Cloud computing has recently emerged as a new paradigm for hosting and delivering services over the Internet. Cloud computing is attractive to business owners as it eliminates the requirement for users to plan ahead for provisioning, and allows enterprises to start from the small and increase resources only when there is a rise in service demand. However, despite the fact that cloud computing offers huge opportunities to the IT industry, the development of cloud computing technology is currently at its infancy, with many issues still to be addressed. In this paper, we present a survey of cloud computing, highlighting its key concepts, architectural principles, state-of-the-art implementation as well as research challenges. The aim of this paper is to provide a better understanding of the design challenges of cloud computing and identify important research directions in this increasingly important area.

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Cloud computing: state-of-the-art and research challenges

Information Theory and Reliable Communication

This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

人教版高中英语新课程教材中,语言运用（Using Language）是每个单元必不可少的部分,提供了围绕单元中心话题的听、说、读、写的综合性练习,是单元中心话题的延续和升华.如何设计Using Language部分的教学,使自己的教学模式既不落俗套,又能真正体现新课程标准所倡导的教学理念,正是广大一线英语教师一直努力探索的问题.

打磨Using Language,倡导新理念

Cloud computing environments allow customers to dynamically scale their applications. The key problem is how to lease the right amount of resources, on a pay-as-you-go basis. Application re-dimensioning can be implemented effortlessly, adapting the resources assigned to the application to the incoming user demand. However, the identification of the right amount of resources to lease in order to meet the required Service Level Agreement, while keeping the overall cost low, is not an easy task. Many techniques have been proposed for automating application scaling. We propose a classification of these techniques into five main categories: static threshold-based rules, control theory, reinforcement learning, queuing theory and time series analysis. Then we use this classification to carry out a literature review of proposals for auto-scaling in the cloud.

A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments

In this paper we study distributed transmission scheduling via power control in wireless ad hoc networks with multiple channels. The target for each node is to manage to be admitted into a channel from the available channels in the network. The aim of this work is twofold: (a) to determine how a wireless node, based on its limited information, will decide which channel to access and (b), to propose a distributed algorithm for each wireless node with which once the channel is chosen a decision is made whether to stay in the channel or not. Here, we propose an algorithm that, if adopted by all the nodes in the network, it converges to a solution that admits most of the wireless nodes in the network, based on limited information only. Simulations in MATLAB justify the good performance of the algorithm.

/pdf/towards-distributed-transmission-scheduling-for-wireless-ad-2hiddjuimb.pdf

Towards distributed transmission scheduling for wireless ad hoc networks

This paper presents prefix codes which minimize various criteria constructed as a convex combination of maximum codeword length and average codeword length, or, a convex combination of the average of an exponential function of the codeword length and the average codeword length. This framework encompasses as a special case several criteria previously investigated in the literature, while relations to universal coding is discussed. The coding algorithm derived is parametric resulting in re-adjusting the initial source probabilities via a weighted probability vector according to a merging rule. An algorithm is presented to compute the weighting vector.

/pdf/lossless-coding-with-generalized-criteria-1f271q69y8.pdf

Lossless coding with generalized criteria

This paper presents lossless prefix codes optimized with respect to a pay-off criterion consisting of a convex combination of maximum codeword length and average codeword length. The optimal codeword lengths obtained are based on a new coding algorithm which transforms the initial source probability vector into a new probability vector according to a merging rule. The coding algorithm is equivalent to a partition of the source alphabet into disjoint sets on which a new transformed probability vector is defined as a function of the initial source probability vector and a scalar parameter. The pay-off criterion considered encompasses a trade-off between maximum and average codeword length; it is related to a pay-off criterion consisting of a convex combination of average codeword length and average of an exponential function of the codeword length, and to an average codeword length pay-off criterion subject to a limited length constraint. A special case of the first related pay-off is connected to coding problems involving source probability uncertainty and codeword overflow probability, while the second related pay-off compliments limited length Huffman coding algorithms.

Optimal Merging Algorithms for Lossless Codes with Generalized Criteria

Classical distributed estimation scenarios typically assume timely and reliable exchange of information over the multi-agent network. This paper, in contrast, considers single time-scale distributed estimation of (potentially) unstable full-rank dynamical systems via a multi-agent network subject to transmission time-delays. The proposed networked estimator consists of two steps: (i) consensus on (delayed) a-priori estimates, and (ii) measurement update. The agents only share their a-priori estimates with their in-neighbors over time-delayed transmission links. Considering the most general case, the delays are assumed to be time-varying, arbitrary, unknown, but upper-bounded. In contrast to most recent distributed observers assuming system observability in the neighborhood of each agent, our proposed estimator makes no such assumption. This may significantly reduce the communication/sensing loads on agents in large-scale, while making the (distributed) observability analysis more challenging. Using the notions of augmented matrices and Kronecker product, the geometric convergence of the proposed estimator over strongly-connected networks is proved irrespective of the bound on the time-delay. Simulations are provided to support our theoretical results.

Delay-Tolerant Consensus-based Distributed Estimation: Full-Rank Systems with Potentially Unstable Dynamics.

This paper addresses the problem of distributed optimization, where a network of agents represented as a directed graph (digraph) aims to collaboratively minimize the sum of their individual cost functions. Existing approaches for distributed optimization over digraphs, such as Push-Pull, require agents to exchange explicit state values with their neighbors in order to reach an optimal solution. However, this can result in the disclosure of sensitive and private information. To overcome this issue, we propose a state-decomposition-based privacy-preserving finite-time push-sum (PrFTPS) algorithm without any global information, such as network size or graph diameter. Then, based on PrFTPS, we design a gradient descent algorithm (PrFTPS-GD) to solve the distributed optimization problem. It is proved that under PrFTPS-GD, the privacy of each agent is preserved and the linear convergence rate related to the optimization iteration number is achieved. Finally, numerical simulations are provided to illustrate the effectiveness of the proposed approach.

Themistoklis Charalambous

Papers

Towards distributed transmission scheduling for wireless ad hoc networks

Lossless coding with generalized criteria

Optimal Merging Algorithms for Lossless Codes with Generalized Criteria

Delay-Tolerant Consensus-based Distributed Estimation: Full-Rank Systems with Potentially Unstable Dynamics.

A Privacy-Preserving Finite-Time Push-Sum based Gradient Method for Distributed Optimization over Digraphs