Workload Analysis and Demand Prediction of Enterprise Data Center Applications

By Daniel Gmach, J. Rolia, L. Cherkasova, Alfons Kemper. IEEE International Symposium on Workload Characterization, 2007

341 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/b25d371020a7fddf2ee2615465c4d9789479b6c4

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In Computer Networks and Communications

In Information Systems

Citations in the corpus

Seer: Leveraging Big Data to Navigate the Complexity of Performance Debugging in Cloud Microservices (2019, methodology): “CloudScale [83], PRESS [48], AGILE [70] and the work by Gmach et al. [47] predict future resource needs on-line, often without a priori knowledge.”
Workload characterization and prediction in the cloud: A multiple time series approach (2012, background): “More recently, some work has been done toward characterizing the workload in a cloud environment [10], [11], [12], [13].”
Paragon: QoS-aware scheduling for heterogeneous datacenters (2013, background): “Additionally, Paragon maintains performance degradation moderate (no cliffs in performance such as for NH in applications [1-1000]) and limits degradation to less than 10% for an additional 33% of applications.”
FIRM: An Intelligent Fine-Grained Resource Management Framework for SLO-Oriented Microservices (2020, methodology): “To enable rapid (re)training of the proposed system as the underlying systems [67] and workloads [40,42,96,98] change in datacenter environments, FIRM uses transfer learning.”
Server Workload Analysis for Power Minimization using Consolidation (2009, background): “This paper does not deal with the problem of translating an application SLA to a resource value and assumes thatC(Ai, t) are available from monitored resource data.”
Quasar: resource-efficient and QoS-aware cluster management (2014, background): “[28] perform online prediction of resource needs, often without a priori workload knowledge.”
A hierarchical edge cloud architecture for mobile computing (2016, background): “Second, current data centers mainly focus on serving enterprise users with high volumes of workloads [11], by providing virtualized cloud resources as Virtual Machines (VMs).”