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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Haodi Lu Haikun Liu Chencheng Ye Xiaofei Liao Fubing Mao Yu Zhang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computing Science and Technology Huazhong University of Science and Technology China

Modern storage systems typically replicate data on multiple servers to provide high reliability and availability. However, most commercially-deployed datastores often fail to offer low latency, high throughput, and strong consistency at the same time. This paper presents Whale, a Remote Direct Memory Access (RDMA) based primary-backup replication system for in-memory datastores. Whale achieves both low latency and strong consistency by decoupling metadata multicasting from data replication for all backup nodes, and using an optimistic commitment mechanism to respond to client write requests earlier. Whale achieves high throughput by propagating writes from the primary node to backup nodes asynchronously via RDMA-optimized chain replication. To further reduce the cost of data replication, we design a log-structured datastore to fully exploit the advantages of one-sided RDMA and Persistent Memory (PM). We implement Whale on a cluster equipped with PM and InfiniBand RDMA networks. Experimental results show that Whale achieves much higher throughput and lower latency than state-of-the-art replication protocols.

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A General Offloading Approach for Near-DRAM Processing-In-Memory Architectures

A General Offloading Approach for Near-DRAM Processing-In-Me...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Dan Chen Hai Jin Long Zheng Yu Huang Pengcheng Yao Chuangyi Gui Qinggang Wang Haifeng Liu Haiheng He Xiaofei Liao Ran Zheng National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Clusters and Grid Computing Lab Huazhong University of Science and Technology China

Processing-in-memory (PIM) is promising to solve the well-known data movement challenge by performing in-situ computations near the data. Leveraging PIM features is pretty profitable to boost the energy efficiency of applications. Early studies mainly focus on improving the programmability for computation offloading on PIM architectures. They lack a comprehensive analysis of computation locality and hence fail to accelerate a wide variety of applications. In this paper, we present a general-purpose instruction-level offloading technique for near-DRAM PIM architectures, namely IOTPIM, to exploit PIM features comprehensively. IOTPIM is novel with two technical advances: 1) a new instruction offloading policy that fully considers the locality of the whole on-chip cache hierarchy, and 2) an offloading performance benefit prediction model that directly predicts offloading performance benefits of an instruction based on the input dataset characterizes, preserving low analysis overheads. The evaluation demonstrates that IOTPIM can be applied to accelerate a wide variety of applications, including graph processing, machine learning, and image processing. IOT-PIM outperforms the state-of-the-art PIM offloading techniques by 1.28×-1.51× while ensuring offloading accuracy as high as 91.89% on average.

关键词： Analytical models Technological innovation Distributed processing systematics Image processing Computer architecture Machine learning

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Grid and Cooperative computing - GCC 2004 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Hai Jin Yi Pan Nong Xiao Jianhua Sun

ISBN: (数字)9783540302087

ISBN: (纸本)9783540235644

Welcome to the proceedings of GCC2004 and the city of Wuhan. Grid computing has become a mainstream research area in computer science and the GCC conference has become one of the premier forums for presentation of new and exciting research in all aspectsofgridandcooperativecomputing. Theprogramcommitteeispleasedtopresent the proceedings of the 3rd International Conference on Grid and Cooperative Comp- ing (GCC2004), which comprises a collection of excellent technical papers, posters, workshops, and keynote speeches. The papers accepted cover a wide range of exciting topics, including resource grid and service grid, information grid and knowledge grid, grid monitoring,managementand organizationtools, grid portal, grid service, Web s- vices and their QoS, service orchestration, grid middleware and toolkits, software glue technologies, grid security, innovative grid applications, advanced resource reservation andscheduling,performanceevaluationandmodeling,computer-supportedcooperative work, P2P computing, automatic computing, and meta-information management. The conference continues to grow and this year a record total of 581 manuscripts (including workshop submissions) were submitted for consideration. Expecting this growth, the size of the program committee was increased from 50 members for GCC 2003 for 70 in GCC 2004. Relevant differences from previous editions of the conf- ence: it is worth mentioning a signi?cant increase in the number of papers submitted by authors from outside China; and the acceptance rate was much lower than for p- vious GCC conferences. From the 427 papers submitted to the main conference, the program committee selected only 96 regular papers for oral presentation and 62 short papers for poster presentation in the program.

关键词： Theory of Computation Computer Communication Networks Operating systems Software Engineering Information systems Applications (incl. Internet) Information Storage and Retrieval

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Towards Effective and Efficient Error Handling Code Fuzzing Based on Software Fault Injection 31

Towards Effective and Efficient Error Handling Code Fuzzing ...

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31st IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2024

作者： Chen, Kang Wen, Ming Jia, Haoxiang Wu, Rongxin Jin, Hai Wuhan430074 China Jin YinHu Laboratory Wuhan430074 China School of Informatics Xiamen University Xiamen361005 China School of Computer Science and Technology HUST Wuhan430074 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security China Cluster and Grid Computing Lab China

ISBN: (纸本)9798350330663

Software systems often encounter various errors or exceptions in practice, and thus proper error handling code is essential to ensure the reliability of software systems. Unfortunately, error handling code is often bug-prone, while sufficiently testing them is challenging as such code often cannot be triggered under normal conditions. Motivated by this, recent studies have proposed to leverage software fault injection (SFI) based fuzzing to discover potential bugs in complicated error handling code. Despite the promising results achieved, their effectiveness and efficiency are still compromised in practice due to the huge search space of error sites, inadequate fuzzing guidance, and the overhead induced by context-sensitive SFI. To achieve effective and efficient testing of error handling code, this study presents AFL-FI, which first utilizes a similarity-based method to identify suspicious error sites, and then incorporates the idea of error site coverage to guide the fuzzing process. Finally, the design of lightweight context-sensitive SFI enables AFL-FI to execute test cases efficiently. We evaluate AFL-FI on eight large-scale open-source projects, and the results show that it can outperform existing state-of-the-art fuzzing tools significantly in terms of branch code coverage. More importantly, AFL-FI has discovered 13 previously unknown bugs, and all of them have been confirmed while 12 of them have been fixed. Besides, our evaluation also demonstrates that all the key designs of AFL-F I are effective that contribute significantly to its overall performance. © 2024 IEEE.

关键词： Errors

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A Replication and Cache based Distributed Metadata Management system for Data Grid

A Replication and Cache based Distributed Metadata Managemen...

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ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed computing (SNPD)

作者： Ke Shi Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

Metadata management is a key technique of data grid. This paper presents a replication and cache based metadata management system (RCMMS) to improve metadata management of Global Distributed Storage system (GDSS). Storage Service Provider (SSP) of GDSS has dedicated buffer to cache metadata. The design, implementation and evaluation of RCMMS are discussed in this paper. RCMMS provides efficient algorithms to manage highly dynamic metadata replicas. The evaluation demonstrates that replica plus cache metadata management system outperforms existing metadata management system of GDSS.

关键词： Least squares approximation technology management Buffer storage Cache storage Peer to peer computing Resource management Data communication Master-slave Software engineering Artificial intelligence

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Cost Minimization for Code Offloading with Cellular Traffic Aggregation

Cost Minimization for Code Offloading with Cellular Traffic ...

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IEEE Symposium on Computers and Communication

作者： Jiao Song Feng Lu Hai Jin Zaiyang Tang Peng Li Xiaofei Liao Xia Xie Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9781467371957

Code offloading has been proposed to improve the performance and energy-efficiency of mobile devices by sending heavy computation tasks to resourceful cloud, instead of executing all tasks on local mobile devices. Unfortunately, current code offloading techniques are not efficient enough because of high communication cost. In this paper, we propose a novel code offloading strategy with cellular traffic aggregation that aggregates offloaded codes to several mobile devices before sending them to cloud, which can significantly reduce tail energy effect. With the objective of minimizing the total cost of computation and communication, we propose an optimization framework by jointly considering code partition and traffic aggregation. Due to the hardness of this problem, we design an efficient heuristic algorithm and evaluate its performance via extensive simulations. Simulation results demonstrate that the proposed algorithm significantly outperforms existing schemes.

关键词： Code Offloading Cellular Network Traffic Aggregation Energy Efficiency

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Scheme for Ensuring Data Security on Cloud Data Storage in a Semi-trusted Third Party Auditor

Scheme for Ensuring Data Security on Cloud Data Storage in a...

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International Conference on Computer Science and Network technology

作者： Zaid Alaa Hussien Hai Jin Zaid Ameen Abduljabbar Mohammed Abdulridha Hussain Salah H. Abbdal Deqing Zou Cluster and Grid Computing Lab Big Data Technology and System Lab Services Computing Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9781467381741

Cloud computing is a promising information technology service that allocates and reallocates resources when a client requires virtual data storage and network facility at any time and place. Cloud computing provides large data storage and management services at the lowest cost. Thus, it is crucial for many organizations and clients who seek such services. Data security and integrity are necessary and play important roles. However, these two issues are facing significant challenges. Therefore, a robust scheme is required to ensure security and privacy while transmitting or storing data in the cloud storage environment. We propose an efficient and robust scheme to ensure data security in a semi-trusted third party auditor. Our scheme adopts an advanced encryption standard to support data owner privacy, a cryptography hash function to maintain data owner integrity, and elliptic curve cryptography to ensure data confidentiality, correctness, and security when transmitting data over unsecure channels. A security analysis confirms that our scheme can withstand man-in-the-middle attack and provides data correctness.

关键词： Cloud storage data security elliptic curve cryptography hash function integrity semi-trusted third party auditor Data Security Elliptic curve cryptography cryptography Cloud computing computer information security Data Storage Scheme auditor Third party customers Management services Transmitting Data

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ChestBox: Enabling Fast State Sharing for Stateful Serverless computing with State Functions

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable computing 2024年

作者： Zhang, Xinmin Wu, Song Gu, Lin He, Qiang Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan Hubei430074 China

This paper presents ChestBox, a novel approach that utilizes state functions to facilitate low-latency state sharing for stateful serverless computing. When an application function needs to share a state, the state function creates a memory space with Linux's shared memory object to store the state. Other application functions can then read the state directly from the shared memory. ChestBox enables fast state sharing that avoids excessive memory overhead without compromising on-demand resource allocation compared to existing solutions. This effectively reduces the energy consumption of serverless computing and promotes sustainable computing. The implementation of ChestBox on Apache OpenWhisk unearths two major implementation challenges, which we address with respective optimization techniques, i.e., state function channel and state swapping. The evaluation of ChestBox with four real-world applications shows that compared with the state-of-The-Art approach, it can reduce state-sharing latency by up to 99.71%, while reducing execution costs by 24.59% and storage costs by 99.76%. © 2016 IEEE.

关键词： Storage allocation (computer)

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Efficient Time-Evolving Stream Processing at Scale

arXiv

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arXiv 2018年

作者： Huang, Yu Services Computing Technology and System Lab Big Data Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

—Time-evolving stream datasets exist ubiquitously in many real-world applications where their inherent hot keys often evolve over times. Nevertheless, few existing solutions can provide efficient load balance on these time-evolving datasets while preserving low memory overhead. In this paper, we present a novel grouping approach (named FISH), which can provide the efficient time-evolving stream processing at scale. The key insight of this work is that the keys of time-evolving stream data can have a skewed distribution within any bounded distance of time interval. This enables to accurately identify the recent hot keys for the real-time load balance within a bounded scope. We therefore propose an epoch-based recent hot key identification with specialized intra-epoch frequency counting (for maintaining low memory overhead) and inter-epoch hotness decaying (for suppressing superfluous computation). We also propose to heuristically infer the accurate information of remote workers through computation rather than communication for cost-efficient worker assignment. We have integrated our approach into Apache Storm. Our results on a cluster of 128 nodes for both synthetic and real-world stream datasets show that FISH significantly outperforms state-of-the-art with the average and the 99th percentile latency reduction by 87.12% and 76.34% (vs. W-Choices), and memory overhead reduction by 99.96% (vs. Shuffle Grouping). Copyright © 2018, The Authors. All rights reserved.

关键词： Fish

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Core maintenance in dynamic graphs: A parallel approach based on matching

arXiv

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arXiv 2017年

作者： Wang, Na Yu, Dongxiao Jin, Hai Hua, Qiang-Sheng Shi, Xuanhua Xie, Xia Services Computing Technology and System Lab Big Data Technology and System Lab Clusters and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

—The core number of a vertex is a basic index depicting cohesiveness of a graph, and has been widely used in large-scale graph analytics. In this paper, we study the update of core numbers of vertices in dynamic graphs with edge insertions/deletions, which is known as the core maintenance problem. Different from previous approaches that just focus on the case of single-edge insertion/deletion and sequentially handle the edges when multiple edges are inserted/deleted, we investigate the parallelism in the core maintenance procedure. Specifically, we show that if the inserted/deleted edges constitute a matching, the core number update with respect to each inserted/deleted edge can be handled in parallel. Based on this key observation, we propose parallel algorithms for core maintenance in both cases of edge insertions and deletions. We conduct extensive experiments to evaluate the efficiency, stability, parallelism and scalability of our algorithms on different types of real-world and synthetic graphs. Comparing with sequential approaches, our algorithms can improve the core maintenance efficiency significantly. Copyright © 2017, The Authors. All rights reserved.

关键词： Maintenance

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