Improving utilization in a full virtualization context

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Table of contents

1 Introduction 
2 Resource Virtualization 
2.1 VirtualMachines: Hardware-level virtualization
2.1.1 Cost of machine virtualization
2.1.2 Improving utilization in a full virtualization context
2.1.3 Improving utilization in a paravirtualization context
2.2 Containers: Operating System-level virtualization
2.2.1 Isolating physical resources with cgroups
2.2.2 Isolating resource visibility with namespaces
2.2.3 Restraining attack surface with security features
2.3 Containers and VMs comparison
2.3.1 Comparing stand-alone overheads
2.3.2 Comparing performance isolation and overcommitment
2.3.3 Should VMsmimic Containers?
2.4 Consolidation and Isolation, the best of both worlds
2.4.1 Resource Consolidation
2.4.2 Performance Isolation
2.4.3 Illustrating Consolidation and Isolation with the CPU cgroups
2.4.4 Block I/O, a time-based resource similar to CPU
2.5 Memory, a spatial but not time-based resource
2.5.1 Conclusion
3 Memory and cgroup 
3.1 Storing data in main memory
3.1.1 Memory Hierarchy
3.1.2 Spatialmultiplexing
3.1.3 Temporal multiplexing
3.1.4 The need for memory cgroup
3.2 Accounting and limiting memory with cgroup
3.2.1 Event, Stat and Page counters
3.2.2 min, max, soft and hard limits
3.3 Isolating cgroupmemory reclaims
3.3.1 Linux memory pool
3.3.2 Splitting memory pools
3.4 Resizing dynamic memory pools
3.4.1 Resizing anon and file memory pools
3.4.2 Resizing cgroup memory pools
3.5 Conclusion
4 Isolation flaws at consolidation 
4.1 Modeling Consolidation
4.1.1 Model assumptions
4.1.2 Countermeasures
4.1.3 Industrial Application atMagency
4.2 Consolidation: once a solution, now a problem
4.2.1 Consolidation with containers
4.2.2 Consolidation without containers
4.2.3 Measuring consolidation errors
4.3 Lesson learned
5 Capturing activity shifts 
5.1 Rotate ratio: a lru dependent metric
5.1.1 Detecting I/O patterns that waste memory with RR
5.1.2 Balancing anon and file memory with RR
5.1.3 RR can produce false negatives
5.1.4 Additional force_scans cost CPU time and impact isolation
5.1.5 Conclusion
5.2 Idle ratio: a lru independent metric
5.2.1 IR accurately monitors the set of idle pages
5.2.2 Trade-offs between CPU time cost and IR’s accuracy
5.2.3 Conclusion
5.3 Conclusion
6 Sustaining isolation of cgroups 
6.1 Refreshing the lrus with force_scan
6.1.1 Conclusion
6.2 Building opt: a relaxed optimal solution
6.2.1 Applying soft_limits at all levels of the hierarchy
6.2.2 Order cgroups by activity levels with reclaim_order
6.2.3 Stacking generic policies
6.3 Guessing the activity levels
6.3.1 AMetric-driven approach to predict activities
6.3.2 An Event-driven approach to react to activity changes
6.4 Conclusion
7 Evaluation of the metric and the event-driven approaches 
7.1 Experimental setup
7.1.1 Workload’s types and inactivitymodels
7.1.2 Schedule of activity shifts and configuration of resources
7.1.3 Throttling issues with Blkio cgroup
7.1.4 Experimental configurations
7.2 Performance analysis
7.2.1 Control Experiments
7.2.2 Event-based solutions
7.2.3 Metric-based solutions
7.3 Page transfer analysis
7.3.1 Rotate ratio solutions
7.3.2 Idle ratio solutions
7.3.3 Event-based solutions
7.4 Conclusion
8 Conclusion and Future works 
8.1 Short-termchallenges
8.1.1 Spreading contention on the most inactive containers
8.1.2 Ensuring properties when all containers are active
8.2 Long-termperspectives
8.2.1 Ensuring isolation and consolidation at the scale of a cluster
8.2.2 Maximizing global performance with limited memory
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