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Measuring interference between live datacenter applications
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-540-85451-7_29openalex-snapshot
Exploration of the Influence of Program Inputs on CMP Co-scheduling
inferredcites →https://doi.org/10.1007/978-3-642-11515-8_16openalex-snapshot
Combining Locality Analysis with Online Proactive Job Co-scheduling in Chip Multiprocessors
inferredcites →https://doi.org/10.1007/s11227-011-0703-4openalex-snapshot
Toward on-chip datacenters: a perspective on general trends and on-chip particulars
inferredcites →https://doi.org/10.1109/hpca.2005.14openalex-snapshot
Enterprise IT Trends and Implications for Architecture Research
inferredcites →https://doi.org/10.1109/hpca.2005.27openalex-snapshot
Predicting Inter-Thread Cache Contention on a Chip Multi-Processor Architecture
inferredcites →https://doi.org/10.1109/ipdps.2006.1639374openalex-snapshot
Exploiting unbalanced thread scheduling for energy and performance on a CMP of SMT processors
inferredcites →https://doi.org/10.1109/ipdps.2010.5470399openalex-snapshot
Performance impact of resource contention in multicore systems
inferredcites →https://doi.org/10.1109/ispass.2007.363750openalex-snapshot
An Analysis of Performance Interference Effects in Virtual Environments
inferredcites →https://doi.org/10.1109/ispass.2010.5452065openalex-snapshot
Cache contention and application performance prediction for multi-core systems
inferredcites →https://doi.org/10.1109/l-ca.2011.14openalex-snapshot
Heterogeneity in “Homogeneous” Warehouse-Scale Computers: A Performance Opportunity
inferredcites →https://doi.org/10.1109/micro.2007.21openalex-snapshot
Stall-Time Fair Memory Access Scheduling for Chip Multiprocessors
inferredcites →https://doi.org/10.1109/micro.2008.4771801openalex-snapshot
Coordinated management of multiple interacting resources in chip multiprocessors: A machine learning approach
inferredcites →https://doi.org/10.1109/micro.2010.51openalex-snapshot
Thread Cluster Memory Scheduling: Exploiting Differences in Memory Access Behavior
inferredcites →https://doi.org/10.1109/mm.2006.73openalex-snapshot
IPC Considered Harmful for Multiprocessor Workloads
inferredcites →https://doi.org/10.1109/mm.2010.68openalex-snapshot
Google-Wide Profiling: A Continuous Profiling Infrastructure for Data Centers
inferredcites →https://doi.org/10.1109/pact.2004.1342546openalex-snapshot
Fair cache sharing and partitioning in a chip multiprocessor architecture
inferredcites →https://doi.org/10.1109/pact.2007.4336197openalex-snapshot
Improving Performance Isolation on Chip Multiprocessors via an Operating System Scheduler
inferredcites →https://doi.org/10.1109/pact.2007.4336224openalex-snapshot
CacheScouts: Fine-Grain Monitoring of Shared Caches in CMP Platforms
inferredcites →https://doi.org/10.1145/1095408.1095418openalex-snapshot
The Future of Microprocessors
inferredcites →https://doi.org/10.1145/1248377.1248396openalex-snapshot
Scheduling threads for constructive cache sharing on CMPs
inferredcites →https://doi.org/10.1145/1269899.1254886openalex-snapshot
QoS policies and architecture for cache/memory in CMP platforms
inferredcites →https://doi.org/10.1145/1273440.1250670openalex-snapshot
Virtual hierarchies to support server consolidation
inferredcites →https://doi.org/10.1145/1399972.1399980openalex-snapshot
Towards modeling & analysis of consolidated CMP servers
inferredcites →https://doi.org/10.1145/1454115.1454146openalex-snapshot
Analysis and approximation of optimal co-scheduling on chip multiprocessors
inferredcites →https://doi.org/10.1145/1531793.1531803openalex-snapshot
Enhancing operating system support for multicore processors by using hardware performance monitoring
inferredcites →https://doi.org/10.1145/1542275.1542342openalex-snapshot
Rate-based QoS techniques for cache/memory in CMP platforms
inferredcites →https://doi.org/10.1145/1693453.1693482openalex-snapshot
Does cache sharing on modern CMP matter to the performance of contemporary multithreaded programs?
inferredcites →https://doi.org/10.1145/1736020.1736033openalex-snapshot
Probabilistic job symbiosis modeling for SMT processor scheduling
inferredcites →https://doi.org/10.1145/1736020.1736058openalex-snapshot
Fairness via source throttling
inferredcites →https://doi.org/10.1145/1810085.1810113openalex-snapshot
An approach to resource-aware co-scheduling for CMPs
inferredcites →https://doi.org/10.1145/1880018.1880019openalex-snapshot
Contention-Aware Scheduling on Multicore Systems
inferredcites →https://doi.org/10.1145/1944862.1944887openalex-snapshot
Directly characterizing cross core interference through contention synthesis
inferredcites →https://doi.org/10.1145/2000064.2000099openalex-snapshot
The impact of memory subsystem resource sharing on datacenter applications
inferredcites →https://doi.org/10.1145/2000417.2000419openalex-snapshot
Contentiousness vs. sensitivity
inferredcites →https://doi.org/10.1145/2024723.2000099openalex-snapshot
The impact of memory subsystem resource sharing on datacenter applications
inferredcites →https://doi.org/10.1145/378993.379244openalex-snapshot
Symbiotic jobscheduling for a simultaneous multithreaded processor
inferredcites →https://doi.org/10.2200/s00193ed1v01y200905cac006openalex-snapshot
The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Techniques to Achieve Energy Proportionality in Data Centers: A Survey
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INDICES: Applying DDDAS Principles for Performance Interference-aware Cloud-to-Fog Application Migration
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Optimizing Egalitarian Performance in the Side-Effects Model of Colocation for Data Center Resource Management
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WiseThrottling: a new asynchronous task scheduler for mitigating I/O bottleneck in large-scale datacenter servers
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Haste makes waste: The On–Off algorithm for replica selection in key–value stores
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Measuring Application Interference With System-Level Instrumentation
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Detection of Performance Interference Among Network Slices in 5G/6G Systems
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Radio: Reconciling Disk I/O Interference in a Para-virtualized Cloud
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VMon: Monitoring and Quantifying Virtual Machine Interference via Hardware Performance Counter
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Kelp: QoS for Accelerated Machine Learning Systems
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Ah-Q: Quantifying and Handling the Interference within a Datacenter from a System Perspective
inferred← citeshttps://doi.org/10.1109/hpcc/smartcity/dss.2019.00274openalex-snapshot
ElaX: Provisioning Resource Elastically for Containerized Online Cloud Services
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ESP: A Machine Learning Approach to Predicting Application Interference
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Information Dissemination for the Adaptive Replica Selection algorithm in Key-Value Stores
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Resource management techniques aware of interference among high-performance computing applications
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