Processing in Memory Assisted MEC 3C Resource Allocation for Computation Offloading

Abstract

The improvement of Internet of Things (IoT) applications has led to a substantial increase in the number of multiple resources of computation, communication, and caching (3C). The fifth generation (5G) and multi-access edge computing (MEC) are promising to enhance the computation offloading of IoT applications with high performance and reliability. According to resource-consuming preferences, IoT applications can be divided into computation-hungry applications and memory-hungry applications. To deal with the computation-hungry applications, Graphics Processing Units (GPUs) are increasingly used to process simple computation tasks. Meanwhile, the running of memory-hungry applications is accompanied by massive data transfers between processing core and memory. These transfers can result in significant energy and performance costs. Processing in memory (PIM) is a computing paradigm that avoids most data movement costs by performing a part of the computations directly in the memory. In this paper, we focus on offloading computation tasks in MEC that require 3C resources with high efficiency and low energy consumption considering latency and resilience constraints in a PIM-assisted multi-core (PAMC) architecture of physical machines (PMs). We formulate an optimization problem to minimize the total weighted resource costs and energy consumption. We also present an algorithm based on the column generation to solve the problem. Simulation results demonstrate that the proposed PAMC architecture can achieve good results in terms of energy consumption and resources utilization in comparison with the traditional PMs’ architecture with the same resources.