Paragon: QoS-aware scheduling for heterogeneous datacenters

By Christina Delimitrou, Christos Kozyrakis. International Conference on Architectural Support for Programming Languages and Operating Systems, 2013

858 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Hardware and Architecture, Information Systems

View Paper: https://www.semanticscholar.org/paper/66ad93a464bf76159c3766047c3030ed177dfd6e

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Citations in the corpus

Seer: Leveraging Big Data to Navigate the Complexity of Performance Debugging in Cloud Microservices (2019, methodology): “Paragon uses classification to determine the impact of platform heterogeneity and workload interference on an unknown, incoming workload [30, 31].”
Flexible Scheduling of Distributed Analytic Applications (2016, methodology): “This can be easily included in an application description, but requires developing additional components to infer statistical models of application properties based on systems observations, as done for example in Tarcil [19], Paragon [15] and Quasar [16].”
Quasar: resource-efficient and QoS-aware cluster management (2014, methodology): “Quasar uses similar classification techniques as those introduced in Paragon [20].”
Heracles: Improving resource efficiency at scale (2015, background): “Proposed interference-aware cluster managers, such as Paragon [18] and Bubble-Up [51], would disallow these colations.”
Towards energy proportionality for large-scale latency-critical workloads (2014, methodology): “Alternatively, we can temporarily delay tasks to create suf.ciently long periods of idleness so that deep sleep modes are effective [28, 29].”
Joint Optimization of Task Scheduling and Image Placement in Fog Computing Supported Software-Defined Embedded System (2016, background): “Delimitrou and Kozyrakis [14] present Paragon, a heterogeneity- and interference-aware online scheduler, which is derived from robust analytical methods, instead of by profiling each application.”
Large-scale cluster management at Google with Borg (2015, background): “But even though the correlations are statistically signiﬁcant, they only explain 5% of the variance we saw in CPI measurements; other factors dominate, such as inherent differences in applications and speciﬁc interference patterns [24, 83].”
FIRM: An Intelligent Fine-Grained Resource Management Framework for SLO-Oriented Microservices (2020, background): “Examples include Borg [119], Mesos [43], Tarcil [28], Paragon [25], Quasar [26], Morpheus [54], DeepDive [73], and Q-clouds [71].”
Who Limits the Resource Efficiency of My Datacenter: An Analysis of Alibaba Datacenter Traces (2019, background): “The stateof-the-art on improving resource efficiency of datacenters mainly focus on CPU utilization of servers [6, 8].”
Twine: A Unified Cluster Management System for Shared Infrastructure (2020, background): “Paragon [12] schedules a job on machines that are beneficial to the job’s performance, but it does not reconfigure a machine.”
SoftSKU: Optimizing Server Architectures for Microservice Diversity @Scale (2019, background): “Other works reduce coscheduled job interference [109–114] or schedule them in a machine characteristics-aware manner [115–118].”