Abstract Social network alignment (SNA) aims to match corresponding users across different platforms, playing a critical role in cross-platform behavior analysis, personalized recommendations, security, and privacy protection. Traditional methods based on attribute and structural features face significant challenges due to the sparsity, heterogeneity, and dynamic nature of social networks, resulting in limited accuracy and efficiency. Recent advances in graph representation learning (GRL) provide promising solutions to these issues by leveraging deep learning to extract network features, effectively addressing sparsity, integrating heterogeneous data, and adapting to network dynamics. This paper presents a comprehensive survey of SNA methods based on GRL. We first introduce key definitions and outline a framework for SNA using GRL. Next, we systematically review state-of-the-art advancements in both static and dynamic networks, considering homogeneous and heterogeneous settings, including emerging approaches integrating large language models (LLMs). We further conduct an in-depth comparative analysis, highlighting the effectiveness of different GRL-based methods, with a particular emphasis on LLM-enhanced techniques. Finally, we discuss open challenges and outline potential future research directions in this rapidly evolving field.

The growing volume of performance-critical parameters in distributed storage systems, coupled with diverse and dynamic workload patterns, has significantly increased the complexity of system configuration. These trends have expanded the parameter space while tightening the time window for tuning convergence, making it challenging to maintain high system performance. Existing tuning strategies often struggle to balance thorough parameter exploration with real-time responsiveness, limiting their effectiveness under fast-evolving workloads and heterogeneous deployment environments. To address these challenges, we propose KGQW, the first framework that formulates automated parameter tuning as a knowledge graph query workflow. KGQW models workload features and system parameters as graph vertices, with performance metrics represented as edges, and constructs an initial knowledge graph through lightweight performance tests. Guided by performance prediction and Bayesian-driven exploration, KGQW progressively expands the graph, prunes insensitive parameters, and refines performance relationships to build an informative and reusable knowledge graph that supports rapid configuration retrieval via graph querying. Moreover, KGQW enables efficient knowledge transfer across clusters, substantially reducing the construction cost for new clusters. Experiments on real-world applications and storage clusters demonstrate that KGQW achieves second-level tuning latency, while maintaining or surpassing the performance of state-of-the-art methods. These results highlight the promise of knowledge-driven tuning in meeting the scalability and adaptability demands of modern distributed storage systems.

For a long time, fully exploiting the multi-level parallelism of SSDs has been an important technique for optimizing SSD performance. By distributing data to different flash parallel units through address mapping policies, SSDs can achieve multi-task parallel processing, thereby significantly improving data read and write performance. However, our experiments show that when SSD blocking causes task queues to stack up, excessive pursuit of flash parallelism can cause performance interference between multiple users, thereby reducing SSD performance. Additionally, in DRAM-less SSDs, differences in data access patterns directly impact the address mapping module, thereby exacerbating the degree of performance degradation. Based on the above analysis, this paper conducts a systematic study of the impact mechanism of different data flows on address mapping policies and proposes ahybrid addressmapping allocationpolicy with dynamic switching (HMpp) for DRAM-less SSDs. This policy identifies and classifies different data access patterns to dynamically select the appropriate address mapping policy, thereby reducing multi-user interference and improving SSD performance. Experimental results based on real cloud block storage workloads show that this policy can achieve 87.2% performance improvement and 77.3% fairness optimization, while reducing the write amplification factor by 32.9% to 40.2%.

Deep neural network (DNN) training is both data-intensive and compute-intensive. As datasets grow, storing them entirely in memory becomes infeasible, making I/O a major bottleneck—often accounting for 30%-90% of total training time due to the widening gap between data loading and computation speed. While caching can mitigate I/O latency, traditional strategies fail under random sampling. Recent work shows that importance sampling can induce data locality, enabling more effective caching; however, loss-based importance ignores semantic attributes, limiting effectiveness in I/O-bound tasks.

Graph embedding maps graph nodes to low-dimensional vectors and is widely used in machine learning tasks. The increasing availability of billion-edge graphs underscores the importance of learning efficient and effective embeddings on large graphs, such as link prediction on Twitter with over one billion edges. Most existing graph embedding methods fall short of reaching high data scalability. In this paper, we present a general-purpose, distributed, information-centric random walk-based, and pipeline-optimized graph embedding framework,$\sf{DistGER-Pipe}$DistGER−Pipe, which scales to embed billion-edge graphs.$\sf{DistGER-Pipe}$DistGER−Pipeincrementally computes information-centric random walks to reduce redundant computations for more effective and efficient graph embedding. It further leverages a multi-proximity-aware, streaming, parallel graph partitioning strategy, simultaneously achieving high local partition quality and excellent workload balancing across machines.$\sf{DistGER-Pipe}$DistGER−Pipealso improves the distributed$\sf{Skip-Gram}$Skip−Gramlearning model to generate node embeddings by optimizing access locality, CPU throughput, and synchronization efficiency. Finally,$\sf{DistGER-Pipe}$DistGER−Pipedesigns pipelined execution that decouples the operators in sampling and training procedures with an inter-round serial and intra-round parallel processing, attaining optimal utilization of computing resources. Experiments on real-world graphs demonstrate that compared to state-of-the-art distributed graph embedding frameworks, including$\sf{KnightKing}$KnightKing,$\sf{DistDGL}$DistDGL,$\sf{Pytorch-BigGraph}$Pytorch−BigGraph, and$\sf{DistGER}$DistGER,$\sf{DistGER-Pipe}$DistGER−Pipeexhibits 3.15×–1053× acceleration, 45% reduction in cross-machines communication, >10% effectiveness improvement in downstream tasks, and 38% enhancement in CPU utilization.

Serverless computing relies on keeping functions alive or pre-warming them before invocation to mitigate the cold start problem, stemming from the overhead of initializing function startup environments. However, under constrained cloud resources, accurately predicting the invocation patterns of sparse functions remains challenging. This limits the formulation of effective pre-warm and keep-alive strategies, leading to frequent cold starts and degraded user satisfaction. To address these challenges, we proposeSPFaaS, a hybrid framework based on sparse function prediction. To enhance the learnability of sparse function invocation data,SPFaaStakes into account the characteristics of cloud service workloads along with the features of pre-warm and keep-alive strategies, transforming function invocation records into probabilistic data. It captures the underlying periodicity and temporal dependencies in the data through multiple rounds of sampling and the combined use of Gated Recurrent Units and Temporal Convolutional Networks for accurate prediction. Based on the final prediction outcome and real-time system states,SPFaaSdetermines adaptive pre-warm and keep-alive strategies for each function. Experiments conducted on two real-world serverless clusters demonstrate thatSPFaaSoutperforms state-of-the-art methods in reducing cold starts and improving user satisfaction.

Haochen Tan, Zhijiang Guo, Zhan Shi, Lu Xu, Zhili Liu, Yunlong Feng, Xiaoguang Li, Yasheng Wang, Lifeng Shang, Qun Liu, Linqi Song. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Modern SSDs achieve low latency and high throughput by utilizing multiple levels of SSD parallelism. Fairness is also a critical design consideration in cloud environments and has inspired great interest in recent years to study it. However, we find that excessive striving for SSD parallelism can inadvertently harm the SSD fairness due to the striped data layout. We observe from our experimental analysis that some common access patterns in cloud loads (e.g., high-intensity I/O) are not addressed by SSD parallelism, which can affect both SSD parallelism and fairness. In this paper, we analyze two address mapping policies with orthogonal characteristics from SSD parallelism and fairness perspectives. Based on extensive experiments and observations, we schedule a hybrid address mapping policy to achieve fairness of SSDs in cloud environments by distinguishing different I/O access modes and cold/hot data, while ensuring SSD performance. Experimental results on the latest block-level I/O traces show that our approach significantly improves performance and fairness by up to 77.3% and 62.8%, respectively, over the previous schemes with negligible cost. Meanwhile, it can also reduce the write amplification by 23.5% to 33.5%.

With the popularity of cloud services, cloud block storage (CBS) systems have been widely deployed by cloud providers. Cloud cache plays a vital role in maintaining high and stable performance in cloud block storage systems. In the past few decades, much research has been conducted on the design of cache replacement policies. Prior work frequently relies on manually-engineered heuristics to capture the most common cache access patterns, or predict the reuse distance and try to identify the blocks that are either cache-friendly or cache-averse. Researchers are now applying recent advances in machine learning to guide cache replacement policy, augmenting or replacing traditional heuristics and data structures. However, most existing approaches depend on a certain environment which restricted their application, e.g., some methods only consider the on-chip cache consisting of program counters (PCs). Moreover, those approaches with attractive hit rates are usually unable to deal with modern irregular workloads, due to the limited feature used. In contrast, we propose a cloud cache replacement framework to automatically learn the relationship between the probability distribution of different replacement policies and workload distribution by using deep reinforcement learning. We train an end-to-end cache replacement policy based on the requested address with two efficient and stable cache replacement policies. Furthermore, by using prioritized experience replay and setting parameter constraints, our framework can accelerate the offline training process without affecting the cloud application. We have evaluated our proposed framework by using block-based I/O traces collected from Alibaba Cloud and Tencent Cloud, two of the largest cloud providers in the world, and several open-source traces. Experimental results show that our method not only outperforms several state-of-the-art cache methods in hit rate, but also reduces request latency and data traffic to the backend storage.

Since cloud service providers adopt the sharing mode to improve the utilization of solid state drives (SSDs) and reduce management costs, fairness is a critical design consideration and has drawn great interest in recent years. There are many methods to achieve fairness at the SSD device level, including cache-based resource allocation and queue rescheduling of transaction scheduling unit (TSU). However, poor locality data in cloud environments makes these methods of achieving fairness through the cache level invalid. In addition, existing fairness approaches at the TSU level treat flash memory as a black box and do not consider some characteristics of flash memory, leading to room for performance improvement. In this work, we propose a novel collaborative SSD fairness scheme, named a coordinated SSD cache and TSU fair scheme (CoFS), that achieves end-to-end full path fairness at the device level. A reinforcement learning-assisted fairness management scheme is designed to coordinate the SSD cache and TSU considering both the cache space and bandwidth resources which are significant for fairness control. The key idea is to enable the front-end SSD cache to achieve workload-level fairness by recognizing workload patterns, while the back-end TSU achieves flash-level fairness by sensing SSD internal status. Finally, CoFS collaborates with them to achieve SSD device-level fairness. In addition, we have designed a flexible reward function mechanism in the cache to balance different optimization objectives and augment TSU queue scheduling to adapt to different types of SSDs. Experimental results show that CoFS improves the overall fairness and performance by 30.8% to 56.7% and 42.1% to 98.7% in different scenarios.

The address allocation policy in SSD aims to translate the logical address of I/O requests into a physical address, and the static address allocation is widely used in modern SSD. Through extensive experiments, we find that there are significant differences in the utilization of SSD parallelism among different static address allocation policies. We also observe that the fixed address allocation design prevents SSDs from continuing to meet the challenges posed by cloud workloads and misses the possibility of further optimization. These situations stem from our excessive reliance on SSD parallelism over time. In this paper, we proposeHsaP, ahybridstatic addressallocationpolicy, that adaptively chooses the best static allocation policy to meet the SSD performance at runtime.HsaPis adynamicscheduling scheme based onstaticaddress allocation policy. The static policy ensures thatHsaPhas stable performance and light-weight overhead, while dynamic scheduling can effectively combine different allocation policies, selecting the best-performing static mapping mode for a given SSD state. Meanwhile,HsaPcan further improve the read and write performance of SSDs simultaneously through plane reallocation and data rewrite. Experimental results show thatHsaPachieves significant read and write performance gain of a wide range of the latest cloud block storage traces compared to several state-of-the-art address allocation approaches.

Since multiple flows (users) compete for a single, shared solid state drives (SSDs) concurrently in cloud environments, fairness has drawn great interest in recent years. Although many fair schemes are proposed on different modules of SSDs, such as resource allocation for the cache and chip queue reordering for the transaction scheduling unit (TSU), they fail to continue to provide fairness for the worse locality of I/O requests due to a deeper cloud storage stack that makes cache performance poor. Moreover, existing methods only treat back-end flash as a black box and do not exploit the differences in flash features across requests. This paper proposes CoFS, a novel coordinated SSD cache and TSU light-weight learning-based fair scheme to achieve complete fairness considering cache and bandwidth resources which are significant for fairness control. The key idea is to make the front-end SSD cache achieves fairness at the workload-level by recognizing dynamic I/O changes, while the back-end TSU achieves fairness at the flash-level by awareness of flash page-types, and finally CoFS collaborates with them to achieve fairness at the SSD device-level. Experimental results show that CoFS achieves significant fairness and performance gain of a wide range of the latest cloud block storage workloads, with an average improvement of 46.3% and 94.5% compared to state-of-the-art fairness policies, respectively.

Object cloud storage systems are deployed with diverse applications that have varying latency service level objectives (SLOs), posting challenges for supporting quality of service with limited storage resources. Existing methods provide prediction-based recommendations for dispatching requests from applications to storage devices, but the prediction accuracy can be affected by complex system topology. To address this issue, Graph3PO is designed to combine storage device queue information with system topological information for forming a temporal graph, which can accurately predict device queue states. Additionally, Graph3PO contains the urgency degree model and cost model for measuring SLO violation risks and penalties of scheduling requests on storage device queues. When the urgency degree of a request exceeds a threshold, Graph3PO determines whether to schedule it in the queue or initiate a hedge request to another storage device. Experimental results show that Graph3PO outperforms its competitors, with SLO violation rates 2.8 to 201.1 times lower.

Graph embedding is becoming widely adopted as an efficient way to learn graph representations required to solve graph analytics problems. However, most existing graph embedding methods, owing to computation-efficiency challenges for large-scale graphs, generally employ a one-size-fits-all strategy to extract information, resulting in a large amount of redundant or inaccurate representations. In this work, we propose HuGE+, an efficient and scalable graph embedding method enabled by an entropy-driven mechanism. Specifically, HuGE+ leverages hybrid-property heuristic random walk to capture node features, which considers both information content of nodes and the number of common neighbors in each walking step. More importantly, to guarantee information effectiveness of sampling, HuGE+ adopts two heuristic methods to decide the random walk length and the number of walks per node, respectively. Extensive experiments on real-world graphs demonstrate that HuGE+ achieves both efficiency and performance advantages over recent popular graph embedding approaches. For three downstream graph tasks, our approach not only offers >10% average gains, but also exhibits 23×–127× speedup over existing sampling-based methods. In addition, HuGE+ significantly reduces memory footprint by an average of 68.9%, facilitating training for billion-node-scale graph embeddings.

As a vital method for computer system design, multilevel cache technology is still a research hotspot in the field of storage. Recently, researchers have designed many multilevel cache replacement algorithms by analyzing the historical trajectories of data blocks between different cache levels but suffer from high overhead and poor adaptability. In addition, these methods still try to determine the most suitable victim to be replaced, given a new block to be loaded into the cache but do not consider how to cooperate between different cache levels. To overcome these challenges, we propose a multi-factor adaptive multi-level cooperative cache replacement policy in block storage systems that leverages the hierarchical characteristics of multilevel cache and multi-factor orthogonality by the probability distribution. This adaptation is mainly reflected in two aspects: each cache level automatically adapts different cache policies based on I/O access patterns, and different cache levels cooperate by adjusting the dynamic frequency threshold. In this work, we choose as our target for optimization the replacement policy of the cloud block cache, because rich storage stacks in cloud environments can be well used as multi-level cache scenarios. We have evaluated our proposed framework by using block-based I/O traces collected from Alibaba Cloud, one of the largest cloud providers in the world, and several open-source traces. Experimental results show that our method improves the hit ratios by 28.9% and reduces the response time by 9.6% to the state-of-the-art cache replacement policies for multi-level cache across different workloads and cache sizes.

In distributed data stores, performance fluctuations generally occur across servers, especially when the servers are deployed in a cloud environment. Hence, the replica selected for a reading request will directly affect the response latency. However, replica selection is challenging in latency-critical data stores (e.g., key-value stores). Such data stores generally deal with small size data, and clients have to select replicas independently. Even the state-of-the-art algorithm of replica selection (C3) still has considerable room for improving the latency. According to our experiments, compared with C3, using the ideal replica selection (Oracle) reduces the 99th latency by about 34-60 percent. In this article, we propose NetRS to address the fundamental factors that prevent replica selection algorithms from being effective. NetRS is a framework that enables in-network replica selection for distributed data stores. It exploits emerging network devices, including programmable switches and network accelerators, to select replicas for requests. NetRS supports diverse algorithms of replica selection and is suited to the network topology of modern data centers. According to our extensive evaluations, compared with the conventional scheme of clients selecting replicas for requests, NetRS reduces the mean latency by up to 50.3 percent, and the 99th latency by up to 56.3 percent. Moreover, NetRS could effectively cut the response latency even when unexpected events (e.g., workload changes, network device failures), and network congestion occur.

Graph embedding is becoming widely adopted as an efficient way to learn graph representations required to solve graph analytics problems. However, most existing graph embedding methods, owing to computation-efficiency challenges for large-scale graphs, generally employ a one-size-fits-all strategy to extract information, resulting in a large amount of redundant or inaccurate representations. In this work, we propose HuGE, an efficient and scalable graph embedding method enabled by an entropy-driven mechanism. Specifically, HuGE leverages hybrid-property heuristic random walk to capture node features, which considers both node degree and the number of common neighbors in each walking step. More importantly, to guarantee information effectiveness of sampling, HuGE adopts two heuristic methods to decide the random walk length and the number of walks per node, respectively. Extensive experiments on real-world graphs demonstrate that HuGE achieves both efficiency and performance advantages over recent popular graph embedding approaches. For link prediction and multi-label classification, our approach not only offers >10% average gains, but also exhibits 22×-126× speedup compared with existing sampling-based methods.

SLO enforcement with the required strong SLO compliance and the desired low level of performance variability is necessary to ensure QoS for user applications with precisely differentiated service levels. However, for the cloud consolidating a large number of VMs rented by users, it is a great challenge to formulate an IO capacity allocation among consolidated VMs under the user-customized QoS constraints of SLO compliance and performance fluctuation for consolidated VMs. To address this challenge, we propose SASLO, an end-to-end VM-oriented control framework that supports users in customizing SLO targets and QoS constraints for each VM. SASLO can dynamically coordinate the throughput target and IO size limit for each VM adapting to the status of SLO enforcement so as to maximize the IO capacity allocation among consolidated VMs under QoS constraints. To accurately enforce time-varying throughput target, SASLO establishes a proportional-integral IO controller for each individual VM to converge the actual throughput to the target with an expected settling time. Our extensive evaluation driven by representative benchmarks demonstrates that SASLO is able to formulate a satisfactory IO capacity allocation plan for consolidated VMs under the constraints of SLO compliance and performance variability.

In distributed key-value stores, performance fluctuations generally occur across servers, especially when the servers are deployed in a cloud environment. Hence, the replica selected for a request will directly affect the response latency. In the context of key-value stores, even the state-of-the-art algorithm of replica selection still has considerable room for improving the response latency. In this paper, we present the fundamental factors that prevent replica selection algorithms from being effective. We address these factors by proposing NetRS, a framework that enables in-network replica selection for key-value stores. NetRS exploits emerging network devices, including programmable switches and network accelerators, to select replicas for requests. NetRS supports diverse algorithms of replica selection and is suited to the network topology of modern data centers. Compared with the conventional scheme of clients selecting replicas for requests, NetRS could effectively cut the response latency according to our extensive evaluations. Specifically, NetRS reduces the average latency by up to 48.4%, and the 99th latency by up to 68.7%.

Storage hierarchy is widely used to mitigate the vast performance gap between different storage components economically, and the cache plays an important role in increasing the efficiency of memory access. However, the in-memory data organization of traditional graph computing framework is not well-optimized for various caches, especially the CPU cache, since classical caches are effectiveless towards irregular access pattern of graph applications. This work presents a cache- friendly graph data layout strategy to improve the efficiency of graph processing. By both considering the parameters of cache line and the pattern of access to adjacent list, we sort the edges to generate a sequential layout, and use BFS (Breadth First Search) algorithm to reorder the vertices for improving locality, thus benefit the CPU cache, without altering the code of graph processing toolkits. The efficiency improvement of Sort layout ranges from 11% up to 78.76% on BGL and SNAP with CC (Connected Components [1]). The BFS layout can benefit 3 classical algorithms on GraphChi: CC, TC (Triangle Counting) and PageRank [2], with the ratios of 15%-20%.

As a fundamental cloud service for modern Web applications, the cloud object storage system stores and retrieves millions or even billions of read-heavy data objects. Serving for a massive amount of requests each day makes the response latency be a vital component of user experiences. Due to the lack of suitable understanding on the response latency distribution, current practice is to use overprovision resources to meet Service Level Agreement (SLA). Hence we build a performance model for the cloud object storage system to predict the percentiles of requests meeting SLA (response latency requirement), in the context of complicated disk operations and event-driven programming model. Furthermore, we find that the waiting time for being accept()-ed at storage servers may introduce significant delay. And we quantify the impacts on system response latency, due to requests waiting for being accept()-ed. In a variety of scenarios, our model reduces the prediction errors by up to 73% compared to baseline models, and the prediction error of our model is 4.44% on average.

Cloud service is being adopted as a utility for large numbers of tenants by renting Virtual Machines (VMs). But for cloud storage, unpredictable IO characteristics make accurate Service-Level-Objective (SLO) enforcement challenging. As a result, it has been very difficult to support simple-to-use and technology-agnostic SLO specifying a particular value for a specific metric (e.g., storage bandwidth). This is because the quality of SLO enforcement depends on performance error and fluctuation that measure the precision of SLO enforcement . High precision of SLO enforcement is critical for user-oriented performance customization and user experiences. To address this challenge, this article presents V-Cup, a framework for VM-oriented customizable SLO and its near-precise enforcement. It consists of multiple auto-tuners, each of which exports an interface for a tenant to customize the desired storage bandwidth for a VM and enable the storage bandwidth of the VM to converge on the target value with a predictable precision. We design and implement V-Cup in the Xen hypervisor based on the fair sharing scheduler for VM-level resource management. Our V-Cup prototype evaluation shows that it achieves satisfying performance guarantees through near-precise SLO enforcement.

Erasure codes are widely used in modern distributed storage systems to prevent data loss and server failures. Regenerating codes are a class of erasure codes that trade storage efficiency and computation for repair bandwidth reduction. However, their nonunified coding parameters and huge computational overhead prohibit their applications. Hence, we first propose a family of General Functional Regenerating (GFR) codes with uncoded repair, balancing storage efficiency and repair bandwidth with general parameters. The GFR codes take advantage of a heuristic repair algorithm, which makes efforts to employ as little repair bandwidth as possible to repair a single failure. Second, we also present a scheduled shift multiplication (SSM) algorithm, which accelerates the matrix product over the Galois field by scheduling the order of coding operations, so encoding and repairing of GFR codes can be executed by fast bitwise shifting and exclusive-OR. Compared to the traditional table-lookup multiplication algorithm, our SSM algorithm gains 1.2 to 2 X speedup in our experimental evaluations, with little effect on the repair success rate.

It is desirable but challenging to simultaneously support latency SLO at a pre-defined percentile, i.e., the Xth percentile latency SLO, and throughput SLO for consolidated VM storage. Ensuring the Xth percentile latency contributes to accurately differentiating service levels in the metric of the application-level latency SLO compliance, especially for the application built on multiple VMs. However, the Xth percentile latency SLO and throughput SLO enforcement are the opposite sides of the same coin due to the conflicting requirements for the level of IO concurrency. To address this challenge, this paper proposes PSLO, a framework supporting the Xth percentile latency and throughput SLOs under consolidated VM environment by precisely coordinating the level of IO concurrency and arrival rate for each VM issue queue. It is noted that PSLO can take full advantage of the available IO capacity allowed by SLO constraints to improve throughput or reduce latency with the best effort. We design and implement a PSLO prototype in the real VM consolidation environment created by Xen. Our extensive trace-driven prototype evaluation shows that our system is able to optimize the Xth percentile latency and throughput for consolidated VMs under SLO constraints.

Erasure codes are widely used in modern distributed storage systems to prevent data loss and server failures. Regenerating codes are a class of erasure codes that trades storage efficiency and computation for repair bandwidth reduction. However, their non-unified coding parameters and huge computation overhead prohibit their applications. Hence, we first propose a family of Functional Regenerating Codes (FRCs) with uncoded repair, balancing storage efficiency and repair bandwidth with general parameters. FRCs take advantage of a heuristic repair algorithm, which makes efforts to employ as little repair bandwidth as possible. Second, we optimize encoding by constructing the generator matrix with a bitmatrix, so encoding of FRCs can be executed by fast bitwise XORs. Further, we also optimize repairing with the Scheduled Shift Multiplication (SSM) algorithm, which accelerates the matrix product over the Galois field during repair. Compared to the traditional table-lookup multiplication algorithm, our SSM algorithm gains 1.2~2X speed-up.

This paper is motivated by our three key observations: (1) there exists a degradation of performance as the interleaved accesses of heterogeneous streams, (2) for the slow stream, sequential accesses suffer huge misses in the prefetching cache, (3) in concurrence paradigm, providing fairness and QoS to concurrent streams is very important which always ignored by the traditional prefetching algorithms. Therefore, we present Fema, a caching management algorithm that enforces the fairness and efficiency for concurrent heterogeneous streams. Fema focuses on three key designs: (1) An adaptive framework (Fema Ada) for prefetching. In the Fema Ada, we propose a rate-aware adjustment of prefetching degree and analysis the optimal partition size. (2) A novel replacement scheme (Fema Rep) in which the accessed data will be firstly evicted to improve the performance. (3) A round robin allocation scheme (Fema Rou) to achieve fairness while as least performance degradation as possible. Results show that Fema is able to achieve averages 81.4% performance improvement over the LRU algorithm, 53.5% over the default Linux Kernel prefetching (LKP) algorithm and 19.0% over the recently proposed practical AMP (adaptive multi-stream prefetching) algorithm. Fema achieves average 74.2% fairness improvement (metric in fair speedup) over the LKP algorithm and 56.5% over the AMP algorithm.

In the background of cloud, self-destructing data mainly aims at protecting the data privacy. All the data and its copies will become destructed or unreadable after a user-specified period, without any user intervention. Besides, anyone cannot get the decryption key after timeout, neither the sender nor the receiver. The Washington's Vanish system is a system for self-destructing data under cloud computing, and it is vulnerable to “hopping attack” and “sniffer attack”. We propose a new scheme in this paper, called Safe Vanish, to prevent hopping attacks by way of extending the length range of the key shares to increase the attack cost substantially, and do some improvement on the Shamir Secret Sharing algorithm implemented in the Original Vanish system. We present an improved approach against sniffing attacks by using the public key cryptosystem to protectt from sniffing operations. In addition, we evaluate analytically the functionality of the proposed Safe Vanish system.

With data amount growing at an ever increasing rate, file system, as the basis of data management, its functionality and content are becoming more and more complex. Thus the file system is forced to be upgraded constantly, in order to satisfy the growing demands. However, the reinvention of new file systems is always a high-cost task. Although there are frameworks such as User-space File System that may help speeding up the development process, ease the pain of deploying new functions, they also bring in significant overhead, especially when file operation frequency is fairly high. We designed and implemented a lightweight file system management framework based on LD_PRELOAD in user-space referred to as User-Space Preload (USP), and tested several management tasks with it. According to the comparison with FUSE, USP-based file system management achieved an obvious performance improvement.

Range query in Peer-to-Peer networks based on Distributed Hash Table (DHT) is still an open problem. The traditional way uses order-preserving hashing functions to create value indexes that are placed and stored on the corresponding peers to support range query. The way, however, suffers from high index maintenance costs. To avoid the issue, a scalable blind search method over DHTs - recursive partition search (RPS) can be used. But, RPS still easily incurs high network overhead as network size grows. Thus, in this paper, a learning-aware RPS (LARPS) is proposed to overcome the disadvantages of two approaches above mentioned. Extensive experiments show LARPS is a scalable and robust approach for range query, especially in the following cases: (a) query range is wide, (b) the requested resources follow Zipf distribution, and (c) the number of required resources is small.

As authentication provides crucial online identity, it is the basis of data security. In this paper, a session based authentication is proposed and the long unique un-guessable session identifier is used as a parameter of an authentication token. It has the advantages of one-timeness, short-lived and no prior knowledge requirement. The session model is established with detailed implementation of communication protocol. The security of this protocol is then analyzed formally and the results show that the protocol can resist various attacks, e.g. session hijacking, message replay and pharming attacks etc. Finally, a case is studied and the performance of the application is evaluated, which indicates that the proposed scheme is simpler and more efficient than the existing schemes.

The storage management initiative specification (SMI-S) has been proposed for years to standardize the management of storage resources in storage area network (SAN). However, current management architecture mainly focuses on local area management. In this paper, we propose a scalable management architecture based on the integration of SMI-S and Chord. Meanwhile, Chord can only deal with exact query, the query of storage resources is significantly more complex. To deal with this problem, we further improve our architecture with a scalable blind search method - recursive partition search (RPS). Experiments show RPS is an effective approach for storage resource range query in the case that the Chord overlay network is not very large.

Ethernet-based network storage protocols such as iSCSI have become increasingly common in today's storage network. In this paper, we propose and implement another new protocol vSCSI (Vl-attached SCSI) for competing with iSCSI in LAN environment, and experimentally compare performance ofvSCSI and iSCSI for environments with no data sharing across machines. Using VI replaces TCP/IP, vSCSI achieves higher throughput than iSCSI, meanwhile maintains low cost. The test results on the Linux platform show that vSCSI outperforms iSCSI for data intensive workloads. In average, vSCSI performs better than iSCSI by 115.12% in the way of sequence write and 35.02% in sequence read. The high-bandwidth and low-latency of VI providing are the primary reasons for this performance difference.

High performance, high bandwidth, and scalable network storage system is the requirement tendency of most data-intensive applications. Distributed RAID, as a storage architecture, is widely used in cluster and distributed computing environments. But the data placement is mechanical and penalty for maintaining consistency is suffered. In this paper we propose a novel block-level storage architecture called iVISA (iSCSI based virtual interface storage architecture) which employs the concepts both distributed RAID and virtualization storage. All storage resources distribute in a high performance VI network and are managed by a metadata server. A number of iSCSI target nodes act as the entrances of iVISA system for accessing through iSCSI connections. The storage resources are dynamically allocated to users during I/O accessing. The users' logical block addresses are intelligently and flexibly mapped to the physical block addresses based on the current workload of storage nodes and data layout. The evaluation result shows that better performance can be achieved by allocation and mapping dynamically and flexibly with optimized allocation strategy. A hybrid strategy considering both data layout and load of storage nodes has a 30% higher I/O performance than conventional distributed RAID.

Geographic Information System Grid integrates geographic information systems and Grid technology for data gathering, accessing, transmitting and service, in different I/O patterns, built upon massive storage systems. Existing non-standardized multi-source and multi-scale data lack spatial information sharing either internally or externally between organizations or departments, especially in national or global applications. HUSt is a massive storage system that was built at Wuhan National Laboratory for Optoelectronics, in China. There are heterogeneous storage areas in the system, including Object-based Storage System for the main data storing especially for the data searched frequently, Virtual Interface based Storage System for the data required at high transfer speed, and InfiniBand based SAN for high performance. HUSt is primarily meant for research on the organization and key technologies of storage systems for the next generation Internet. The goal is to unify network storage and construct a peta-byte storage system, which supports GIS Grid and applications.

The ever growing storage demands of modern applications cultivate a promising area of storage system management, centered upon metadata servers. Some of the works are done at the file system level, which care little about device level data organization. In this paper we present the design of managing metadata at block-level by using storage virtualization techniques. We propose both static and dynamic block mapping policies to meet the diverse needs of applications, balance disk workload and improve storage system performance. We build our prototype in a Virtual Interface based storage area network, and the evaluation results demonstrate that metadata management at block-level can be efficient and flexible.