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Resource capacity is often over provisioned to primarily deal with short periods of peak load. Shaping these peaks by shifting them to low utilization periods (valleys) is referred to as "resource consumption shaping". While originally aimed at the data center level, the resource consumption shaping we consider focuses on local resources, like CPU or I/O as we have identified that individual...
Hadoop, an open source implementation of MapReduce, uses slots to represent resource sharing. The number of slots in a Hadoop cluster node specifies the concurrency of task execution. Thus, the slot configuration has a significant impact on performance. The number of slots is by default hand-configured (static) and slots share resources "fairly". As resource capacity (e.g., #cores) continues...
We present a user-scheduling scheme for multiuser multiple-input-multiple-output (MIMO) systems with random unitary beamforming (RUB) in this paper. The new scheme, which is termed as adaptive beam activation based on the conditional best beam index feedback (ABA-CBBI), requires low average feedback load by imposing a feedback threshold on the users' signal-to-interference-plus-noise ratio (SINR)...
A new user scheduling strategy is proposed for multiuser MIMO system with random unitary beamforming (RUB) in this paper. This strategy imposes a threshold on the signal to interference and noise ratio (SINR) of mobile users to reduce the feedback load. The basestation only uses those beams that are requested by feedback users. The exact analytical expression of the sum rate of proposed strategy is...
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