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 ...
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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.
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 in...
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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.
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 sing...
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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.
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 red...
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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.
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 l...
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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.
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 ...
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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.
Popular service providers, such as Google and Amazon, have turned their vast resources into a cloud computing model and enforced their businesses to run applications on the servers of such new model. To ensure securit...
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ISBN:
(纸本)9781467381741
Popular service providers, such as Google and Amazon, have turned their vast resources into a cloud computing model and enforced their businesses to run applications on the servers of such new model. To ensure security and privacy in this environments, customers have to encrypt their data before uploading them into the cloud servers. Unfortunately, modern unbreakable encryption methods are inadequate because they do not have the ability to execute database queries on the encrypted data. In this paper, we address the problem of how to calculate the geographical distance over an encrypted dataset. Specifically, the data owner, Alice, sends her encrypted dataset of geographical locations into the cloud server. At any time, Bob would like to check the proximity of his submitted query from the locations of Alice. Our proposed scheme enables the untrusted server to perform such task without compromising the privacy of either the dataset of Alice or the query of Bob. Among various distance metrics, we employ the efficient principle of approximate matching to obtain the proximity between query and data locations. Furthermore, we use the inner product similarity to formalize such principle for similarity measurement. Several experiments have been conducted to investigate the overhead and the efficiency of the proposed scheme.
Recently, security issues are obstructing the development and using of cloud computingservices. Authentication and integrity play an important role in the cloud security, and numerous concerns have been raised to rec...
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Recently, security issues are obstructing the development and using of cloud computingservices. Authentication and integrity play an important role in the cloud security, and numerous concerns have been raised to recognize any tampering with exchanges of the image document between two entities (sender and receiver) within the cloud environment. However, none of the existing solutions reduce the probability of known attacks by combining cryptographic hash function with a strong factor that should be periodically changed. For this reason, in this paper we propose a robust one-time image document authentication scheme based on combining non-interactive onetime biometric key and a robust wavelet-based cryptographic hashing scheme. The result of the combination is one-time image document authentication code (OMAC). OMAC is hidden in an image document as a cover image through reversible data embedding steganography. The proposed scheme has several important security attributes, such as key agreement, biometric key management, robust OMAC, invulnerability, and efficiency. In biometric key management, key generation, key selection, and key update algorithms are performed autonomously by the sender and the receiver; thus, no interaction between them is needed.
Datacenter demand response is envisioned as a promising approach for mitigating operational instability faced by smart grids. It enables significant potentials in peak load shedding and facilitates the incorporation o...
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Graph model has been widely applied in docu- ment summarization by using sentence as the graph node, and the similarity between sentences as the edge. In this paper, a novel graph model for document summarization is p...
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Graph model has been widely applied in docu- ment summarization by using sentence as the graph node, and the similarity between sentences as the edge. In this paper, a novel graph model for document summarization is presented, that not only sentences relevance but also phrases relevance information included in sentences are utilized. In a word, we construct a phrase-sentence two-layer graph structure model （PSG） to summarize document（s） . We use this model for generic document summarization and query-focused sum- marization. The experimental results show that our model greatly outperforms existing work.
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