Virtualization has become a prominent tool in data centers and is extensively leveraged in cloud environments: it enables multiple virtual machines (VMs) - with multiple operating systems and applications - to run wit...
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Virtualization has become a prominent tool in data centers and is extensively leveraged in cloud environments: it enables multiple virtual machines (VMs) - with multiple operating systems and applications - to run within a physical server. However, virtualization introduces the challenging issue of preserving the high disk utilization (i.e., reducing the seek delay and rotation overhead) when allocating disk resources to VMs. Exploiting spatial locality, a key technique for improving disk utilization and performance, faces additional challenges in the virtualized cloud because of the transparency feature of virtualization (hyper visors do not have the information about the access patterns of applications running within each VM). To this end, this paper contributes a novel disk I/O scheduling framework, named Pregather, to improve disk I/O efficiency through exposure and exploitation of the special spatial locality in the virtualized environment (regional and sub-regional spatial locality corresponds to the virtual disk space and applications' access patterns, respectively), thereby improving the performance of disk-intensive applications without harming the transparency feature of virtualization (without a priori knowledge of the applications' access patterns). The key idea behind Pregather is to implement an intelligent model to predict the access regularity of sub-regional spatial locality for each VM. We implement the Pregather disk scheduling framework and perform extensive experiments that involve multiple simultaneous applications of both synthetic benchmarks and a MapReduce application on Xen-based platforms. Our experiments demonstrate the accuracy of our prediction model and indicate that Pregather results in the high disk spatial locality and a significant improvement in disk throughput and application performance.
Recently, graphics processing units (GPUs) have opened up new opportunities for speeding up general-purpose parallel applications due to their massive computational power and up to hundreds of thousands of threads ena...
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ISBN:
(纸本)9781450319003
Recently, graphics processing units (GPUs) have opened up new opportunities for speeding up general-purpose parallel applications due to their massive computational power and up to hundreds of thousands of threads enabled by programming models such as CUDA. However, due to the serial nature of existing micro-architecture simulators, these massively parallel architectures and workloads need to be simulated sequentially. As a result, simulating GPGPU architectures with typical benchmarks and input data sets is extremely time-consuming. This paper addresses the GPGPU architecture simulation challenge by generating miniature, yet representative GPGPU kernels. We first summarize the static characteristics of an existing GPGPU kernel in a profile, and analyze its dynamic behavior using the novel concept of the divergence flow statistics graph (DFSG). We subsequently use a GPGPU kernel synthesizing framework to generate a miniature proxy of the original kernel, which can reduce simulation time significantly. The key idea is to reduce the number of simulated instructions by decreasing per-thread iteration counts of loops. Our experimental results show that our approach can accelerate GPGPU architecture simulation by a factor of 88X on average and up to 589X with an average IPC relative error of 5.6%.
Mitogen-activated protein kinase(MAPK) cascade,consisting of three Ser/Thr protein kinases,namely,MAPKKK,MAPKK and MAPK,is a universal module in signal transduction pathways in *** proteins have been implicated in div...
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Mitogen-activated protein kinase(MAPK) cascade,consisting of three Ser/Thr protein kinases,namely,MAPKKK,MAPKK and MAPK,is a universal module in signal transduction pathways in *** proteins have been implicated in diverse cellular processes including cell growth,proliferation,differentiation,survival, development and in responses to a diversity of environmental stimuli including cold, heat,reactive oxygen species,UV,drought and pathogen attack.
Recent advance of virtualization technology provides a new approach to check-point/restart at the virtual machine(VM) *** contrast to traditional process-level checkpointing,checkpointing at the virtualization layer b...
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Recent advance of virtualization technology provides a new approach to check-point/restart at the virtual machine(VM) *** contrast to traditional process-level checkpointing,checkpointing at the virtualization layer brings up several advantages,such as compatibility,transparence,flexibility and ***,because the virtualization layer has little semantic knowledge about the operation system and the applications running atop,VM-layer checkpointing requires saving the entire operating system state rather than a single *** overhead may render the approach *** reduce the size of VM checkpoint,in this paper we propose a page eviction scheme and an incremental checkpointing mechanism to avoid saving unnecessary VM pages in the *** keep the system online transparently,we propose a live checkpointing mechanism by saving the memory image in a copy-on-write(COW) *** implement the performance optimization mechanisms in a prototype system,called *** results with a group of representative applications show that our page eviction scheme and incremental checkpointing can significantly reduce the checkpoint file size by up to 87% and shorten the total checkpointing/restart time by a factor of up to 71%,in comparison with the Xens default checkpointing *** observed application downtimes due to checkpointing can be reduced to as small as 300 ms.
Recently, graphics processing units (GPUs) have opened up new opportunities for speeding up general-purpose parallel applications due to their massive computational power and up to hundreds of thousands of threads ena...
详细信息
ISBN:
(纸本)9781450319003
Recently, graphics processing units (GPUs) have opened up new opportunities for speeding up general-purpose parallel applications due to their massive computational power and up to hundreds of thousands of threads enabled by programming models such as CUDA. However, due to the serial nature of existing micro-architecture simulators, these massively parallel architectures and workloads need to be simulated sequentially. As a result, simulating GPGPU architectures with typical benchmarks and input data sets is extremely time-consuming. This paper addresses the GPGPU architecture simulation challenge by generating miniature, yet representative GPGPU kernels. We first summarize the static characteristics of an existing GPGPU kernel in a profile, and analyze its dynamic behavior using the novel concept of the divergence flow statistics graph (DFSG). We subsequently use a GPGPU kernel synthesizing framework to generate a miniature proxy of the original kernel, which can reduce simulation time significantly. The key idea is to reduce the number of simulated instructions by decreasing per-thread iteration counts of loops. Our experimental results show that our approach can accelerate GPGPU architecture simulation by a factor of 88X on average and up to 589X with an average IPC relative error of 5.6%.
Map/Reduce style data-parallel computation is characterized by the extensive use of user-defined functions for data processing and relies on data-shuffling stages to prepare data partitions for parallel computation. I...
Due to great advances in computing and Internet technologies, organizations have been enabled to collect and generate a large amount of data. Most of these organizations tend to analyze their data to discover new patt...
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Due to great advances in computing and Internet technologies, organizations have been enabled to collect and generate a large amount of data. Most of these organizations tend to analyze their data to discover new patterns. Usually, analyzing such amount of data requires huge computational power and storage facilities that may not be available to these organizations. Cloud computing offers the best way to solve this problem. Storing the private data of different organizations in the same cloud server enhances the mining process, but at the same time, raises privacy concerns. Therefore, it is highly recommended to support privacy preserving data mining algorithms in the cloud environment. This paper introduces an efficient and accurate cryptography-based scheme for mining the cloud data in a secure way without loss of accuracy. Specifically, we address the problem of K-nearest neighbor (KNN) classification over horizontally distributed databases without revealing any unnecessary information. We have utilized the recently developed cryptography primitive, order preserving symmetric encryption (OPSE), to integrate securely the local classifications at a lower cost than the previously presented privacy preserving data mining schemes. Empirical results on real datasets demonstrate that the proposed scheme has similar performance with the naive mining systems in terms of classification accuracy.
Advances in cloud computing and Internet technologies have pushed more and more data owners to outsource their data to remote cloud servers to enjoy with huge data management services in an efficient cost. However, de...
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Advances in cloud computing and Internet technologies have pushed more and more data owners to outsource their data to remote cloud servers to enjoy with huge data management services in an efficient cost. However, despite its technical advances, cloud computing introduces many new security challenges that need to be addressed well. This is because, data owners, under such new setting, loss the control over their sensitive data. To keep the confidentiality of their sensitive data, data owners usually outsource the encrypted format of their data to the untrusted cloud servers. Several approaches have been provided to enable searching the encrypted data. However, the majority of these approaches are limited to handle either a single keyword search or a Boolean search but not a multikeyword ranked search, a more efficient model to retrieve the top documents corresponding to the provided keywords. In this paper, we propose a secure multi-keyword ranked search scheme over the encrypted cloud data. Such scheme allows an authorized user to retrieve the most relevant documents in a descending order, while preserving the privacy of his search request and the contents of documents he retrieved. To do so, data owner builds his searchable index, and associates with each term document with a relevance score, which facilitates document ranking. The proposed scheme uses two distinct cloud servers, one for storing the secure index, while the other is used to store the encrypted document collection. Such new setting prevents leaking the search result, i.e. the document identifiers, to the adversary cloud servers. We have conducted several empirical analyses on a real dataset to demonstrate the performance of our proposed scheme.
Virtualization can divide or aggregate the underlying resource flexibly, and it attracts attention from both academic and industry in recent years. Guest operating systems in virtual machines can be various, and the s...
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Virtualization can divide or aggregate the underlying resource flexibly, and it attracts attention from both academic and industry in recent years. Guest operating systems in virtual machines can be various, and the states of virtual machines changes all the time. Due to the complexity of virtual computing environment, it brings tremendous challenges for security monitoring. Existing monitoring mechanism can not adapt to the dynamic and diversity of virtual machines. Therefore, a comprehensive monitoring framework, named ComMon, is proposed in this paper, which implements comprehensive monitoring from three aspects: network, process, and file. It has the characteristics of real-time, transparency and generality.
Cloud security represents a main hindrance that causes to retard its widespread adoption. Authentication considers a significance element of security in cloud environment, aiming to verify a user's identity when a...
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Cloud security represents a main hindrance that causes to retard its widespread adoption. Authentication considers a significance element of security in cloud environment, aiming to verify a user's identity when a user wishes to request services from cloud. There are many authentication schemes that depend on username/password, but they are considered weak techniques of cloud authentication. A more secure scheme is the two-factor authentication that does not only verify the username/password pair, but also needs a second factor such as a token device, biometric. However, the feasibility of second-factor authentication is limited by the deployment complexity, high cost and the cloud security is compromised when the token is missing or purloined. Furthermore, these schemes are failed to resist well-known attacks such as replay attacks, reflection attacks. This paper proposes two-factor authentication scheme based on Schnorr digital signature and feature extraction from fingerprint to overcome above aforementioned issues. Security analysis and experimental results illustrate that our proposed scheme can withstand the common security attacks as well, and has a good performance of password authentication.
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