OpenStack managed Cloud Foundry service Market · Openstack community contributor, experienced in...
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1 SOFT CONTAINER TOWARDS 100% RESOURCE UTILIZATION ACCELA ZHAO, LAYNE PENG
Transcript of OpenStack managed Cloud Foundry service Market · Openstack community contributor, experienced in...
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SOFT CONTAINER TOWARDS 100% RESOURCE UTILIZATION
ACCELA ZHAO, LAYNE PENG
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Accela Zhao, Technologist at EMC OCTO, active Openstack community contributor, experienced in cloud scheduling and container technologies.
WHO ARE THOSE GUYS …
Layne Peng, Principal Technologist at EMC OCTO, experienced cloud architect, one of the earliest contributors to Cloud Foundry in China, 9 patents owner and a book author.
Mail: [email protected]
Mail: [email protected] Twitter: @layne_peng
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WHAT IS RESOURCE UTILIZATION?
This is what we buy
This is what we use
A gap of $$$ wasted
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ENERGY AND RESOURCE UTILIZATION
Energy-related costs 42% of total (including buy new machines)
An idle server consumes even 70% as much energy as running in full-speed
Low resource utilization is energy inefficient Waste energy, waste money
Real world resource utilization is usually low: around 20% or less
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A CLOSER LOOK TO CLOUD
The key advantage - cloud consolidation
Less machines, more apps. Energy-efficient and saves money.
Improved resource utilization
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• Scheduling - choose the best resource placement when app starts – Examples: Green Cloud, Paragon. And the schedulers in
Openstack, Kubernetes, Mesos, …
• Migration - continuously optimize the resource placement when app is running – Examples: Openstack Watcher, VMware DRS
• Soft Container - elastic, and dynamically adjust resource constraints in response to co-located apps – Related: Google Heracles
RESOURCE UTILIZATION ON CLOUD
Soft Container
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RESOURCE UTILIZATION ON CLOUD
Scheduler
Migration
Apps
Soft Container
Manages resource utilization at app kick-off
Manages resource utilization cross hosts while app running
Manages resource utilization at fine granularity inside host
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RESOURCE UTILIZATION ON CLOUD
A battle of putting more apps in each host
vs. guaranteed app SLA
The key problem: resource interference
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• What is resource interference? – Apps co-located in one host share resources like CPU,
cache, memory, …
– They interfere with each other, result in poor performance compared to running standalone
– Resource interference make SLA unenforceable
• Related readings – Google Heracles: an analysis of resource interference
– Paragon: resource interference-aware scheduling
– Bubble-up: to measure resource interference
THE KEY PROBLEM: RESOURCE INTERFERENCE
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RESOURCE INTERFERENCE: HOW IT LOOKS?
MySQL standalone running vs co-located with a CPU & disk hungry task
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• Bubble-up – The setup
• Run app co-located with resource benchmarks, each benchmark stresses one type of resource
– App tolerated resource interference • Slowly increase resource benchmark stress until app fails its SLA.
• The critical point shows how much resource interference the app can tolerate.
– App caused resource interference • Run app at what its SLA requires.
• The stress it causes on each type of resource is the app’s caused resource interference.
• Where to use it? – Better resource utilization management
– Scheduling, Migration, Soft Container, …
RESOURCE INTERFERENCE: HOW TO MEASURE?
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RESOURCE INTERFERENCE: HOW TO MEASURE?
MySQL standalone running, vs co-located with CPU stress, vs disk stress. In my case, MySQL is much more sensitive to CPU interference.
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• Motivations – Increase resource utilization by co-locating more apps
• E.g. Business services is critical but may not use all resources on the host. Add the low priority hadoop batching tasks to fill what is left.
– Respond to the dynamic nature of time-varying workload • E.g. Business service may become more idle at lunch time, hadoop
tasks can then expand its resource bubble and utilize the leftover.
– Guarantee the SLA of critical apps • E.g. When the business service suddenly requires more resource for
processing, hadoop tasks will shrink instantly to give out resources.
• Challenges – Resource control and isolation of interference
– Respond to dynamic workload change
INTRODUCING TO SOFT CONTAINER
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• What does “Soft” mean? – Varying container resources needs based upon neighbors
and SLAs. (The container becomes elastic)
– “Expanding” (bubble up) resources when idle resources exist
– Shrinking resources on a specific container, when another critical app demands more resources
INTRODUCING TO SOFT CONTAINER
Container resource bubble
Time
Resource
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THE FEEDBACK CONTROL LOOP
Controller
Watcher Limiter
Containers
Soft Container
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RESOURCES TO LIMIT
• CPU – Core
– Time Quota
– …
• Memory – Size
– Bandwidth
– …
• Disk I/O – IOPS
– Throughput
– …
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RESOURCES TO LIMIT - MISSING
• CPU – Core
– Time Quota
– …
• Cache – LLC
– …
• Memory – Size
– Bandwidth*
– …
• GPU – …
• Device* – …
• Network – Ulimit
– Bandwidth
– …
…
• Disk I/O – IOPS
– Throughput
– …
Kernel 3.6, most supports can be found in the community…
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ISOLATION THE RESOURCES - NAMESPACE
/proc/<pid>/ns: • lrwxrwxrwx 1 root root 0 Jun 21 18:38 ipc -> ipc:[4026532509] • lrwxrwxrwx 1 root root 0 Jun 21 18:38 mnt -> mnt:[4026532507] • lrwxrwxrwx 1 root root 0 Jun 16 18:24 net -> net:[4026532512] • lrwxrwxrwx 1 root root 0 Jun 21 18:38 pid -> pid:[4026532510] • lrwxrwxrwx 1 root root 0 Jun 21 18:38 user -> user:[4026531837] • lrwxrwxrwx 1 root root 0 Jun 21 18:38 uts -> uts:[4026532508]
• clone(): create a new process and attached to a new namespace • unshare(): create a new namespace and attaches to a existed process • setns(): Set a a process to a existing namespace
• security namespace • security keys namespace • device namespace • time namespace
We are still waiting…
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LIMIT THE RESOURCE - CGROUP
Task, Control Group & Hierarchy Subsystem – Control options
• blkio • cpu • cpuacct • cpuset • devices
• freezer • memory • net_cls • net_prio • ns
Create a cgroup subsystem Change the limitation…
Usage
# echo 524288000 > /sys/fs/cgroup/memory/foo/memory.limit_in_bytes
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MISSING - NETWORK
Isolation, does not means resource controlling
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Suppose two containers in a machine, totally 100Gbps b/w
80
100Gbps
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MISSING - NETWORK
Isolation, does not means resource controlling
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Suppose two containers in a machine, totally 100Gbps b/w
80
100Gbps
95
100Gbps
If the GREEN container consumes the majority of b/w, which may have a negative impact on the BLUE one… How we can avoid this case from happening?
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MISSING - NETWORK
Community attempts: Base on Traffic Control (tc)
Nightmare of the PaaS providers…
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MISSING - NETWORK
Community attempts: Base on Traffic Control (tc)
Nightmare of the PaaS providers…
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MISSING - GPU
Nvidia’s efforts:
a. GPU exposed as separated normal devices in /dev
Ref: https://github.com/NVIDIA/nvidia-docker/wiki/GPU-isolation
b. devices cgroup: • Allow/Deny/List • Access
i. R ii. W iii. M
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MISSING - GPU
Nvidia’s efforts:
a. GPU exposed as separated normal devices in /dev
Ref: https://github.com/NVIDIA/nvidia-docker/wiki/GPU-isolation
b. devices cgroup: • Allow/Deny/List • Access
i. R ii. W iii. M
Usable, but insufficient… 1. Launch multiple jobs in parallel, each one us a subset of avaiable GPUs; 2. How about share GPU between Jobs with proper isolation? Can we share
a GPU like we can a CPU?
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MISSING - CACHE
Intel’s efforts:
Cache Monitor Technology (CMT) • For an OS or VMM to indicate a software-
defined ID for each of applications or VMs that are scheduled to run on a core. This ID is called the Resource Monitoring ID (RMID).
• To Monitor cache occupancy on a per RMID basis
• For an OS or VMM to read LLC occupancy for a given RMID at any time.
Cache Allocation Technology (CAT) • The ability to enumerate the CAT capability and
the associated LLC allocation support via CPUID.
• Interfaces for the OS/hypervisor to group applications into classes of service (CLOS) and indicate the amount of last-level cache available to each CLOS. These interfaces are based on MSRs (Model-Specific Registers).
Code and Data Prioritization (CDP) • Extension to CAT • a new CPUID feature flag is added within the
CAT sub-leaves at CPUID.0x10.[ResID=1]:ECx[bit 2] to indicate support
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MISSING – MEMORY BANDWIDTH
Monitor
Memory Bandwidth Monitoring (MBM) • Mechanisms in hardware to monitor cache
occupancy and bandwidth statistics as applicable to a given product generation on a per software-id basis.
• Mechanisms for the OS or hypervisor to read back the collected metrics such as L3 occupancy or Memory Bandwidth for a given software ID at any point during runtime.
Control
Ref Memory Bandwidth Management for Efficient Performance Isolation in Multi-core Platform: http://pertsserver.cs.uiuc.edu/~mcaccamo/papers/private/IEEE_TC_journal_submitted_C.pdf Code: https://github.com/heechul/memguard
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MISSING – MEMORY BANDWIDTH
Monitor
Memory Bandwidth Monitoring (MBM) • Mechanisms in hardware to monitor cache
occupancy and bandwidth statistics as applicable to a given product generation on a per software-id basis.
• Mechanisms for the OS or hypervisor to read back the collected metrics such as L3 occupancy or Memory Bandwidth for a given software ID at any point during runtime.
Control
Ref Memory Bandwidth Management for Efficient Performance Isolation in Multi-core Platform: http://pertsserver.cs.uiuc.edu/~mcaccamo/papers/private/IEEE_TC_journal_submitted_C.pdf Code: https://github.com/heechul/memguard
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• Latencies – App request latency
– Disk IO await
– Network response time
• Queue length – CPU load average
– Disk request queue size
– Network queue length
• Utilization – CPU util rate
– Disk util rate
– Network util rate
WATCH THE WORKLOAD CHANGE
• Bandwidth – DRAM bandwidth
– CPU bandwidth
– Disk bandwidth
• Request count – App request count
– Disk IOPS / req/s
– Network IOPS / req/s
• Granularity – Global level
– Per container level
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THE FEEDBACK CONTROL LOOP
Controller
Watcher Limiter
Containers
Soft Container
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THE FEEDBACK CONTROL LOOP
Controller
Watcher Limiter
Containers
Soft Container
Immediate response
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THE FEEDBACK CONTROL LOOP
Controller
Watcher Limiter
Containers
Soft Container
Immediate response
How to immediately resize the containers?
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HOW WE LOOK AT RESIZE?
a. Create a new container; b. Live migrate the contents to new container:
1. Transfer existed data to new container; 2. Transfer the instant data to new container.
c. Stop the old container d. Start the new container e. Route the traffic to new container
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9527 /usr/sbin/httpd
Control Groups (cgroup): • CPU time: 20 • System memory: 1G • Disk bandwidth: 2000 • Network bandwidth: 100Mbs
Control Groups (cgroup): • CPU time: 70 • System memory: 5G • Disk bandwidth: 8000 • Network bandwidth: 1Gbs
a. Mount to new cgroup or change the value of the cgroup
b. Done!
IN CONTAINER’S WORLD…
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9527 /usr/sbin/httpd
Control Groups (cgroup): • CPU time: 20 • System memory: 1G • Disk bandwidth: 2000 • Network bandwidth: 100Mbs
Control Groups (cgroup): • CPU time: 70 • System memory: 5G • Disk bandwidth: 8000 • Network bandwidth: 1Gbs
a. Mount to new cgroup or change the value of the cgroup
b. Done!
IN CONTAINER’S WORLD…
We need to take a fresh look at the resources management from
Container’s perspective.
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SOFT CONTAINER: IMPLEMENTATION
Controller Algorithm ”expand”
Algorithm ”pin_idle”
Algorithm plugin N
Watcher CPU plugin
Disk plugin
Watcher plugin N
Limiter RunC plugin
Docker plugin
Limiter plugin N
Metrics Store
CPU statistics
Disk …
More …
Container Repo
RunC plugin
Docker plugin
Container type N
Containers
Auto discovery
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• Early version
• Support RunC and Docker containers
• A few controller algorithms which are effective
• Able to expand with more plugins
SOFT CONTAINER: CURRENT STATUS
Completely runnable!
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BENCHMARK RESULTS: BEFORE
If uncontrolled, MySQL workload is severely interfered by co-located low priority task
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BENCHMARK RESULTS: BEFORE
The CPU utilization is far from saturation while workload varies by time (Although in my case, disk IO is highly utilized)
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BENCHMARK RESULTS: SOFT CONTAINER
With Soft Container (green line), latency impact is controlled. (We can improve the algorithm to cope better with peak workload)
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BENCHMARK RESULTS: SOFT CONTAINER
Soft Container helps improve CPU utilization by co-locating new tasks with MySQL
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BENCHMARK RESULTS: SOFT CONTAINER
CPU utilization looks close to saturation, after adding in iowait time
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• Soft Container monitors app resource needs and overall resource utilization in realtime
• Soft Container issues resource controls in realtime, to guard app SLA and balance resource utilization
HOW DOES SOFT CONTAINER DID THIS?
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BENCHMARK RESULTS: SOFT CONTAINER
How the resource bubble floats under the control of Soft Container. (The vibration threshold are made very sensitive to workload change)
Q&A
