Energy-Aware Data Allocation With Hybrid Memory for Mobile Cloud Systems

Abstract

Resource scheduling is one of the most important issues in mobile cloud computing due to the constraints in memory, CPU, and bandwidth. High energy consumption and low performance of memory accesses have become overwhelming obstacles for chip multiprocessor (CMP) systems used in cloud systems. In order to address the daunting “memory wall” problem, hybrid on-chip memory architecture has been widely investigated recently. Due to its advantages in size, real-time predictability, power, and software controllability, scratchpad memory (SPM) is a promising technique to replace the hardware cache and bridge the processor–memory gap for CMP systems. In this paper, we present a novel hybrid on-chip SPM that consists of a static random access memory (RAM), a magnetic RAM (MRAM), and a zero-capacitor RAM for CMP systems by fully taking advantages of the benefits of each type of memory. To reduce memory access latency, energy consumption, and the number of write operations to MRAM, we also propose a novel multidimensional dynamic programming data allocation (MDPDA) algorithm to strategically allocate data blocks to each memory. Experimental results show that the proposed MDPDA algorithm can efficiently reduce the memory access cost and extend the lifetime of MRAM.