Remote associations play a crucial role in enhancing creativity during design ideation, yet designers face challenges in effectively creating and integrating them. Our formative study (N = 5) shows the potential of AI-generated remote associations to facilitate this process, but there are still challenges to understand and apply them. To address these, we propose StarBurst, which supports design ideation through AI-generated remote associations. It consists of three core components: (1) generating diverse remote associations from images to expand creative possibilities; (2) constructing an attribute map to explore connections between associative elements; and (3) providing suggestions to integrate these associations into the final design idea. Through two forms of user studies (N = 32, N = 16), we found that StarBurst outperformed designers in generating remote associations and provided more effective support for diverse and creative idea development compared to the baseline system. Additionally, we discussed how users’ usage patterns and perceptions influence StarBurst’s effectiveness.

AI literacy is critical for young children as AI is rapidly integrated into people’s daily lives. However, the complexity of AI knowledge presents significant learning challenges, and there is currently a lack of effective approaches for converting complex AI concepts into easy-comprehend content. Based on the formative analysis, we propose Hello!AI, an interactive rhyme-based AI literacy education game targeted at children in Grades 2–6 of primary schools. Hello!AI comprises 3 modules: (i) Algorithm Adventure, focusing on basic AI concept learning, (ii) Algorithm Handbook, promoting thinking and reflection on AI algorithms, and (iii) City Builder, emphasizing the application of AI algorithm to solve real-life problems. We developed the prototype system, iterated it through pilot study, and then conducted a user study. The results demonstrate that Hello!AI can effectively engage children and, to a certain extent, improve their ability to understand and apply AI knowledge, as well as their thinking and reflective capabilities regarding AI technologies.

From image to video understanding, the capabilities of Multi-modal LLMs (MLLMs) are increasingly powerful. However, most existing video understanding benchmarks are relatively short, which makes them inadequate for effectively evaluating the long-sequence modeling capabilities of MLLMs. This highlights the urgent need for a comprehensive and integrated long video understanding benchmark to assess the ability of MLLMs thoroughly. To this end, we propose ALLVB (ALL-in-One Long Video Understanding Benchmark). ALLVB's main contributions include: 1) It integrates 9 major video understanding tasks. These tasks are converted into video QA formats, allowing a single benchmark to evaluate 9 different video understanding capabilities of MLLMs, highlighting the versatility, comprehensiveness, and challenging nature of ALLVB. 2) A fully automated annotation pipeline using GPT-4o is designed, requiring only human quality control, which facilitates the maintenance and expansion of the benchmark. 3) It contains 1,376 videos across 16 categories, averaging nearly 2 hours each, with a total of 252k QAs. To the best of our knowledge, it is the largest long video understanding benchmark in terms of the number of videos, average duration, and number of QAs. We have tested various mainstream MLLMs on ALLVB, and the results indicate that even the most advanced commercial models have significant room for improvement. This reflects the benchmark's challenging nature and demonstrates the substantial potential for development in long video understanding.

In the era of widespread digital art dissemination, visible watermarks provide immediate copyright identification by overlaying visible markers, addressing the lag issue of invisible watermarks that are difficult to prevent in advance due to post hoc evidence collection, thus meeting artists' needs for preemptive prevention and explicit protection. However, existing approaches struggle to balance aesthetics and functionality. To address this challenge, we conducted an exploratory study with watermarking experts, identifying key principles, six common design patterns, and a systematic watermarking workflow. Based on these insights, we developed an end-to-end, perceptual-aware framework for aesthetic-preserving watermark embedding, modeled after expert workflows in 5 phases. Using the Chain-of-Thought strategy, we optimized prompt instructions to guide the Vision-Language Model in emulating experts' decision-making, generating effective watermarking schemes and conducting objective visual evaluations. Iterative feedback optimization ensures watermarked images adhere to aesthetic principles. Quantitative and qualitative experiments demonstrate the system's superiority over baseline methods in preserving aesthetics and ensuring effective copyright protection.

This paper presents CrePoster, a data-driven framework to generate aesthetic posters for Chinese cultural relics, aiming to enhance the exhibition experience and promote cultural spread. CrePoster comprises three modules: (1) object segmentation module, (2) content generation module, and (3) poster generation module. Upon processing a cultural relic image, the object segmentation module first leverages a cascaded U2Net-SAM structure to obtain the visual target. Secondly, the content generation module utilizes a multi-target learning-enabled caption generator to produce professional captions. Thirdly, the Multimodal Large Language Model (MLLM) based poster generation module adaptively creates aesthetic parameters, including layout and color scheme, ultimately rendering them into refined posters.

Sequential data learning is vital to harnessing the encompassed rich knowledge for diverse downstream tasks, particularly in healthcare (e.g., disease prediction). Considering data sensitiveness, privacy-preserving learning methods, based on federated learning (FL) and split learning (SL), have been widely investigated. Yet, this work identifies, for the first time, existing methods overlook that sequential data are generated by different patients at different times and stored in different hospitals, failing to learn the sequential correlations between different temporal segments. To fill this void, a novel distributed learning frameworkSTSLis proposed by training a model on the segments in order. Considering that patients have different visit sequences,STSLfirst implements privacy-preserving visit ordering based on a secure multi-party computation mechanism. Then batch scheduling participates patients with similar visit (sub-)sequences into the same training batch, facilitating subsequent split learning on batches. The scheduling process is formulated as an NP-hard optimization problem on balancing learning loss and efficiency and a greedy-based solution is presented. Theoretical analysis proves the privacy preservation property ofSTSL. Experimental results on real-world eICU data show its superior performance compared with FL and SL ($5\% \sim 28\%$better accuracy) and effectiveness (a remarkable 75% reduction in communication costs).

Graphic Layout Generation focuses on providing layout references for various visual design tasks, such as advertisement design and poster design. Researchers have conducted exploratory studies from different dimensions, such as human-computer interaction and layout representation. With the success of deep learning techniques, there has been a recent surge in research on graphic layout generation using deep generative models. Yet there are still no comprehensive studies on graphic layout generation. In this paper, we review methods for graphic layout generation from two perspectives: implementation and interactivity. We analyze the current status, the advantageous application scenarios, and the challenges of different methods. Based on the analysis results, we summarize the workflow of designers collaborating with graphic automatic layout systems and propose potential directions for future research.

The unique artistic style is crucial to artists’ occupational competitiveness, yet prevailing Art Commission Platforms rarely support style-based retrieval. Meanwhile, the fast-growing generative AI techniques aggravate artists’ concerns about releasing personal artworks to public platforms. To achieve artistic style-based retrieval without exposing personal artworks, we propose FedStyle, a style-based federated learning crowdsourcing framework. It allows artists to train local style models and share model parameters rather than artworks for collaboration. However, most artists possess a unique artistic style, resulting in severe model drift among them. FedStyle addresses such extreme data heterogeneity by having artists learn their abstract style representations and align with the server, rather than merely aggregating model parameters lacking semantics. Besides, we introduce contrastive learning to meticulously construct the style representation space, pulling artworks with similar styles closer and keeping different ones apart in the embedding space. Extensive experiments on the proposed datasets demonstrate the superiority of FedStyle.

Designers widely accept AI as a partner in the design process for its efficient and intelligent decision-making. However, AI is often not perfect, and AI error often makes humans dumbfounded. Literature has pointed out the value of such AI error, while still leaving its inspiration essence and application strategies uncharted from the practice perspective. This work focuses on bridging the practice gap by looking into and exploiting the imaginative "mislabeled" objects of object detection models. To gain insights into the inspiration of AI "error", we collected a dedicated AI "error" dataset from object detection and invited eight designers to share divergent comments on the "mislabeled" objects. Coding was then performed on the comments, which summarizes the inspiration of AI "error" into six atomic dimensions. Subsequently, we took a step further to an exploratory study, a comparative ideation experiment with 20 designers, investigating how to apply these inspiration dimensions to create ideas. Questionnaire and interview results revealed that essential inspiration of AI "error" could positively activate creativity, especially the "Outline" dimension. A design model CETR is then formulated by summarizing the application of atomic inspiration of "error" into four forms of creativity, which could be taken as a guideline for cooperative design with AI "error". In addition, we also sketch two approaches to generate more inspiring and applicable AI "error", elaborate on two principal characteristics of AI "error" for promoting creativity, and propose three strategies for better co-creating with AI "error". Finally, we provide insight into design research about AI self-awareness and human-AI collaboration.

Increasing artwork plagiarism incidents stresses the urgent need for proper copyright protection on behalf of the creators. The latest development in this context focuses on embedding watermarks via deep encoder-decoder networks. However, we find that deep watermarking has a serious vulnerability on its robustness when facing deliberate plagiarism. To manifest it, we construct an attack that misuses watermarking encoder as a plagiarism lookout for bypassing copyright detection. As a remedy, we propose a patch-level deep watermarking framework (DIPW) to retain copyright evidence in essential patches with plagiarism resistance, inspired by a user study observation that subject elements in artworks are the principal plagiarism entities. Technically, DIPW adaptively finds the embedding patches by identifying a subset of non-overlapping and feature-rich objects; and tailors the model with dual-distortion losses and adversarial plagiarism noise injection for robustness. Experimental results demonstrate the superiority of DIPW in facilitating better robustness, secrecy, and imperceptibility with acceptable time burden.

Recently, a hierarchical fine-grained fusion mechanism has been proved effective in cross-modal retrieval between videos and texts. Generally, the hierarchical fine-grained semantic representations (video-text semantic matching is decomposed into three levels including global-event representation matching, action-relation representation matching, and local-entity representation matching) to be fused can work well by themselves for the query. However, in real-world scenarios and applications, existing methods failed to adaptively estimate the effectiveness of multiple levels of the semantic representations for a given query in advance of multilevel fusion, resulting in a worse performance than expected. As a result, it is extremely essential to identify the effectiveness of hierarchical semantic representations in a query-adaptive manner. To this end, this article proposes an effective query-adaptive multilevel fusion (QAMF) model based on manipulating multiple similarity scores between the hierarchical visual and text representations. First, we decompose video-side and text-side representations into hierarchical semantic representations consisting of global-event level, action-relation level, and local-entity level, respectively. Then, the multilevel representation of the video-text pair is aligned to calculate the similarity score for each level. Meanwhile, the sorted similarity score curves of the good semantic representation are different from the inferior ones, which exhibit a "cliff" shape and gradually decline (see Fig. fig1 as an example). Finally, we leverage the Gaussian decay function to fit the tail of the score curve and calculate the area under the normalized sorted similarity curve as the indicator of semantic representation effectiveness, namely, the area of good semantic representation is small, and vice versa. Extensive experiments on three public benchmark video-text datasets have demonstrated that our method consistently outperforms the state-of-the-art (SoTA). A simple demo of QAMF will soon be publicly available on our homepage: https://github.com/Lab-ANT.

Artificial Intelligence technology-driven Creativity Support Tools (AI-CSTs) provide specific field capability support for human creative activities. In this paper, we compare and analyze the current situation and trend of AI-CSTs design space in four aspects: creative stage, support form, support technology, and role diversity. Through a coding study and comparative analysis of 50 AI-CSTs cases, we discuss the impact of AI-CSTs on traditional workflows, the boundaries of AI-CSTs as co-creators, and how to treat AI errors, which provides insights for future AI-CSTs design. We summarize the collaboration framework in AI-CSTs. Finally, this paper also studies the information technology requirements and challenges of AI-CSTs research, which provides a new perspective to understanding the landscape of AI-CSTs.

Personalized Federated Learning (FL) handles the data heterogeneous problem by tailoring local models for each distributed data owner. Previous studies first train a highly-adaptable global model and then transfer it for personalization. However, the additional training aggravates burden of resource-limited end devices. Training a personalized local sub-network is a promising efficient solution. It normally prunes the global model by parameters’ scalar magnitude. In this paper, we found that the vector magnitude, i.e. the parameter stability, could further promote personalized FL. Driven by the local data characteristics, the values of some model parameters are hardly changed in their updates. But they consume the same resources as the changed ones. Thus, we propose Star-PFL, a STability-AwaRe algorithm for efficient FL Personalization. In Star-PFL, the data owner focuses on training non-stabilized parameters, and decreases the resource wastes on stabilized ones. Experimental results on two real-world biomedical datasets demonstrate that Star-PFL improves the accuracy (3.1%↑) and decreases the resource costs (communication 36.3%↓, computation 18.3%↓) than 5 typical baselines. The code is available at https://github.com/Guoyeting/Star-PFL.

Increasing artwork plagiarism incidents underscores the urgent need for reliable copyright protection for high-quality artwork images. Although watermarking is helpful to this issue, existing methods are limited in imperceptibility and robustness. To provide high-level protection for valuable artwork images, we propose a novel invisible robust watermarking framework, dubbed as IRWArt. In our architecture, the embedding and recovery of the watermark are treated as a pair of image transformations’ inverse problems, and can be implemented through the forward and backward processes of an invertible neural networks (INN), respectively. For high visual quality, we embed the watermark in high-frequency domains with minimal impact on artwork and supervise image reconstruction using a human visual system(HVS)-consistent deep perceptual loss. For strong plagiarism-resistant, we construct a quality enhancement module for the embedded image against possible distortions caused by plagiarism actions. Moreover, the two-stagecontrastive training strategy enables the simultaneous realization of the above two goals. Experimental results on 4 datasets demonstrate the superiority of our IRWArt over other state-of-the-art watermarking methods. Code: https://github.com/1024yy/IRWArt.

The pervasive view of the mobile crowd bridges various real-world scenes and people's perceptions with the gathering of distributed crowdsensing photos. To elaborate informative visuals for viewers, existing techniques introduce photo selection as an essential step in crowdsensing. Yet, the aesthetic preference of viewers, at the very heart of their experiences under various crowdsensing contexts (e.g., travel planning), is seldom considered and hardly guaranteed. We propose CrowdPicker, a novel photo selection framework with adaptive aesthetic awareness for crowdsensing. With the observations on aesthetic uncertainty and bias in different crowdsensing contexts, we exploit a joint effort of mobile crowdsourcing and domain adaptation to actively learn contextual knowledge for dynamically tailoring the aesthetic predictor. Concretely, an aesthetic utility measure is invented based on the probabilistic balance formalization to quantify the benefit of photos in improving the adaptation performance. We prove the NP-hardness of sampling the best-utility photos for crowdsourcing annotation and present a (1-1/e) approximate solution. Furthermore, a two-stage distillation-based adaptation architecture is designed based on fusing contextual and common aesthetic preferences. Extensive experiments on three datasets and four raw models demonstrate the performance superiority of CrowdPicker over four photo selection baselines and four typical sampling strategies. Cross-dataset evaluation illustrates the impacts of aesthetic bias on selection.

Joint extraction of entities and their relations benefits from the close interaction between named entities and their relation information. Therefore, how to effectively model such cross-modal interactions is critical for the final performance. Previous works have used simple methods, such as label-feature concatenation, to perform coarse-grained semantic fusion among cross-modal instances but fail to capture fine-grained correlations over token and label spaces, resulting in insufficient interactions. In this article, we propose a dynamic cross-modal attention network (CMAN) for joint entity and relation extraction. The network is carefully constructed by stacking multiple attention units in depth to dynamic model dense interactions over token-label spaces, in which two basic attention units and a novel two-phase prediction are proposed to explicitly capture fine-grained correlations across different modalities (e.g., token-to-token and label-to-token). Experiment results on the CoNLL04 dataset show that our model obtains state-of-the-art results by achieving 91.72% F1 on entity recognition and 73.46% F1 on relation classification. In the ADE and DREC datasets, our model surpasses existing approaches by more than 2.1% and 2.54% F1 on relation classification. Extensive analyses further confirm the effectiveness of our approach.

Word-character lattice models have been proved to be effective for some Chinese natural language processing (NLP) tasks, in which word boundary information is fused into character sequences. However, due to the inherently unidirectional sequential nature, prior approaches have only learned sequential interactions of character-word instances but fail to capture fine-grained correlations in word-character spaces. In this article, we propose a lattice-aligned attention network (LAN) that aims to model dense interactions over word-character lattice structure for enhancing character representations. By carefully combining cross-lattice module, gated word-character semantic fusion unit, and self-lattice attention module, the network can explicitly capture fine-grained correlations across different spaces (e.g., word-to-character and character-to-character), thus significantly improving model performance. Experimental results on three Chinese NLP benchmark tasks demonstrate that LAN obtains state-of-the-art results compared to several competitive approaches.

As a decentralized training paradigm, Federated learning (FL) promises data privacy by exchanging model parameters instead of raw local data. However, it is still impeded by the resource limitations of end devices and privacy risks from the ‘curious’ cloud. Yet, existing work predominately ignores that these two issues are non-orthogonal in nature. In this article, we propose a joint design (i.e., AHFL) that accommodates both the efficiency expectation and privacy protection of clients towards high inference accuracy. Based on a cloud-edge-end hierarchical FL framework, we carefully offload the training burden of devices to one proximate edge for enhanced efficiency and apply a two-level differential privacy mechanism for privacy protection. To resolve the conflicts of dynamical resource consumption and privacy risk accumulation, we formulate an optimization problem for choosing configurations under correlated learning parameters (e.g., iterations) and privacy control factors (e.g., noise intensity). An adaptive algorithmic solution is presented based on performance-oriented resource scheduling, budget-aware device selection, and adaptive local noise injection. Extensive evaluations are performed on three different data distribution cases of two real-world datasets, using both a networked prototype and large-scale simulations. Experimental results show that AHFL relieves the end's resource burden (w.r.t. computation time 8.58%$\downarrow$, communication time 59.35%$\downarrow$and memory consumption 43.61%$\downarrow$) and has better accuracy (6.34%$\uparrow$) than 3 typical baselines under the limited resource and privacy budgets. The code for our implementation is available athttps://github.com/Guoyeting/AHFL.

The wide sharing and rapid dissemination of digital artworks has aggravated the issues of plagiarism, raising significant concerns in cultural preservation and copyright protection. Yet, modes of plagiarism are formally uncharted, causing rough plagiarism detection practices with duplicate checking. This work is thus devoted to understanding artwork plagiarism, with poster design as the running case, for building more dedicated detection techniques. As the first study of such, we elaborate on 8 elements that form unique posters and 6 judgement criteria for plagiarism using an exploratory study with designers. Second, we build a novel poster dataset with plagiarism annotations according to the criteria. Third, we propose models, leveraging the combination of primary elements and criteria of plagiarism, to find suspect instances in a retrieval process. The models are trained under the context of modern artwork and evaluated on the poster plagiarism dataset. The proposal is shown to outperform the baseline with superior Top-K accuracy (~33%) and retrieval performance (~42%).

This paper explores resource allocation strategy in the Baidu Over The Edge system to enable mobile edge computing (MEC) datacenters to effectively support cloud-native services downstream to the network edge. There are many challenges to this issue. First, MEC datacenters are resource-constrained to fully meet resource demands. Second, previous works regard the resource requirements of each service as an indivisible unit, resulting in idle MEC resources, even if the resources can meet the demands of some microservices decoupled by the service. Third, they are confined to optimize the allocation for a single slot, failing to adapt to the dynamic demands. To improve resource utilization, we propose performability-aware resource allocation (PARA), a PARA on the edges for cloud-native services. It takes microservices as the unit of resource allocation and allows services to perform with degraded services when only part of microservices' demands are met. It also considers dependency among microservices, dynamic resource requirements, and resource supply characteristics of MEC and cloud. Performability is a unified performance-reliability measure for evaluating such degradable systems. To maximize the long-term overall performability, we model the resource optimization problem and then develop an online greedy heuristic algorithm. The algorithm predicts services' resource demands and then adapts the online allocation. The experimental results show that PARA reduces the reallocation overhead by 47.7%–53.6%, and improves the long-term overall performability by 23.14%–43.25% of existing state-of-the-art works.

Smart cameras, as typical IoT devices, are widely adopted to provide surveillance on individuals, homes, and the environment. The unavoidably captured sensitive visuals via these cameras may raise significant security concerns, while the prevalent software defects and authentication misconfiguration issues aggravate the vulnerability of such devices. However, traditional cryptography techniques are inadequate to provide full protection of these devices due to the large computation overhead. In this context, realizing accountability for these surveillance systems shall be the last line of defense in the presence of fast-evolving and high-influential threats. We propose EviChain, a scalable blockchain-based solution to trace the operations on intelligent surveillance cameras and reserve the evidence for any misuse in tamper-proofing manipulation records. Building a blockchain over the distributed cameras is challenging due to the limited capacity of on-board memory. To tackle this challenge, we design a cooperative mechanism that enables cameras to adaptively join in groups and share storage for recording blocks. In addition, we present a computation efficiency and delay-aware block generation strategy to reduce the cost of the consensus process. We perform extensive simulations to validate the superior performance of EviChain over other baselines, for example, Practical Byzantine Fault Tolerance (PBFT).

Word-character lattice models have been proved to be effective for Chinese named entity recognition (NER), in which word boundary information is fused into character sequences for enhancing character representations. However, prior approaches have only used simple methods such as feature concatenation or position encoding to integrate word-character lattice information, but fail to capture fine-grained correlations in word-character spaces. In this paper, we propose DCSAN, a Dynamic Cross- and Self-lattice Attention Network that aims to model dense interactions over word-character lattice structure for Chinese NER. By carefully combining cross-lattice and self-lattice attention modules with gated word-character semantic fusion unit, the network can explicitly capture fine-grained correlations across different spaces (e.g., word-to-character and character-to-character), thus significantly improving model performance. Experiments on four Chinese NER datasets show that DCSAN obtains stateof-the-art results as well as efficiency compared to several competitive approaches.

The emerging federated learning (FL) paradigm allows multiple distributed devices to cooperatively train models in parallel with the raw data retained locally. The local-computed parameters will be transferred to a centralized server for aggregation. However, the vanilla aggregation method ignores the heterogeneity of the distributed data, which may lead to slow convergence and low training efficiency. Yet, existing data scheduling and improved aggregation methods either incur privacy concerns or fail to consider the fine-grained heterogeneity. We propose FedCav, a contribution-aware model aggregation algorithm that differentiates the merit of local updates and explicitly favors the model-informed contributions. The intuition is that the local data showing higher inference loss is likely to facilitate better performance improvement. To this end, we design a novel global loss function with explicit optimization preference on informative local updates, theoretically prove its convex property, and use it to regulate the gradient descent process iteratively. Additionally, we propose to identify abnormal updates with fake loss by auditing historic local training statistics. The results of extensive experiments demonstrate that FedCav needs fewer training rounds (~34%) for convergence and achieves better inference accuracy (~2.4%) than the baselines (i.e., FedAvg and FedProx). We also observe that FedCav can actively mitigate the model replacement attacks with agile recovery capability towards the aggregation.

With the development of edge computing, edge storage solutions are attracting widespread attention. When facing the requirements of lower latency and faster access speed from end devices, edge storage solutions are considered to be an alternative to the cloud. However, edges are usually owned by small organizations which have limited operations and maintenance capabilities. This makes these edge devices can be easily disabled by external attacks or internal hardware failures. Besides, the heterogeneity of the edge devices will also make it difficult to price the edge resources uniformly. To tackle these problems, we propose SmartStore: an auction mechanism based on blockchain to allocate edge resources. Considering centralized solutions have access bottlenecks and trust issues, we built SmartStore on the smart contract. With Bayesian game theory, SmartStore can analyze how data owners (DO) and edges price the resources can maximize their benefits. From an economic perspective, both DO and edges can make full use of edge heterogeneous resources with SmartStore. Besides, a two-stage submission strategy is proposed to complete the sealed auction. Furthermore, considering the reliability of edge storage, we propose a cluster-based block distribution algorithm for SmartStore's intelligent edge recommendation process. SmartStore ensures the reliability of edge storage while maximizing the benefits and resource utilization of both parties. Finally, we conduct specific experiments on the proposed auction smart contract through “Ethereum” and the experimental results of implementation show the effectiveness and efficiency of our SmartStore.

Edge computing emerges as an alternative to cloud computing in the scenarios where the end devices require lower latency and faster access speeds. Edge nodes are deployed at the proximity of the end devices to reduce response time. On the other hand, the edge nodes are usually owned by small organizations that have limited operations and maintenance capabilities. Data on the edge may be easily damaged, due to external attacks or internal hardware failures. Therefore, it is essential to verify data integrity in edge computing. However, edge environment requires a different trust model compared with other computing and storage paradigm. Besides, compared with cloud storage, edge storage is decentralized and storage service participants may pose greater internal and external threats. This paper proposes a blockchain-based intelligent crowdsourcing audit approach (Crowdauditing) to achieve on-chain and off-chain credibility of audit results. The model relies on an untrusted auditor committee from the crowd to audit data integrity and uses smart contracts as the core of the intelligent system to ensure the reliability of result submission, the accuracy of the result judgment, and reasonable punishments and rewards. Specifically, an unbiased selection algorithm is proposed to achieve fairness during the auditor committee construction. An innovative two-stage submission strategy is proposed to ensure that the auditor committee can reach a consensus on the off-chain audit results. An incentive mechanism is carefully designed to force auditors providing audit services honestly to maximize their own rewards. Moreover, we modeled that as a game of n players, which proves the reliability of the result. Finally, we implement a prototype of Crowdauditing based on smart contracts. The extensive experimental results demonstrate the effectiveness of Crowdauditing.

With the explosive emergence of computation-intensive and latency-sensitive applications, data processing could be envisioned to perform closer to the data source. Similar to edge and fog computing, dispersed computing is considered as a complementary computing paradigm, which can excavate potential computation resources in the network to users, and serve as a supplement for sharing the computational burden when the edge is overloaded. In this article, we first make full use of idle and geographically dispersed computation resources via task offloading, contributing to conserve energy for mobile devices. Especially, a dispersed computing offloading framework concerning the interests of users and networked computation points is proposed. We further transform the initial problem into a multiobjective optimization problem subject to latency and resource constraints. To tackle such a complex problem, an energy-saving bilateral matching algorithm is designed to obtain the optimal task offloading strategy. The simulation results demonstrate that our proposed algorithm can outperform the benchmark schemes in terms of user fairness and can achieve a relatively balanced energy cost ratio. Furthermore, comparative experiments with edge computing are implemented in Amber Response and Disaster Relief scenarios, respectively, to reveal the advantages of the proposed framework.

Surveillance cameras have been extensively used in smart cities and high security zones. However, with the exploding deployment of smart cameras, the rapid growth of cloud workloads from vision-based IoT applications are becoming a huge burden for all cloud service providers. Some researchers have proposed mechanisms, such as compression and deduplication to reduce the video traffic size, but these methods cannot offset the enormous growth of data volume. Most of the surveillance video data do not need to be proceeded in real time. By making use of the IoT camera’s onboard resources to store the data, the cloud workloads can be fundamentally reduced. However, recent incidents have posed a new, powerful geo-range attack, where the attacker may compromise a group of surveillance cameras located within an area. Existing simple onboard solutions cannot offer secure defense against such geo-range attacks. To tackle the problem, we developCentipede, a cooperative video data storage system that distributes video content across geographically dispersed surveillance cameras. It generates secure copies for the video content and enhances data security by judiciously distributing erasure-coded video blocks across optimally-chosen surveillance cameras. In this article, we implementCentipedeand evaluate its performance.Centipedeis the first solution that can fundamentally reduce the cloud workload and defend against geo-range attacks.

Network anomalies, such as wide-area congestion and packet loss, can seriously degrade network performance. To this end, it is critical to accurately identify network anomalies on end-to-end paths for high quality network services in practice. In this work, we propose an unsupervised two-step method for the detection and characterization of general network anomalies. It first finds the change-points in large-scale RTT time series by formalizing an optimization problem in terms of data series segmentation. Then we mark the segments as normal or abnormal on different sides of a change-point through exploitation of their distribution statistics. After detecting an anomaly, a further step is introduced to analyze the relations between links with state changes and localize the entities (nodes or links) that most likely cause the corresponding event. We believe such unsupervised and light-weighed method can provide valuable insights on anomaly mining in large-scale time series data. Extensive experiments on both simulated (artificial time series with ground truth) and real-network (RIPE Atlas traceroute measurements) datasets are performed. The results demonstrate that the proposed method can achieve better performance, w.r.t. accuracy and efficiency, than existing solutions.

With the prosperity of artificial intelligence, neural networks have been increasingly applied in healthcare for a variety of tasks for medical diagnosis and disease prevention. Mobile wearable devices, widely adopted by hospitals and health organizations, serve as emerging sources of medical data and participate in the training of neural network models for accurate model inference. Since the medical data are privacy-sensitive and non-shareable, federated learning has been proposed to train a model across decentralized data, which involves each mobile device running a training task with its own data in parallel. However, due to the ever-increasing size and complexity of modern neural network models, it becomes inefficient, and may even infeasible, to perform training tasks on wearable devices that are resource-constrained. In this paper, we propose a FEderated Edge Learning system, FEEL, for efficient privacy-preserving mobile healthcare. Specifically, we design an edge-based training task offloading strategy to improve the training efficiency. Further, we build our system on the basis of federated learning to make use of distributed user data to improve the inference performance. In addition, during model training, we provide a differential privacy scheme to strengthen the privacy protection. A prototype system has been implemented to evaluate the training efficiency, inference performance and noise sensitivity, respectively. And the results have demonstrated that our proposal could train models in an efficient and privacy-preserving way.

Joint extraction of entities and their relations benefits from the close interaction between named entities and their relation information. Therefore, how to effectively model such cross-modal interactions is critical for the final performance. Previous works have used simple methods such as label-feature concatenation to perform coarse-grained semantic fusion among cross-modal instances, but fail to capture fine-grained correlations over token and label spaces, resulting in insufficient interactions. In this paper, we propose a deep Cross-Modal Attention Network (CMAN) for joint entity and relation extraction. The network is carefully constructed by stacking multiple attention units in depth to fully model dense interactions over token-label spaces, in which two basic attention units are proposed to explicitly capture fine-grained correlations across different modalities (e.g., token-to-token and labelto-token). Experiment results on CoNLL04 dataset show that our model obtains state-of-the-art results by achieving 90.62% F1 on entity recognition and 72.97% F1 on relation classification. In ADE dataset, our model surpasses existing approaches by more than 1.9% F1 on relation classification. Extensive analyses further confirm the effectiveness of our approach.

With the widespread application of infotainment services in intelligent connected vehicles (ICVs), network traffic has grown exponentially, bringing huge burden and energy consumption to the ICV network. Edge caching, which enables edges [e.g., vehicles or roadside units (RSUs)] with cache storages, is a promising technology to alleviate this problem. In this article, in terms of the hybrid communication mode of vehicle to vehicle (V2V) and vehicle to RSU (V2R), an energy-aware caching scheme for infotainment services is proposed. Considering the geographical distribution of vehicles and RSUs as well as the size of transmission content, the energy consumption model in the ICV network is formulated to implement the optimal selection of cache nodes. Then, the selection of the cache node in the ICV network is transformed into the optimal stopping problem and solved by the optimal stopping theory. Finally, we propose a new algorithm for optimal energy-efficiency cache node selection (OEECS). The simulation results show that the proposed OEECS can obtain higher energy saving and lower average access latency than other baseline schemes.

IT companies need to monitor various Key Performance Indicators (KPIs) and detect anomalies in real time to ensure the quality and reliability of Internet-based services. However, due to the diversity of KPIs, the ambiguity and scarcity of anomalies and the lack of labels, anomaly detection for various KPIs has been a great challenge. Existing KPI anomaly detection methods have not explored the properties of anomalies in KPIs in detail to our best knowledge. Therefore, we explore anomalies in KPIs and recognize a common and important form of anomalies namedabrupt changes, which often indicate potential failures in the relevant services. Forabrupt changesin various KPIs, we proposeDDCOL, an unsupervised online anomaly detection algorithm with parameter adaptation from the perspective of anomalies for the first time. We propose three techniques: high order${D}$ifference extraction and combination,${D}$ensity-based${C}$lustering with parameter adaptation and${O}\text{n}{L}$ine detection with subsampling (DDCOL). Compared with traditional statistical methods and unsupervised learning methods, extensive experimental results and analysis on a large number of public KPIs show the competitive performance ofDDCOLand the significance ofabrupt changes. Furthermore, we provide an interpretation for the promising results, which shows thatDDCOLcan be robust to KPI expected concept drifts, and obtain a good feature distribution of normal data in KPIs.

Driven by the visions of Internet of Things and 5G communications, the edge computing systems integrate computing, storage, and network resources at the edge of the network to provide computing infrastructure, enabling developers to quickly develop and deploy edge applications. At present, the edge computing systems have received widespread attention in both industry and academia. To explore new research opportunities and assist users in selecting suitable edge computing systems for specific applications, this survey paper provides a comprehensive overview of the existing edge computing systems and introduces representative projects. A comparison of open-source tools is presented according to their applicability. Finally, we highlight energy efficiency and deep learning optimization of edge computing systems. Open issues for analyzing and designing an edge computing system are also studied in this paper.

In the aftermath of earthquakes, floods, and other disasters, photos are increasingly playing more significant roles, such as finding missing people and assessing disasters, in rescue and recovery efforts. These disaster photos are taken in real time by the crowd, unmanned aerial vehicles, and wireless sensors. However, communications equipment is often damaged in disasters, and the very limited communication bandwidth restricts the upload of photos to the cloud center, seriously impeding disaster rescue endeavors. Based on edge computing, we propose Echo, a highly time-efficient disaster rescue framework. By utilizing the computing, storage, and communication abilities of edge servers, disaster photos are preprocessed and analyzed in real time, and more specific visuals are immensely helpful for conducting emergency response and rescue. This article takes the search for missing people as a case study to show that Echo can be more advantageous in terms of disaster rescue. To greatly conserve valuable communication bandwidth, only significantly associated images are extracted and uploaded to the cloud center for subsequent facial recognition. Furthermore, an adaptive photo detector is designed to utilize the precious and unstable communication bandwidth effectively, as well as ensure the photo detection precision and recall rate. The effectiveness and efficiency of the proposed method are demonstrated by simulation experiments.

Security is a pivotal issue for the development of Cyber Physical Systems (CPS). The trusted computing of CPS includes the complete protection mechanisms, such as hardware, firmware, and software, the combination of which is responsible for enforcing a system security policy. A Trust Detection-based Secured Routing (TDSR) scheme is proposed to establish security routes from source nodes to the data center under malicious environment to ensure network security. In the TDSR scheme, sensor nodes in the routing path send detection routing to identify relay nodes’ trust. And then, data packets are routed through trustworthy nodes to sink securely. In the TDSR scheme, the detection routing is executed in those nodes that have abundant energy; thus, the network lifetime cannot be affected. Performance evaluation through simulation is carried out for success of routing ratio, compromised node detection ratio, and detection routing overhead. The experiment results show that the performance can be improved in the TDSR scheme compared to previous schemes.

Apache Spark is a lightning-fast unified analytics engine for large-scale data processing. When executing an application with Spark, it runs many jobs in parallel. These jobs are divided into stages based on the shuffle boundary. However, shuffling data across the stages in a cluster is time-consuming because it will place significant burden on operating system on both the source and the destination by requiring many remote files and network I/Os. Meanwhile, the latest Spark is based on Netty which is written with Java Sockets and will produce a large number of data copies during the shuffle phase. This has become the major bottleneck for Apache Spark and motivates us to use RDMA technology to accelerate data shuffle. RDMA, with the function of zero-copy transfers, reducing latency and CPU overhead, can reduce stress on operating system during the shuffle phase and improve the throughput of the whole system. In this paper, we present a high-performance RDMA-based design for accelerating data shuffle in Apache Spark framework by providing tiering memory pool and different mechanisms to transfer messages of different sizes. The experimental results show that compared to the default Spark running with IP over InfiniBand (IPoIB), our proposed design can achieve up to 89.8% performance improvement for Spark RDD operation benchmarks (e.g., GroupBy and SortBy), up to 49% performance improvement for iterative algorithms (e.g., TriangleCount and SVM in SparkBench). And the evaluation results also show that our RDMA-based design slightly outperforms Crail-Spark-IO, a recent open-source Spark shuffle plugin from IBM.

Wearable technology is one of the greatest applications of the Internet of Things. The popularity of wearable devices has led to a massive scale of personal (user-specific) data. Generally, data holders (manufacturers) of wearable devices are willing to share these data with others to get benefits. However, significant privacy concerns would arise when sharing the data with the third party in an improper manner. In this paper, we first propose a specific threat model about the data sharing process of wearable devices’ data. Then we propose a K -anonymity method based on clustering to preserve privacy of wearable IoT devices’ data and guarantee the usability of the collected data. Experiment results demonstrate the effectiveness of the proposed method.

Medical Internet of Things, also well known as MIoT, is playing a more and more important role in improving the health, safety, and care of billions of people after its showing up. Instead of going to the hospital for help, patients’ health-related parameters can be monitored remotely, continuously, and in real time, then processed, and transferred to medical data center, such as cloud storage, which greatly increases the efficiency, convenience, and cost performance of healthcare. The amount of data handled by MIoT devices grows exponentially, which means higher exposure of sensitive data. The security and privacy of the data collected from MIoT devices, either during their transmission to a cloud or while stored in a cloud, are major unsolved concerns. This paper focuses on the security and privacy requirements related to data flow in MIoT. In addition, we make in-depth study on the existing solutions to security and privacy issues, together with the open challenges and research issues for future work.

Both traditional coarse-grained and fine-grained Flash Translation Layer schemes are unsuitable for ultra-large SSDs. They produce overmuch mapping entries which fail to be kept in embedded DRAM completely and can suffer severely from low spatial and temporal localities. In this paper, we propose a novel KV-FTL for ultra-large SSDs, which mostly maps logical addresses to physical addresses via a simple hash function, while handles hash collisions and out-of-place data updates by the traditional manner, i.e., the mapping table. Our KV-FTL can accelerate address translation by avoiding loading mapping table from flash memory to DRAM, thus improve performance; as well as reduce the write-traffic incurred by the mapping table, thus extend the lifespan of SSDs. Experimental results show that our KV-FTL facilitates SSDs to survive longer lifespan by a factor of up to 18.7% with an average of 13.6%; improves read performance ranging from 18.4% to 50.7% with an average of 39% with optimization, and in the case of extremely intensive requests, improves the access performance for requests with an average of 47%.

Emerging non-volatile memories enable persistent memory, which offers the opportunity to directly access persistent data structures residing in main memory. In order to keep persistent data consistent in case of system failures, most prior work relies on persist ordering constraints which incurs significant overheads. Strand persistency minimizes persist ordering constraints. However, there is still no proposed persistent memory design based on strand persistency due to its implementation complexity. In this work, we propose a novel persistent memory system based on strand persistency, called SPMS. SPMS consists of cacheline-based strand group tracking components, a volatile strand buffer and ultra-capacitors incorporated in persistent memory modules. SPMS can track each strand and guarantee its atomicity. In case of system failures, committed strands buffered in the strand buffer can be flushed back to persistent memory within the residual energy window provided by the ultra-capacitors. Our evaluations show that SPMS outperforms the state-of-the-art persistent memory system by 6.6% and has slightly better performance than the baseline without any consistency guarantee. What's more, SPMS reduces the persistent memory write traffic by 30%, with the help of the strand buffer.

Medical institutes use Electronic Medical Record (EMR) to record a series of medical events, including diagnostic information (diagnosis codes), procedures performed (procedure codes) and admission details. Plenty of data mining technologies are applied in the EMR data set for knowledge discovery, which is precious to medical practice. The knowledge found is conducive to develop treatment plans, improve health care and reduce medical expenses, moreover, it could also provide further assistance to predict and control outbreaks of epidemic disease. The growing social value it creates has made it a hot spot for experts and scholars. In this paper, we will summarize the research status of data mining technologies on EMR, and analyze the challenges that EMR research is confronting currently.

Photos obtained via crowdsourcing can be used in image sensing for disaster management. Due to the weak communication environment after a disaster, it is difficult to transfer the huge amount of crowdsourced photos. To address this problem, we propose COCO, a content-aware crowdsourcing system that leverages edge computing to support real-time image sensing in disaster environment. COCO filters the crowdsourced images at the data source and only uploads the images that contain relevant objects which the application is interested in. We use a machine-learning based computer vision detector to understand the content of images. Considering the resource constraints of mobile devices, we implement the computer vision detector at the edge server which located in the close proximity to data source. As the unstable network bandwidth is normal in disaster environment, we propose an adaptive mechanism to further improve the sensing performance. We have implemented the COCO prototype which is evaluated via a real-world dataset. The experimental results demonstrate the effectiveness of COCO.

As the expected emerging Non-Volatile Memory (NVM) technologies, such as 3DXPoint, are in production, there has been a recent push in the big data processing community from storage-centric towards memory-centric. Generally, in large-scale systems, distributed memory management through traditional network with TCP/IP protocol exposes performance bottleneck. Briefly, CPU- centric network involves context switching, memory copy etc. Remote Direct Memory Access (RDMA) technology reveals the tremendous performance advantage over than TCP/IP: Allowing access to remote memory directly bypassing OS kernel. In this paper, we propose Megalloc, a distributed NVM allocator exposes NVMs as a shared address space of a cluster of machines based-on RDMA. Firstly, it makes memory allocation metadata accessed directly by each machine, allocating NVM in coarse-grained way; secondly, adopting fine-grained memory chunk for applications to read or store data; finally, it guarantees high distributed memory allocation performance.

The non-volatile memory (NVM) has the merits of byte-addressability, fast speed, persistency and low power consumption, which make it attractive to be used as main memory. Commonly, user process dynamically acquires memory through memory allocators. However, traditional memory allocators designed with in-place data writes are not appropriate for the non-volatile main memory (NVRAM) due to the limited endurance. In this article, first, we quantitatively analyze the wear-oblivious of DRAM-oriented designed allocator—glibc malloc and the inefficiency of wear-conscious allocator NVMalloc. Then, we propose WAlloc, an efficient wear-aware manual memory allocator designed for NVRAM: (1) decouples metadata and data management; (2) distinguishes metadata with volatility; (3) redirects the data writes around to achieve wear-leveling; (4) redesigns an efficient and effective NVM copy mechanism, bypassing the CPU cache partially and prefetching data explicitly. Finally, experimental results show that the wear-leveling of WAlloc outperforms that of NVMalloc about 30% and 60% under random workloads and well-distributed workloads, respectively. Besides, WAlloc reduces the average data memory writes in 64 bytes block by 1.5 times comparing with glibc malloc. With the fulfillment of data persistency, cache bypassing NVM copy is better than cache line flushing NVM copy with performance improvement circa 14%.

To facilitate efficient context switches, GPUs usually employ a large-capacity register file to accommodate a massive amount of context information. However, the large register file introduces high power consumption, flowing to high leakage power SRAM cells. Emerging non-volatile STT-RAM memory has recently been studied as a potential replacement to alleviate the leakage challenge when constructing register files on GPUs. Unfortunately, due to the long write latency and high energy consumption associated with write operations in STT-RAM, simply replacing SRAM with STTRAM for register files would incur non-trivial performance overhead and only bring marginal energy benefits.

To address the high energy consumption issue of SRAM on GPUs, emerging Spin-Transfer Torque (STT-RAM) memory technology has been intensively studied to build GPU register files for better energy-efficiency, thanks to its benefits of low leakage power, high density, and good scalability. However, STT-RAM suffers from a reliability issue, read disturbance, which stems from the fact that the voltage difference between read current and write current becomes smaller as technology scales. The read disturbance leads to high error rates for read operations, which cannot be effectively protected by SECDEC ECC on large-capacity register files of GPUs.

High-Performance network technology, Remote Direct Memory Access (RDMA), has revealed its tremendous advantage over traditional TCP/IP. With its ultra-low latency and high bandwidth, RDMA has been extensively adopted in distributed environment, especially for in-memory key-value stores. However, although RDMA does provide the ability to interact with remote user space memory directly, memory copy still exists between data memory area and communication memory area with two-sided communication semantics in in-memory key-value store. In addition, using high performance one-sided communication semantics will expose memory totally, hence an inadvertent corrupt data operation could crash system. In this paper, we propose a tactful cache mechanism for RDMA-based in-memory key-value store -- InnerCache. Our design concerns two dimensions with respect to improve the system performance with two-sided communication semantics and make system less vulnerable. It merges one-sided and two-sided communication model through making communication memory cacheable. Experimental results show that InnerCache can efficiently improve the performance of RDMA-based in-memory key-value store.

To address the high energy consumption issue of SRAM on GPUs, emerging Spin-Transfer Torque (STT-RAM) memory technology has been intensively studied to build GPU register files for better energy-efficiency, thanks to its benefits of low leakage power, high density, and good scalability. However, STT-RAM suffers from the read disturbance issue, which stems from the fact that the voltage difference between read current and write current becomes smaller as technology scales. The read disturbance leads to high error rates for read operations, which cannot be effectively protected by the SEC-DED ECC on large-capacity register files of GPUs. Prior schemes (e.g., read-restore) to mitigate the read disturbance usually incur either non-trivial performance loss or excessive energy overhead, thus not applicable for the GPU register file design that aims to achieve both high performance and energy-efficiency. To combat the read disturbance, we propose a novel software-hardware co-designed solution (i.e., Red-Shield ), which consists of three optimizations to overcome the limitations of the existing solutions. First, we identify dead reads at compiling stage and augment instructions to avoid unnecessary restores. Second, we employ a small read buffer to accommodate register reads with high-access locality to further reduce restores. Third, we propose an adaptive restore mechanism to selectively pick the suitable restore scheme, according to the busy status of corresponding register banks. Experimental results show that our proposed design can effectively mitigate the performance loss and energy overhead caused by restore operations while still maintaining the reliability of reads.

Solid State Drives (SSDs) have been extensively deployed as the cache of hard disk-based storage systems. The SSD-based cache generally supplies ultra-large capacity, whereas managing so large a cache introduces excessive memory overhead, which in turn makes the SSD-based cache neither cost-effective nor energy-efficient. This work targets to reduce the memory overhead introduced by the replacement policy of SSD-based cache. Traditionally, data structures involved in cache replacement policy reside in main memory. While these in-memory data structures are not suitable for SSD-based cache any more since the cache is much larger than ever. We propose a memory-efficient framework which keeps most data structures in SSD while just leaving the memory-efficient data structure (i.e., a new bloom proposed in this work) in main memory. Our framework can be used to implement any LRU-based replacement policies under negligible memory overhead. We evaluate our proposals via theoretical analysis and prototype implementation. Experimental results demonstrate that, our framework is practical to implement most replacement policies for large caches, and is able to reduce the memory overhead by about$10 \times$.

The non-volatile memory (NVM) has the illustrious merits of byte-addressability, fast speed, persistency and low power consumption, which make it attractive to be used as main memory. Commonly, user process dynamically acquires memory through memory allocators. However, traditional memory allocators designed with in-place data writes are not appropriate for non-volatile main memory (NVRAM) due to the limited endurance. For instance, the number of write operations is merely 108 times per PCM cell. In this paper, we quantitatively analyze the wear-oblivious of DRAM-oriented designed allocator-glibc malloc and the inefficiency of wear-conscious allocator-NVMalloc. For example, the average imbalance factor (the maximum/the average) of memory allocation is about 7.5 and 3, respectively. Based on our observations, we propose WAlloc, an efficient wear-aware manual memory allocator designed for NVRAM, decouples metadata and data, uses Less Allocated First Out allocation policy and redirects the data writes. Experimental results show that the wear-leveling of WAlloc outperforms that of NVMalloc about 30% and 60% under random workloads and well-distributed workloads, respectively. In addition, considering the trade-off between space and wear-leveling, WAlloc reduces average data memory writes in 64 bytes block by average 1.5X comparing with malloc with almost 8% extra space overhead.

NAND flash-based Solid State Drives (SSDs) have been widely deployed in data centers of cloud computing due to their high performance compared with hard disks, while the limited lifespan of flash memory makes SSDs not very suitable for write-intensive applications. Deduplication is an effective method used to reduce the write traffic of applications thus can be used to extend the lifespan of SSDs. However, traditional deduplication schemes rely on the time-consuming fingerprint computing process to find duplicated data, which may impair the write performance of SSDs. Accordingly, Pre-hashing was proposed to reduce the chances of fingerprint computing thus improving the performance of SSDs with deduplication, but at the cost of degrading deduplication rate. In this paper, we propose NF-Dedupe, a new deduplication scheme that needs no fingerprint computing for flash-based SSDs. NF-Dedupe determines whether a write page is duplicated or not by comparing the write page with its potential duplicated page read from underlying flash chips byte by byte, rather than relying on the comparison of fingerprints. As flash memory is known for its high parallelism and low read latency, reading a page from flash chip and comparing two pages byte by byte introduce lower overhead than the fingerprint computing does. We evaluate the NF-Dedupe via trace-driven simulations. Experimental results have shown that NF-Dedupe outperforms the other approaches and can achieve the deduplication rate ranging from 5.3% to 29.9% and the write latency is improved by a factor of up to 21% with an average of 12%.

Flash memory, especially NAND flash memory, has become a popular alternative for the design of storage system. Index schemes of conventional file systems do not take the flash memory characteristics into account and will cause poor performance. The current flash file systems work well only in the case of small capacity. To address the problem, we propose a new hybrid indexing scheme both in directory structure and file data index for NAND flash file system in this paper, which is called HIFFS (a Hybrid Index for Flash File System). HIFFS contains two components: hash tree directory and adaptive file data index. The hash tree directory uses a hash-based index to get better update performance and search efficiency. The adaptive file data index uses different file index strategy according to file size. The experiment results show that HIFFS outperforms the state-of-the-art file systems both in throughput.

Solid state drives (SSDs) are increasingly deployed to construct storage arrays (RAIDs) in enterprise environments. The design decisions in RAID are traditionally devised for HDD RAIDs, which cannot fully exploit the characteristics of SSDs. In particular, SSD lacks the ability to update pages in-place. Random writes in traditional parity-based RAIS (SSD RAID) systems that has static striping result in significantly more writes, degraded performance, and shortened SSD lifetime. By dynamically forming full stripes, log-based design originally proposed in HDD RAID can mitigate the write-hole problem caused by random writes. However, it needs a directory to record locations for all data blocks, resulting in large space overhead and consequently sacrificing addressing efficiency. In this paper, we propose CD-RAIS, a compromise between static striping and dynamic striping. CD-RAIS groups requests that are from different SSD drives and places their corresponding unnecessarily consecutive logical blocks in one stripe. It mitigates the write-hole problem, meanwhile remains the same addressing efficiency as static striping. To enable dynamic data striping fit SSDs, CD-RAIS performs lazy data invalidation and consolidates updates to parity blocks. CD-RAIS greatly alleviates the write request increase due to parity block update in RAIS. Our experimental results show that, for random write dominated workloads, CD-RAIS achieves 65% response time improvement and 31% longer SSD lifespan over traditional RAIS schemes.

NoSQL (Not only SQL) data stores become a vital component in many big data computing platforms due to its inherent horizontal scalability. HBase is an open-source distributed NoSQL store that is widely used by many Internet enterprises to handle their big data computing applications (e.g. Facebook h

In personal computing devices that rely on a cloud storage environment for data backup, an imminent challenge facing source deduplication for cloud backup services is the low deduplication efficiency due to a combination of the resource-intensive nature of deduplication and the limited system resources. In this paper, we present ALG-Dedupe, an Application-aware Local-Global source deduplication scheme that improves data deduplication efficiency by exploiting application awareness, and further combines local and global duplicate detection to strike a good balance between cloud storage capacity saving and deduplication time reduction. We perform experiments via prototype implementation to demonstrate that our scheme can significantly improve deduplication efficiency over the state-of-the-art methods with low system overhead, resulting in shortened backup window, increased power efficiency and reduced cost for cloud backup services of personal storage.

Virtual machine (VM) live storage migration techniques significantly increase the mobility and manageability of virtual machines in the era of cloud computing. On the other hand, as solid state drives (SSDs) become increasingly popular in data centers, VM live storage migration will inevitably encounter heterogeneous storage environments. Nevertheless, conventional migration mechanisms do not consider the speed discrepancy and SSD's wear-out issue, which not only causes significant performance degradation but also shortens SSD's lifetime. This paper, for the first time, addresses the efficiency of VM live storage migration in heterogeneous storage environments from a multi-dimensional perspective, i.e., user experience, device wearing, and manageability. We derive a flexible metric (migration cost), which captures various design preference. Based on that, we propose and prototype three new storage migration strategies, namely: 1) Low Redundancy (LR), which generates the least amount of redundant writes; 2) Source-based Low Redundancy (SLR), which keeps the balance between IO performance and write redundancy; and 3) Asynchronous IO Mirroring, which seeks the highest IO performance. The evaluation of our prototyped system shows that our techniques outperform existing live storage migration by a significant margin. Furthermore, by adaptively mixing our proposed schemes, the cost of massive VM live storage migration can be even lower than that of only using the best of individual mechanism.

Solid-state drives (SSDs) are widely used in storage systems. However, algorithms adopted by existing operating systems generally consider the underlying devices as hard disks, and thus are rarely optimized for SSDs. In this paper, we focus on a classical research issue, the cache replacement policy, and design a new policy by taking the parallelism of SSDs into account.

The market for cloud backup services in the personal computing environment is growing due to large volumes of valuable personal and corporate data being stored on desktops, laptops and smart phones. Source deduplication has become a mainstay of cloud backup that saves network bandwidth and reduces storage space. However, there are two challenges facing deduplication for cloud backup service clients: (1) low deduplication efficiency due to a combination of the resource-intensive nature of deduplication and the limited system resources on the PC-based client site, and (2) low data transfer efficiency since post-deduplication data transfers from source to backup servers are typically very small but must often cross a WAN. In this paper, we present AA-Dedupe, an application-aware source deduplication scheme, to significantly reduce the computational overhead, increase the deduplication throughput and improve the data transfer efficiency. The AA-Dedupe approach is motivated by our key observations of the substantial differences among applications in data redundancy and deduplication characteristics, and thus is based on an application-aware index structure that effectively exploits this application awareness. Our experimental evaluations, based on an AA-Dedupe prototype implementation, show that our scheme can improve deduplication efficiency over the state-of-art source-deduplication methods by a factor of 2-7, resulting in shortened backup window, increased power-efficiency and reduced cost for cloud backup services.

NAND flash has some inherent peculiarities which increase the access delay seriously. We propose the Page to Block mapping Flash Translation Layer (PBFTL). Solid State Drives (SSDs) adopting PBFTL have lower response time. To achieve low response time for read requests, PBFTL adopts hybrid-level mapping scheme. But, hybrid-level FTL behaves awkwardly for write due to the high overhead of garbage collection. PBFTL takes two measures to optimize garbage collection. The first is to direct hot and cold data to separate blocks, which mitigates write amplification significantly. The second is to reduce the latency of reclaiming a block, which enables PBFTL to spend less time on garbage collection. User's requests are unlikely to be congested for a long time. Trace-driven simulations show that, PBFTL achieves low response for both read- and write-intensive workloads.

Providing resistance against hardware attacks is important to ensure trusted or secure computing. Approach of inter-chip authentication is proposed, which can be applied to detect malicious tamper to chips/components equipped on the circuit board. The proposed approach is to utilize the I/O physical timing characters to obtain a specified fingerprint for each specified chip on the board, and to check the validity of the chip by matching its fingerprint for later usage. To obtain the required fingerprint, an inner sampling logic is set after the I/O pins to get the timing characters reflected on the lines connected between the master chip and the slave chip to be verified. From the sampling result, the physical character associated with the slave chip is extracted and compared with the valid one. Because tampers will influence the physical character to distort the fingerprint, it has the abilities to detect tamper behaviors like chip replacement and faked signal injection. A logic analyzer and 8051-MCU based evaluation system is constructed. The test result shows that it can effectively identify the valid chip from the faked ones. Hence, the proposed approach can be deployed into the circuit board to protect the chips equipped on the board against hardware attacks.

Network virtualization has been proposed as a powerful vehicle for running multiple customized networks on a shared infrastructure. Virtual network embedding is a critical step for network virtualization that deals with efficient mapping of virtual nodes and virtual links onto the substrate network resources. Previous work in virtual network embedding primarily focused on designing heuristic algorithms for static networks. Virtual network infrastructure should be reconfigured or redeployed in response to network growth. In this paper, we address the problem of optimally redeploying the existing virtual network infrastructure as the network evolves. This problem focus on minimizing the upgrading cost of virtual network, with satisfying node resource constraint and path delay constraint. It is shown that this problem is NP-hard. A heuristic algorithm is proposed and its effectiveness is validated by simulations evaluation.

The development of flash memory drives flash based SSD to enter into large-scale storage systems. The performance of SSD is highly dependent on the design of FTL. For the last few years, several FTL schemes have been proposed. Such as FAST, BAST, SAST etc. we design a novel FTL based on page-level mapping scheme. Since one of the major troubles of page-level mapping FTL is the unendurable memory consuming of the fine-grained mapping table. We propose a dedicated cache replacement policy called SRC to mitigate the memory pressure. Our FTL based on SRC is able to distinguish hot data from the cold. This capability highlights the garbage collection efficiency of page-level mapping schemes. As a result, the hot data-aware FTL reduces extra read/write operations by 10 times or more compared with FAST and BAST. As our FTL erases less blocks, the lifetime of SSD is extended by more than 30%. Further experiment shows that the hot data-aware FTL outperforms hybrid-level FTLs on workloads with varied read/write ratios.

The development of flash memory drives flash based SSDs to enter into enterprise-scale storage systems. As the kernel of SSD, flash translation layer (FTL) attracts many attentions. Generally, there are two types of FTLs according to the granularity of address mapping: block-level and page-level mapping FTLs. We focus on the latter one. Typically, page-level mapping scheme must employ a cache to alleviate the memory pressure introduced by the big mapping table. We argue that classic cache replacement policies aren't competent for the page table cache of FTLs. The major contribution of this work is to design a dedicated cache replacement policy called Two Filters (abbreviated as 2F) for page-level mapping FTLs. 2F aims at two goals. The first is higher hit ratio as all the replacement policies pursue. As 2F not only protects frequently accessed pages, but also protects sequentially accessed pages at little cost, it does achieve a higher hit ratio. The second goal is to distinguish hot pages from the cold. This goal is special for page table of FTLs. If hot and cold pages are directed to separate blocks, garbage collection will be more efficient. In order to achieve this goal, 2F employs two filters. One is used for containing sequentially accessed pages. Another is used for selecting hot pages. Trace driven simulations present that 2F outperforms classic replacement policies in both hit ratio and data classification.

Hash tree is a secure way to authenticate stored data. However, it is difficult to maintain a consistent state between the authentication result and data, which is necessary for permanent data storage of disk. Incremental node updating is proposed to solve such problem, it synchronizes data modification and authentication result with low cost. The reason is that the path from leaf node to root node of the tree can be very short, and each step on the path can be finished quickly as no sibling nodes are required. Thus, it greatly reduce additional disk I/O for authentication synchronization. Together with a low cost logging mechanism implemented by NVRAM, system can make fast recovery to still keep the required consistency after any failures. Related approach is elaborated, as well as testing results. Theoretical analysis and experimental simulations show that it is a practical and available way for mass data storage authentication.

This paper presents our replacement algorithm named RED for storage caches. RED is exclusive. It can eliminate the duplications between a storage cache and its client cache. RED is high performance. A new criterion Resident Distance is proposed for making an efficient replacement decision instead of Recency and Frequency. Moreover, RED is non-intrusive to a storage client. It does not need to change client software and could be used in a real-life system. Previous work on the management of a storage cache can attain one or two of above benefits, but not all of them. We have evaluated the performance of RED by using simulations with both synthetic and real-life traces. The simulation results show that RED significantly outperforms LRU, ARC, MQ, and is better than DEMOTE, PROMOTE for a wide range of cache sizes.

With the wide applications of data streams in many fields, such as sensor network monitoring and internet traffic control, query processing over data streams has become increasingly important. In these applications, multiple aggregate queries are registered in the system, and have different sliding window sizes and different frequency upper bounds. How to share the results of these queries is a challenge. Prior work studies how to detect common tasks of these queries and share the results by computing the common tasks only once. Hybrid scheduling first addressed this problem and used the earliest-deadline-first (EDF) method. However, this work did not present a method for computing the scheduling. We formulate the scheduling problem among multiple aggregate queries with different sliding window sizes and different frequency upper bounds over data streams and propose a combination rule to classify these queries. Then, we present an efficient scheduling algorithm to decide whether a query should be executed more often than necessary, as long as the interval between two consecutive executions is less than the frequency upper bound. We also combine our scheduling algorithm with EDF to handle under loaded and overloaded situations. An experimental study shows that our scheduling algorithms are more efficient than no scheduling and EDF in terms of the number of scanned tuples, the throughput and the latency.

To protect hard disk data confidentiality and integrity, AEIVV associates one unique IV with each disk sector; then, it applies authenticated encryption of AES-CCM to the protected sector and constructs hash tree upon IV storage. Through assuring IV to be trusted or un-tampered, data can be protected firmly. To make it an available way for disk protection, various optimizing measures are applied to quicken the running speed. With the emphasis of reducing extra latencies caused by protection, IV/MAC storage is allocated using interlaced layout to decrease seek time of disk I/O, IV checking penalty is reduced by buffering the frequently used hash tree nodes and IV/MAC values. Related approaches are elaborated, as well as experimental results. It shows that AEIVV is a practical and available way to build secure disk.

The ever-growing creation of data requires large-scale network storage systems. One of the key issues related such systems is how to place several petabytes of data among large number of devices. Itpsilas necessary to design a reliable, fair, adaptable data placement algorithm. All proposed approaches are oblivious to the reliability-based requirements of data in such systems. In this paper, we present a reliability-aware data placement algorithm for large-scale network storage systems. With considering the reliability-aware differences of storage devices and the reliability-based requirement of data, we formulate the problem as an integer programming to minimize the reliability cost and propose a polynomial-time algorithm to solve the problem. For each reliability level, we use a fair and adaptive data placement to distribute data. It can support arbitrary heterogeneous storage systems, distribute data in a fair way and allow an efficient adaptation to a changing set of devices. The theoretical analysis as well as the experimental study show that the approach can meet the reliability-based requirements of data, distribute data evenly among devices, and adapt well to the changes of devices.

As the performance gap between disks and processors continues to increase, dozens of cache replacement policies come up to handle the problem. Unfortunately, most of the policies are static. Nimrod Megiddo etc put forward a low overhead adaptive policy called ARC. It outperforms most of the static policies in most situations. But, ARC adapts itself to the workloads by the feedback of the missed pages. It hasn 't carried out the adaption before missed pages are discovered. We propose a high performance adaptive replacement policy. It adapts itself to the workloads by the feedback of the hit pages, so, it is more sensitive to the changes of the workloads than ARC. As the policy stares at the tails of the queues regardless of other pages, we name the policy as short-sighted adaptive replacement policy. The ARC usually regrets for the missed pages and wishes to rescue the neighborhood of them. However, SSARC endeavors to protect the would-be-reused pages from being replaced aggressively. So, it outperforms ARC in most situations. We compared SSARC with LRU, 2Q and ARC. The trace-driven experiments represent that SSARC gains higher performance.

Hash tree based disk storage integrity protection suffers from performance penalty and possible losing of consistency. FI-Tree deploys a fixed-structure tree and applies incremental-hash to tree node updating to solve the difficulties of performance and consistency. The biggest advantage of FI-Tree comes from that: to allow tree nodes to be cached to optimize performance, it can maintain consistency between the tree and the protected data with low cost at the same time. Basing on FI-Tree, TNSD constructs an instance of secure disk. TNSD associates one nonce with each data block to be protected, and applies FI-Tree to ensure the nonce to be un-tampered. In such way, data protection can be fulfilled with resistance against any attacks. Related approaches are elaborated, as well as testing results. Theoretical analysis and experimental simulation show that it is a practical and available way to build secure disk.

Link bandwidth utilization is obviously critical for numerous network management tasks. Using the flow-conservation law, we could reduce the number of activated monitor agents. The problem of efficiently monitoring link-bandwidth based on flow-conservation law could be reduced to weak vertex cover problem, which is NP-hard. In this paper, we demonstrate an approximation preserving reduction from the vertex cover problem to weak vertex cover problem. Due to this reduction, it follows that it is very difficult to get an approximation algorithm with approximation ratio lower than 2 for weak vertex cover problem. Using the primal-dual method, we give an approximation algorithm with approximation ratio 2 to solve the problem. The effectiveness of our monitoring algorithm is validated by simulations evaluation over a wide range of network topologies. We also demonstrate the problem of weak vertex cover with blackout vertices could be reduce to weak vertex cover problem. Hence we could use the approximation algorithms for weak vertex cover problem to solve the problem of weak vertex cover with blackout vertices.