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

Full-duplex relaying is an enabling technique of sixth generation (6G) mobile networks, promising tremendous rate and spectral efficiency gains. In order to improve the performance of full-duplex communications, power control is a viable way of avoiding excessive loop interference at the relay. Unfortunately, power control requires channel state information of both source-relay and relay-destination channels, as well as of the loop interference channel, thus resulting in increased overheads. Aiming to offer a low-complexity alternative for power control in full-duplex relay networks, we adopt reward-based learning in the sense of multi-armed bandits. More specifically, we provide bandit-based power control algorithms, relying on acknowledgements/negative-acknowledgements observations by the relay. The proposed algorithms avoid the need for channel state information acquisition and exchange, and can be employed in a distributed manner. Performance evaluation results in terms of outage probability, average throughput and accumulated regret over time highlight an interesting performance-complexity trade-off compared to optimal power control with full channel knowledge and significant performance gains over the cases without power control and random power level selection.

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Bandit-Based Power Control in Full-Duplex Cooperative Relay Networks

As virtualization technologies enable real-time CPU allocation, it is important to build controllers that adjust the allocation in a timely fashion avoiding resource saturation and hence, dissatisfaction of the end users of services. In this work, adaptive neuro-fuzzy inference, trained on Kalman and H ∞ filters, has been used to adjust the CPU allocations based on observations of past utilization. When evaluating the performance of the proposed controller it is demonstrated that it provides even better performance than the filters it is trained on. In addition, there are no assumptions on the noise characteristics and due to the fact that the neuro-fuzzy controller can, in general, capture non-linear level processes, our controller is more robust than linear model based approaches, such as the Kalman and the H ∞ filters.

On the use of fuzzy logic controllers to comply with virtualized application demands in the cloud

Buffer-aided (BA) relaying improves the diversity of cooperative networks often at the cost of increasing end-to-end packet delays. This characteristic renders BA relaying unsuitable for delay-sensitive applications. However, the increased diversity makes BA relaying appealing for ultra-reliable communications. Towards enabling ultra-reliable low-latency communication (URLLC), we aim at enhancing BA relaying for supporting delay-sensitive applications. In this paper, reliable full-duplex (FD) network operation is targeted and for this purpose, hybrid relay selection algorithms are formulated, combining BA successive relaying (SuR) and delay- and diversity-aware (DDA) half-duplex (HD) algorithms. In this context, a hybrid FD DDA algorithm is presented, namely LoLa4SOR, switching between SuR and HD operation. Additionally, a low-complexity distributed version is given, namely d-LoLa4SOR, providing a trade-off among channel state information requirements and performance. The theoretical analysis shows that the diversity of LoLa4SOR equals to two times the number of available relays $K$ , i.e., $2K$ , when the buffer size $L$ is greater than or equal to 3. Comparisons with other HD, SuR and hybrid algorithms reveal that LoLa4SOR offers superior outage and throughput performance while, the average delay is reduced due to SuR-based FD operation and the consideration of buffer state information for relay-pair selection. d-LoLa4SOR, as one of the few distributed algorithms in the literature, has a reasonable performance that makes it a more practical approach.

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LoLa4SOR: Leveraging Successive Transmissions for Low-Latency Buffer-Aided Opportunistic Relay Networks

Buffer-aided (BA) relaying has been proposed as an efficient way to improve the diversity of multi-hop networks Nonetheless, it results in increased end-to-end delay, as packets tend to reside in the relays’ buffers for many time-slots In order to enable the adoption of BA relays in delay-intolerant applications, the combination with successive relaying can increase the number of packets that are transmitted In this work, hybrid relay selection algorithms are studied, where delay- and diversity aware (DDA) half-duplex algorithms are complemented with successive BA relaying Moreover, a hybrid DDA algorithm is presented, namely LoLA4SOR that adapts to the transmit conditions, switching between half-duplex (HD) and successive relaying (SuR) The performance of LoLA4SOR is compared to several HD, successive and hybrid algorithms, in terms of outage probability, throughput and delay The results indicate that LoLA4SOR provides superior outage and throughput performance, while its delay performance is not affected by the buffer size and, as the number of relays increase, its delay reduces due to the high probability for successive transmissions

LoLA4SOR: A Low-Latency Algorithm for Successive Opportunistic Relaying

In recent years, wireless node density has increased rapidly, as more base stations, users, and machines coexist. Exploiting this node density, cooperative relaying has been deployed to improve connectivity throughout the network. Such a configuration, however, often demands relay scheduling, which comes with increased channel estimation and signaling overheads. To reduce these overheads, in this paper, we propose low-complexity relay scheduling mechanisms with the aid of a multi-armed bandit (MAB) framework. More specifically, this MAB framework is used for relay scheduling, based only on observing the acknowledgements/negative-acknow-ledgements (ACK/NACK) of packet transmissions. Hence, a bandit-based opportunistic relay selection (BB - ORS) mechanism is developed, recovering eventually the performance of classical opportunistic relay selection (0RS) when channel state information (CSI) is available without requiring any CSI. In addition, a distributed implementation of BB - ORS is presented, herein called d - BB - ORS, where distributed timers are used at the relays for relay selection, thus reducing the signaling overhead significantly. BB - ORS is compared to optimal scheduling with full CSI and the negligible performance gap is compensated by the low-complexity low-overhead implementation, while it surpasses the performance of ORS with outdated CSI.

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Themistoklis Charalambous

Papers

Bandit-Based Power Control in Full-Duplex Cooperative Relay Networks

On the use of fuzzy logic controllers to comply with virtualized application demands in the cloud

LoLa4SOR: Leveraging Successive Transmissions for Low-Latency Buffer-Aided Opportunistic Relay Networks

LoLA4SOR: A Low-Latency Algorithm for Successive Opportunistic Relaying

Bandit-Based Relay Selection in Cooperative Networks Over Unknown Stationary Channels