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Rainbow: Capacity Oriented Virtualized Computing Framework
for Virtualized Data Center
Ying Song & Yuzhong [email protected]
Key Laboratory of Computer System and Architecture, Institute of Computing Technology,
Chinese Academy of Sciences
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AgendaMotivationRAINBOW: Capacity Service Computing FrameworkMulti-tiered Resource Scheduling SchemeResource Flowing ModelOn-demand Resource Flowing AlgorithmsImplementation and Performance Evaluation Future WorkRAINBOW Prototype Introduction
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Motivation
Utility computing & Cloud computingunder-utilized and idle-working, barrier:
the computer architecture and the operating systemlack of efficient, on-demand and fine-grained resource scheduling
Server consolidationIsolation - Virtualization
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Virtualization leads to Capacity Service ComputingVirtualization leads to Capacity Service Computing
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Capacity Service Computing
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Challenge and Chance
New chance for resource management in capacity service computing, which results from the virtualization：
Finer-grainedTwo-dimension
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Problem：
How to control on-demand resource allocation?
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RAINBOW gains improvements in most cases, especially in the case of hosting different resource-bound services (improves 26%~324% in service performance, as well as 26% in CPU utilization over traditional service computing framework)
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Schedule
Virtual Machine Virtual Machine Virtual Machine
…
On-Demand Resource Flowing Should Solve the Problems：
Which resource will flow? When will such resource flow? Which VMs will be the source and target of flow? How many resources will flow
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Multi-tiered Resource Scheduling Scheme
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We consider the problem of resource flowing among VMs in RAINBOW, and model it by optimization theory. This model is a general one which can be respectively used by CPU, memory or other resources.
⎪⎩
⎪⎨⎧
=≥
≤
−
=∑
),,2,1(..
min
1KiCR
RRtsiit
K
iit
L
Where, function Qit=fi(Rit,Dit) denotes that QoS (i.e. response time) is decided by the required (Dit) and allocated (Rit) resources. SPi is the static priority of service hosted in VMi.Фi is defined to be the acceptable quality of service i.
∑=
×Φ
=K
ii
i
ititit SP
DRfUMin
1
),(
Resource Flowing Model
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With plentiful experiments, we extract functions Qit=fi(Rit,Dit)of several typical enterprise services including web and database (DB for short) services, and of different resources, i.e. CPU and memory. Then we simplify these relationships to be: q=A*D+B*R+C.
0100200300400500600
30 90 150 210
rate(reqs/s)
response time (ms)
mem_64
mem_256
mem_320
mem_384
mem_512
mem_640
mem_768
mem_896
mem_1024
mem_1536
0
20
40
60
80
100
1000 2000 3000 4000 5000
request number
response time (s)
40% CPU
60% CPU
80% CPU
100% CPU
⎪⎪⎩
⎪⎪⎨
⎧
<−−−≤<−+−
−≤<+−≤−−
=
DRsensenoRDRRD
RDRRDRD
q
5.25.15.25.125.165.113.252.255.1
25.165.124.214.0138.00.14.0
QoS of web service impacted by CPU
⎪⎪⎩
⎪⎪⎨
⎧
<++−+≤<+−+≤<−
≤
=
DRRRDRRDRDRRD
RD
q
384006.0000297.0384256000333.0000698.02560000095.0000123.0
009.0
QoS of web service impacted by memory
Difficulty and challenge: how to extract such functions automatically within a short period.
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Putting these functions into our model, we get the following formulation:
⎪⎩
⎪⎨⎧
=≥
≤
−
=∑
),,2,1(..
min
1KiCR
RRtsiit
K
iit
L
where m denotes the maximum number of stages for fi(Rit,Dit). We resolve the above model to get the close-to-optimal resource allocation Rit at time t, using the Simplex Method.
∑∑
=
= ×Φ
++=
K
ii
i
m
jijitijitij
t SPCRBDA
MinU1
1**
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We simplify the above model to design our local resource (CPU & memory) flowing algorithms. All the functions fi(Rit,Dit)/Фi are set as: if Dit>Rit, fi(Rit,Dit)/Фi=Dit-Rit; else, fi(Rit,Dit)/Фi=0.
The relationship between Dit and Rit can be directly reflected by the resource utilization of VMi. Thus, our local algorithms control resource flowing according to the resource utilization of each VM and priority of each service.
Local Resource Flowing Algorithms
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Algorithm of CPU Flowing among VMs (CFaVM)
0%
Td 70%
Tu 90%100%
Adjusting weight
Priority
0%
Td 70%
Tu 90%100%
0%
Td 70%
Tu 90%100%
CFaVM
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Algorithm of Lazy Memory Flowing among VMs (LMFaVM)
Do not flow!
On-demand
Lazy
Priority
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Experimental platform Hardware
Server pool：4 servers2190MHZ Dual Core AMD Operon(2 cores per cpu)1024KB cache4GB RAM1000Mb Ethernet
Workload generation servers：6 serversNetwork
Gigabit Ethernet LANSoftware
Operating System: Centos4.4Xen Version: 3.0.4Webserver: ApacheHPC: Conder （MPI application）Database: MysqlOffice: Openoffice, Firefox, Adobe Reader, etc.
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Experimental platformBenchmarks
SpecWeb2005Test Web server;
TPC-WTest database server;
LinpackTest HPC;
XneeTest office server；
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Various scheduling algorithms in RAINBOW vs. RAINBOW-NoFlow (‘NF’ for short)
Condition Comparison Office web HPC Resource utility
BN:CPU CFaVM : NF 25% 1% -7% CPU:2 %
BN : memory LMFaVM : NF 37% 1% 1% mem:8%
BN:CPU&mem RFaVM(CFaVM&LMFaVM) : NF
42% 2% 1% CPU: 2% mem:6%
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The goal of the global resource flowing model is to optimize resource allocation among services. Based on the resource flowing model, we propose a global resource flowing algorithm (GRFA) as a complement to the local resource flowing algorithms.
Global Resource Flowing Algorithm
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CPU
utilization
CPU
utilization
CPU
utilization
Tu
Td
100%
70%
90%Tu
Td
100%
60%
80%
…
Service #1 Service #KService #2
Tu
Td
100%
90%
Compute the optimal resources allocation according to the model
Compare the current resources allocation and the optimal one
Adjust threshold of resource overload to each service
Global Resource Flowing Algorithm
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Multi-tiered resource scheduling vs. RAINBOW-NF in prototype hosting database, office and web services
Cases database Office Web CPU utilization Memory utilization
LRFaVM 6% 16% 1% 1% 1%
GRFA 9%(75% of the maximum margin)
10% -2% 1% 5%
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RAINBOW vs. Padala’s work in Eurosys07resources working
intervalthreshold improv
ementdegradation
improv-degrad
Padala’s CPU 10s Fixed 28% 41% -13% RAINBOW CPU&mem 1s(CPU), 5s
(memory)Auto adjusted
19% 2% 17%
Padala’s work only focuses on CPU reallocation among VMs within a server and uses fixed reallocation threshold according to experience.
RAINBOW focuses on both CPU and memory flowing not only among VMswithin a server but also among services, as well as automatically adjusts threshold of overload according to the time varying workloads of the hosted services.
The working intervals of RAINBOW are 1s for CPU and 5s for memory, which are much smaller than that of Padala’s work (10s). This implies that RAINBOW has faster response to the change of resource requirements by the hosted services. The hosted web-based services are interactive with sudden demands on resources. Thus such faster response results in better service performance too.
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Future Work
Practical problem of our algorithms:Extracting resource functions in resource flowing model automatically within a short period.
Optimizing overhead of algorithms:analyzing the potential and overhead caused by each tier of the multi-tiered resource scheduling.
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Prototype Introduction
RAINBOW
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The RAINBOW Monitor
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The RAINBOW Monitor
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Capacity Service Computing & Cloud Computing
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RAINBOW-Based Desktop Virtualization
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RAINBOW-Based Desktop Virtualization
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The Monitor on Desktop Virtualization
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The Key Technology on Desktop Virtualization
Transparent Desktop Technology
Server View Client ViewProcessing on transparent desktop
