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

This paper considers lossless uniquely decodable source codes for a class of distributions describedby a ball with respect to the total variational distance, centered at a nominal distribution with a givenradius. The coding problem is formulated using minimax techniques, in which the maximum of theaverage codeword length over the class of distributions is minimized subject to Kraft inequality. Firstly,the maximization over the class of distributions is characterized resulting in an equivalent pay-off.consisting of the maximum and minimum codeword length and the average codeword length withrespect to the nominal distribution. Secondly, an algorithm is introduced which computes the optimalweight vector as a function of the class radius. Finally, the optimal codeword length vector is found asa function of the weight vector. I. I NTRODUCTION Lossless source codes for known probability distributions are investigated for several pay-offs,such as the average codeword length [1] and the average redundancy of the codeword length,the average of an exponential function of the codeword length [2]–[4], and the average of anexponential function of the redundancy of the codeword length [4]–[6]. For the average codewordlength pay-off the average redundancy is bounded below by zero and above by one. On the otherhand, if the true probability distribution of the source is unknown and the code is designed solely

Lossless Source Codes for a Class Described by Variational Distance and Optimal Merging Algorithm

This paper considers a network of collaborating agents for local resource allocation subject to nonlinear model constraints. In many applications, it is required (or desirable) that the solution be anytime feasible in terms of satisfying the sum-preserving global constraint. Motivated by this, sufficient conditions on the nonlinear mapping for anytime feasibility (or non-asymptotic feasibility) are addressed in this paper. For the two proposed distributed solutions, one converges over directed weight-balanced networks and the other one over undirected networks. In particular, we elaborate on uniform quantization and discuss the notion of ε-accurate solution, which gives an estimate of how close we can get to the exact optimizer subject to different quantization levels. This work, further, handles general (possibly non-quadratic) strictly convex objective functions with application to CPU allocation among a cloud of data centers via distributed solutions. The results can be used as a coordination mechanism to optimally balance the tasks and CPU resources among a group of networked servers while addressing quantization or limited server capacity.

Distributed CPU Scheduling Subject to Nonlinear Constraints

In this paper we propose a load balancing problem formulation where agents cooperate with the aim of simultaneously minimizing both the workload disparity among the agents and the overall workload transfer, under network capacity constraints. Notably, in our computational setting, the network is not just a device for the distributed solution of an optimization problem; on the contrary, the problem shares the same sparsity pattern as the network, and this aspect allows to solve it without the need for the agents to store large amount of data. In particular, while the load balancing process occurs over directed links, agents' communication is assumed to be bidirectional. For this optimization setting, first, an optimality condition is derived; then, a provably convergent distributed algorithm to compute the optimal solution is developed, and an upper bound on the convergence rate is characterized. Simulation results are provided to corroborate the validity and performance of our theoretical findings. 

Best Effort Workload Disparity Minimization in Multi-Agent Systems with Capacity Constraints

In this chapter the impact of realistic communication channels and uncertain location information on formation control of multi-agent systems aiming to achieve a common task is highlighted. First, the work is motivated by elucidating the need to incorporate realistic communication models as well as the need to model the agents’ location uncertainty. Second, it is discussed how control can be utilised to reduce the agents positioning error in cooperative systems to achieve a higher level goal, such as steering a group of agents towards a destination. Third, the impact of location uncertainty on channel gain prediction is addressed for formation control. Finally, conclusions and an outlook on future directions for controlled multi-agent systems are provided.

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Formation control of multi-agent systems with location uncertainty

This paper studies the internal stability and string stability of a vehicle platooning of constant time headway spacing policy with a varying-speed leader using a multiple-predecessor-following strategy via vehicle-to-vehicle communication. Unlike the common case in which the leader's speed is constant and different kinds of Proportional-Integral-Derivative controllers are implemented, in this case, the fact that the leader has a time-varying speed necessitates the design of an observer. First, in order to estimate its position, speed and acceleration error with respect to the leader, each follower designs an observer. The observer is designed by means of constructing an observer matrix whose parameters should be determined. We simplifies the design of the matrix of the observer in such a way that the design boils down to choosing a scalar value. The resulting observer turns out to have a third order integrator dynamics, which provides an advantage of simplifying the controller structure and, hence, derive conditions for string stability using a frequency response method. A new heuristic searching algorithm is developed to deduce the controller parameter conditions, given a fixed time headway, for string stability. Additionally, a bisection-like algorithm is incorporated into the above algorithm to obtain the minimum (with some deviation tolerance) available value of the time headway by fixing one controller parameter. The effectiveness of the internal and string stabilities of the proposed observer-based controller is demonstrated via comparison examples.

Themistoklis Charalambous

Papers

Lossless Source Codes for a Class Described by Variational Distance and Optimal Merging Algorithm

Distributed CPU Scheduling Subject to Nonlinear Constraints

Best Effort Workload Disparity Minimization in Multi-Agent Systems with Capacity Constraints

Formation control of multi-agent systems with location uncertainty

Observer-based Control for Vehicle Platooning with a Leader of Varying Speed