1,721,005 research outputs found
Bandwidth management in live virtual machine migration
Full text linkIn this thesis I investigated the bandwidth management problem on live migration of virtual machine in different environment. First part of the thesis is dedicated to intra-data-center bandwidth optimization problem, while in the second part of the document I present the solution for wireless live migration in 5G and edge computing emerging technologies.
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 and improve data centers’ flexibility. However, the bandwidth consumption and latency of current state-of-the-art live VM migration techniques still reduce the experienced benefits to much less than their potential. Motivated by this consideration I analytically characterize and test the optimal bandwidth manager for intra-data-center live migration of VMs. The goal is to min- imize the migration-induced communication energy consumption under service level agreement (SLA)-induced hard constraints on the total migration time, downtime, slowdown of the migrating applications and overall available bandwidth
Distributed and adaptive resource management in Cloud-assisted Cognitive Radio Vehicular Networks with hard reliability guarantees
No full textIn 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
Energy-efficient adaptive networked datacenters for the QoS support of real-time applications
No full textIn 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
Multi-Frame s-Persistent Neighbor Discovery strategy in DTNs with resource-constrained RFID devices
No full textIn this work a Multi-Frame Neighbor Discovery Protocol in DTNs over a RFID devices is proposed. Our approach, which is based on a Sift-distribution (we named it s-Persistent) function, takes Single-Frame and Multi-Frame scenarios into consideration. Our s-Persistent approach is applied in a simulated test and in a real test-bed in order to see the effectiveness of the proposal in comparison with a well-known technique such as p-Persistent protocol. Therefore Performance Evaluation has been considered in terms of number of discovered neighbors under different numbers of nodes and frames, moreover we regarded an intra-frame time evaluation. Obtained results show that our solution performs better than p-Persistent, increasing the number of discovered neighbors with better results in intra-frame time evolution. © 2014 IEEE
Resource-Management for Vehicular Real-Time Application under Hard Reliability Constraints
No full textIn this paper, we design and test a full distributed and scalable resource-management scheduler for Vehicular Real-Time applications. We dynamically allocate the access time window (at the RoadSide Units) and the access rate and traffic flows (at the Vehicular Clients) under hard reliability collision constraints. We provide the optimal memoryless scheduler for network utility maximization, showing as it presents no loss in the network average utility with respect to not real-time soft reliability schedulers. Finally, the proposed scheduler exploits an ad-hoc designed soft-input/soft-output data fusion algorithm, able to supply in real-time reliable context-information, even in the presence of fading-affected and intermittent vehicular-to-infrastructure connectivity. © 2014 IEEE
Primary-secondary resource-management on vehicular networks under soft and hard collision constraints
No full textIn this paper, a primary-secondary resource-management controller on Vehicular Networks is designed and tested. We cast the resource-management problem into a suitable constrained stochastic Network Utility Maximization problem and derive the optimal cognitive resource management controller, which dynamically allocates the access time-windows at the primary users (the serving Roadside Units) and the access rates and traffic flows at the secondary users (the served Vehicular Clients). We provide the optimal mem-oryless controllers under hard and soft primary-secondary collision constraints, showing as the hard controller presents no optimality gap in the average utility with respect to the soft one. Finally we generalize the framework integrating the controllers with the data fusion techniques
Bandwidth management VMs live migration in wireless fog computing for 5G networks
No full textLive 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
Hard and soft optimal resource allocation for primary and secondary users in infrastructure vehicular networks
No full textIn this paper, a primary-secondary resource management controller on Vehicular Networks is designed and tested. We cast the resource-management problem into a suitable constrained stochastic Network Utility Maximization problem and derive the optimal cognitive resource management controller, which dynamically allocates the access time-windows. We provide the optimal steady-state memoryless controllers under hard and soft primary-secondary collision constraints, showing as the hard controller does not present any optimality gap in the average utility with respect to the soft one, while, on the contrary, it is able to make the outage-probability vanishing. Then we generalize the framework integrating the controllers with different data fusion techniques, and test the controller behaviour in a non-stationary application scenario. Finally we provide the optimal steady-state hard controller with memory and compare it with the memoryless one.In this paper, a primary-secondary resource-management controller on Vehicular Networks is designed and tested. We cast the resource-management problem into a suitable constrained stochastic Network Utility Maximization problem and derive the optimal cognitive resource management controller, which dynamically allocates the access time-windows. We provide the optimal steady-state memoryless controllers under hard and soft primary-secondary collision constraints, showing as the hard controller does not present any optimality gap in the average utility with respect to the soft one, while, on the contrary, it is able to make the outage-probability vanishing. Then we generalize the framework integrating the controllers with different data fusion techniques, and test the controller behaviour in a non-stationary application scenario. Finally we provide the optimal steady-state hard controller with memory and compare it with the memoryless one. © 2015 IEEE
Networking-computing resource allocation for hard real-time Green Cloud applications
No full textPerforming real-time applications on top of virtualized cloud systems requires that the overall per-job delay due to the in-cloud processing is upper bounded in a hard way. This opens the question about the optimal dynamic joint allocation of both computing and networking resources hosted in the Cloud. This is the focus of this contribution, where we develop in closed-form the optimal fully scalable energy-saving scheduler for the joint allocation of the task sizes, communication rates and processing rates in delay-constrained Clouds composed by multiple frequency-scalable parallel Virtual Machines (VMs). © 2014 IEEE
Energy-saving adaptive computing and traffic engineering for real-time-service data centers
No full textIn 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
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