The Effective Transmission and Processing of Mobile Multimedia

Ph.DDOCTOR OF PHILOSOPH

System-level modeling and analysis of multimedia-soc platforms

Ph.DDOCTOR OF PHILOSOPH

Adaptive Resource Management for Uncertain Execution Platforms

Embedded systems are becoming increasingly complex. At the same time, the components that make up the system grow more uncertain in their properties. For example, current developments in CPU design focuses on optimizing for average performance rather than better worst case performance. This, combined with presence of 3rd party software components with unknown properties, makes resource management using prior knowledge less and less feasible. This thesis presents results on how to model software components so that resource allocation decisions can be made on-line. Both the single and multiple resource case is considered as well as extending the models to include resource constraints based on hardware dynam- ics. Techniques for estimating component parameters on-line are presented. Also presented is an algorithm for computing an optimal allocation based on a set of convex utility functions. The algorithm is designed to be computationally efficient and to use simple mathematical expres- sions that are suitable for fixed point arithmetics. An implementation of the algorithm and results from experiments is presented, showing that an adaptive strategy using both estimation and optimization can outperform a static approach in cases where uncertainty is high

Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing

The availability of many-core computing platforms enables a wide variety of technical solutions for systems across the embedded, high-performance and cloud computing domains. However, large scale manycore systems are notoriously hard to optimise. Choices regarding resource allocation alone can account for wide variability in timeliness and energy dissipation (up to several orders of magnitude). Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing covers dynamic resource allocation heuristics for manycore systems, aiming to provide appropriate guarantees on performance and energy efficiency. It addresses different types of systems, aiming to harmonise the approaches to dynamic allocation across the complete spectrum between systems with little flexibility and strict real-time guarantees all the way to highly dynamic systems with soft performance requirements. Technical topics presented in the book include: Load and Resource Models Admission Control Feedback-based Allocation and Optimisation Search-based Allocation Heuristics Distributed Allocation based on Swarm Intelligence Value-Based Allocation Each of the topics is illustrated with examples based on realistic computational platforms such as Network-on-Chip manycore processors, grids and private cloud environments.Note.-- EUR 6,000 BPC fee funded by the EC FP7 Post-Grant Open Access Pilo

Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing

The availability of many-core computing platforms enables a wide variety of technical solutions for systems across the embedded, high-performance and cloud computing domains. However, large scale manycore systems are notoriously hard to optimise. Choices regarding resource allocation alone can account for wide variability in timeliness and energy dissipation (up to several orders of magnitude). Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing covers dynamic resource allocation heuristics for manycore systems, aiming to provide appropriate guarantees on performance and energy efficiency. It addresses different types of systems, aiming to harmonise the approaches to dynamic allocation across the complete spectrum between systems with little flexibility and strict real-time guarantees all the way to highly dynamic systems with soft performance requirements. Technical topics presented in the book include: • Load and Resource Models• Admission Control• Feedback-based Allocation and Optimisation• Search-based Allocation Heuristics• Distributed Allocation based on Swarm Intelligence• Value-Based AllocationEach of the topics is illustrated with examples based on realistic computational platforms such as Network-on-Chip manycore processors, grids and private cloud environments

Dynamic bandwidth allocation in ATM networks

Includes bibliographical references.This thesis investigates bandwidth allocation methodologies to transport new emerging bursty traffic types in ATM networks. However, existing ATM traffic management solutions are not readily able to handle the inevitable problem of congestion as result of the bursty traffic from the new emerging services. This research basically addresses bandwidth allocation issues for bursty traffic by proposing and exploring the concept of dynamic bandwidth allocation and comparing it to the traditional static bandwidth allocation schemes

Mobile cloud computing and network function virtualization for 5g systems

The recent growth of the number of smart mobile devices and the emergence of complex multimedia mobile applications have brought new challenges to the design of wireless mobile networks. The envisioned Fifth-Generation (5G) systems are equipped with different technical solutions that can accommodate the increasing demands for high date rate, latency-limited, energy-efficient and reliable mobile communication networks. Mobile Cloud Computing (MCC) is a key technology in 5G systems that enables the offloading of computationally heavy applications, such as for augmented or virtual reality, object recognition, or gaming from mobile devices to cloudlet or cloud servers, which are connected to wireless access points, either directly or through finite-capacity backhaul links. Given the battery-limited nature of mobile devices, mobile cloud computing is deemed to be an important enabler for the provision of such advanced applications. However, computational tasks offloading, and due to the variability of the communication network through which the cloud or cloudlet is accessed, may incur unpredictable energy expenditure or intolerable delay for the communications between mobile devices and the cloud or cloudlet servers. Therefore, the design of a mobile cloud computing system is investigated by jointly optimizing the allocation of radio, computational resources and backhaul resources in both uplink and downlink directions. Moreover, the users selected for cloud offloading need to have an energy consumption that is smaller than the amount required for local computing, which is achieved by means of user scheduling. Motivated by the application-centric drift of 5G systems and the advances in smart devices manufacturing technologies, new brand of mobile applications are developed that are immersive, ubiquitous and highly-collaborative in nature. For example, Augmented Reality (AR) mobile applications have inherent collaborative properties in terms of data collection in the uplink, computing at the cloud, and data delivery in the downlink. Therefore, the optimization of the shared computing and communication resources in MCC not only benefit from the joint allocation of both resources, but also can be more efficiently enhanced by sharing the offloaded data and computations among multiple users. As a result, a resource allocation approach whereby transmitted, received and processed data are shared partially among the users leads to more efficient utilization of the communication and computational resources. As a suggested architecture in 5G systems, MCC decouples the computing functionality from the platform location through the use of software virtualization to allow flexible provisioning of the provided services. Another virtualization-based technology in 5G systems is Network Function Virtualization (NFV) which prescribes the instantiation of network functions on general-purpose network devices, such as servers and switches. While yielding a more flexible and cost-effective network architecture, NFV is potentially limited by the fact that commercial off-the-shelf hardware is less reliable than the dedicated network elements used in conventional cellular deployments. The typical solution for this problem is to duplicate network functions across geographically distributed hardware in order to ensure diversity. For that reason, the development of fault-tolerant virtualization strategies for MCC and NFV is necessary to ensure reliability of the provided services