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

Various problems arising in control and data analysis can be formulated as large-scale convex optimization problems with a composite objective structure. Within the black-box optimization framework, such problems are typically solved by using accelerated first-order methods. The celebrated examples of such methods are the Fast Gradient Method and the Accelerated Multistep Gradient Method, designed by using the estimating sequences framework. In this work, we present a new class of estimating sequences, which are constructed by making use of a tighter lower bound on the objective function together with the gradient mapping technique. Based on the newly introduced estimating sequences, we construct a new method, which is also equipped with an efficient line-search strategy that provides robustness to the imperfect knowledge of the Lipschitz constant. Our proposed method enjoys the accelerated convergence rate, and our theoretical results are corroborated by numerical experiments conducted on real-world datasets. The experimental results also demonstrate the robustness of the initialization of the proposed method to the imperfect knowledge of the strong convexity parameter of the objective function.

A new accelerated gradient-based estimating sequence technique for solving large-scale optimization problems with composite structure

In \cite{butman1976} the linear coding scheme is applied, $X_t =g_t\Big(\Theta - {\bf E}\Big\{\Theta\Big|Y^{t-1}, V_0=v_0\Big\}\Big)$, $t=2,\ldots,n$, $X_1=g_1\Theta$, with $\Theta: \Omega \to {\mathbb R}$, a Gaussian random variable, to derive a lower bound on the feedback rate, for additive Gaussian noise (AGN) channels, $Y_t=X_t+V_t, t=1, \ldots, n$, where $V_t$ is a Gaussian autoregressive (AR) noise, and $\kappa \in [0,\infty)$ is the total transmitter power. For the unit memory AR noise, with parameters $(c, K_W)$, where $c\in [-1,1]$ is the pole and $K_W$ is the variance of the Gaussian noise, the lower bound is $C^{L,B} =\frac{1}{2} \log \chi^2$, where $\chi =\lim_{n\longrightarrow \infty} \chi_n$ is the positive root of $\chi^2=1+\Big(1+ \frac{|c|}{\chi}\Big)^2 \frac{\kappa}{K_W}$, and the sequence $\chi_n \triangleq \Big|\frac{g_n}{g_{n-1}}\Big|, n=2, 3, \ldots,$ satisfies a certain recursion, and conjectured that $C^{L,B}$ is the feedback capacity. 
In this correspondence, it is observed that the nontrivial lower bound $C^{L,B}=\frac{1}{2} \log \chi^2$ such that $\chi >1$, necessarily implies the scaling coefficients of the feedback code, $g_n$, $n=1,2, \ldots$, grow unbounded, in the sense that, $\lim_{n\longrightarrow\infty}|g_n| =+\infty$. The unbounded behaviour of $g_n$ follows from the ratio limit theorem of a sequence of real numbers, and it is verified by simulations. It is then concluded that such linear codes are not practical, and fragile with respect to a mismatch between the statistics of the mathematical model of the channel and the real statistics of the channel. In particular, if the error is perturbed by $\epsilon_n>0$ no matter how small, then $X_n =g_t\Big(\Theta - {\bf E}\Big\{\Theta\Big|Y^{t-1}, V_0=v_0\Big\}\Big)+g_n \epsilon_n$, and $|g_n|\epsilon_n \longrightarrow \infty$, as $n \longrightarrow \infty$.

On the Fragile Rates of Linear Feedback Coding Schemes of Gaussian Channels with Memory.

We present a new accelerated gradient-based method for solving smooth unconstrained optimization problems, which we name SuperFast Gradient Method (SFGM). Our goal is to understand how the heavy-ball momentum can be embedded into the Fast Gradient Method (FGM). For this purpose, we construct a generalization of Nesterov's estimating sequences, which allows for encoding any form of information about the cost function that can aid in further accelerating the minimization process. In the black box framework, the proposed generalized estimating sequences suggest minimizing a regularized version of the objective function. We show that the regularizers can be obtained by exploiting the history of the previously constructed estimating functions. From the viewpoint of efficiency estimates, we prove that the lower bound on the number of iterations for the proposed SFGM is $\mathcal{O} \left(\sqrt{\frac{\kappa}{2}}\right)$. Our theoretical results are further corroborated by extensive numerical experiments on various types of optimization problems. Both synthetic and real-world datasets are utilized to demonstrate the effciency of SFGM in decreasing the distance to the optimal solution and in decreasing the norm of the gradient.

Generalizing the estimating sequences yields a SuperFast Gradient Method

In this paper, we consider the problem of power scheduling of a sensor that transmits over a (possibly) unknown Gilbert-Elliott (GE) channel for remote state estimation. The sensor supports two power modes, namely low power, and high power. The scheduling policy determines when to use low power or high power for data transmission over a fading channel with temporal correlation while satisfying the energy constraints. Although error-free acknowledgement/negative-acknowledgement (ACK/NACK) signals are provided by the remote estimator, they only provide meaningful information about the underlying channel state when low power is utilized. This leads to a partially observable Markov decision process (POMDP) problem and we derive conditions that preserve the optimality of a stationary schedule derived for its fully observable counterpart. However, implementing this schedule requires knowledge of the parameters of the GE model which are not available in practice. To address this, we adopt a Bayesian framework to learn these parameters online and propose an algorithm that is shown to satisfy the energy constraint while achieving near-optimal performance via simulation.

Power Allocation for Remote Estimation Over Known and Unknown Gilbert-Elliott Channels

This paper investigates the distributed consensus tracking control problem for general linear multi-agent systems (MASs) with external disturbances and heterogeneous time-varying input and communication delays under a directed communication graph topology, containing a spanning tree. First, for all agents whose state matrix has no eigenvalues with positive real parts, a communication-delay-related observer, which is used to construct the controller, is designed for followers to estimate the leader's state information. Second, by means of the output regulation theory, the results are relaxed to the case that only the leader's state matrix eigenvalues have non-positive real parts and, under these relaxed conditions, the controller is redesigned. Both cases lead to a closed-loop error system of which the stability is guaranteed via a Lyapunov-Krasovskii functional with sufficient conditions in terms of input-delay-dependent linear matrix inequalities (LMIs). An extended LMI is proposed which, in conjunction with the rest of LMIs, results in a solution with a larger upper bound on delays than what would be feasible without it. It is highlighted that the integration of communication-delay-related observer and input-delay-related LMI to construct a fully distributed controller (which requires no global information) is scalable to arbitrarily large networks. The efficacy of the proposed scheme is demonstrated via illustrative numerical examples.

Themistoklis Charalambous

Papers

A new accelerated gradient-based estimating sequence technique for solving large-scale optimization problems with composite structure

On the Fragile Rates of Linear Feedback Coding Schemes of Gaussian Channels with Memory.

Generalizing the estimating sequences yields a SuperFast Gradient Method

Power Allocation for Remote Estimation Over Known and Unknown Gilbert-Elliott Channels

Multi-agent consensus with heterogeneous time-varying input and communication delays in digraphs