Zakarya, Muhammad, Gillam, Lee, Chalak Qazani, Mohamadreza, Khan, Ayaz Ali, Salah, Khaled, and Rana, Omer (2025) BackFillMe: An Energy and Performance Efficient Virtual Machine Scheduler for IaaS Datacenters. IEEE Transactions on Services Computing, 18 (2). pp. 660-672.

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Abstract

Backfilling refers to the practice of allowing small jobs to be completed ahead of schedule as long as they do not cause the first job in the line to wait. Users are expected to offer estimates of how long jobs will take to complete in order to make these decisions possible, and these projections are often based on historical data. However, predictions are very hard and may not be accurate, particularly in cloud computing scenarios where jobs or applications run on Virtual Machines (VMs). In addition, scheduling and consolidation techniques can improve the energy efficiency and performance of applications. Consolidation involves VM migrations that can have a negative impact on workload performance and users’ costs. Backfilling can be used as an alternative technique for consolidation (short-term) and/or can be used along with consolidation (long-term). Backfilling methods are well-utilised in single computing systems, but are relatively unexplored in cloud resource allocation. A backfilling-based resource allocation and consolidation technique is proposed. Using real workloads from the Google cluster traces, we investigate the impact of backfilling on infrastructure energy efficiency and performance. For 12583 heterogeneous servers and approximately three million jobs that belong to three different applications, we observed that approximately 19% energy savings and 6% workload performance improvements are achievable using the backfilling approach. Furthermore, our evaluation suggests that using VM runtime as a criterion for the backfilling approach is approximately 3.56%–7.78% more energy and 1.91%–3.38% more performance efficient than using priority as a backfilling criterion.

Item ID:	86694
Item Type:	Article (Research - C1)
ISSN:	1939-1374
Keywords:	Clouds, resource allocation, backfilling, service migration, energy efficiency, performance
Copyright Information:	© 2025 IEEE. All rights reserved, including rights for text and data mining, and training of artificial intelligence and similar technologies.
Date Deposited:	27 Aug 2025 02:50
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4601 Applied computing > 460199 Applied computing not elsewhere classified @ 40% 46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460509 Query processing and optimisation @ 60%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering @ 30% 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences @ 70%
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