Paper Info
Title
Co-optimizing Latency and Energy for IoT services using HMP servers in Fog Clusters
Abstract
Fog computing has the potential to be an energy-efficient alternative to cloud computing for guaranteeing latency requirements of Latency-critical (LC) IoT services. However, even in fog computing low energy-efficiency of homogeneous multi-core server processors can be a major contributor to energy wastage. Recent studies have shown that Heterogeneous Multi-core Processors (HMPs) can improve energy efficiency of servers by adapting to dynamic load changes of LC-services. However, proposed approaches optimize energy only at a single server level. In our work, we demonstrate that optimization at the cluster-level across many HMP-servers can offer much greater energy savings through optimal work distribution across the HMP-servers while still guaranteeing the Service Level Objectives (SLO) of LC-services. In this paper, we present Greeniac, a cluster-level task manager that employs Reinforcement Learning to identify optimal configurations at the server- and cluster-levels for different workloads. We develop a server-level service scheduler and a cluster-level load balancing module to assign services and distribute tasks across HMP servers based on the learned configurations. In addition to meeting the required SLO targets, Greeniac achieves up to 28% energy saving compared to best-case cluster scheduling techniques with local HMP-aware scheduling on a 4-server fog cluster, with potentially larger savings in a larger cluster.
Year
DOI
Venue
2019
10.1109/FMEC.2019.8795353
2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)
Keywords
Field
DocType
service level objectives,heterogeneous multicore processors,energy savings,latency-critical IoT services,optimal work distribution,energy wastage,homogeneous multicore server processors,fog computing low energy-efficiency,latency requirements,cloud computing,4-server fog cluster,HMP servers,cluster-level load balancing module,server-level service scheduler,optimal configurations,cluster-level task manager,LC-services,HMP-servers
Task manager,Service level objective,Load balancing (computing),Efficient energy use,Scheduling (computing),Computer science,Server,Distributed computing,Reinforcement learning,Cloud computing
Conference
ISBN
Citations 
PageRank 
978-1-7281-1797-3
0
0.34
References 
Authors
15
5
Name
Order
Citations
PageRank
Sambit Shukla100.34
Dipak Ghosal22848163.40
Kesheng Wu31231108.30
Alex Sim424342.76
Matthew K. Farrens512.38