Heterogeneity Aware Workload Management in Distributed Sustainable Datacenters

Dazhao Cheng, Xiaobo Zhou, Zhaojun Ding, Yu Wang, Mike Ji

Research output: Contribution to journalArticlepeer-review

Abstract

The tremendous growth of cloud computing and large-scale data analytics highlight the importance of reducing datacenter power consumption and environmental impact of brown energy. While many Internet service operators have at least partially powered their datacenters by green energy, it is challenging to effectively utilize green energy due to the intermittency of renewable sources, such as solar or wind. We find that the geographical diversity of internet-scale services can be carefully scheduled to improve the efficiency of applying green energy in datacenters. In this paper, we propose a holistic heterogeneity-aware cloud workload management approach, sCloud, that aims to maximize the system goodput in distributed self-sustainable datacenters. sCloud adaptively places the transactional workload to distributed datacenters, allocates the available resource to heterogeneous workloads in each datacenter, and migrates batch jobs across datacenters, while taking into account the green power availability and QoS requirements. We formulate the transactional workload placement as a constrained optimization problem that can be solved by nonlinear programming. Then, we propose a batch job migration algorithm to further improve the system goodput when the green power supply varies widely at different locations. Finally, we extend sCloud by integrating a flexible batch job manager to dynamically control the job execution progress without violating the deadlines. We have implemented sCloud in a university cloud testbed with real-world weather conditions and workload traces. Experimental results demonstrate sCloud can achieve near-to-optimal system performance while being resilient to dynamic power availability. sCloud with the flexible batch job management approach outperforms a heterogeneity-oblivious approach by 37 percent in improving system goodput and 33 percent in reducing QoS violations.
Original languageEnglish
Pages (from-to)375-387
Number of pages13
JournalIEEE Transactions on Parallel and Distributed Systems
Volume30
Issue number2
Early online date17 Aug 2018
DOIs
Publication statusPublished - 1 Feb 2019

Keywords

  • cloud computing
  • computer centres
  • green computing
  • nonlinear programming
  • power aware computing
  • quality of service
  • scheduling
  • sustainable development
  • large-scale data analytics
  • Internet-scale services
  • QoS requirements
  • constrained optimization problem
  • batch job migration algorithm
  • transactional workload placement
  • green power availability
  • heterogeneous workloads
  • distributed datacenters
  • distributed self-sustainable datacenters
  • holistic heterogeneity-aware cloud workload management approach
  • green energy
  • Internet service operators
  • brown energy
  • datacenter power consumption
  • distributed sustainable datacenters
  • heterogeneity aware workload management
  • heterogeneity-oblivious approach
  • flexible batch job management approach
  • sCloud
  • dynamic power availability
  • workload traces
  • real-world weather conditions
  • flexible batch job manager
  • green power supply
  • Power supplies
  • Green products
  • Task analysis
  • Cloud computing
  • System performance
  • Quality of service
  • Clouds
  • Sustainable datacenter
  • heterogeneity
  • job migration
  • optimization
  • system goodput
  • workload placement

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