/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

A team of researchers at Google's DeepMind Technologies has been working on a means to increase the capabilities of computers by combining aspects of data processing and artificial intelligence and have come up with what they are calling a differentiable neural computer (DNC.) In their paper published in the journal Nature, they describe the work they are doing and where they believe it is headed. To make the work more accessible to the public team members, Alexander Graves and Greg Wayne have posted an explanatory page on the DeepMind website. [13]

Convolutional Neural Network를 이용한 한국어 영화평 감성 분석

An overview of virtual machine placement schemes in cloud computing

Within the framework of statistical learning theory we analyze in detail the so-called elastic-net regularization scheme proposed by Zou and Hastie [H. Zou, T. Hastie, Regularization and variable selection via the elastic net, J. R. Stat. Soc. Ser. B, 67(2) (2005) 301-320] for the selection of groups of correlated variables. To investigate the statistical properties of this scheme and in particular its consistency properties, we set up a suitable mathematical framework. Our setting is random-design regression where we allow the response variable to be vector-valued and we consider prediction functions which are linear combinations of elements (features) in an infinite-dimensional dictionary. Under the assumption that the regression function admits a sparse representation on the dictionary, we prove that there exists a particular ''elastic-net representation'' of the regression function such that, if the number of data increases, the elastic-net estimator is consistent not only for prediction but also for variable/feature selection. Our results include finite-sample bounds and an adaptive scheme to select the regularization parameter. Moreover, using convex analysis tools, we derive an iterative thresholding algorithm for computing the elastic-net solution which is different from the optimization procedure originally proposed in the above-cited work.

Elastic-net regularization in learning theory

Fog computing, also called “clouds at the edge,” is an emerging paradigm allocating services near the devices to improve the quality of service (QoS) The explosive prevalence of Internet of Things, big data, and fog computing in the context of cloud computing makes it extremely challenging to explore both cloud and fog resource scheduling strategy so as to improve the efficiency of resources utilization, satisfy the users’ QoS requirements, and maximize the profit of both resource providers and users This paper proposes a resource allocation strategy for fog computing based on priced timed Petri nets (PTPNs), by which the user can choose the satisfying resources autonomously from a group of preallocated resources Our strategy comprehensively considers the price cost and time cost to complete a task, as well as the credibility evaluation of both users and fog resources We construct the PTPN models of tasks in fog computing in accordance with the features of fog resources Algorithm that predicts task completion time is presented Method of computing the credibility evaluation of fog resource is also proposed In particular, we give the dynamic allocation algorithm of fog resources Simulation results demonstrate that our proposed algorithms can achieve a higher efficiency than static allocation strategies in terms of task completion time and price

Resource Allocation Strategy in Fog Computing Based on Priced Timed Petri Nets

Network Function Virtualization (NFV) has the potential to significantly reduce the capital and operating expenses, shorten product release cycle, and improve service agility. In this paper, we focus on minimizing the total number of Virtual Network Function (VNF) instances to provide a specific service (possibly at different locations) to all the flows in a network. Certain network security and analytics applications may allow fractional processing of a flow at different nodes (corresponding to datacenters), giving an opportunity for greater optimization of resources. Through a reduction from the set cover problem, we show that this problem is NP-hard and cannot even be approximated within a factor of (1 − o(1))lnm (where m is the number of flows) unless P=NP. Then, we design two simple greedy algorithms and prove that they achieve an approximation ratio of (1 − o(1))ln m + 2, which is asymptotically optimal. For special cases where each node hosts multiple VNF instances (which is typically true in practice), we also show that our greedy algorithms have a constant approximation ratio. Further, for tree topologies we develop an optimal greedy algorithm by exploiting the inherent topological structure. Finally, we conduct extensive numerical experiments to evaluate the performance of our proposed algorithms in various scenarios.

Provably efficient algorithms for joint placement and allocation of virtual network functions

Network Function Virtualization (NFV) has the potential to significantly reduce the capital and operating expenses, shorten product release cycle, and improve service agility. In this paper, we focus on minimizing the total number of Virtual Network Function (VNF) instances to provide a specific service (possibly at different locations) to all the flows in a network. Certain network security and analytics applications may allow fractional processing of a flow at different nodes (corresponding to datacenters), giving an opportunity for greater optimization of resources. Through a reduction from the set cover problem, we show that this problem is NP-hard and cannot even be approximated within a factor of (1 - o(1)) ln(m) (where m is the number of flows) unless P=NP. Then, we design two simple greedy algorithms and prove that they achieve an approximation ratio of (1 - o(1)) ln(m) + 2, which is asymptotically optimal. For special cases where each node hosts multiple VNF instances (which is typically true in practice), we also show that our greedy algorithms have a constant approximation ratio. Further, for tree topologies we develop an optimal greedy algorithm by exploiting the inherent topological structure. Finally, we conduct extensive numerical experiments to evaluate the performance of our proposed algorithms in various scenarios.

Provably Efficient Algorithms for Joint Placement and Allocation of Virtual Network Functions

The pay-as-you-go service model impels cloud customers to reduce the usage cost of bandwidth. Traffic Redundancy Elimination (TRE) has been shown to be an effective solution for reducing bandwidth costs, and thus has recently captured significant attention in the cloud environment. By studying the TRE techniques in a trace driven approach, we found that both short-term (time span of seconds) and long-term (time span of hours or days) data redundancy can concurrently appear in the traffic, and solely using either sender-based TRE or receiver-based TRE cannot simultaneously capture both types of traffic redundancy. Also, the efficiency of existing receiver-based TRE solution is susceptible to the data changes compared to the historical data in the cache. In this paper, we propose a Cooperative end-to-end TRE solution (CoRE) that can detect and remove both short-term and long-term redundancy through a two-layer TRE design with cooperative operations between layers. An adaptive prediction algorithm is further proposed to improve TRE efficiency through dynamically adjusting the prediction window size based on the hit ratio of historical predictions. Besides, we enhance CoRE to adapt to different traffic redundancy characteristics of cloud applications to improve its operation cost. Extensive evaluation with several real traces show that CoRE is capable of effectively identifying both short-term and long-term redundancy with low additional cost while ensuring TRE efficiency from data changes.

CoRE: Cooperative End-to-End Traffic Redundancy Elimination for Reducing Cloud Bandwidth Cost

Storage as a service has become an important paradigm in cloud computing for its great flexibility and economic savings. However, the development is hampered by data privacy concerns: data owners no longer physically possess the storage of their data. In this work, we study the issue of privacy-preserving set-valued data publishing. Existing data privacy-preserving techniques (such as encryption, suppression, generalization) are not applicable in many real scenes, since they would incur large overhead for data query or high information loss. Motivated by this observation, we present a suite of new techniques that make privacy-aware set-valued data publishing feasible on hybrid cloud. On data publishing phase, we propose a data partition technique, named extended quasi-identifier-partitioning (EQI-partitioning), which disassociates record terms that participate in identifying combinations. This way the cloud server cannot associate with high probability a record with rare term combinations. We prove the privacy guarantee of our mechanism. On data querying phase, we adopt interactive differential privacy strategy to resist privacy breaches from statistical queries. We finally evaluate its performance using real-life data sets on our cloud test-bed. Our extensive experiments demonstrate the validity and practicality of the proposed scheme.

Towards Privacy Preserving Publishing of Set-Valued Data on Hybrid Cloud

Recent years have witnessed an opportunity to improve trial efficiency and quality by predictive analysis of massive judgment documents. A practical legal judgment prediction (LJP) system should provide a judge with feasible judgment suggestions, including the charges, applicable law articles, and prison term, whereas most existing works focus on only part of the LJP task. Inspired by the impressive success of deep neural networks in a wide range of application scenarios, we propose a multichannel attentive neural network model, MANN, which learns from previous judgment documents and performs the integrated LJP task in a unified framework. In general, MANN takes the textual description of a criminal case as the input for attention-based neural networks to learn its latent feature representations oriented to the case fact, the defendant persona, and relevant law articles. Moreover, we adopt a two-tier structure to empower attentive sequence encoders to hierarchically model the semantic interactions from different parts of case description at both the word and sentence levels. The experiments are conducted on four real-world datasets of criminal cases in mainland China. The experimental results demonstrate that MANN achieves state-of-the-art LJP performance on all evaluation metrics.

Lin Ye

Papers

Provably efficient algorithms for joint placement and allocation of virtual network functions

Provably Efficient Algorithms for Joint Placement and Allocation of Virtual Network Functions

CoRE: Cooperative End-to-End Traffic Redundancy Elimination for Reducing Cloud Bandwidth Cost

Towards Privacy Preserving Publishing of Set-Valued Data on Hybrid Cloud

MANN: A Multichannel Attentive Neural Network for Legal Judgment Prediction