Academic literature on the topic 'Buffer storage (Computer science)'

Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.<br>Includes bibliographical references (leaves 77-79). Also available in electronic version. Access restricted to campus users.

In networks, using large buffers tend to increase end-to-end packet delay and its deviations, conflicting with real-time applications such as online gaming, audio-video services, IPTV, and VoIP. Further, large buffers complicate the design of high speed routers, leading to more power consumption and board space. According to Moore's law, switching speeds double every 18 months while memory access speeds double only every 10 years. Hence, as memory requirements increasingly become a limiting aspect of router design, studying networks in finite-buffer regime seems necessary for network engineers. This work focuses on both practical and theoretical aspects of finite-buffer networks. In Chapters 1-7, we investigate the effects of finite buffer sizes on the throughput and packet delay in different networks. These performance measures are shown to be linked to the stationary distribution of an underlying irreducible Markov chain that exactly models the changes in the network. An iterative scheme is proposed to approximate the steady-state distribution of buffer occupancies by decoupling the exact chain to smaller chains. These approximate solutions are used to analytically characterize network throughput and packet delay, and are also applied to some network performance optimization problems. Further, using simulations, it is confirmed that the proposed framework yields accurate estimates of the throughput and delay performance measures and captures the vital trends and tradeoffs in these networks. In Chapters 8-10, we address the problem of modeling and analysis of the performance of finite-memory random linear network coding in erasure networks. When using random linear network coding, the content of buffers creates dependencies which cannot be captured directly using the classical queueing theoretical models. A careful derivation of the buffer occupancy states and their transition rules are presented as well as decodability conditions when random linear network coding is performed on a stream of arriving packets.

Current computing infrastructures use virtualization to increase resource utilization by deploying multiple virtual machines on the same hardware. Virtualization is particularly attractive for data center, cloud computing, and hosting services; in these environments computer systems are typically configured to have fast processors, large physical memory and huge storage capable of supporting concurrent execution of virtual machines. Subsequently, this high demand for resources is directly translating into higher energy consumption and monetary costs. Increasingly managing energy consumption of virtual machines is becoming critical. However, virtual machines make the energy management more challenging because a layer of virtualization separates hardware from the guest operating system executing inside a virtual machine. This dissertation addresses the challenge of designing energy-efficient storage, memory and buffer cache for virtual machines by exploring innovative mechanisms as well as existing approaches. We analyze the architecture of an open-source virtual machine platform Xen and address energy management on each subsystem. For storage system, we study the I/O behavior of the virtual machine systems. We address the isolation between virtual machine monitor and virtual machines, and increase the burstiness of disk accesses to improve energy efficiency. In addition, we propose a transparent energy management on main memory for any types of guest operating systems running inside virtual machines. Furthermore, we design a dedicated mechanism for the buffer cache based on the fact that data-intensive applications heavily rely on a large buffer cache that occupies a majority of physical memory. We also propose a novel hybrid mechanism that is able to improve energy efficiency for any memory access. All the mechanisms achieve significant energy savings while lowering the impact on performance for virtual machines.