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Serverless computing has become increasingly popular for cloud applications, due to its compelling properties of high-level abstractions, lightweight runtime, high elasticity and pay-per-use billing. In this revolutionary computing paradigm shift, challenges arise when adapting data analytics applications to the serverless environment, due to the lack of support for efficient state sharing, which attract ever-growing research attention. In this paper, we aim to exploit the advantages of task level orchestration and fine-grained resource provisioning for data analytics on serverless platforms, with the hope of fulfilling the promise of serverless deployment to the maximum extent. To this end, we present ACTS, an autonomous cost-efficient task orchestration framework for serverless analytics. ACTS judiciously schedules and coordinates function tasks to mitigate cold-start latency and state sharing overhead. In addition, ACTS explores the optimization space of fine-grained workload distribution and function resource configuration for cost efficiency. We have deployed and implemented ACTS on AWS Lambda, evaluated with various data analytics workloads. Results from extensive experiments demonstrate that ACTS achieves up to 98% monetary cost reduction while maintaining superior job completion time performance, in comparison with the state-of-the-art baselines.
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Goldilocks: Adaptive Resource Provisioning in Containerized Data Centers
Power management in data centers is challenging because of fluctuating workloads and strict task completion time requirements. Recent resource provisioning systems, such as Borg and RC-Informed, pack tasks on servers to save power. However, current power optimization frameworks based on packing leave very little headroom for spikes, and the task completion times are compromised. In this paper, we design Goldilocks, a novel resource provisioning system for optimizing both power and task completion time by allocating tasks to servers in groups. Tasks hosted in containers are grouped together by running a graph partitioning algorithm. Containers communicating frequently are placed together, which improves the task completion times. We also leverage new findings on power consumption of modern- day servers to ensure that their utilizations are in a range where they are power-proportional. Both testbed implementation measurements and large-scale trace-driven simulations prove that Goldilocks outperforms all the previous works on data center power saving. Goldilocks saves power by 11.7%-26.2% depending on the workload, whereas the best of the implemented alternatives, Borg, saves 8.9%-22.8%. The energy per request for the Twitter content caching workload in Goldilocks is only 33% of RC-Informed. Finally, the best alternative in terms of task completion time, E-PVM, has 1.17-3.29 times higher task completion times than Goldilocks across different workloads.
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- Award ID(s):
- 1763929
- PAR ID:
- 10120194
- Date Published:
- Journal Name:
- 39th IEEE International Conference on Distributed Computing Systems (ICDCS 2019)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
