In this paper, 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 goal is to allow energy and computing-limited car smartphones to utilize the available vehicle-to-infrastructure (V2I) WiFi connections for performing traffic offloading toward local or remote clouds by opportunistically acceding to a spectral-limited wireless backbone built up by multiple roadside units (i.e., cloudlets). We cast the resource management problem into a suitable constrained stochastic network utility maximization problem and derive the optimal cognitive resource manager that 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). 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 able to acquire context information about the currently available bandwidth-energy resources to quickly adapt to the mobility-induced abrupt changes in the state of the vehicular network.

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.

Live virtual machine migration aims at enabling the dynamic balanced use of the networking/computing physical resources of virtualized data-centers, so to lead to reduced energy consumption. Here, we analytically characterize, prototype in software and test an optimal bandwidth manager for live migration of VMs in wireless channel. In this paper we present the optimal tunable-complexity bandwidth manager (TCBM) for the QoS live migration of VMs under a wireless channel from smartphone to access point. The goal is the minimization of the migration-induced communication energy under service level agreement (SLA)-induced hard constrains on the total migration time, downtime and overall available bandwidth.