Journal
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
Volume 173, Issue -, Pages -Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jnca.2020.102869
Keywords
Datacenters; Virtualisation; Containerisation; Resource management; Server consolidation; Workload migration; Energy efficiency; Performance
Categories
Funding
- Abdul Wali Khan University Mardan (AWKUM), Pakistan
- Higher Education Commission (HEC), Pakistan
- Australian Research Council (ARC)
Ask authors/readers for more resources
Major IT companies like Google, Rackspace, and AWS use virtualization and containerization technologies to run customer workloads and applications, consuming substantial energy with ecological impacts. Resource management plays a crucial role in hybrid cloud environments, affecting data center energy efficiency and user costs.
In major Information Technology (IT) companies such as Google, Rackspace and Amazon Web Services (AWS), virtualisation and containerisation technologies are usually used to execute customers' workloads and applications. The computational resources are provided through large-scale datacenters, which consume substantial amount of energy and have, therefore, ecological impacts. Since long, Google runs users' applications in containers, Rackspace offers bare-metal hardware, whereas AWS runs them either in VMs (EC2), containers (ECS) and/or containers inside VMs (Lambda); therefore, making resource management a tedious activity. The role of a resource management system is of the greatest importance, principally, if IT companies practice various kinds of sand-boxing technologies, for instance, bare-metal, VMs, containers, and/or nested containers in their data centers (hybrid platforms). The absence of centralised, workload-aware resource managers and consolidation policies produces questions on datacenters energy efficiency, workloads performance, and users' costs. In this paper, we demonstrate, through several experiments, using the Google workload data for 12,583 hosts and approximately one million tasks that belong to four different kinds of workload, the likelihood of: (i) using workload-aware resource managers in hybrid clouds; (ii) achieving energy and cost savings, in heterogeneous hybrid datacenters such that the workload performance is not affected, negatively; and (iii) how various allocation policies, combined with different migration approaches, will impact on datacenter's energy and performance efficiencies. Using plausible assumptions for hybrid datacenters set-up, our empirical evaluation suggests that, for no migration, a single scheduler is at most 16.86% more energy efficient than distributed schedulers. Moreover, when migrations are considered, our resource manager can save up to 45.61% energy and can improve up to 17.9% workload performance.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available