Traditional short video recommendations primarily enhance user retention by reinforcing existing user preferences, potentially leading to information cocoons. Conversely, proactive recommendations aim to diversify user interests by exposing users to content beyond their historical preferences. However, current proactive approaches face three limitations: (1) homogeneous receptivity assumption, neglecting individual differences in users' openness to new interests; (2) short-term item exposure without interest anchoring, focusing on item-level shifts rather than interest evolution; and (3) static feedback utilization, failing to incorporate dynamic user feedback during the recommendation adequately. To address these challenges, we propose ProRec-Video, a proactive framework that guides hierarchical interest transitions through three innovations. First, User Receptivity Profiling assesses individual openness for new interests, ensuring personalized transition pacing. Second, Hierarchical Interest Transition Planning decomposes complex interest shifts into intermediate steps to generate smooth interest transition paths and semantically coherent video sequences, addressing overemphasis on item exposure. Third, Dynamic Feedback Adaptation integrates agent-based simulation and Reflexion mechanisms to refine interest transition paths and video sequences based on real-time user feedback, enhancing adaptability and satisfaction. Extensive experiments on two datasets demonstrate that ProRec-Video achieves a significant improvement in proactive recommendation performance, with an interest transition success rate of 85% and a user satisfaction rate of 78.3%.

Instruction tuning (IT) improves large language models (LLMs) by aligning their outputs with human instructions, but its success depends critically on training data quality, and datasets such as Alpaca often contain noisy or suboptimal examples that undermine fine-tuning.Prior selection strategies score samples using general-purpose LLMs (e.g., GPT), leveraging their strong language understanding yet introducing inherent biases that misalign with the target model's behavior and yield unstable downstream performance.Influence-based methods address this by estimating each example's marginal contribution to overall performance, but they typically assume additive contributions and therefore overlook higher-order interactions among samples.To overcome these limitations, we propose JI 2 S, a novel framework that jointly models both marginal and combinatorial influences within sample groups.Applying JI 2 S to select the top 1,000 most influential examples from Alpaca, we fine-tune LLaMA2-7B, Mistral-7B, and LLaMA2-13B and evaluate them on Open LLM Benchmarks, MT-Bench, and GPT-4-judged pairwise comparisons.Our experiments show that JI 2 S consistently outperforms full-dataset training and strong baselines, highlighting the value of capturing joint influence for high-quality instruction fine-tuning.We provide our code in this GitHub repository.

Abstract The typical aim of relational triple extraction is to identify entities along with their relations from unstructured text, which is a crucial task in information extraction. Recent methods achieve considerable performance by mining semantic associations within the input sentence but they hardly exploit the equally important semantic meaning of relations. Most methods simply represent relations as numeric labels and the rich semantic information is not fully utilized to enhance performance. To address the issue, we decompose the task into two sequential subtasks, entity pairing and relation matching, from a novel perspective and then propose a semantic-preserving model SPRel. Specifically, SPRel first extracts entity pairs associated with at least one relation, and then matches them with certain relations according to the descriptive text of relations. Comprehensive experiments on two widely used datasets demonstrate that SPRel outperforms previous methods, particularly in handling complex scenarios.

User identity linkage aims to correlate multiple virtual identities of the same person across different social networks. It is widely used in person profile integration, potential friend recommendation, user behavior prediction, identity verification, etc. Since the data inconsistency and network heterogeneity among social networks, extracting features from profiles, contents, and network structures may lead to severe random noises. To this end, we propose PsyLink, a user identity linkage method via psychological characteristic modeling. To reduce the data noise, PsyLink extracts both internal personality characteristics and external interest characteristics to represent an individual. Furthermore, we design a neighborhood enhancement strategy to capture latent higher-order structural features through a graph contrastive learning technique. Under various parameter settings, the experimental results demonstrate that the macro-f1 of PsyLink improves 19.23% on average compared with state-of-the-art deep learning models. Combined with the psychological characteristics and graph contrastive learning, PsyLink is able to adequately learn the similarities and differences between user identities across social networks.

Both 5G Internet and COVID-19 pandemic have increasingly prompted thousands of companies and organizations to shift from a centralized office model to a distributed home model, which poses a new requirement: how to securely and rapidly share private data for coworkers on Internet. The covert communication systems are widely used to deliver private information because of the possibility of extending the system to Internet-scale size. However, most existing systems are inadequate to solve the requirement, since either the servers in centralized systems face the risk of being monitored and infiltrated, or the multi-hop routing schemes in decentralized systems lead to diverse attacks and high delivery latency. To this end, we proposed a scalable covert communication service for coworkers, called SC2. For security and hiddenness, we adopt the content slicing and multichannel routing to prevent adversary from monitoring and analyzing data. For scalability, we design a two-hop logic overlay to support low latency routing, and an adaptive channel selction technique to exploit the available bandwidth of the system. To evaluate the performance of SC2, we deploy the system in various IoT clouds and storage clouds. The experimental results demonstrate that SC2 is able to transmit both short messages and bulk content. Under various parameter settings, the delivery latency of SC2 linearly decreases with the number of channels, and SC2 takes full advantage of the available bandwidth with the growing number of users.

Distance Measuring between two mixed data objects is the basis of many learning algorithms. The complex relevance between heterogeneous – various types/scales – attributes has a significant influence on the measured results. In this paper, we propose an End-to-End Distance Measuring method for mixe d data based on deep relevance learning, called E2DM. Existing methods confuse the attributes space by mapping the discrete attribute values to new continuous values, or discretize continuous attributes values without considering the relevance. In contrast, E2DM directly manipulates on the original data with data conversion and relevance learning simultaneously to avoid information loss and attribute space confusion. E2DM firstly estimates internal relevance (i.e., relevance within the attribute) influenced distance by considering the categorical attribute value frequency and mapping numerical attribute values into multiple bins. Then it takes a wrapper approach to iteratively optimize relevance influenced distance and bin boundaries using a Frobenius-norm deviation as its objective function. Co-occurrence Mover’s Distance is proposed to explicitly explore relevance between attributes in each iteration. Finally, the distance for numerical attribute values is refined based on the original values and the fallen bin centers. Experimental results on a number of real-world datasets demonstrate that E2DM outperforms the state-of-the-art methods.

Summary Many modern distributed storage systems adopt erasure coding to protect data from frequent server failures for cost reason. Reconstructing data in failed servers efficiently is vital to these erasure‐coded storage systems. To this end, tree‐structured reconstruction mechanisms where blocks are transmitted and combined through a reconstruction tree have been proposed. However, existing tree‐structured reconstruction mechanisms build reconstruction trees from the perspective of available network bandwidths between servers, which are fluctuating and difficult to measure. Besides, these reconstruction mechanisms cannot reduce data transmission. In this study, we overcome these limitations by proposing LAR, a locality‐aware tree‐structured reconstruction mechanism. LAR builds reconstruction trees from the perspective of data locality, which is stable and easy to obtain. More importantly, by building reconstruction trees that combine blocks closer to each other first, LAR can reduce the data transmitted through the network core and hence speed up reconstruction. We prove that a minimum spanning tree is an optimal reconstruction tree that minimizes core bandwidth usage. We also design and implement a general reconstruction framework that supports all tree‐structured reconstruction mechanisms and nearly all erasure codes. Large‐scale simulations on commonly deployed network topologies show that LAR consumes 20%–61% less core bandwidth than previous reconstruction mechanisms. Thorough experiments on a testbed consisting of 40 physical servers show that LAR improves proactive recovery throughput by 23% at least and improves degraded read rate by up to 68%.

Analysis of high-dimensional data often suffers from the curse of dimensionality and the complicated correlation among dimensions. Dimension reduction methods often are used to alleviate these problems. Existing outlier detection methods based on dimension reduction usually only rely on reconstruct ion error to detect outlier or apply conventional outlier detection methods to the reduced data, which could deteriorate the performance of outlier detection as only considering part of the information from data. Few studies have been done to combine these two strategies to do outlier detection. In this paper, we proposed an outlier detection method based on Variational Autoencoder (VAE), which combines low-dimensional representation and reconstruction error to detect outliers. Specifically, we first model the data use VAE, then extract four outlier scores from VAE model, finally propose an ensemble method to combine the four outlier scores. The experiments conducted on six real-world datasets show that the proposed method performs better than or at least comparable to state of the art methods.

Recently, sequence anomaly detection has been widely used in many fields. Sequence data in these fields are usually multi-dimensional over the data stream. It is a challenge to design an anomaly detection method for a multi-dimensional sequence over the data stream to satisfy the requirements of accuracy and high speed. It is because: (1) Redundant dimensions in sequence data and large state space lead to a poor ability for sequence modeling; (2) Anomaly detection cannot adapt to the high-speed nature of the data stream, especially when concept drift occurs, and it will reduce the detection rate. On one hand, most existing methods of sequence anomaly detection focus on the single-dimension sequence. On the other hand, some studies concerning multi-dimensional sequence concentrate mainly on the static database rather than the data stream. To improve the performance of anomaly detection for a multi-dimensional sequence over the data stream, we propose a novel unsupervised fast and accurate anomaly detection (FAAD) method which includes three algorithms. First, a method called “information calculation and minimum spanning tree cluster” is adopted to reduce redundant dimensions. Second, to speed up model construction and ensure the detection rate for the sequence over the data stream, we propose a method called “random sampling and subsequence partitioning based on the index probabilistic suffix tree.” Last, the method called “anomaly buffer based on model dynamic adjustment” dramatically reduces the effects of concept drift in the data stream. FAAD is implemented on the streaming platform Storm to detect multi-dimensional log audit data. Compared with the existing anomaly detection methods, FAAD has a good performance in detection rate and speed without being affected by concept drift.

Unavoidable noise in real-world categorical data presents significant challenges to existing outlier detection methods because they normally fail to separate noisy values from outlying values. Feature subspace-based methods inevitably mix noisy values when retaining an entire feature because a feature may contain both outlying values and noisy values. Pattern-based methods are normally based on frequency and are easily misled by noisy values, resulting in many faulty patterns. This paper introduces a novel unsupervised framework termed OUVAS, and its parameter-free instantiation RHAC to explore a high-quality outlying value set for detecting outliers in noisy categorical data. Based on the observation that the relations between values reflect their essence, OUVAS investigates value similarities to cluster values into different groups and combines cluster-level analysis and value-level refinement to identify an outlying value set. RHAC instantiates OUVAS by three successive modules (i.e., the combination of Ochiai coefficient and LOUVAIN algorithm to cluster values, hierarchical value coupling learning to perform cluster-level analysis, and a threshold to divide fake and real outlying values in value-level refinement). We show that (i) RHAC-based outlier detector significantly outperforms five state-of-the-art outlier detection methods; (ii) Extended RHAC-based feature selection method successfully improves the performance of existing outlier detectors and performs better than two latest outlying feature selection methods.

Erasure coding has received considerable attentions due to the better tradeoff between the space efficiency and reliability. The frequent update of the stored data in the distributed storage systems has posed a new challenge for erasure codes: how to update the erasure-coded data in a general, efficient and adaptive way. However, existing update schemes of erasure codes are inadequate to meet these requirements, since their code-related update manners lead to a low generality, their star-structured data transmission manners lead to a low update efficiency, and their redo manners when encountering the node failure lead to a low adaptivity. In this paper, we propose an adaptive update scheme with the tree-structured transmission, called TA-Update, which consists of a code-independent update framework and three algorithms: the rack-aware tree construction algorithm, the top-down data processing algorithm and the rollback-based failure processing algorithm. For generality, we propose a code-independent update framework with the tree structure to support the MDS code with any coding parameter. For efficiency, a rack-aware tree construction algorithm is proposed to achieve the high available bandwidth, which organizes the data node and parity nodes as an update tree. Moreover, a top-down data processing algorithm is proposed to achieve the high transmission and computation efficiency, which pipelines the data transmission along the update tree and distributes the encoding computations among all the participating nodes. For adaptivity, we propose a rollback-based failure processing algorithm to achieve high adaptivity, which handles the node failure during update with the existing update tree in a rollback manner. To evaluate the performance of TA-Update, we conduct experiments on HDFS-RAID under various parameter settings on both 30 physical and 200 virtual machines. Extensive experiments confirm that TA-Update could support the various erasure codes with any parameter, improve the update efficiency by 30 percent and the adaptivity by 47 percent on average compared with the state-of-the-art approaches under various parameter settings.

Summary Characterized by the increasing arrival rate of live content, emergency applications pose a great challenge: how to disseminate data with diverse sizes to interested users in a real‐time manner. Most file sharing applications focus on the dissemination of bulk content with less consideration of users' interests. On the other hand, existing publish/subscribes are designed for notifying interested users with small‐sized content. To bridge this gap, we propose CAPS, a cloud‐assisted publish/subscribe service for time‐critical bulk content dissemination. In CAPS, a hybrid 2‐layer architecture is proposed to knit servers in the cloud and clients in the internet. Through dividing each event into attribute‐value pairs and the data content, CAPS provides both event matching service and data distribution in a parallel manner. To improve the upload bandwidth of data distribution, we propose a helper‐based content distribution protocol, where the servers not only guide the clients with similar interests to exchange their received data blocks but also contribute their own upload capacities to clients. Moreover, a volume‐aware helper renting scheme is proposed to adaptively adjust the scale of servers according to the churn of data volume, leading to a high‐performance price ratio. So as to evaluate the performance of CAPS, about 1000 virtual machines are deployed in our Cloud‐Stack testbed. Extensive experiments confirm that CAPS can linearly reduce the download completion time with the growing number of servers, adaptively adjust the upload capacity in tens of seconds according to the change of the workloads, and ensure reliable data dissemination even if a large number of nodes frequently churn or instantaneously fail. Compared with the state‐of‐the‐art approaches, CAPS demonstrates better performance under various parameter settings.

Summary The increasing data volume in a large number of applications presents a dire need for supporting the reliable data management in distributed storage systems. Existing classical erasure codes, such as the Reed‐Solomon codes and locally reconstruction codes, are widely adopted by many distributed storage systems. However, existing researches mainly focus on proposing new optimized codes, ignoring the optimization of the encoding process with the classical codes, where inefficient encoding process greatly degrades the encoding performance of the distributed storage systems. Thus, how to complete the encoding process in an efficient way has become the challenge for adopting the classical codes. In this paper, we propose a decentralized redundancy generation scheme on the basis of the codes with locality, called D2CP, where a 2‐step framework is proposed to support both the data patterns (replication to encodinganddirect encoding) and codes with locality with any parameter set. For improving the insertion throughput, D2CP adopts a data placement technique with consistent hashing to guide the selection of nodes. For reducing the network traffic cost, D2CP adopts a data sending scheduling technique to schedule the transmission of the source nodes and a cooperative parity generation technique to generate the parity data cooperatively. To evaluate the performance of D2CP, we conduct experiments on our RAID distributed storage system under various parameter settings with both 30 physical and 200 virtual servers. Extensive experiments confirm that D2CP can improve the encoding throughput by 20% and 32% and reduce the network traffic cost by 16% and 33% compared with the typical approaches on average for the 2 data patterns respectively.

The big data era is characterized by the emergence of live data with high volume and fast arrival rate, it poses a new challenge to stream processing applications: how to process the unbounded live data in real time with high throughput. The sliding window technique is widely used to handle the unbounded live data by storing the most recent history of streams. However, existing centralized solutions cannot satisfy the requirements for high processing capacity and low latency due to the single-node bottleneck. Moreover, existing studies on distributed windows primarily focus on specific operators, while a general framework for processing various window-based operators is wanted. In this paper, we firstly classify the window-based operators to two categories: data-independent operators and data-dependent operators. Then, we propose GDSW, a general framework for distributed count-based sliding window, which can handle both of data-independent and data-dependent operators. Besides, in order to balance system load, we further propose a dynamic load balance algorithm called DAD based on buffer usage. Our framework is implemented on Apache Storm 0.10.0. Extensive evaluation shows that GDSW can achieve sub-second latency, and 10X improvement in throughput compared with centralized processing, when processing rapid data rate or big size window.

Erasure coding has received considerable attention due to the better tradeoff between the space efficiency and reliability. However, it consumes large network traffic and long time to complete the update, involving updates of both data nodes and parity nodes. Existing solutions to this problem mainly focus on proposing new class of codes with lower update complexity to reduce the network traffic, ignoring the optimization of data transmission structure. In fact, the data transmission structure has great impact on the update. In this paper, we propose T-Update, a tree-structured update scheme with top-down transmission that minimizes the update time for erasure-coded data with no additional network traffic. Specially, we propose a rack-aware tree construction technique to construct an update tree to organize the data connections, with the data node as the root and the parity nodes as the children. To maximize the update efficiency, we propose a top-down data transmission technique to guide the data transmission and distribute the data computation for updating the parity nodes. To evaluate the performance of T-Update, we conduct experiments on HDFS-RAID under various parameter settings on both 30 physical and 200 virtual servers. Extensive experiments confirm that T-Update reduces the update time by 27% and 32% on average compared with two typical update schemes respectively.

Nowadays sequence data tends to be multi-dimensional sequence over data stream, it has a large state space and arrives at unprecedented speed. It is a big challenge to design a multi-dimensional sequence outlier detection method to meet the accurate and high speed requirements. The traditional methods can't handle multi-dimensional sequence effectively as they have poor abilities for multi-dimensional sequence modeling, and can't detect outlier timely as they have high computational complexity. In this paper we propose a variable Markovian based outlier detection method for multi-dimensional sequence over data stream, VMOD, which consists of two algorithms: mutual information based feature selection algorithm (MIFS), variable Markovian based sequential analysis algorithm (VMSA). It uses MIFS algorithm to reduce the state space and redundant features, and uses VMSA algorithm to accelerate the outlier detection. Through VMOD method, we can improve the detection rate and detection speed. The MIFS algorithm uses mutual information as similarity measures and adopt clustering based strategy to select features, it can improve the abilities for sequence modeling through reducing the state space and redundant features, consequently, to improve the detection rate. The VMSA algorithm use random sample and index structure to accelerate the variable Markovian model construction and reduce the model complexity, consequently, to quicken the outlier detection. The experiments show that VMOD can detect outlier effectively, and reduce the detection time by at least 50% compared with the traditional methods.

How to design a low-latency and accurate approach for user behavior anomaly detection over data streams has become a great challenge. However, existing studies cannot meet low-latency and accurate requirements, due to a large number of subsequences and sequential relationship in behaviors. This paper presents BADSM, a user behavior anomaly detection approach based on sequence mining over data streams that seeks to address such challenge. BADSM uses self-adaptive behavior pruning algorithm to adaptively divide data stream into behaviors and decrease the number of subsequences to improve the efficiency of sequence mining. Meanwhile, the top-k abnormal scoring algorithm is used to reduce the complexity of traversal and obtain quantitative detection result to improve accuracy. We design and implement a streaming anomaly detection system based on BADSM to perform online detection. Extensive experiments confirm that BADSM significantly reduces processing delay by at least 36.8% and false positive rate by 6.4% compared with the classic sequence mining approach PrefixSpan.

Summary Repairs of multiple failures in distributed storage systems have posed the challenges for erasure coding: how to minimize the repair time with the least extra repair network traffic cost. However, existing repair schemes designed for single failure suffer from the high network traffic cost due to the serial repairs for multiple failures. Repair schemes designed for multiple failures suffer from long repair time due to the centralized repair structure. In this paper, we propose a decentralized adaptive repair scheme, called DARS, to minimize the repair time with the least extra network traffic cost. Specially, we propose a three‐layer repair model to support the repairs for both the single and multiple failures. For low repair time, a bandwidth‐aware node selection technique is proposed to guide the selection of nodes, and a line‐structured data transmission technique is proposed to organize the data transmission between the providers and the newcomer. For the least extra network traffic cost, a core‐based data distribution technique is proposed to organize the data transmission between the coordinator and other newcomers, and an intersection provider adjustment technique is proposed to adaptively adjust the number of intersection providers. Moreover, we adopt the ‘lazy repair’ within a stripe to further reduce the repair network traffic cost. We implement and evaluate DARS on our raid distributed storage system under various parameter settings with 30 physical machines and 200 virtual machines. Experimental results confirm that DARS reduces the repair time by 29% and 55% on average compared with tree‐structured repair and CORE, respectively. Copyright © 2015 John Wiley & Sons, Ltd.

SUMMARY Characterized by the emergence of a large number of live content, the emergency applications have received increasing attention in recent years. Providing a general and scalable event, matching service can precisely notify users latest information that they are interested in. However, because the live content arrival rate may churn significantly in a short time and subscriptions with various patterns tend to be skewed, it is challenging to increase the generality, scalability, and elasticity of the matching process. We propose a novel parallel event matching service based on the cloud computing environment, called GSEM, to satisfy these requirements. GSEM first presents a two‐hop framework and a general subscription pattern to handle various patterns of subscriptions. To provide scalable matching service, ahybrid content space partitionscheme is proposed to divide large skewed subscriptions into multiple small clusters managed by a group of parallel servers. To adapt to the sudden change of event arrival rate, GSEM elastically adjusts the scale of servers and rebalances their workloads through aperformance‐aware detectiontechnique. A prototype deployment on the OpenStack platform shows that GSEM achieves scalable matching throughput with the growth of servers, elastic service capacity with the change of event arrival rate, and significantly outperforms the existing cloud based systems in various workloads. Copyright © 2014 John Wiley & Sons, Ltd.

Characterized by the increasing arrival rate of live content, the emergency applications pose a great challenge: how to disseminate large-scale live content to interested users in a scalable and reliable manner. The publish/subscribe (pub/sub) model is widely used for data dissemination because of its capacity of seamlessly expanding the system to massive size. However, most event matching services of existing pub/sub systems either lead to low matching throughput when matching a large number of skewed subscriptions, or interrupt dissemination when a large number of servers fail. The cloud computing provides great opportunities for the requirements of complex computing and reliable communication. In this paper, we propose SREM, a scalable and reliable event matching service for content-based pub/sub systems in cloud computing environment. To achieve low routing latency and reliable links among servers, we propose a distributed overlay SkipCloud to organize servers of SREM. Through a hybrid space partitioning technique HPartition, large-scale skewed subscriptions are mapped into multiple subspaces, which ensures high matching throughput and provides multiple candidate servers for each event. Moreover, a series of dynamics maintenance mechanisms are extensively studied. To evaluate the performance of SREM, 64 servers are deployed and millions of live content items are tested in a CloudStack testbed. Under various parameter settings, the experimental results demonstrate that the traffic overhead of routing events in SkipCloud is at least 60 percent smaller than in Chord overlay, the matching rate in SREM is at least 3.7 times and at most 40.4 times larger than the single-dimensional partitioning technique of BlueDove. Besides, SREM enables the event loss rate to drop back to 0 in tens of seconds even if a large number of servers fail simultaneously.

The big data era is characterized by the emergence of live content with increasing complexities of data dimensionality and data sizes, which poses a new challenge to emergency applications: how to timely disseminate large-scale live content to users who are interested in. The publish/subscribe (pub/sub) model is widely used to disseminate data because of its possibility of expanding the system to Internet-scale size. However, existing pub/sub systems are inadequate to meet the requirement of disseminating live content in the big data era, since their multi-hop routing techniques and coarse-grained partitioning techniques lead to a low matching throughput, and their upload capacities do not scale well. In this paper, we propose a general scalable and elastic pub/sub service based on the cloud computing environment, called GSEC. For generality, we propose a two-layer pub/sub framework to support the dissemination with diverse data sizes and data dimensionality. For scalability, a hybrid space partitioningtechnique is proposed to achieve high matching throughput, which divides subscriptions into multiple clusters in a hierarchical manner. Moreover, a helper-based content distribution technique is proposed to achieve high upload bandwidth, where servers act as both providers and coordinators to fully explore the upload capacity of the system. For elasticity, we propose a performance-aware provisioningtechnique to adjust the scale of servers to adapt to the churn workloads. To evaluate the performance of GSEC, about 1,000 servers are deployed and hundreds of thousands of live content items are tested in our CloudStack-based testbed. Extensive experiments confirm that GSEC can linearly increase the capacities of event matching and content distribution with the growth of servers, adaptively adjust these capacities in tens of seconds according to the churn workloads, and significantly outperforms the state-of-the-art approaches under various parameter settings.

To guarantee the reliability of distributed storage systems, erasure coding, as a redundant scheme, has received increasingly attention because it can greatly improve the space efficiency compared with the replica schemes. However, it takes a long time and consumes a lot of network bandwidth for erasure coding to repair the lost data on failed nodes. The state-of-art studies focus on the repairing optimization for the single-node-failure context. Real-world experiments have clearly shown that multi-node failures indeed happen in cloud storage systems. Borrowing single-node repairing techniques to the multi-node setting faces challenges on the efficiency. We propose a mutually cooperative recovery scheme MCRTREE based on the tree structure for multiple node failures. MCRTREE improves the bandwidth utilization and reduces the repair time by the construction of regeneration trees between each new node (denoted as newcomers) and alive nodes (denoted as providers). Further, MCRTREE reduces the size of the data volumes to be transmitted for the repair process. Numerical experiments show that MCRTREE consumes less storage cost and the maintenance bandwidth compared with other redundancy recovery schemes. Trace-driven simulation results reveal that the MCRTREE reduces the regeneration time by 30% - 50%, improves the successful regeneration probability by 10% - 20% and the data availability by 10% - 20% compared with the typical repair schemes.

Network size is one of the fundamental information of distributed applications. The approach to estimate network size must feature both high accuracy and robustness in order to adapt to the dynamic environment in different topologies. However, existing approaches fail to guarantee accuracy and robustness simultaneously in dynamic topologies due to the randomness of nodes sampled. In this paper, we propose a churn adaptive approach to network size estimation – CANSE, which collects closest nodes in identification to each node’s identification by sampling nodes periodically. Each node collects closest identifications by two schemes. One scheme is sampling random nodes from random walks along the topology. The other one is exchanging the closest identifications with other nodes. Finally, each node calculates the average spacing of the closest identifications collected to estimate network size. Compared with existing approaches, extensive experiments show that CANSE provides accurate estimation values quickly in various dynamic topologies.