Dynamic Server Provisioning for Energy Efficient Large Scale Video-on-Demand Systems
نویسندگان
چکیده
منابع مشابه
Energy-Efficient Dynamic Capacity Provisioning in Server Farms
A central question in designing server farms today is how to efficiently provision the number of serversto handle unpredictable demand patterns, so as to extract the best performance while not wastingenergy. While one would like to turn servers off when they become idle to save energy, the largesetup cost (both, in terms of setup time and energy penalty) needed to switch the server ...
متن کاملScheduling Video Streams in a Large-Scale Video-On-Demand Server
This paper addresses the design problems concerning a large scale parallel video on demand server that consists of multiple clusters of nodes connected by a high performance interconnection network In order to e ciently control the ow of video streams we propose two scheduling algorithms for data retrieval and communica tion First we present a disk scheduling algorithm called round scheduling w...
متن کاملDistributed joint optimization for large-scale video-on-demand
http://dx.doi.org/10.1016/j.comnet.2014.09.014 1389-1286/ 2014 Elsevier B.V. All rights reserved. q This work was supported, in part, by Hong Kong Research Grant Council (RGC) General Research Fund (610713) and The Hong Kong Innovation and Technology Fund (UIM/246). ⇑ Corresponding author. E-mail addresses: [email protected] (D. Ren), [email protected] (S.-H.G. Chan), [email protected] (G. ...
متن کاملFair Dynamic Routing in Large-Scale Heterogeneous-Server Systems
In a call center, there is a natural trade-off between minimizing customer wait time and fairly dividing the workload amongst agents of different skill levels. The relevant control is the routing policy; that is, the decision concerning which agent should handle an arriving call when more than one agent is available. We formulate an optimization problem for a call center with heterogeneous agen...
متن کاملEfficient Large Scale Video Classification
Video classification has advanced tremendously over the recent years. A large part of the improvements in video classification had to do with the work done by the image classification community and the use of deep convolutional networks (CNNs) which produce competitive results with handcrafted motion features. These networks were adapted to use video frames in various ways and have yielded stat...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Studies in Informatics and Control
سال: 2011
ISSN: 1220-1766,1841-429X
DOI: 10.24846/v20i4y201103