm to make such different links decodable. We implement the graph filter and the associated topology calibration method. Comprehensive evaluations indicate that our method finishes the topology calibration task efficiently with high probability, incurs the least space overhead, and supports invertible decoding reasonably.

The topology of data centers changes dynamically due to link malpositions, hardware failures or software crushes. However, many topology enabled protocols or applications must know the current topology of data center precisely, which triggers the topology calibration problem. Topology calibration needs to deduce the different nodes and links between two given topologies effectively. Based on the existing method, deriving the different nodes is relatively simple, since they can be uniquely identified by their IP or MAC addresses. On the contrary, picking the different links from the massive links can be costly. Therefore, we envision a method to locate the different links with respect to the following rationales: 1) efficient, the caused storage cost or communication overhead should be low; 2) without priori knowledge, there is no support information, thus the different links should be decoded inversely. However, the existing strategies based on Bloom filter, Hash table, or Search trees fail to achieve the two rationales simultaneously. Thus, we propose graph filter, a space-efficient data structure to represent and deduce the different links in an invertible manner. To this end, the associated encoding, subtracting and decoding algorithms are proposed. The simulations highlight the strength of graph filter reasonably.

The component-based business architecture integration of military information systems is a popular research topic in the field of military operational research. Identifying enterprise-level business components is an important issue in business architecture integration. Currently used methodologies for business component identification tend to focus on software-level business components, and ignore such enterprise concerns in business architectures as organizations and resources. Moreover, approaches to enterprise-level business component identification have proven laborious. In this study, we propose a novel approach to enterprise-level business component identification by considering overall cohesion, coupling, granularity, maintainability, and reusability. We first define and formulate enterprise-level business components based on the component business model and the Department of Defense Architecture Framework (DoDAF) models. To quantify the indices of business components, we formulate a create, read, update, and delete (CRUD) matrix and use six metrics as criteria. We then formulate business component identification as a multi-objective optimization problem and solve it by a novel meta-heuristic optimization algorithm called the ‘simulated annealing hybrid genetic algorithm (SHGA)’. Case studies showed that our approach is more practical and efficient for enterprise-level business component identification than prevalent approaches.

Set synchronization is an essential job for distributed applications. In many cases, given two sets A and B, applications need to identify those elements that appear in set A but not in set B, and vice versa. Bloom filter, a spaceefficient data structure for representing a set and supporting membership queries, has been employed as a lightweight method to realize set synchronization with a low false positive probability. Unfortunately, bloom filters and their variants can only be applied to simple sets rather than more general multisets, which allow elements to appear multiple times. In this paper, we first examine the potential of addressing the multiset synchronization problem based on two existing variants of the bloom filters: the IBF and the counting bloom filter (CBF). We then design a novel data structure, invertible CBF (ICBF), which represents a multiset using a vector of cells. Each cell contains two fields, id and count, which record the identifiers and number of elements mapped into them, respectively. Given two multisets, based on the encoding results, the ICBF can execute the dedicated subtracting and decoding operations to recognize the different elements and differences in the multiplicities of elements between the two multisets. We conduct comprehensive experiments to evaluate and compare the three dedicated multiset synchronization approaches proposed in this paper. The evaluation results indicate that the ICBF-based approach outperforms the other two approaches in terms of synchronization accuracy, timeconsumption, and communication overhead.

Recent results have made a promising case for offering oversubscribed wired data center networks (DCN) with extreme costs. Inter-rack wireless networks are drawing intensive attention to augment such wired DCNs with a few wireless links. Inspired by the promise of easy deployment and plug-and-play, we present VLCcube, a novel inter-rack wireless solution that extends the design of wireless DCN into three further dimensions: (1) all inter-rack links are wireless; (2) there is no imposition of any infrastructure-level alteration on wired production data centers; and (3) it should be plug-and-play, without any need of additional mechanical or electronic control operations. This vision, if realized, will lead to increased flexibility, reduced reconstructing cost, simplified configuration and usage, and outstanding compatibility with existing wired DCNs. Previous proposals, however, are opposed to the last two design rationales. To achieve this vision, the proposed VLCcube augments Fat-Tree, a representative DCN in production data centers, by organizing all racks into a wireless Torus structure via the emerging visible light links. We further present the topology design, hybrid routing, and flow scheduling schemes for VLCcube. Extensive evaluations indicate that VLCcube outperforms Fat-Tree significantly under the existing ECMP flow scheduling scheme, irrespective of the undergoing traffic pattern. Moreover, the performance of VLCcube can be significantly promoted by our congestion-aware flow scheduling scheme. More precisely, compared to ECMP, our flow scheduling scheme makes VLCcube achieve$\times 1.50$throughput under batched flows,$\mathrm{\times}2.21$and$\times 2.59$throughput under two different kinds of online flows.

Distributed computing systems like MapReduce in data centers transfer massive amount of data across successive processing stages. Such shuffle transfers contribute most of the network traffic and make the network bandwidth become a bottleneck. In many commonly used workloads, data flows in such a transfer are highly correlated and aggregated at the receiver side. To lower down the network traffic and efficiently use the available network bandwidth, we propose to push the aggregation computation into the network and parallelize the shuffle and reduce phases. In this paper, we first examine the gain and feasibility of the in-network aggregation with BCube, a novel server-centric networking structure for future data centers. To exploit such a gain, we model the in-network aggregation problem that is NP-hard in BCube. We propose two approximate methods for building the efficient IRS-based incast aggregation tree and SRS-based shuffle aggregation subgraph, solely based on the labels of their members and the data center topology. We further design scalable forwarding schemes based on Bloom filters to implement in-network aggregation over massive concurrent shuffle transfers. Based on a prototype and large-scale simulations, we demonstrate that our approaches can significantly decrease the amount of network traffic and save the data center resources. Our approaches for BCube can be adapted to other servercentric network structures for future data centers after minimal modifications.

Localization is an enabling technique for many sensor network applications. Real-world deployments demonstrate that, in practice, a network is not always entirely localizable, leaving a certain number of theoretically nonlocalizable nodes. Previous studies mainly focus on how to tune network settings to make a network localizable. However, the existing methods are considered to be coarse-grained, since they equally deal with localizable and nonlocalizable nodes. Ignoring localizability induces unnecessary adjustments and accompanying costs. In this study, we propose a fine-grained approach, localizability-aided localization (LAL), which basically consists of three phases: node localizability testing, structure analysis, and network adjustment. LAL triggers a single round adjustment, after which some popular localization methods can be successfully carried out. Being aware of node localizability, all network adjustments made by LAL are purposefully selected. Experiment and simulation results show that LAL effectively guides the adjustment while makes it efficient in terms of the number of added edges and affected nodes.

SUMMARY Several novel data center network structures have been proposed to improve the topological properties of data centers. A common characteristic of these structures is that they are designed for supporting general applications and services. Consequently, these structures do not match well with the specific requirements of some dedicated applications. In this paper, we propose a hyper‐fat‐tree network (HFN): a novel data center structure for MapReduce, a well‐known distributed data processing application. HFN possesses the advanced characteristics of BCube as well as fat‐tree structures and naturally supports MapReduce. We then address several challenging issues that face HFN in supporting MapReduce. Mathematical analysis and comprehensive evaluation show that HFN possesses excellent properties and is indeed a viable structure for MapReduce in practice. Copyright © 2011 John Wiley & Sons, Ltd.

Localization is an enabling technique for many sensor and ad-hoc network applications. Real-world deployments demonstrate that, in practice, a network is not always entirely localizable, leaving a certain number of theoretically non-localizable nodes. Previous studies mainly focus on how to tune network settings to make a network localizable; however, they are considered to be coarse-grained, since they equally deal with localizable and non-localizable nodes. Ignoring localizability induces unnecessary adjustments and accompanying costs. In this study, we propose a fine-grained approach, Localizability-aided Localization (LAL), which basically consists of three phases: node localizability testing, component tree construction, and network adjustment. LAL triggers a single round adjustment, after which some popular localization methods can be successfully carried out. Being aware of node localizability, all adjustments made by LAL are purposefully selected. Simulation results show that LAL effectively guides the adjustment.

A Bloom filter is an effective, space-efficient data structure for concisely representing a set, and supporting approximate membership queries. Traditionally, the Bloom filter and its variants just focus on how to represent a static set and decrease the false positive probability to a sufficiently low level. By investigating mainstream applications based on the Bloom filter, we reveal that dynamic data sets are more common and important than static sets. However, existing variants of the Bloom filter cannot support dynamic data sets well. To address this issue, we propose dynamic Bloom filters to represent dynamic sets, as well as static sets and design necessary item insertion, membership query, item deletion, and filter union algorithms. The dynamic Bloom filter can control the false positive probability at a low level by expanding its capacity as the set cardinality increases. Through comprehensive mathematical analysis, we show that the dynamic Bloom filter uses less expected memory than the Bloom filter when representing dynamic sets with an upper bound on set cardinality, and also that the dynamic Bloom filter is more stable than the Bloom filter due to infrequent reconstruction when addressing dynamic sets without an upper bound on set cardinality. Moreover, the analysis results hold in stand-alone applications, as well as distributed applications.

There is a growing need for enabling reprogramming in a working sensor network. We prefer to meet the requirements remotely instead of collecting all deployed sensors. Identifying the version difference of data items, having the same key, could significantly reduce the communication overhead, because only those out-of-date items should be updated at each sensor. Previous protocols need to exchange multiple messages to identify a version difference between two items with the same key. In this paper, we propose a reliable and energy efficient data dissemination protocol (BDP) with less propagation delay. BDP uses Bloom filters to identify a version difference between two items with the same key, and find the new one between two items having the same key but different versions. Through comprehensive simulations, we show that BDP outperforms previous work in terms of energy cost and propagation delay of updating new items with high reliability

The elements for constituting a complete workflow model should include control, data, resource, and time factors. But most of the current workflow modeling methods only bias towards one or some of the elements, and they all lack the capacity to describe a workflow by comprehensive considering all the elements. For solving this problem, a new method based on Petri- net named as multi-dimensional workflow model (MD_WFM) is produced in the paper. Though classifying the places and the transitions, the MD_WFM not only achieve the unification of control flow, data flow, resource flow in the workflow model, but also improve the workflow model description capacity and the model analysis capability. The graphical expression for the MD_WFM is put forward and the MD_WFM patterns are discussed in the paper, which make the workflow modeling maneuverable and easy.

The topological properties of peer-to-peer overlay networks are critical factors that dominate the performance of these systems. Several non-constant and constant degree interconnection networks have been used as topologies of many peer-to-peer networks. One of these has many desirable properties: the Kautz digraph. Unlike interconnection networks, peer-to-peer networks need a topology with an arbitrary size and degree, but the complete Kautz digraph does not possess these properties. In this paper, we propose Moore: the first effective and practical peer-to-peer network based on the incomplete Kautz digraph withO(logdN) diameter and constant degree under a dynamic environment. The diameter and average routing path length are [logd(N) - logd(1 + 1/d)] and logdN, respectively, and are shorter than that of CAN, butterfly, and cube-connected-cycle. They are close to that of complete de Bruijn and Kautz digraphs. The message cost of node joining and departing operations are at most 2.5dlogdNand (2.5d+ 1) logdN, and onlydand 2dnodes need to update their routing tables. Moore can achieve optimal diameter, high performance, good connectivity and low congestion evaluated by formal proofs and simulations.

Abstract — A bloom filter is a simple, space-efficient, randomized data structure for concisely representing a static data set, in order to support approximate membership queries. It has great potential for distributed applications where systems need to share information about what resources they have. The space efficiency is achieved at the cost of a small probability of false positive in membership queries. However, for many applications the space savings and short locating time consistently outweigh this drawback. In this paper, we introduce dynamic bloom filters (DBF) to support concise representation and approximate membership queries of dynamic sets, and study the false positive probability and union algebra operations. We prove that DBF can control the false positive probability at a low level by adjusting the number of standard bloom filters used according to the actual size of current dynamic set. The space complexity is also acceptable if the actual size of dynamic set does not deviate too much from the predefined threshold. Furthermore, we present multidimension dynamic bloom filters (MDDBF) to support concise representation and approximate membership queries of dynamic sets in multiple attribute dimensions, and study the false positive probability and union algebra operations through mathematic analysis and experimentation. We also explore the optimization approach and three network applications of bloom filters, namely bloom joins, informed search, and global index implementation. Our simulation shows that informed search based on bloom filters can obtain higher recall and success rate of query than the blind search protocol.

As the increasing development of application technology and infrastructure, many kinds of application and resources can be encapsulated as Web service and its variations, and service-oriented computing become research hot. But, we believe service-oriented computing should establish on virtualized service rather than concrete service instance directly. In this paper, we present formalized model of virtualized service and service instance, and define the concept of service virtualization. Then, we propose the implementation solution of virtualized service-oriented application from perspective of global and local instantiation process.