Large Audio-language Models (LAMs) have recently enabled powerful speech-based interactions by coupling audio encoders with Large Language Models (LLMs). However, the security of LAMs under adversarial attacks remains underexplored, especially through audio jailbreaks that craft malicious audio prompts to bypass alignment. Existing efforts primarily rely on converting text-based attacks into speech or applying shallow signal-level perturbations, overlooking the impact of human speech’s expressive variations on LAM alignment robustness. To address this gap, we propose StyleBreak, a novel style-aware audio jailbreak framework that systematically investigates how diverse human speech attributes affect LAM alignment robustness. Specifically, StyleBreak employs a two-stage style-aware transformation pipeline that perturbs both textual content and audio to control linguistic, paralinguistic, and extralinguistic attributes. Furthermore, we develop a query-adaptive policy network that automatically searches for adversarial styles to enhance the efficiency of LAM jailbreak exploration. Extensive evaluations demonstrate that LAMs exhibit critical vulnerabilities when exposed to diverse human speech attributes. Moreover, StyleBreak achieves substantial improvements in attack effectiveness and efficiency across multiple attack paradigms, highlighting the urgent need for more robust alignment in LAMs.

Large Language Models (LLMs) aligned with human feedback have recently garnered significant attention. However, it remains vulnerable to jailbreak attacks, where adversaries manipulate prompts to induce harmful outputs. Exploring jailbreak attacks enables us to investigate the vulnerabilities of LLMs and further guides us in enhancing their security. Unfortunately, existing techniques mainly rely on handcrafted templates or generated-based optimization, posing challenges in scalability, efficiency and universality. To address these issues, we present JailPO, a novel black-box jailbreak framework to examine LLM alignment. For scalability and universality, JailPO meticulously trains attack models to automatically generate covert jailbreak prompts. Furthermore, we introduce a preference optimization-based attack method to enhance the jailbreak effectiveness, thereby improving efficiency. To analyze model vulnerabilities, we provide three flexible jailbreak patterns. Extensive experiments demonstrate that JailPO not only automates the attack process while maintaining effectiveness but also exhibits superior performance in efficiency, universality, and robustness against defenses compared to baselines. Additionally, our analysis of the three JailPO patterns reveals that attacks based on complex templates exhibit higher attack strength, whereas covert question transformations elicit riskier responses and are more likely to bypass defense mechanisms.

Incorporating privacy regulations and business requirements, enterprises should securely manage unstructured textual data from diverse domains. Sensitive information classification is a critical component of data security, but it poses challenges due to complex textual contexts. With the ever-increasing data volumes, rule-based and lexicon-based classification methods require substantial maintenance and become inefficient. Additionally, supervised named entity recognition is not feasible for multi-domain sensitive information classification due to numerous type-specific annotations and the scarcity of training data. This work presents ToSIC, a new sensitive information classification approach to tackle these problems. Specifically, ToSIC simplifies multi-domain type labeling through a hierarchical structure and employs a rapid learning mechanism based on prototype distance classification to mitigate extensive training data. Experiments indicate that ToSIC outperforms existing methods by significantly reducing inference time and error rates while ensuring classification performance. Moreover, ToSIC demonstrates exceptional expandability to unknown types, showcasing potential value in practical applications.

Existing large language model (LLM) serving systems typically batch the compute-bound prefill and I/O-bound decoding phases together.This co-location approach not only leads to significant interference between the two phases but also limits resource allocation and placements.To address these limitations, recent work proposes disaggregating the prefill and decoding phases to enhance performance.However, these works often rely on coarse-grained static scheduling strategies, resulting in imbalanced and insufficient resource utilization.For instance, compute resources for the prefill phase may be overloaded while those for the decoding phase remain idle, resulting in performance bottlenecks.In this paper, we propose WindServe, an efficient phase disaggregated LLM serving system that leverages stream-based, finegrained dynamic scheduling to enhance resource utilization and performance.WindServe features a global scheduler that monitors compute and memory resource usage to dynamically orchestrate cross-phase jobs, effectively reducing queuing delay and KV cache swapping overhead.We also introduce a stall-free rescheduling strategy to saturate the memory resources while minimizing the scheduling overhead from KV cache transfers.Furthermore, we design a stream-based approach to mitigate interference between prefill and decoding jobs.Our evaluation demonstrates that Wind-Serve achieves remarkable stability and SLO attainment under highload scenarios, outperforming state-of-the-art phase-disaggregated LLM serving systems by delivering a 4.28× improvement in TTFT median latency and a 1.5× reduction in TPOT P99 latency.

Content Delivery Networks (CDNs) rely heavily on caching algorithms to minimize content delivery latency and optimize network performance. While machine learning approaches have emerged as promising solutions for handling complex request patterns in caching systems, current learningbased caching methods face critical limitations in processing granularity and operational efficiency. Existing approaches either process requests in coarse-grained time windows or struggle with throughput bottlenecks during object-level operations. To address these challenges, we present UniCache, a novel batchlevel content caching algorithm that balances processing granularity and system efficiency. UniCache introduces a Batch Queue architecture coupled with specialized Batch-level Inference components, enabling high-throughput processing while providing fine-grained request information. This design prevents both suboptimal caching decisions and request accumulation delays during prediction phases. Furthermore, UniCache overcomes the common limitation of treating admission and eviction policies as separate entities, by implementing a unified model that jointly optimizes both processes based on object popularity patterns. The integration of tiered cache storage enhances the system's resilience to prediction inaccuracies while facilitating effective identification and retention of popular objects. Experimental evaluation on Wiki CDN and Tencent Photo datasets demonstrates that UniCache achieves$\mathbf{1 2 \%} \sim \mathbf{5 3 \%}$improvement in Object Hit Ratio (OHR) compared to state-of-the-art methods while maintaining real-time processing capabilities. Comprehensive ablation studies validate the effectiveness of each architectural component in the overall system design.

Graph Neural Networks (GNNs) have achieved significant success in anomaly detection across various domains. However, existing GNN approaches face notable challenges when applied to directed multigraphs where edge attributes serve as the sole source of information. This limitation complicates the learning of meaningful node representations and the accurate detection of anomalies, particularly in transaction networks. Moreover, existing methods typically suffer from dimensionality explosion and suboptimal use of structural and semantic information, leading to reduced detection performance. To address these challenges, we propose Feature Reconstruction for Anomaly Detection on Directed Multigraphs (FRAD-DM), a novel preprocessing framework tailored to enhance graph representations in node feature-absent scenarios. FRAD-DM employs advanced edge feature derivation techniques, temporal and structural subgraph analysis for node representation generation, and a reinforcement learning-based adaptive feature selection mechanism. This comprehensive framework optimizes feature spaces by balancing informativeness, task relevance, and computational efficiency, ensuring robust and scalable anomaly detection. Extensive experiments on real-world datasets under barely supervised settings demonstrate the effectiveness of FRAD-DM. By integrating it into various GNN architectures for node-level and edge-level anomaly detection tasks, negative-class F1-scores improve by 5.44% to 20.98%. These results highlight its capability to address the challenges of directed multigraphs in practical applications.

Byte-addressable Persistent Storage (BPS), such as persistent memory and CXL-enabled SSDs, has become an extension of main memory. This opens up new possibilities for indexes that operate and persist data directly on the memory bus. Recent learned indexes exploit data distribution and have shown great potential for some workloads. Despite some work proposed for integrating learned indexes into BPS, they are mainly based on Intel’s first-generation persistent memory. The current design suffers from the following problems: 1) Excessive storage line accesses due to large node in learned indexes; 2) Inefficient concurrency control due to volatile cache; 3) Write amplification due to mismatch access granularity.

With the rapid development and wide deployment of high-speed network technologies like RDMA and the relatively stagnant evolution of intra-host resources, intra-host network congestion has become a potential issue that may affect the QoS of network applications. Offloading hotspot data to modern Smart-NICs, enabling hotspot data access completion on the SmartNIC, and reducing intra-host network traffic, is a promising solution to this issue. However, due to the limited SmartNIC resources and the complexity of network application requirements, achieving efficient offload is challenging.We present Magician, an architecture to mitigate intra-host network congestion with SmartNIC. Magician adopts a client-driven data access approach to avoid performance degradation caused by limited SmartNIC resources. Magician also introduces a SmartNIC-oriented hotspot data update strategy that dynamically refreshes hotspot data with minimal overhead. Moreover, we design a server-centric data consistency mechanism to ensure data consistency under concurrent access. We implement Magician within the key-value store. Evaluation of the key-value store with and without Magician suggests that, in the presence of intra-host network congestion, Magician significantly mitigates intra-host network congestion, leading to improved performance of network applications.

Embedding-based recommendation systems are now widely used to recommend content for users, and have strict requirements on their latency and throughput. However, the latest recommendation models often exceed GPU HBM memory capacity, and the system is often deployed separately on computing nodes for GPU calculating and Parameter Servers for embedding tables’ storage. This architecture leads to a significant amount of network I/O during the inference process and reduces GPU utilization.In this paper, we propose SmartEmb, an inference framework that accelerates the network I/O of embedding table lookups through a specialized control plane of task reordering, prefetching and cache management. We offload these control planes on SmartNIC to avoid contention with the host CPU and gain better performance. We implemented the SmartEmb prototype on BlueField-2 and evaluated its performance. Our evaluation demonstrates that compared to the Nvidia HugeCTR HPS, SmartEmb can improve the quality of service by achieving up to 217% improvement in throughput and reducing latency by up to 190% of overall embedding layer look-ups in inference scenarios.

Distributed machine learning methods like Federated Learning (FL) and Split Learning (SL) meet the growing demands of processing large-scale datasets under privacy restrictions. Recently, FL and SL are combined in hybrid SLFL (SFL) frameworks to exploit both methods’ advantages to facilitate ubiquitous intelligence in the Internet of Things (IoT), for example, smart finance. Despite its significant impact on the performance and costs of SFL, model decomposition that splits an ML model into the client-server pair has not been sufficiently studied, especially for SFL in a large-scale dynamic IoT environment. In this paper, we propose a new SFL framework HSFL with a lightweight model decomposition method to offload a part of model training to the edge server. Specifically, we develop a method for estimating the training latency of HSFL and designed a metric for measuring privacy leakage in HSFL, based on which we formulate model decomposition in HSFL as an optimization problem with privacy protection as a constraint. Then, we transform the formulated problem into a contextual bandit problem and design an efficient algorithm to solve it. We have conducted thorough evaluations of the proposed HSFL framework through extensive experiments on a prototype testbed and a simulation platform. The experimental results validate the superiority of HSFL over the state-of-the-art benchmarks in terms of training latency, efficiency, scalability, and privacy protection.

While edge computing has the potential to offer low-latency services and overcome the limitations of traditional cloud computing, it presents new challenges in terms of trust, security, and privacy (TSP) in Internet of Things environments. Cooperative edge computing (CEC) has emerged as a solution to address these challenges through resource sharing among edge nodes. However, for multi-infrastructure providers, incentive and trust mechanisms among edge nodes are crucial technical issues that must be addressed alongside system latency and reliability to meet performance requirements. In this article, we propose a blockchain-assisted intelligent edge cooperation system (BIECS) to systematically solve these issues. By leveraging blockchain technology, we construct trust among edge nodes and employ an incentive mechanism for resource sharing among multi-infrastructure providers. We formulate the system performance optimization as a multiobjective joint optimization problem and solve it efficiently through a two-stage strategy for selecting edge nodes. We first design an improved long short term memory (LSTM) model for resource prediction and then select edge nodes for executing offloaded tasks and handling the corresponding blockchain process related to each task execution. To evaluate the performance of BIECS, we implement the system based on Hyperledger Fabric and design extensive experiments. Our proposed system achieves better performance in terms of system delay, throughput, and resource utilization compared to state-of-the-art schemes for edge cooperation.

As a new distributed machine learning (ML) framework for privacy protection, federated learning (FL) enables substantial Internet of Things (IoT) devices (e.g., mobile phones, tablets, etc.) to participate in collaborative training of an ML model. FL can protect the data privacy of IoT devices without exposing their raw data. However, the diversity of IoT devices may degrade the overall training process due to the straggler issue. To tackle this problem, we propose a gear-based asynchronous FL (AsyFed) architecture. It adds a gear layer between the clients and the FL server as a mediator to store the model parameters. The key insight is that we group these clients with similar training abilities into the same gear. The clients within the same gear conduct synchronous training. These gears then communicate with the global FL server asynchronously. Besides, we propose a T-step mechanism to reduce the weight from the slow gear when they are communicating with the FL server. The extensive experiment evaluations indicate that AsyFed outperforms FedAvg (baseline synchronous FL scheme) and some state-of-the-art asynchronous FL methods in terms of training accuracy or speed under different data distributions. The only negligible overhead is that we leverage the extra layer (gear layer) to preserve part of the model parameters.

Edge computing can provide high bandwidth and low-latency service for big data tasks by leveraging the edge side’s computing, storage, and network resources. With the development of microservice and docker technology, service providers can flexibly and dynamically cache microservice at the edge side to respond efficiently with limited resources. Automatically caching needed services on the nearest edge nodes and dynamically scheduling users’ requests can realize that computing power and software services flow with the users to provide continuous services. However, achieving the goal needs to overcome many challenges, such as the significant fluctuation of user devices’ requests at the edge side and the lack of collaboration among edge nodes. In this article, dynamic computing power scheduling and collaborative task scheduling among edge nodes are comprehensively developed. The problem is considered a multiobjective optimization problem, including sequentially minimizing the deadline missing rate of requests and the average task completion time. We propose an adaptive mechanism for dynamically collaborative computing power and task scheduling (ADCS) in the edge environment to solve this problem. It adopts the greedy decision method to schedule computing tasks to meet their deadline requirements. At the same time, it uses the best-fit method to adjust the computing resources according to the changes of users’ requests. The simulation results show that ADCS can decrease the deadline missing rate and reduce the average completion time. Compared with DSR and CoDSR, the deadline missing rate is reduced by 59.91% and 19.95%, respectively. The average completion time is decreased by 37.87% and 6.71%.

Federated learning (FL) offers a promising approach to efficient machine learning with privacy protection in distributed environments, such as Internet of Things (IoT) and mobile-edge computing (MEC). The effectiveness of FL relies on a group of participant nodes that contribute their data and computing capacities to the collaborative training of a global model. Therefore, preventing malicious nodes from adversely affecting the model training while incentivizing credible nodes to contribute to the learning process plays a crucial role in enhancing FL security and performance. Seeking to contribute to the literature, we propose a blockchain-empowered secure and incentive FL (BESIFL) paradigm in this article. Specifically, BESIFL leverages blockchain to achieve a fully decentralized FL system, where effective mechanisms for malicious node detections and incentive management are fully integrated in a unified framework. The experimental results show that the proposed BESIFL is effective in improving FL performance through its protection against malicious nodes, incentive management, and selection of credible nodes.

In data center networks, applications such as dis-tributed key-value stores consume a lot of CPU resources. The performance of the entire system drops significantly under heavy load conditions. In order to improve the performance of key-value stores, many existing studies use RDMA (Remote Direct Memory Access) to reduce the communication overhead. However, RDMA primitives can only offload simple operations to the NIC, such as reading and writing remote memory. With the emergence of new hardware like SmartNICs, we consider whether we can offload more complex operations in distributed key-value stores to SmartNICs to reduce the load on CPU. In this paper we present SKV, a SmartNIC-offloaded distributed key-value store. In order to make full use of the offload ability of the SmartNIC, we make a detailed analysis on the characteristic and architecture of SmartNICs and distributed key-value stores. SKV offloads operations such as data replication to the SmartNIC. We design a new replication mechanism, which enables the server to separate background processing from the interaction with clients in the front. We implement SKV on the Mellanox BlueField SmartNIC. Our evaluations show that SKV improves the overall throughput by 14% and reduces latency by 21 % compared with baseline.

Emerging network-attached resource disaggregation architecture requires ultra-low latency rack-scale communication. However, current hardware offloading (e.g., RDMA) and user-space (e.g., mTCP) communication schemes still rely on heavily layered protocol stacks which requires the translation between PCIe bus and network protocol, or complex connection/memory resource management within RNICs, inevitably bringing latency overhead.

Inter-process communications form a performance bottleneck for large-scale brain simulations. The sparse and imbalanced communication patterns of human brain make it particularly challenging to design a communication system for supporting large-scale brain simulations. In this paper, we tackle the communication challenges posed by large-scale brain simulations with sparse and imbalanced communication patterns. We design a virtual communication topology with a merge and forward algorithm that exploits the sparsity to regularize inter-process communications. To balance the communication loads of different processes, we formulate voxel partition in brain simulations as a k-way graph partition problem and propose a constrained deterministic greedy algorithm to solve the problem effectively. We conducted extensive simulation experiments for evaluating the performance of the proposed communication scheme and found that the proposed method may significantly reduce communication overheads and shorten simulation time for large-scale brain models.

Although the emerging edge computing paradigm offers a promising approach to overcoming some limitations of conventional cloud computing, the heterogeneous edge nodes with highly diverse system capacities bring new challenges to service provisioning especially for latency-sensitive services. Cooperative edge computing (CEC) has been proposed for facing such challenges through resource sharing among edge nodes. However, some technical issues must be fully addressed to make CEC effective, among which incentive and trust mechanisms and performance optimization are crucial for latency-sensitive service provision. In this paper, we design a novel blockchain-based intelligent edge cooperation system named BIECS to tackle these challenges systematically. BIECS provides incentive to edge nodes for resource sharing and enables trust among cooperative nodes upon a distributed platform leveraging the blockchain technology. In order to optimize system performance for meeting the requirements of latency-sensitive services, we propose a two-stage strategy for node selection in BIECS that chooses the most appropriate edge nodes for executing offloaded tasks and recording related transactions in the blockchain. We also implemented a prototype of BIECS based on Hyperledger Fabric and conducted extensive experiments for evaluating the performance of BIECS. The obtained experiment results verify that the proposed BIECS achieves better performance in system delay and throughput compared to the state-of-the-art methods for edge cooperation.

The wide deployment of advanced robots with industrial IoT (IIoT) technologies in smart factories generates a large volume of data during production and a wide variety of data processing workloads are launched to maintain productivity and safety of smart manufacture. The emerging fog computing paradigm offers a promising solution to enhancing data processing performance in a smart factory environment while on the other hand brings in new challenges to resource management, which call for a more effective approach for recommending resource configurations to heterogeneous workloads. In this paper, we propose an Optimized Recommendations of Heterogeneous Resource Configurations (ORHRC) model that employs machine learning techniques to provide resource configuration recommendations for the heterogeneous workloads in a fog computing-based smart factory environment. ORHRC learns a recommendation model by leveraging the operating characteristics and execution time of workloads on fog servers with different configurations. We also design a decision model in ORHRC to further improve prediction accuracy and reduce operational overheads. Experiment results show that ORHRC outperforms the state of art configuration recommendation methods in terms of average prediction accuracy.Note to Practitioners—The various data processing workloads in a smart factory environment need to be processed by the computational resources with optimal configurations for meeting their performance requirements. In this paper, we employ machine learning technologies for enabling automatic recommendation of resource configurations to heterogeneous workloads. Specifically, we develop an Optimized Recommendations of Heterogeneous Resource Configurations (ORHRC) model that can identify the optimal resource configurations for various workloads. We also conducted extensive experiments that verify the effectiveness of the proposed ORHRC model.

Anomaly detection is crucial to the reliability and safety of rail transit systems. The rapid development of Internet of Things (IoT) and cloud technologies together with recent advances in machine learning offered various cloud-based data-driven approaches to automatic anomaly detection. However, the challenges introduced by the different types of equipment in rail transit systems with highly diverse data distributions and the lack of labeled anomaly data have not been sufficiently addressed. In this article, we attempt to cope with such challenges by proposing an improved long short term memory (LSTM)-based time-series anomaly detection scheme. The key elements of the proposed scheme include an improved LSTM model that may achieve more accurate time-series prediction for various rail transit devices and a method for determining an appropriate error threshold for detecting anomalies based on the prediction errors. In order to further enhance anomaly detection performance, we also propose a pruning algorithm for reducing the number of false anomalies. Our method does not rely on scarce anomaly labels but dynamically determines a threshold of prediction errors to identify anomalies; therefore, it overcomes the challenge of the extremely uneven distribution of rail transit data. We conducted extensive experiments in a real metro operation environment for performance evaluation. The experiment results prove the effectiveness of the proposed scheme and show a superior performance of the scheme compared to existing anomaly detection methods.

In recent years, the commodities industry has grown rapidly under the stimulus of domestic demand and the expansion of cross-border trade. It has also been combined with the rapid development of e-commerce technology in the same period to form a flexible and efficient e-commerce system for bulk commodities. However, the hasty combination of both has inspired a lack of effective regulatory measures in the bulk industry, leading to constant industry chaos. Among them, the problem of lagging evidence in regulatory platforms is particularly prominent. Based on this, we design a blockchain-based evidential and secure bulk-commodity supervisory system (abbr. BeBus). Setting different privacy protection policies for each participant in the system, the solution ensures effective forensics and tamper-proof evidence to meet the needs of the bulk business scenario.

The edge-cloud hybrid environment requires complex deployment strategies to enable the smart Internet-of-Things (IoT) system. However, current service deployment strategies use simple, generalized heuristics and ignore the heterogeneous characteristics in the edge-cloud hybrid environment. In this article, we devise a method to find a microservice-based service deployment strategy that can reduce the average waiting time of IoT devices in the hybrid environment. For this purpose, we first propose a microservice-based deployment problem (MSDP) based on the heterogeneous and dynamic characteristics in the edge-cloud hybrid environment, including heterogeneity of edge server capacities, dynamic geographical information of IoT devices, and changing device preference for applications and complex application structures. We then propose a multiple buffer deep deterministic policy gradient (MB_DDPG) to provide more preferable service deployment solutions. Our algorithm leverages reinforcement learning and neural network to learn a deployment strategy without any human instruction. Therefore, the service provider can make full use of limited resources to improve the Quality of Service (QoS). Finally, we implement MB_DDPG based on real-world data sets and some synthetic data, and we also implement another two algorithms, genetic algorithm and random algorithm, as a contrast. The experimental results demonstrate that MB_DDPG is able to learn a preferable strategy which, in terms of average waiting time, outperforms genetic algorithm and the random algorithm by 32% and 44%, respectively.

The deployment of datasets in the heterogeneous edge-cloud computing paradigm has received increasing attention in state-of-the-art research. However, due to their large sizes and the existence of private scientific datasets, finding an optimal data placement strategy that can minimize data transmission as well as improve performance, remains a persistent problem. In this study, the advantages of both edge and cloud computing are combined to construct a data placement model that works for multiple scientific workflows. Apparently, the most difficult research challenge is to provide a data placement strategy to consider shared datasets, both within individual and among multiple workflows, across various geographically distributed environments. According to the constructed model, not only the storage capacity of edge micro-datacenters, but also the data transfer between multiple clouds across regions must be considered. To address this issue, we considered the characteristics of this model and identified the factors that are causing the transmission delay. The authors propose using a discrete particle swarm optimization algorithm with differential evolution (DE-DPSO) to distribute dataset during workflow execution. Based on this, a new data placement strategy named DE-DPSO-DPS is proposed. DE-DPSO-DPS is evaluated using several experiments designed in simulated heterogeneous edge-cloud computing environments. The results demonstrate that our data placement strategy can effectively reduce the data transmission time and achieve superior performance as compared to traditional strategies for data-sharing scientific workflows.

The cloud-fog orchestrated computing environments devolve computing tasks from the cloud center to the fog nodes, providing more heterogeneous configurations for the operation of workloads. Compared to the conventional cloud computing environment, the physical conditions at the fog nodes in the cloud-fog orchestrated computing environments are more complex and changeable. Therefore, the configurations that the fog nodes provide are heterogeneous and varying. This requires the configuration selection model to adapt to changeable configurations. The previous configuration selection models are applied to the limited and fixed configurations in the conventional cloud environment, but not to the complex cloud-fog orchestrated computing environments. To address this problem, we propose Optimized Recommendations of Heterogeneous Resource Configurations(ORHRC), a model that provides users with a reliable cloud configuration recommendation service. ORHRC uses the matrix factorization algorithm and neural network to build a recommendation model, which combines the operating characteristics of workloads as the explicit ratings and implicit feedback, to give configuration recommendations. Comprehensive experiments on a real-world dataset demonstrate that the hit rate of configurations of ORHRC is 24% higher than Micky and 15% higher than Selecta.

Known as a distributed ledger, blockchain is becoming prevalent due to its decentralization, traceability and tamper resistance. Particularly, permissioned blockchain such as Hyperledger Fabric shows great application prospects as the infrastructure of IoT security, credit management, etc. Many cloud platforms like AWS, Azure, Oracle and IBM cloud currently provide blockchain as a service, in which tenants can quickly build permissioned blockchain and run smart contract based applications. However, the transactions throughput and scalability in the permissioned blockchain are not ideal, despite many optimization efforts in consensus protocol and parallel chain. Existing solutions still reveals some limitations like excessive CPU scheduling, inefficient block broadcast and high latency of initial blocks synchronization when new nodes join blockchain network. Inspired by the emerging RDMA (Remote Direct Memory Access) network, we propose BoR, an RDMA-based permissioned blockchain framework. By offloading the block transfer transaction into RDMA NICs, it can increase block broadcast speed and reduce block sync delay. We exploit the RDMA primitives to redesign the block synchronization protocol and accelerate DPoS (Delegated Proof of Stake) consensus process for higher throughput and lower latency in kernel-bypass manner. As demonstrated in our evaluation with different workloads, BoR with lower CPU utilization significantly outperforms the state-of-the-art EoS blockchain.

Video-on-demand (VoD) services historically rely on commercial content distribution networks (CDNs) for on-demand capacity provisioning. Content providers gradually prefer a self-managed content infrastructure because of its full control and customization. However, such a dedicated physical infrastructure could be costly in initial capital investment, and complex in management. It has become a promising alternative to host VoD services on pay-as-you-go cloud platforms, on which using dynamic server provisioning to reduce server rental cost is the key objective of content providers. In this paper we address two major challenges to reducing cost: to minimize content reorganization and to tolerate imperfect workload prediction. We first present a practical VoD servicing system design based on a pay-as-you-go cloud. We prove that previous works, focusing exclusively on cost savings, cause significant content reorganization and are vulnerable to imperfect workload prediction. To address such issues, we propose a novel idea called workload absorber, and design a provisioning algorithm called Absorb Window based on the idea. Workload absorbers eliminate the bandwidth wastage and significantly reduce content reorganization. We conduct extensive evaluations with real VoD access traces, and demonstrate the superior scalability of the proposed algorithm by producing highly optimized provisioning in seconds for thousands of servers.

Resources dynamical allocation and management is always an important feature in cloud computing. Auto Scale allows users to scale their cloud resources capacity according to elastic loads timely, which has been widely used in mature public cloud. For private cloud, there are some different features from public cloud. It is more flexible to use Auto Scale technique to provide QoS guarantees and ensure system health. In this paper, we design a novel Auto Load-aware Scale scheme for private cloud environment. We describe scale in and scale out strategy based on prediction algorithm. We implement our scheme on OpenStack platform. Both simulation and experiments are carried out to evaluate our work. The experiments show that our scheme has better performance in resource utilization while providing high SLA levels.

Although many streaming application providers have relied on CDN services, there are several barriers to making CDN a more common service: expensive construction cost and fixed service mode. The rise of cloud computing requires "CDN as a Service" with a more open service mode, which requires content services available on-demand and be utilized in an open and loosely-coupled fashion. In most of the current streaming systems, CDN provides serve requesters with a passive and static state, there is no dynamic interaction with P2P systems, so the total CDN-P2P-Hybrid efficiency is not high. In this paper, we present CPDID: a CDN-P2P Dynamic Interactive Delivery scheme for Live Streaming. We explore CPDID architecture based on REST interface and JSON message format. And then we propose an identifying and selecting 'upload amplification nodes' algorithm to more efficiently utilize CDN resource. Our experimental results show that CPDID achieves at least 10-25% performance improvement compared with the existing native CDN-P2P-Hybrid schemes. At last, we analyze the prospective research direction and propose our future work.

Web Services standard-based system resource management is a new direction. In this paper, in order to verify the WS-Management standard whether to satisfy the realistic system resource management requirements, we use IBM MAPE categories to find more WS-Management related use cases. We design three typical use cases based on MAPE principles. Finally, we make a conclusion and propose our next-step work.

The paper proposes a novel schema — MARDO for dynamic composite web services resources QoS optimizing using cooperative agents. The paper first introduces the QoS model of web service resources and its mathematical definitions. Then provides MARDO to monitor and optimize the runtime status of composite web services. It parses the structure and the QoS requirement of the composite service workflow into the specific QoS demand. And then selects appropriate primitive web services. Furthermore MARDO could adjust itself to fit the new demand when the requirement is modified. The schema also provides a flexible way to manipulate heterogeneous web services which provide different management interfaces by taking advantage of agents assistant and web service based management protocol. The contribution of the paper is providing an efficient schema to help composite web services providers optimize their services dynamically according to on demand requirement.

Cloud computing environment requires a more open and loosely-coupled service and resource management model. Web Services for Management specification (WS-Management), as an initiative of DMTF organization, can help to manage IT resources cross multiple domains in cloud environment. In this paper, we propose a novel WS-Management-based Cloud-oriented resource management model. We describe the main components of this model. And then, we discuss our management model verification experimental scheme focusing on DASH resource. Finally, we present conclusion and future work.

From the analysis of some hard drawbacks faced with system service and composite service management today, a novel multi-agent-assisted and WS-management-based composite service management scheme: MWS-MCS is introduced in this paper. Firstly, we propose model architecture of this scheme. The prototype system had proved the feasibility of this design scheme. At last, we conclude this paper and analyze the prospective research challenges.

Nowadays, system and network resource management software should deal with more and more heterogeneous specific interfaces of different resource. This is a tightly-coupled management model. Recent years, Web Services have become the major technology and architecture of SOA for enterprise applications. Web Services provides a loosely-coupled management model. In this paper, based on the Web Services-based management protocol-WSManagement, we propose the distributed System and Network Resource Management Middleware Model. In this model, every managed IT resource provides the manageability interfaces via WS-Management specification. Furthermore, we utilize WSManagement Java implementation prototype-wiseman and WMI management interface to carry out the scheme implementation and test case work of the novel model, and then analyze the experiment results. At last, we analyze the prospective research direction and challenges in this field.

In this paper, we firstly analyze the mechanism of PeerCast as a P2P live streaming solution. An then, based on the shortcoming analysis of tree-based PeerCast, we propose a improved tree-mesh-hybrid scheme-MultiPeerCast, including Multi-thread media transmission, multiple-to-one overlay network reconstruct, optimized buffer design, media retrieving style changing from push mode to pull mode. As part of experiment work, we discuss an improved P2P live streaming prototype system implementation based on MultiPeerCast. The experiment results verify MultiPeerCast scheme is more stable and efficient than PeerCast. At last, from our research experiences and related survey, we analyze the prospective research direction and challenges in this field.