Nicola Cordeschi

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.

FUGE: A joint meta-heuristic approach to cloud job scheduling algorithm using fuzzy theory and a genetic method

Job scheduling is one of the most important research problems in distributed systems, particularly cloud environments/computing. The dynamic and heterogeneous nature of resources in such distributed systems makes optimum job scheduling a non-trivial task. Maximal resource utilization in cloud computing demands/necessitates an algorithm that allocates resources to jobs with optimal execution time and cost. The critical issue for job scheduling is assigning jobs to the most suitable resources, considering user preferences and requirements. In this paper, we present a hybrid approach called FUGE that is based on fuzzy theory and a genetic algorithm (GA) that aims to perform optimal load balancing considering execution time and cost. We modify the standard genetic algorithm (SGA) and use fuzzy theory to devise a fuzzy-based steady-state GA in order to improve SGA performance in term of makespan. In details, the FUGE algorithm assigns jobs to resources by considering virtual machine (VM) processing speed, VM memory, VM bandwidth, and the job lengths. We mathematically prove our optimization problem which is convex with well-known analytical conditions (specifically, Karush–Kuhn–Tucker conditions). We compare the performance of our approach to several other cloud scheduling models. The results of the experiments show the efficiency of the FUGE approach in terms of execution time, execution cost, and average degree of imbalance.

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.

Optimal MIMO UWB-IR Transceiver for Nakagami-fading and Poisson-Arrivals

In this contribution, we develop a (novel) fam- ily of Multiple-Input Multiple-Output (MIMO) UWB Impulse-Radio (UWB-IR) transceivers for Orthogonal PPM- modulated (OPPM) coded transmissions over (baseband) multipath-faded MIMO channels. To by-pass expensive channel-estimation procedures, the MIMO channel path- gains are assumed to be fully unknown at the receiver. Thus, according to the UWB-IR statistical channel-models currently reported in the literature for both indoor/outdoor application scenarios, we develop and analyze three versions of the resulting noncoherent transceiver, that are optimal for Nakagami, Gaussian, and Log-normal distributed channel- gains, respectively. As dictated by the Saleh-Valenzuela (SV) UWB model, the resulting noncoherent Maximum- Likelihood (ML) Decoder explicitly accounts for the Poisson- distribution of the path-arrivals. Hence, after analytically evaluating the performance of the proposed noncoherent transceiver via suitable versions of the Union-Chernoff bound, we prove that the family of Space-Time OPPM (STOPPM) recently presented in the Literature is able to at- tain full-diversity in the considered multipath-affected appli- cation scenario. To corroborate the carried out performance analysis, we report several numerical results supporting both the medium/long coverage ranges attained by the proposed STOPPM-coded noncoherent transceiver, and its perfor- mance robustness against the degrading effects induced by Inter-Pulse-Interference (IPI), spatially-correlated multipath fading and mistiming.

Energy-saving adaptive computing and traffic engineering for real-time-service data centers

In this paper, we propose a traffic engineering-based adaptive approach to dynamically reconfigure the computing-plus-communication resources of networked data centers which support in real-time the service requirements of mobile clients connected by TCP/IP energy-limited wireless backbones. The goal is to maximize the energy-efficiency, while meeting hard QoS requirements on the delivered transmission rate and processing delay. In order to cope with the (possibly, unpredictable) fluctuations of the offered workload, the proposed optimal cross-layer resource controller is adaptive. It jointly performs: i) the balanced control and dispatching of the admitted workload; ii) the dynamic reconfiguration of the Virtual Machines (VMs) instantiated onto the parallel computing platform at the data center; and iii) the rate control of the traffic injected into the wireless backbone for delivering the service to the requiring clients. Our experimental results show that the proposed technique improves energy consumption of servers by 25% compared to state of the art improvement on average in the entire data center.

Stochastic traffic engineering for real-time applications over wireless networks

In this work, we focus on the Stochastic Traffic Engineering (STE) problem arising from the support of QoS-demanding real-time media-streaming applications over fading and congestion affected TCP-friendly/IP multiantenna wireless pipes. First, after recasting the tackled STE problem in the form of a suitable cross-layer nonlinear stochastic optimization problem, we develop a traffic analysis of the overall underlying multiple-input multiple-output (MIMO) wireless pipe that points out the relative effects of both fading-induced errors and congestion-induced packet losses on the goodput offered by the resulting end-to-end connection. Second, we develop an optimal cross-layer resource management policy that allows a joint scheduling of the media encoding rate (i.e., playin rate), transmit energy and delivery rate (i.e., playout rate) of each end-to-end connection active over the considered access network. Salient features of the presented joint scheduling policy are that: (i) it is self-adaptive; (ii) it is able to provide hard (i.e., deterministic) QoS guarantees, in terms of hard limited playout delay and playout rate-jitter; and (iii) it explicitly accounts for the performance interaction of the protocols implemented at all layers of the considered stack.

Energy-efficient adaptive networked datacenters for the QoS support of real-time applications

In this paper, we develop the optimal minimum-energy scheduler for the adaptive joint allocation of the task sizes, computing rates, communication rates and communication powers in virtualized networked data centers (VNetDCs) that operate under hard per-job delay-constraints. The considered VNetDC platform works at the Middleware layer of the underlying protocol stack. It aims at supporting real-time stream service (such as, for example, the emerging big data stream computing (BDSC) services) by adopting the software-as-a-service (SaaS) computing model. Our objective is the minimization of the overall computing-plus-communication energy consumption. The main new contributions of the paper are the following ones: (i) the computing-plus-communication resources are jointly allotted in an adaptive fashion by accounting in real-time for both the (possibly, unpredictable) time fluctuations of the offered workload and the reconfiguration costs of the considered VNetDC platform; (ii) hard per-job delay-constraints on the overall allowed computing-plus-communication latencies are enforced; and, (iii) to deal with the inherently nonconvex nature of the resulting resource optimization problem, a novel solving approach is developed, that leads to the lossless decomposition of the afforded problem into the cascade of two simpler sub-problems. The sensitivity of the energy consumption of the proposed scheduler on the allowed processing latency, as well as the peak-to-mean ratio (PMR) and the correlation coefficient (i.e., the smoothness) of the offered workload is numerically tested under both synthetically generated and real-world workload traces. Finally, as an index of the attained energy efficiency, we compare the energy consumption of the proposed scheduler with the corresponding ones of some benchmark static, hybrid and sequential schedulers and numerically evaluate the resulting percent energy gaps.

Optimal self-adaptive QoS resource management in interference-affected multicast wireless networks

In this paper, we focus on the quality-of-service (QoS)-constrained jointly optimal congestion control, network coding, and adaptive distributed power control for connectionless wireless networks affected by multiple access interference (MAI). The goal is to manage the available network resources, so as to support multiple multicast sessions with QoS requirements when intrasession network coding (NC) is allowed. To cope with the nonconvex nature of the resulting cross-layer optimization problem, we propose a two-level decomposition that provides the means to attain the optimal solution through suitable relaxed convex versions of its comprising subproblems. Sufficient conditions for the equivalence of the primary nonconvex problem and its related convex version are derived, occurrence of such conditions investigated, and performance with respect to conventional routing-based layered solutions analyzed. Moreover, we develop a distributed algorithm to compute the actual solution of the resource allocation problem that quickly adapts to network time-evolutions. Performance of this algorithm and its adaptivity are evaluated in the presence of varying network/fading conditions and noisy measurements.