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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Feng Chen Weizhong Qiang Hai Jin Deqing Zou Duoqiang Wang Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan China

Softwares usually need to be updated to fix bugs or add new features. On the other hand, some critical softwares, such as cloud applications, need to provide service continuously, thus should be updated without downtime. Conventional Dynamic Software Updating (DSU) systems try to update programs while running, but they hardly consider the communication of the program to be updated with other programs, which may lead to some inconsistency problems. We handle the problem with an improved DSU system by using multi-version execution. When a new update arrives, instead of updating the application to the new version, we fork a new process of the old version and dynamically update it to the new version, then make these two versions run concurrently until the update finishes. We implement a prototype system called MUC (Multi-vesion for Updating of Cloud) on Linux. To verify our prototype, we apply MUC to cloud applications Redis and Ice cast, and evaluate the overhead of MUC at runtime.

关键词： Servers Software Synchronization Prototypes Linux Analytical models Process control

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Evaluating Latency-Sensitive Applications' Performance Degradation in Datacenters with Restricted Power Budget

Evaluating Latency-Sensitive Applications' Performance Degra...

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International Conference on Parallel Processing

作者： Song Wu Chuxiong Yan Haibao Chen Hai Jin Wei Guo Zhen Wang Deqing Zou Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Tencent Inc

ISBN: (纸本)9781467375887

For datacenters with limited power supply, restricting the servers' power budget (i.e., the maximal power provided to servers) is an efficient approach to increase the server density (the server quantity per rack), which can effectively improve the cost-effectiveness of the datacenters. However, this approach may also affect the performance of applications in servers. Hence, the prerequisite of adopting the approach in datacenters is to precisely evaluate the application performance degradation caused by restricting the servers' power budget. Unfortunately, existing evaluation methods are inaccurate because they are either improper or coarse-grained, especially for the latency-sensitive applications widely deployed in datacenters. In this paper, we analyze the reasons why state-of-the-art methods are not appropriate for evaluating the performance degradation of latency-sensitive applications in case of power restriction, and we propose a new evaluation method which can provide a fine-grained way to precisely describe and evaluate such degradation. We verify our proposed method by a real-world application and the traces from Tencent's datecenter with 25328 servers. The experimental results show that our method is much more accurate compared with the state of the art, and we can significantly increase datacenter efficiency by saving servers' power budget while maintaining the applications' performance degradation within controllable and acceptable range.

关键词： Servers degradation of performance power budget evaluation method state of the art Budgets

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SKVM: Scaling In-Memory Key-Value Store on Multicore

SKVM: Scaling In-Memory Key-Value Store on Multicore

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IEEE Symposium on Computers and Communication

作者： Ran Zheng Wenjin Wang Hai Jin Qin Zhang Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology

ISBN: (纸本)9781467371957

SKVM is a high performance in-memory Key-Value (KV) store for multicore, which is designed for high concurrent data access. There are some problems of existing systems dealing with high concurrent data processing on multicore: lock competition, cache coherency overhead, and large numbers of concurrent network connections. To solve the problems and make the in-memory KV store scale well on multicore, high concurrent data access is divided into two steps: high concurrent connection processing and high concurrent data processing. Half sync/half async model (HSHA) is adopted to eliminate network bottle-neck, which can support high concurrent network connection. Through data partition, lock competition is eliminated and cache movement is reduced. Furthermore, consistent hash is adopted as data distribution strategy which can improve the scalability of system on multicore. Though some of these ideas appear elsewhere, SKVM is the first to combine them together. The experimental results show that SKVM can reach at most 2.4× higher throughput than Memcached, and scales near linearly with the number of cores under any workload.

关键词： In-Memory KV store Multicore Scalability Concurrent data access

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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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UaaS: Software Update as a Service for the IaaS Cloud

UaaS: Software Update as a Service for the IaaS Cloud

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IEEE International Conference on services computing (SCC)

作者： Kai Liu Deqing Zou Hai Jin Cluster and Grid Computing Lab Services Huazhong University of Science and Technology Wuhan China

With the development of cloud computing, there is a growing number of virtual machines (VMs) in the IaaS cloud. The VM owners can install different kinds of software on demand. However, if the software is not updated in time, it would be a great threat to the security of the cloud. But for the VM owners, it is a tedious task to keep all of the installed software up to date. In this paper we present a new software update model called UaaS (Update as a Service) to handle the VM (online or offline) update automatically. Multiple VMs share one software update service and multiple update strategies are provided for single software, which can be customized at any time. The ability of UaaS has been evaluated by our experiments, and the results show that UaaS can provide software update service successfully and complete update task for lots of VMs with multiple update strategies efficiently.

关键词： Software Databases Security Linux Monitoring Computer architecture Algorithm design and analysis

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A Novel Fast Approach for Convolutional Networks with Small Filters Based on GPU

A Novel Fast Approach for Convolutional Networks with Small ...

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IEEE International Conference on High Performance computing and Communications (HPCC)

作者： Wenbin Jiang Yiming Chen Hai Jin Bin Luo Ye Chi Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781479989386

Recently, convolutional networks have achieved great successes in the field of computer vision. In order to improve the efficiency of convolutional networks, large amount of solutions focusing on training algorithms and parallelism strategies have been proposed. In this paper, a novel algorithm based on look-up table is proposed to speed up convolutional networks with small filters by applying GPU. By transforming multiplication operations in the convolution computation to some table-based summation operations, the overhead of convolution computation can be reduced largely. The process of creating table and looking up table is very appropriate for parallelization on GPU. Experiment results show that the proposed approaches can improve the speed of convolution computation by 20%-30%, compared with state-of-the-art existing works.

关键词： Convolution Graphics processing units Training Computational modeling Filtering algorithms Kernel Digital filters

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Cost-Effective Hybrid Replacement Strategy for SSD in Web Cache

Cost-Effective Hybrid Replacement Strategy for SSD in Web Ca...

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IEEE International Conference on Ubiquitous computing and Communications

作者： Qu Li Xiaofei Liao Hai Jin Li Lin Xia Xie Qiongjie Yao Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9781509001552

The increasing popularity of photo sharing in social networking service (SNS) complicates the challenge of storing and transmitting large photo data for SNS providers. Distributed web caches are generally used by SNS providers to improve data transmission performance effectively. Two critical factors affect the efficiency of web caches: storage media and replacement algorithms. Solid-state drive (SSD) is often used in web caches due to its good performance. Random write performance and SSD endurance are directly affected by the characteristic of write amplification;this characteristic is also related to replacement algorithms. The least recently used (LRU) algorithm effectively computes cache hit ratio but induces severe write amplification. First in first out (FIFO) method minimizes write amplification, whereas a weak hit ratio increases bandwidth consumption. In this paper, we present a hybrid replacement strategy, namely, F-SkLRU. This strategy facilitates a good trade-off between performance and the cost of web cache. F-SkLRU takes advantage of sequential writing to reduce SSD wear. This strategy also consolidates multiple factors to improve hit ratio. Simulation results show that F-SkLRU can reduce bandwidth consumption by 14.11% to 23.4% in comparison with that of FIFO and LRU. The proposed strategy can also reduce the cost of SSD by up to 300% more than LRU can in extreme situations.

关键词： Web cache Bandwidth SSD Write amplification

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A Map-reduce Method for Training Autoencoders on Xeon Phi

A Map-reduce Method for Training Autoencoders on Xeon Phi

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IEEE International Conference on Ubiquitous computing and Communications

作者： Qiongjie Yao Xiaofei Liao Hai Jin Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9781509001552

The stacked autoencoder is a deep learning model that consists of multiple autoencoders. This model has been widely applied in numerous machine learning applications. A significant amount of effort has been made to increase the size of the deep learning model with respect to the size of the training dataset and the parameter of the model to improve performance. However, training a large deep learning model is highly time consuming. Recent studies have applied the CPU cluster with thousands of machines as well as the single GPU or the GPU cluster, to train large scale deep learning models. As a high-performance coprocessor like GPU, the Xeon Phi can be an alternative tool for training large scale deep learning models on a single machine. The Xeon Phi can be recognized as a small cluster which features about 60 cores, and each core supports four hardware threads. Massive parallelism offsets the low computing capacity of every core, but challenges an efficient parallel autoencoders design. In this paper, we analyze the training algorithm of autoencoders based on the matrix operation and point out the thread oversubscription problem, which results in performance degradation. Based on the observation, we propose our map-reduce implementation of autoencoders on the Xeon Phi coprocessor. Our basic idea is to parallelize multiple autoencoder model replicas with bulk synchronous parallel (BSP) communication model where the parameters are updated after the computations of all replicas are completed. Each thread is responsible for one model replica, and all replicas work together on the same mini-batch. This data parallelism method is suitable for training autoencoders on the Xeon Phi, and can extend to asynchronous parallel training method without thread oversubscription. In our experiment the speedup is four times higher than that of sequential implementation. Enlarging the size of the autoencoder model, our method still gets stable speedup.

关键词： Deep Learning Autoencoder Xeon Phi Mapreduce BSP

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URMG: Enhanced CBMG-Based Method for Automatically Testing Web Applications in the Cloud

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Tsinghua Science and technology 2014年第1期19卷 65-75页

作者： Xiaolin Xu Hai Jin Song Wu Lixiang Tang Yihong Wang Services Computing Technology and System Lab and Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology

To satisfy the rapid growth of cloud technologies, a large number of web applications have been developed and deployed, and these applications are being run in clouds. Due to the scalability provided by clouds, a single web application may be concurrently visited by several millions or billions of users. Thus, the testing and performance evaluations of these applications are increasingly important. User model based evaluations can significantly reduce the manual work required, and can enable us to determine the performance of applications under real runtime environments. Hence, it has become one of the most popular evaluation methods in both industry and academia. Significant efforts have focused on building different kinds of models using mining web access logs, such as Markov models and Customer Behavior Model Graph （CBMG）. This paper proposes a new kind of model, named the User Representation Model Graph （URMG）, which is built based on CBMG. It uses an algorithm to refine CBMG and optimizes the evaluations execution process. Based on this model, an automatic testing and evaluation system for web applications is designed, implemented, and deployed in our test cloud, which is able to execute all of the analysis and testing operations using only web access logs. In our system, the error rate caused by random access to applications in the execution phase is also reduced, and the results show that the error rate of the evaluation that depends on URMG is 50% less than that which depends on CBMG.

关键词： cloud web application performance evaluation customer behavior user representation

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Ciphertext-Policy Attribute-Based Encryption with Short Keys

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Chinese Journal of Electronics 2014年第4期23卷 655-660页

作者： XU Peng TANG Yong JIANG Wenbin JIN Hai ZOU Deqing Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology

Ciphertext-policy attribute-based encryption(CP-ABE)allows a user with some attributes to decrypt the ciphertexts associated with these *** several CP-ABE schemes with the constant size ciphertext were proposed to reduce the communication cost,their master public and secret keys still have the size linear in the total number of *** schemes are unpractical for the attribute-scalable and many-attributes scenario.A new CP-ABE scheme is *** attribute is mapped to a mathematical value by a combination *** master public and secret keys of the proposed CP-ABE scheme have the size linear in the binary size of a hash function’s *** has the comparable performance with existing schemes in the aspects like the time costs of encryption and decryption,the expressiveness of access policy and the provable security.

关键词： Ciphertext-policy attribute-based encryption Expressiveness policy Provable security

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