Virtual memory is considered to be an unlimited resource in desktop or notebook computers with high storage memory capabilities. However, in wireless mobile devices like palmtops and personal digital assistants (PDA), storage memory is limited or absent due to weight, size and power constraints. As a consequence, swapping over remote memory devices can be considered as a viable alternative. Nevertheless, power hungry wireless network interface cards (WNIC) may limit the battery lifetime and application performance if not efficiently exploited. In this chapter we explore performance and energy of network swapping in comparison with swapping on local microdrives and flash memories. Our study points out that remote swapping over power-manageable WNICs can be more efficient than local swapping and that both energy and performance can be optimized through power-aware reshaping of data requests. Experimental results show that application-level prefetching can be applied to save up to 60% of swapping energy while also improving performance.

Power-Aware Network Swapping for Wireless Palmtop PCs

LATTANZI, EMANUELE;BOGLIOLO, ALESSANDRO
2004

Abstract

Virtual memory is considered to be an unlimited resource in desktop or notebook computers with high storage memory capabilities. However, in wireless mobile devices like palmtops and personal digital assistants (PDA), storage memory is limited or absent due to weight, size and power constraints. As a consequence, swapping over remote memory devices can be considered as a viable alternative. Nevertheless, power hungry wireless network interface cards (WNIC) may limit the battery lifetime and application performance if not efficiently exploited. In this chapter we explore performance and energy of network swapping in comparison with swapping on local microdrives and flash memories. Our study points out that remote swapping over power-manageable WNICs can be more efficient than local swapping and that both energy and performance can be optimized through power-aware reshaping of data requests. Experimental results show that application-level prefetching can be applied to save up to 60% of swapping energy while also improving performance.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11576/1889122
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