15,922 research outputs found
Minimizing buffer requirements in video-on-demand servers
23rd Euromicro Conference EUROMICRO 97: 'New Frontiers of Information Technology', Budapest, Hungary, 1-4 Sept 1997Memory management is a key issue when designing cost effective video on demand servers. State of the art techniques, like double buffering, allocate buffers in a per stream basis and require huge amounts of memory. We propose a buffering policy, namely Single Pair of Buffers, that dramatically reduces server memory requirements by reserving a pair of buffers per storage device. By considering in detail disk and network interaction, we have also identified the particular conditions under which this policy can be successfully applied to engineer video on demand servers. Reduction factors of two orders of magnitude compared to the double buffering approach can be obtained. Current disk and network parameters make this technique feasible.Publicad
Efficient memory management in VOD disk array servers usingPer-Storage-Device buffering
We present a buffering technique that reduces video-on-demand server memory requirements in more than one order of magnitude. This technique, Per-Storage-Device Buffering (PSDB), is based on the allocation of a fixed number of buffers per storage device, as opposed to existing solutions based on per-stream buffering allocation. The combination of this technique with disk array servers is studied in detail, as well as the influence of Variable Bit Streams. We also present an interleaved data placement strategy, Constant Time Length Declustering, that results in optimal performance in the service of VBR streams. PSDB is evaluated by extensive simulation of a disk array server model that incorporates a simulation based admission test.This research was supported in part by the National R&D Program of Spain, Project Number TIC97-0438.Publicad
Computing server power modeling in a data center: survey,taxonomy and performance evaluation
Data centers are large scale, energy-hungry infrastructure serving the
increasing computational demands as the world is becoming more connected in
smart cities. The emergence of advanced technologies such as cloud-based
services, internet of things (IoT) and big data analytics has augmented the
growth of global data centers, leading to high energy consumption. This upsurge
in energy consumption of the data centers not only incurs the issue of surging
high cost (operational and maintenance) but also has an adverse effect on the
environment. Dynamic power management in a data center environment requires the
cognizance of the correlation between the system and hardware level performance
counters and the power consumption. Power consumption modeling exhibits this
correlation and is crucial in designing energy-efficient optimization
strategies based on resource utilization. Several works in power modeling are
proposed and used in the literature. However, these power models have been
evaluated using different benchmarking applications, power measurement
techniques and error calculation formula on different machines. In this work,
we present a taxonomy and evaluation of 24 software-based power models using a
unified environment, benchmarking applications, power measurement technique and
error formula, with the aim of achieving an objective comparison. We use
different servers architectures to assess the impact of heterogeneity on the
models' comparison. The performance analysis of these models is elaborated in
the paper
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