HEXO: Offloading Long-Running Compute- and Memory-Intensive Workloads on Low-Cost, Low-Power Embedded Systems - Olivier, Pierre; Fazla Mehrab, A. K.M.; Errabelly, Sandeep; Lankes, Stefan; Karaoui, Mohamed Lamine; Lyerly, Robert; Kim, Sang Hoon; Barbalace, Antonio; Ravindran, Binoy (IEEE Transactions on Cloud Computing, 2024)
OS-capable embedded systems exhibiting a very low power consumption are available at an extremely low price point. It makes them highly compelling in a datacenter context. We show that sharing long-running, compute-intensive datacenter workloads between a server machine and one or a few connected embedded boards of negligible cost and power consumption can yield significant performance and energy benefits. Our approach, named Heterogeneous EXecution Offloading (HEXO), selectively offloads Virtual Machines (VMs) from server-class machines to embedded boards. Our design tackles several challenges. We address the Instruction Set Architecture (ISA) difference between typical servers (x86) and embedded systems (ARM) through hypervisor and guest OS-level support for heterogeneous-ISA runtime VM migration. We cope with the low amount of resources in embedded systems by using lightweight VMs - unikernels - and by using the server's free RAM as remote memory for embedded boards through a transparent lightweight memory disaggregation mechanism for heterogeneous server-embedded clusters, called Netswap. VMs are offloaded based on an estimation of the slowdown expected from running on a given board. We build a prototype of HEXO and demonstrate significant increases in throughput (up to 67%) and energy efficiency (up to 56%) using benchmarks representative of compute-intensive long-running workloads.
Keywords: Datacenter; Embedded boards; Embedded-system; Heterogeneous ISA; Instruction set architecture; Low power embedded systems; Low-costs; Migration; Offloading; Unikernel; Heterogeneous ISAs; migration; offloading; unikernels