Enzo Baccarelli

Energy-efficient Adaptive Resource Management for Real-time Vehicular Cloud Services

Providing real-time cloud services to Vehicular Clients (VCs) must cope with delay and delay-jitter issues. Fog computing is an emerging paradigm that aims at distributing small-size self-powered data centers (e.g., Fog nodes) between remote Clouds and VCs, in order to deliver data-dissemination real-time services to the connected VCs. Motivated by these considerations, in this paper, we propose and test an energy-efficient adaptive resource scheduler for Networked Fog Centers (NetFCs). They operate at the edge of the vehicular network and are connected to the served VCs through Infrastructure-to-Vehicular (I2V) TCP/IP-based single-hop mobile links. The goal is to exploit the locally measured states of the TCP/IP connections, in order to maximize the overall communication-plus-computing energy efficiency, while meeting the application-induced hard QoS requirements on the minimum transmission rates, maximum delays and delay-jitters. The resulting energy-efficient scheduler jointly performs: (i) admission control of the input traffic to be processed by the NetFCs; (ii) minimum-energy dispatching of the admitted traffic; (iii) adaptive reconfiguration and consolidation of the Virtual Machines (VMs) hosted by the NetFCs; and, (iv) adaptive control of the traffic injected into the TCP/IP mobile connections. The salient features of the proposed scheduler are that: (i) it is adaptive and admits distributed and scalable implementation; and, (ii) it is capable to provide hard QoS guarantees, in terms of minimum/maximum instantaneous rates of the traffic delivered to the vehicular clients, instantaneous rate-jitters and total processing delays. Actual performance of the proposed scheduler in the presence of: (i) client mobility; (ii) wireless fading; and, (iii) reconfiguration and consolidation costs of the underlying NetFCs, is numerically tested and compared against the corresponding ones of some state-of-the-art schedulers, under both synthetically generated and measured real-world workload traces.

Energy-efficient dynamic traffic offloading and reconfiguration of networked data centers for big data stream mobile computing: review, challenges, and a case study

Big data stream mobile computing is proposed as a paradigm that relies on the convergence of broadband Internet mobile networking and real-time mobile cloud computing. It aims at fostering the rise of novel self-configuring integrated computing-communication platforms for enabling in real time the offloading and processing of big data streams acquired by resource-limited mobile/wireless devices. This position article formalizes this paradigm, discusses its most significant application opportunities, and outlines the major challenges in performing real-time energy-efficient management of the distributed resources available at both mobile devices and Internet-connected data centers. The performance analysis of a small-scale prototype is also included in order to provide insight into the energy vs. performance tradeoff that is achievable through the optimized design of the resource management modules. Performance comparisons with some state-of-the-art resource managers corroborate the discussion. Hints for future research directions conclude the article.

Distributed and adaptive resource management in Cloud-assisted Cognitive Radio Vehicular Networks with hard reliability guarantees

Abstract In this contribution, we design and test the performance of a distributed and adaptive resource management controller, which allows the optimal exploitation of Cognitive Radio and soft-input/soft-output data fusion in Vehicular Access Networks. The ultimate goal is to allow energy and computing-limited car smartphones to utilize the available Vehicular-to-Infrastructure WiFi connections for performing traffic offloading towards local or remote Clouds by opportunistically acceding to a spectral-limited wireless backbone built up by multiple Roadside Units. For this purpose, we recast the afforded resource management problem into a suitable constrained stochastic Network Utility Maximization problem. Afterwards, we derive the optimal cognitive resource management controller, which dynamically allocates the access time-windows at the serving Roadside Units (i.e., the access points) together with the access rates and traffic flows at the served Vehicular Clients (i.e., the secondary users of the wireless backbone). Interestingly, the developed controller provides hard reliability guarantees to the Cloud Service Provider (i.e., the primary user of the wireless backbone) on a per-slot basis. Furthermore, it is also capable to self-acquire context information about the currently available bandwidth-energy resources, so as to quickly adapt to the mobility-induced abrupt changes of the state of the vehicular network, even in the presence of fadings , imperfect context information and intermittent Vehicular-to-Infrastructure connectivity. Finally, we develop a related access protocol, which supports a fully distributed and scalable implementation of the optimal controller.

Combined channel estimation and data detection using soft statistics for frequency-selective fast-fading digital links

A novel adaptive nonlinear equalizer for fast time-varying multipath channels that combines the channel estimation and data detection tasks is presented. The a posteriori probabilities (APPs) of the states of the intersymbol interference (ISI) channel are recursively computed from the received data by a symbol-by-symbol (SbS) detector and are then employed by a Kalman-type nonlinear channel estimator. Robust channel tracking and good data-detection performance are obtained, with a reasonable receiver complexity.

Energy-saving self-configuring networked data centers

In this paper, we develop the optimal minimum-energy scheduler for the dynamic online joint allocation of the task sizes, computing rates, communication rates and communication powers in virtualized Networked Data Centers (NetDCs) that operates under hard per-job delay-constraints. The referred NetDCs infrastructure is composed by multiple frequency-scalable Virtual Machines (VMs), that are interconnected by a bandwidth and power-limited switched Local Area Network (LAN). Due to the nonlinear power-vs.-communication rate relationship, the resulting Computing-Communication Optimization Problem (CCOP) is inherently nonconvex. In order to analytically compute the exact solution of the CCOP, we develop a solving approach that relies on the following two main steps: (i) we prove that the CCOP retains a loosely coupled structure, that allows us to perform the lossless decomposition of the CCOP into the cascade of two simpler sub-problems; and, (ii) we prove that the coupling between the aforementioned sub-problems is provided by a (scalar) constraint, that is linear in the offered workload. The resulting optimal scheduler is amenable of scalable and distributed online implementation and its analytical characterization is in closed-form. After numerically testing its actual performance under randomly time-varying synthetically generated and real-world measured workload traces, we compare the obtained performance with the corresponding ones of some state-of-the-art static and sequential schedulers.

Fog of Everything: Energy-Efficient Networked Computing Architectures, Research Challenges, and a Case Study

Fog computing (FC) and Internet of Everything (IoE) are two emerging technological paradigms that, to date, have been considered standing-alone. However, because of their complementary features, we expect that their integration can foster a number of computing and network-intensive pervasive applications under the incoming realm of the future Internet. Motivated by this consideration, the goal of this position paper is fivefold. First, we review the technological attributes and platforms proposed in the current literature for the standing-alone FC and IoE paradigms. Second, by leveraging some use cases as illustrative examples, we point out that the integration of the FC and IoE paradigms may give rise to opportunities for new applications in the realms of the IoE, Smart City, Industry 4.0, and Big Data Streaming, while introducing new open issues. Third, we propose a novel technological paradigm, the Fog of Everything (FoE) paradigm, that integrates FC and IoE and then we detail the main building blocks and services of the corresponding technological platform and protocol stack. Fourth, as a proof-of-concept, we present the simulated energy-delay performance of a small-scale FoE prototype, namely, the V-FoE prototype. Afterward, we compare the obtained performance with the corresponding one of a benchmark technological platform, e.g., the V-D2D one. It exploits only device-to-device links to establish inter-thing “ad hoc” communication. Last, we point out the position of the proposed FoE paradigm over a spectrum of seemingly related recent research projects.

Optimal discrete bit loading for DMT based constrained multicarrier systems

We present the solution for the optimal discrete bit loading in multicarrier systems employing discrete multitone (DMT) modulation, when additional constraints on either the maximum allowable energy for each subchannel or the maximal cardinality of the QAM constellations to be used are given. Conditions for the optimality of greedy algorithms in this context are also provided. The solution is based on some results in matroid theory about combinatorial optimization.

Evaluation of the reliable data rates supported by multiple-antenna coded wireless links for QAM transmissions

We present some novel results about the reliable information-rate supported by point-to-point multiple-antenna Rayleigh-faded wireless links for coded transmissions that employ two-dimensional (QAM or PSK) data constellations. After deriving the symmetric capacity of these links, we present fast-computable analytical upper and lower bounds that are asymptotically exact both for high and low SNRs, and give rise to a reliable evaluation of the link capacity when perfect channel state information (CSI) is available at the receiver. Furthermore, asymptotically exact simple upper bounds are also presented for a tight evaluation of the outage probability.

Some simple bounds on the symmetric capacity and outage probability for QAM wireless channels with Rice and Nakagami fadings

In this article, quickly computable upper and lower bounds are presented on the symmetric capacity of flat-faded Rice and Nakagami channels with side information (SI) for data-transmissions via finite-size quadrature amplitude modulation (QAM) constellations. The proposed bounds exhibit the appealing feature to be tight and asymptotically exact both for high and low signal-to-noise ratios (SNRs). Furthermore, exponentially tight Chernoff-like formulas are also presented for an analytical evaluation of the resulting system outage probabilities when interleaved packet transmissions are carried out.

New results about analysis and design of TCM for ISI channels and combined equalization/decoding

New upper bounds for the performance of the optimum combined symbol-by-symbol (SbS) Abend-Fritchman-like equalizer and decoder are presented, and a related criterion for the actual design of good trellis-coded-modulated (TCM) schemes effectively matched to the distortion introduced by the intersymbol interference (ISI)-corrupted transmission channel is developed. The actual application of the proposed design criterion is also addressed.