FlexCloud: A Flexible and Extendible Simulator for Performance Evaluation of Virtual Machine Allocation

6.2 MB·English

by  Minxian Xu

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FlexCloud: A Flexible and Extendible
Simulator for Performance Evaluation of
Virtual Machine Allocation
Minxian Xu, Wenhong Tian, Xinyang Wang, Qin Xiong
5 (cid:70)
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n
aAbstract—Cloud Data centers aim to provide reliable, sustainable consideredinitsinfancy.Youseffetal.[19]introducea
Jandscalableservicesforallkindsofapplications.Resourceschedul- detailed ontology of dissecting Cloud into five main
3 ingisoneofkeystocloudservices.Tomodelandevaluatedifferent layers from top to down: Cloud application (SaaS),
2schedulingpoliciesandalgorithms,weproposeFlexCloud,aflexible Cloud software environment (PaaS), Cloud software
and scalable simulator that enables users to simulate the process
infrastructure (IaaS), software kernel and hardware
]ofinitializingclouddatacenters,allocatingvirtualmachinerequests
Cand providing performance evaluation for various scheduling algo- (HaaS), and illustrates their interrelations as well
Drithms. FlexCloud can be run on a single computer with JVM to as their inter-dependency on preceding technologies.
simulatelargescalecloudenvironmentswithfocusoninfrastructure From structure perspective, Cloud data center can be
.
sasaservice;adoptsagiledesignpatternstoassuretheflexibilityand regarded as a distributed network, containing many
cextensibility;modelsvirtualmachinemigrationswhichislackinthe
[ computing nodes, storage nodes, or network devices.
1 euxraistitoinngstaonodls;repprolavyidinegs.uCsoemr-fpriaernindglytionteexrifsatcinegsfsoimrcuulasttoorms,izFeldexcConlofiugd- Each node is composite of a series of resources such
as CPU, memory, network bandwidth and so on. In
vhascombiningfeaturesforsupportingpubliccloudproviders,load-
9balanceandenergy-efficiencyscheduling.FlexCloudhasadvantage this paper, we focus on Infrastructure as a service
8incomputingtimeandmemoryconsumptiontosupportlarge-scale (IaaS) in Cloud data centers, and proposing general
7simulations. The detailed design of FlexCloud is introduced and and flexible definition as well as model that could be
5performanceevaluationisprovided. used by various cloud providers.
0
An essential technology in Cloud datacenter is re-
.Index Terms—Cloud Data Centers; Resource Scheduling Algo-
1 source scheduling. One challenge problem related to
rithms;VirtualMachineAllocation;PerformanceEvaluation;Flexibil-
0
ityandExtensibility scheduling in Cloud data center is to consider alloca-
5
tionandmigrationofreconfigurablevirtualmachines
1
v:1 INTRODUCTION and integrated features of hosting physical machines.
Different from existing load-balancing scheduling al-
i
XWith various recent advancements in virtualization, gorithmsthatconsideronlyphysicalserverswithone
rlike Grid computing, Web computing, utility com- factor such as CPU, the new algorithms treat CPU,
aputing and related technologies, Cloud computing memory and network bandwidth integrated for both
obtains great development. Cloud computing aims physicalmachines(PMs)andvirtualmachines(VMs).
to provide both infrastructure and services on de- Besides that, real-time virtual machine allocation for
mand through the Internet or intranet [20], and its multiple parallel jobs and physical machines is taken
benefits can be concluded as hiding and abstraction into consideration. With the development of cloud
of complexity, virtualized resources and efficient use computing, the size and density of the cloud data
of distributed resources. Cloud computing allows the center become huge, and problems which need to
sharing, allocation and aggregation of software, com- be solved therewith. For instance, how to manage
putationalandstoragenetworkresourcesondemand. physical resources and virtual resources intensively
Currently, quite a few IT enterprises products, like and use them dynamically, to improve elasticity and
Amazon EC2 [4], Google App Engine [14], IBM blue flexibility which can improve service and reduce cost
Cloud [17] and Microsoft Azure [22] have shown and risk management; and how to help customers
their practice of emerging Cloud computing plat- build flexible, dynamic, and business growth adapt-
forms. Whereas there are many challenging issues ing infrastructure as well as ensure the sustainable
to be resolved [20] [1] [25], Cloud computing is still development in the future.
Because of the uncertainty of network environ-
Dr.Tian,Mr.WangandMissXiongareintheschoolofComputerSci-
ments, it is extremely hard to research widely for
ence,UniversityofElectronicScienceandTechnologyofChina(UESTC),
610054;Mr.XuisintheschoolofsoftwareengineeringofUESTC. all these problems in real Internet platform. In ad-
2
dition, the network conditions cannot be predicted [28] introduce techniques for virtual machine migra-
or controlled accurately, but affect the validation of tion and propose some migration algorithms. Zhang
strategies. A considerate way in research is develop- et al. [29] compare major load balancing scheduling
ing a simulation system, which supports visualized algorithms for traditional web servers. Singh et al.
modeling and simulation in large-scale applications [5] propose a novel load balancing algorithm called
in cloud infrastructure. Data center simulation sys- VectorDot for handling the hierarchical and multi-
tem can describe the application workload statement, dimensional resource constraints by considering both
whichincludesuserinformation,datacenterposition, servers and storage in Cloud computing. Doyle et al.
theamountofusersanddatacenters,andtheamount [18]proposeasystemnamedStratustodeterminethe
of resources in each data center. Using this informa- routine decisions for data center requests.
tion,datacentersimulationsystemgeneratesresponse Buyyaetal.introduceGridSim[23]toolkitformod-
requests and allocates these requests to virtual ma- eling and simulation of distributed resource manage-
chines. By using data center scheduling simulation ment for grid computing. Dumitrescu and Foster [7]
system, researchers can evaluate suitable strategies introduce GangSim tool for grid scheduling. Buyya
such as distributing reasonable data center resources, et al. [24] introduce modeling and simulations of
selecting data center to match special requirements, Cloud computing environments at application level,
reducingcosts,findingefficientschedulingalgorithms a few simple scheduling algorithms such as time-
and so on. sharedandspace-sharedarediscussedandcompared.
Themajorcontributionsofthispaperareasfollow- CloudSim [24] is one of Cloud computing simulators,
ing: which provides: modeling large-scale cloud comput-
• theproposalofanewcloudsimulator,FlexCloud, ing infrastructure; models for the data center, service
with light weight design to simulate cloud envi- agency, scheduling and distributing strategies; virtual
ronment; engines,whichishelpfultocreateandmanageseveral
• the design and implementation of a flexible and independent and collaborative virtual services in a
extendible architecture model that resource, re- data center node; switching flexibly between process-
questspecificationandschedulingalgorithmscan ingcoreswithspace-sharingandtime-sharing.Cloud-
be easily added; Analyst [6] aims to achieve the optimal scheduling
• the validation of the simulator, which has been among user groups and data centers based on the
carried out by comparing realistic data with ac- currentconfiguration.BothCloudSimandCloudAna-
tual results collected from Lawrence Livermore lystarebasedonSimJava[12]andGridSim[23].Also
National Lab [16] trace; CloudSimandCloudAnalysttreataClouddatacenter
• the performance comparison with CloudSim, asalargeresourcepoolandconsideronlyapplication-
whichshowsFlexCloudhasstrengthintimecost levelworkloads,maynotsuitableforInfrastructureas
and memory consumption. aservice(IaaS)simulationwhereeachvirtualmachine
The remainder parts of this paper are organized as as resource is considered to be requested and allo-
follows:section2describestherelatedworkonvirtual cated. A CloudSim-based simulation tool considering
machine allocation algorithms and compares existing DVFSenergymodelisproposedin[27].Kliazovichet
cloud computing simulators from several categories. al. propose an energy-aware simulation environment
At the end of this section, the major contributions of namedGreenCloudforClouddatacenters[11].Nunez
thispaperaregiven.Section3demonstratesthearchi- et al. [3] introduce a new simulator of cloud infras-
tectural model of the newly proposed simulator from tructure named iCanCloud using C++ and compare
severalaspects,includingitslayeredarchitecture,sce- the performance with CloudSim.
nario, datacenter modeling, VM requests modeling, Table 1 shows the comparison of some state-of-art
scheduling algorithms modeling, implemented per- cloud simulators as well as FlexCloud proposed in
formance metrics, VM migration modeling, schedul- this paper. We compare these cloud simulators from
ing process modeling and etc. Section 4 presents several categories.
implementation details and design patterns adopted Platform: CloudSim and FlexCloud are both imple-
inFlexCloud.Section5andsection6demonstratethe mented with Java, so they can be executed on any
validation and evaluation of FlexCloud respectively. machineinstalledJVM.BuiltinGridSimandSimJava,
Finally,thispaperendswiththebriefconclusionsand CloudSim is heavy to execute. MDCSim is written in
a discussion on future work. CSIM, as for GreenCloud and iCanCloud, they are
based on NS2 and OMNET respectively.
2 RELATED WORKS
Language: The languages implemented with the
A mount of research has been conducted in resource simulatorsarerelatedtotheplatforms.CloudSimand
schedulingalgorithms,whicharesignificantforcloud FlexCloud are implemented with Java, MDCSim can
data centers. Mastroaianni [8] et al. present a self- be implemented with C++ and Java, and GreenCloud
organizing and adaptive approach for the consolida- needs combining C++ and OTcl, which is difficult for
tion of VMs on CPU and RAM resources. Wood et al. developers.
3
TABLE1
SummaryofCloudSimulators
Items CloudSim MDCSim GreenCloud iCanCloud FlexCloud
Platform any CSIM NS2 OMNET,MPI any
ProgrammingLanguage Java C++/Java C++/OTcl C++ Java
Availability OpenSource Commercial OpenSource OpenSource OpenSource
GraphicalSupport Limited(ViaCloudAnalyst) None Limited(ViaNam) Full Full
PhysicalModels None None Limited(ViaPlug-in) Full Full
Modelsforpubliccloud None None None Amazon Amazon
SupportforParallelexperiments No No No Yes No
SupportforEnergyConsumptionModel Yes Yes Yes No Yes
SupportforMigrationalgorithms Yes No No No Yes
Availability: Only MDCSim is commercial, and FlexCloudundermulti-datacenterenvironment.They
other four simulators are free or open-source. Flex- would use FlexCloud to explore suitable algorithms
Cloud can be fetched from [13]. for their company applications.
Graphical support: MDCSim doesnt support inter-
face operations. The original CloudSim support no 3 THE ARCHITECTURAL MODEL OF FLEX-
graphicalinterface,butwithCloudAnalyst,thegraph-
CLOUD
ical interface are supported. However, full support is
not provided in CloudAnalyst, in a whole scheduling Fig.1 shows the overview architecture of FlexCloud
process, only the configurations and results can be with layered components. The top layer is Client
presented. So we label it limited, the same reason Layer that provides the interface for user to config-
for GreenCloud. FlexCloud and iCanCloud support ure requests properties and have results feedbacks
whole scheduling process to be showed on the inter- from lower layers. At this layer, a GUI implemented
faces. with Java Swing supports user to configure algo-
Physical models: iCanCloud and FlexCloud pro- rithm types, set PM and VM specifications and select
vide detailed simulation for physical analogs for the scheduling algorithms. After all settings are com-
scheduling. GreenCloud needs to use a plug-in to pleted,thedefinedconfigurationswouldbesubmitted
simulate that. to lower layer and a sequence of scheduling steps
Modelsforpubliccloudproviders:BothiCanCloud wouldbeprocessed.Comparisondiagramsaswellas
and FlexCloud use the model suggested by Amazon, result outputs would be sent as feedback to Client
in which physical machine and virtual machine spec- Layer. At lower layer, a Requests Broker is imple-
ifications are pre-defined. mented at Broker Layer acting as a mediator between
Parallel experiments: Supporting for multiple ma- Client Layer and Scheduler Layer. This Layer is re-
chines running the experiments together is a main sponsible for verifying the inputs from Client Layer
feature of iCanCloud and that feature is under de- and transforming the settings into recognized com-
velopment. As for FlexCloud, we are working to mands at Scheduler Layer. For instance, the number
implement that function as well. of VM requests submitted from Client Layer would
Power consumption model: Except for iCanCloud, be written into a configuration file, which could be
otherfoursimulatorscansupportpowerconsumption read in the process of scheduling at Scheduler Layer.
modeling. Scheduler Layer implements the core functions for
Migrationalgorithm:CloudSimandFlexCloudsup- FlexCloud system. At this layer, the scheduling pro-
port migration algorithms, while other 3 simulators cess is defined: VM Requests Generation component
haven’t supported that. generatestheVMrequestswithconfiguredproperties
In our teaching practice in our university, we have on user interface; Datacenter Scheduler component
adopted CloudSim, a mature simulator, as a teaching schedules the particular algorithms to allocate VMs
tool assisted, but according to the students feedback, to corresponding PM according to algorithms; VM
CloudSim is a bit complex to use and heavy to exe- RequestsAllocationcomponentmanagestheallocated
cute. That complexity is also a feature of iCanCloud. VMs, including checking the allocation conditions
AsforMDCSim,acommercialtool,isnotappropriate and removing VMs at the end of their lifecycles.
for researching. Apart from that, its not easy to use At bottom layer, Resource Layer contains a Resource
several languages together in GreenCloud, since it is Management component providing resource that VM
implemented with C++ and OTcl. requests require and supporting services for higher
The main contribution of FlexCloud lies in that it levels. Besides the component, the physical resource,
is implemented with light weight design, flexible to such as servers, network and storage are resources of
extendaswellaseasytostart.Besidesthebenefitsfor the whole system.
teaching,wealsocooperatewithacompanyresearch- Fig.2showsanapplicationscenariowithFlexCloud.
ing in resource scheduling to boost the functions of This figure shows the three main components: user,
4
Fig.1. LayeredFlexCloudarchitecture
TABLE2
FlexCloudschedulercenterandothercomputingcen-
3typesofphysicalmachines(PMs)suggested
ters.TheFlexCloudschedulercenterisresponsiblefor
the following main tasks: (1) accepting the VM re-
PMPoolType ComputeUnits Memory Storage
quests sentby users; (2) managingcomputing centers
Type1 16units 30GB 3380GB
thatinservice;(3)findingavailablecomputingunitto Type2 52units 136GB 3380GB
allocate requests; (4) sending feedback information to Type3 40units 14GB 3380GB
users.ComputingcentersrepresentapoolofPhysical
Machines (PMs) or Virtual Machines (VMs), each one
configured with a pre-defined specification such as
CPU, memory and storage. Users are represented as bedynamicallyset.Thetypeandpropertyvalues,like
component that submits a set of jobs to be allocated CPU, memory, storage and power, are recorded in a
tospecificPMincomputingcenter.Thesesubmissions configuration XML file (in Fig. 3), which would be
submitted directly to the FlexCloud scheduler center. loadedintosystem.Becausethesepropertyvaluesare
Then, the requests are managed by this module to in XML file, modification can be easily done either
be allocated to specific PM in the corresponding data for exactly value or new added property elements.
center. After all requests have been processed, a feed- For instance, if more types of PMs are needed, the
back report would be sent back to the user. pair < pminfo > type − id < pminfo > could be
created and other values of this type PM could so
3.1 ModelingthedatacenterinFlexCloud also be added. Besides the load balance algorithms,
we also implement energy-saving algorithms that
Fromcomputingresourcepointofview,adatacenter
contain a new property named power consumption.
consists of a number of physical servers (PMs), net-
This property is added in the configuration XML
work devices, storages and other related equipment.
file and corresponding methods are added in class
A PM contains several kinds of resource, like CPU,
PhysicalMachine.Thecorrespondingmethodsinclass
memory, storage and bandwidth, etc. Before VM re-
PhysicalMachine are responsible for accessing these
quests are coming, the PMs are at the state of turned-
property values.
on, which means the class of Physical Machine is
instantiated in FlexCloud. The number of instances More detailed information related to comparison
depends on the number of PMs would provide ser- indices can be found in Section 3.3. In datacenter
vices. model, the left resource capacity decides whether a
InTABLE2,the3suggestedtypesofheterogeneous VM request can be allocated to that PM. At initializa-
PMsinFlexCloudarelisted,andtheconfigurationcan tionstage,PMhasafullcapacityresourcetoofferser-
5
Fig.2. AscenarioarchitecturewithFlexCloud
time,whichcanbetreatedasasecondoraminutethat
arequestisin.Forinstance,VM2occupiestimeslot3
to 5, so lifecycle of VM2 is 3 slots. The value 0.0625 is
proportionofresourceoccupation,meaningthatVM2
wouldoccupy6.25%resourceofthePMitisallocated
to, during time slot 3 to 5. In our model, several VM
requests can share the capacity of the same PM at the
same time slot only if the capacity is enough.
0.0625 0.0625 0.0625
Fig.4. anexampleofVMrequests
TABLE 3 shows the corresponding CPU, memory,
Fig.3. PMspecificationinXMLfile storage values for different VMs. Also for extensi-
ble reason, these property values are also recorded
into a configuration XML file. Once a VM request is
allocated to a PM, the left resource capacity would
vices.Eithertheallocationorremoveoperationwould
be decreased by the value of that request, and the
update the available capacity value and influence the
capacity is increased back when request is released.
later requests allocation.
In FlexCloud, several VM requests generation ap-
proaches have been implemented, in which requests
3.2 ModelingVMrequestsinFlexCloud
can be generated in Poisson, Normal and Random
We use a simple example to show how VM re- distributions. When the specific distribution is se-
quests are modeled in FlexCloud in Fig. 4. Slots lected, the start time or duration of the generated
#1,#2,...,#6 represent the time slots in discrete requests would follow the distribution. In section 5,
6
TABLE3
requests’ allocation (line 11 to 12) after their end-time
8typesofvirtualmachines(VMs)inAmazonEC2
expired. This example is based on singe data center,
while under multiple data centers, the index genera-
ComputeUnits Memory Storage VMType
tion process would be involved with data center id
1units 1.7GB 160GB 1-1(1)
4units 7.5GB 850GB 1-2(2) generation, rack id generation and PM id generation
8units 15GB 1690GB 1-3(3) rather than only PM id generation.
6.5units 17.1GB 420GB 2-1(4)
13units 34.2GB 850GB 2-2(5)
26units 68.4GB 1690GB 2-3(6)
5units 1.7GB 350GB 3-1(7)
20units 7GB 1690GB 3-2(8)
we would show the data collected from different
distributions. Moreover, its available for FlexCloud to
import requests data from file in the Generate VMs
step (see section 3.6), which means it can be tested
under realistic data.
3.3 ModelingSchedulingAlgorithmsinFlexCloud
Four kinds of scheduling algorithms are provided
in FlexCloud based on scheduling goals and request
types.Forrequesttypes,schedulingalgorithmscanbe
dividedintoonlinealgorithmsandofflinealgorithms,
Fig.5. ThepseudocodeofRandomalgorithm
thedifferenceliesinwhethertherequestsinformation
isallknownbeforescheduling.Requestswouldcome
andbeoperatedonebyoneinonlinealgorithm,while ThealgorithmprocessofR-RandLSisquitesimilar
requests sequence can be adjusted by processing time to Random except the way the index is generated for
or end time because all requests information have PMs or VMs. In R-R algorithm, index generation is
beencollectedbeforeschedulinginofflinealgorithms. in a round robin way while the index refers to the
Another division principle is via goal: we consider PMwiththeleastaverageutilizationinLSalgorithm.
load balancing and energy saving in FlexCloud. The more complex algorithms, Post Migration algo-
When comparing the effects of different algo- rithms and Prepartition Algorithm, that considering
rithms, the scheduling process would be same ex- migrations operations introduced in section 3.5 could
cept that the scheduling algorithms are different. For also be modeled based on these principles. As for
online load balancing comparison, Random, Round- offline load balancing algorithms, a procedure of re-
Robin (Round), List Scheduling (LS) algorithms have quest processing should be added before line 4. The
been implemented. Under the layered architectural processing procedure may change the requests order
model and related design pattern (introduced in byprocessingtimeorendtimeorotherfeatures.Also,
later section), new created algorithms can be added theprocessofonline/offlineenergy-savingalgorithms
to scheduling algorithm library, without influencing taken is similar to online/offline load balancing algo-
other existed algorithms. rithms.
WetaketheRandomalgorithm,oneofthesimplest
algorithms, as an example to show scheduling algo-
3.4 PerformanceMetricsinFlexCloud
rithm could be modeled, mapped and extended in
FlexCloud. Fig.5 shows the pseudo-code of Random In this section, we introduce the major performance
algorithm.Afteradatacenterschedulerhasinitialized metrics we used in FlexCloud:
the PMs and VM requests, Random algorithm would For load balancing algorithms:
randomlygenerateanindexintherangeof0andM-1 Average utilization: Each PM would have the
(line3)forPMs.ThenVMrequestwillbeallocatedto utilization value in scheduling process, and average
the PM with generated index (line 5), if allocation is utilization is the arithmetic average value of all PMs
successful by checking whether the PM has available in the data center;
resource, then allocated PM needs updating its left PMresource:PM (i,PCPU ,PMem ,PStorage ),iis
i i i i
capacity (line 7), another index would be generated the index number of PM, PCPU ,PMem ,PStorage
i i i
if allocation is failed and the VM request should be are the CPU, memory, storage capacity of that a PM
allocated again with a new index (line 8-9). As VM can provide.
requests have lifecycles, the VM requests should be VM resource:
released from hosting PM to prepare for other VM VM (j,VCPU ,VMem ,VStorage ,Tstart,Tend), j
j j j j j j
7
is the VM type ID, VCPU ,VMem ,VStorage are load balancing algorithm for the new situation as
j j j
the CPU, memory, storage requirements of VM , follow:
j
Tstart,Tend are the start time and end time, which Skewofcapacity makespanisdefinedastheminimal
j j
are used to represent the life cycle of a VM. capacity makespanovermaximalcapacity makespan
Time slot: we consider a time span from 0 to T be on all machines (referring to equation (6)):
divided into parts with same length. Then n parts (cid:80)
min c t
can be defined as [(t1−t0),(t2−t1),...,(tn−tn−1)], skew(capacity makespan)= (cid:80)j∈A(i) j j (7)
each time slot Tk means the time span (tk−tk−1). max j∈A(i)cjtj
Average CPU utilization of PM during slot 0 and
i where c is the capacity (for example CPU) requests
T : j
n (cid:80)n (PCPUTk ×T ) ofVMj andtj isthespanofrequestj (i.e.,thelength
PCPUiU = k=0 (cid:80)n Ti k (1) of processing time of request j).
k=0 k Forenergysavingalgorithms,followingindicesare
And memory PMemU and storage PStorageU uti- provided:
i i
lization of both PMs and VMs can be computed in 1) The total number of PMs turned-on during the
the same way. Similarly, average CPU utilization of a scheduling;
VM can be computed. 2) Rejected number of VM requests: VM requests
Integrated load imbalance value ILB of PM . The which cannot be served by the data center resources;
i i
variance is widely used as a measure of how far a set 3)Totalenergyconsumption:theenergyconsumption
of values is spread out from each other in statistics. of all PMs (including VMs allocated on them); a
Using variance, an integrated load imbalancing value less total energy consumption value reflects a better
ILB of server i is defined energy saving effect for a given set of requests.
i
(Avg −CPUA)2 (Avg −MemA)2 In [1], authors found that CPU utilization is typ-
ILBi = i 3 u + i 3 u ically proportional to the overall system load, and
proposed a power model defined in equation (8):
(Avg −StorageA)2
+ i u (2)
3 P(u)=kP +(1−k)P u (8)
max max
where
where P is the maximum power consumed when
PCPUU +PMemU +PStoargeU max
Avg = i i i (3) the server is fully utilized; k is the fraction of power
i 3
consumed by the idle server (studies show that on
and CPUA,MemA,StorageA are respectively the av- average it is about 70%); and u is the CPU utiliza-
u u u
erage utilization of CPU, memory and storage in a tion. This paper focuses on CPU power consumption,
Cloud data center. which accounts for main part of energy comparing
ILB is applied to indicate load imbalance level to the other resources such as memory, disk storage
i
comparing utilization of CPU, memory and network andnetworkdevices.InFlexCloud,weusethepower
bandwidth of a single server itself. model defined in (8). Equation (8) is further reduced
Makespan: is as same as traditional definition, and to (9):
therefore the capacity makespan of all PMs can be P =P +(P −P )u (9)
min max min
formulated as below:
where P is the power of given PM when its CPU
min
capacity makespan=max(Li) (4) utilization is zero (the PM is idle without any VM
i
running). In real environment, the utilization of the
Load efficiency (skew of makespan): is defined as the
CPU may change over time due to the workload
(minimal average load divided by maximal average
variability. Thus, the CPU utilization is a function of
load) on all machines:
time and is represented as u(t). Therefore, the total
skew(makespan)= mini(Li) (5) energy consumption by a PM (Ei) can be defined as
max (L ) an integral value of the power consumption function
i i
over a period of time as in (10):
where L is the load of PM i. Skew shows the load
i
balancing efficiency to some degree. (cid:90) t1
Capacity makespan:InanyallocationofVMrequests Ei = P(u(t))dt (10)
toPMs,wecanletA(i)denotethesetofVMrequests t0
allocated to machine PM , under this allocation, ma- If u(t) is constant over time, for example average uti-
i
chine PMi will have total loads, lizationisadopted,u(t)=u,thenEi =P(u)×(t1−t0).
(cid:88) Thetotalenergyconsumptionofaclouddatacenter
L = c t (6)
i j j is computed as (11):
j∈A(i)
n
(cid:88)
Based on the above definitions and equations, we E = E (11)
DC i
have developed another metric, capacity skew on
i=1
8
It is the sum of energy consumed by all PMs. Notes thePMtobehigherthantheup-threshold.Theremay
that energy consumption of all VMs on PMs is in- be still some VMs left in the list, finally the algorithm
cluded. allocates the left VMs to the PMs with the lowest
Also confidence intervals can be calculated for dif- capacity makespan until the list is empty.
ferent metrics as follows: Let x ,x ,x ,...,x be the Capacity makespan Prepartition Algorithm: novel
1 2 3 n
calculated metrics (such as IBL and E values work proposed by ourselves. For a given set of VM
tot cdc
etc.) from n times of repeated simulations. Then the reservations,letusconsidertherearemPMsinadata
mean is center and denote OPT as the optimal solution for a
n
1 (cid:88) given set of J VM reservations. Firstly define
x = x (12)
mean n i
i=1 1 (cid:88)J
P =max{maxJ CM , CM }≤OPT (15)
and the standard deviation s is 0 j=1 j m j
j=1
(cid:115)
s= (cid:80)ni=1(xmean−xi)2 (13) P0 isalowerboundonOPT.TheCapacity makespan
n−1 Prepartition algorithm is introduced in detailed in
[32]. It firstly computes balance value by equation
andtheconfidenceintervalat95%confidenceisgiven (15), defines partition value (k) and finds the length
by of each partition (i.e. (cid:100)P /k(cid:101), which is the max time
s s 0
(xmean−1.96√n,xmean+1.96√n) (14) length a VM can continuously run on a PM). For
each request, Prepartition equally partitions it into
multiple (cid:100)P /k(cid:101) subintervals if its CM is larger than
0
Abovearebasicthemetricsthatalreadyimplemented
(cid:100)P /k(cid:101), and then finds a PM with the lowest average
0
inFlexCloud.Othermetricscouldalsobeincludedfor
capacity makespan and available capacity, and up-
further research.
datestheloadoneachPM.Afterallrequestsareallo-
cated,thealgorithmcomputesthecapacity makespan
3.5 Modeling Virtual Machine Migrations in Flex- ofeachPMandfindstotalpartition(migration)num-
Cloud bers. For practice, the scheduler has to record all
possible subintervals and their hosting PMs of each
There is lack of virtual machine migration modeling
request so that migrations of VMs can be conducted
in existing simulation tools. In [32], the detailed algo-
in advance to reduce overheads.
rithmsaboutmigrationareintroducedandcompared.
FlexCloud therefore can evaluate the performance of
In this section, we provide brief introduction to vir-
differentmigrationalgorithms;theevaluationprocess
tual machine migration modeling in FlexCloud. The
is similar to allocation algorithms.
key difference from allocation is that the migration
objectives and the choose of resource and destination
3.6 TheSchedulingProcessinFlexCloud
PMs.Twotypicalmigrationalgorithmsareintroduced
in FlexCloud: The major steps of the scheduling process in Flex-
Post Migration algorithm: Firstly, it processes the Cloud are as followings:
requests in the same way as LPT (Longest Processing 1). Booting PMs: it loads the configuration XML file
Timefirst)does.Thentheaveragecapacity makespan containing PM specifications set by user from user
of all jobs is calculated. The up-threshold and interface. After needed information is collected, in-
low-threshold of the capacity makespan for the stances of PMs are created to prepare for VM allo-
post migration are calculated through the average cation.
capacity makespan multiplied by a factor (in this 2). Generating traces (VM requests): it loads the con-
paper we set the factor as 0.1, so the up-threshold is figuration XML file containing VM specifications and
averagecapacity makespanmultipliedby1.1andthe VM traces from user interfaces.
low-thresholdismultipliedby0.9).Offcoursethefac- 3). Comparing scheduling algorithms: two or more
tor can be set dynamically to meet different require- iterators would collect the compared algorithms and
ments; however, the larger the factor is, the higher compared indices. All selected algorithms will be
imbalance is. A migration list is formed by collecting compared and corresponding indices are collected.
the VMs taken from PMs with capacity makespan 4)Outputresults:thecomparisonresultsareoutputed
higher than the low-threshold. The VMs would be in both text or diagrams format.
taken from a PM only if the operation would not For building a more flexible system, the scheduling
lead the capacity makespan of the PM to be less processonlydefinesthebasicframeworkprocessand
than the low threshold. After that, the VMs in the customization may be improved based on this pro-
migration list would be re-allocated to a PM with cess.BeforebootingPMs,thePMspecificationscanbe
capacity makespan less than the up-threshold. The modified in configuration file. As for generating VM
VMs would be allocated to a new PM only if the requests traces, besides the configuration file, more
operation would not lead the capacity makespan of VM requests creation methods can be implemented.
9
metrics are flexible and extendable to add in;
currently load-balancing and energy-efficiency
scheduling algorithms are considered.
(5) Virtual machine migration is modeled, this is still
lack in current simulation tools.
4.2 DesignPatternsinFlexCloud
Fig.6. SchedulingprocessinFlexCloud
Various algorithms can be developed in algorithm
scheduling process and other results format may
be adopted for better visual effects. Fig. 6 shows a
Fig.7. DecoratorpatterninFlexCloud
scheduling process combing user interface configura-
tions and basic scheduling process.
FlexCloud has adopted some design patterns to
meet the extendible goal. Fig.7 shows decorator pat-
4 IMPLEMENTATION OF FLEXCLOUD
tern to meet the requirement that requests gener-
In this section, we will introduce the detailed imple-
ation approaches may differ. Class CreateVMDec-
mentation of FlexCloud from design patterns’ point
oratorA and CreateVMDecorateB extend the creat-
of view. The design principles are mainly aiming
eVM() method of CreateVM and add a new behavior
at satisfying agile system goals with flexibility and
method. With this pattern, when new requests gen-
extendibility.
eration approaches are needed, we can rewrite the
method addedBehavior(). Under this method, func-
4.1 MainFeaturesinFlexCloud tion of class CreateVM can be dynamically added
Considering design principles, FlexCloud mainly has or deleted. For instance, new resource is needed,
following novel features: resource collection codes can be put in the addedBe-
(1) FlexCloud is built on Java platform and can be havior()methodratherthanchangetheexistingcodes
run on a single computer installed JVM to simulate or add new classes.
large scale cloud infrastructure as a service (IaaS).
A computer with 4 GB memory can simulate larger
scale applications. We test the condition when the
Java environment would throw an OutOfMemory
exception by increasing requests gradually. With a
4 GB memory computer, experiments can simulate
scheduling process of more than 100,000 requests. We
have extended our tests with computers with 2GB
memory, that configuration can simulate requests
ranging from 25,000 to 50,000.
(2) A user-friendly GUI is provided and lots of
customized configurations can be set to satisfy
Fig.8. AbstractFactorypatterninFlexCloud
various simulation assumptions. The basic operation
includes: select algorithm type, set VM numbers, set
average duration, set start time, set total number of Fig.8 shows the Abstract Factory pattern to meet
PMs, select comparison algorithms and indices. Of the requirement of different compositions of requests
course, without GUI, user can also simulate a cloud generation approaches and scheduling algorithms.
datacenter and scheduling process in .java class file An instance of LoadBalanceFactory would combine
as well. a request generation approach from CreateVM and
(3) A scheduling process framework is defined, each scheduling algorithms from generalization of Allo-
step of process can be extended easily in agile style. cateAlgorihm. These different combinations can pro-
(4) New scheduling algorithms and performance duce diverse scheduling process, like set requests
10
TABLE4
order byprocessing timeand scheduledby asubtype
TheoreticalandsimulationresultscomparisonofLS
of class OnlineAlgorithm. Adopting this design pat-
algorithm
tern can avoid fixed composition and gain a better
extendable effect.
LSIndices Theoretical Simulation
AverageUtilization 0.5 0.5
ImbalanceDegree 0.0 0.0
Makespan 0.5 0.5
Skew(makespan) 1 1
Capacity makespan 50 50
Skew(capacity makespan) 1 1
rithms and indices should extend from their base
class. The strength for this design pattern is also
Fig.9. StrategypatterninFlexCloud a favorable extendibility. It’s intelligent when new
algorithms and indices are implemented, the Iterator
would compare results only if they are appended to
Strategy pattern in Fig.9 defines a series encapsu-
it.
late scheduling algorithm classes: Random, Round-
Robin, and OLRSA [18] for OnlineAlgorithm and
LPT(LongestProcessTime)etc.forOfflineAlgorithm, 4.3 CommunicationsBetweenEntities
which can be substituted with each other, enabling
schedulingalgorithmsbeindependentonthechanges
fromusers.Withstrategydesignpattern,whenanew
algorithm is joined, only allocate() method in new
joined algorithm should be implemented. After that,
thenewjoinedalgorithmcanworkassameasexisting
algorithms.
Fig.11. Sequencediagramofbasicprocess
Fig.11 depicts the flow of communication among
important FlexCloud entities. At the beginning of
the simulation, a DataCenter entity sends necessary
messages that BootPM entity needs to start PMs pro-
viding services in datacenter. CreateVM entity would
also accept messages it needs to create VM requests.
AlgorithmIteratorandIndexIteratorentitiesacttorun
scheduling algorithms and send calculated indices
values back to DataCenter entity.
Thecommunicationflowdescribedaboveisabasic
flow in a simulated experiment. Some variations in
Fig.10. IteratorpatterninFlexCloud
this flow are possible depending on the scheduling
process. For example, before bootPM() and creat-
Fig.10 shows the Iterator pattern to meet the re- eVM(), the message sent by a datacenter should be
quirement of algorithm and indices results compar- verified.
ison in data centers. After user have selected the
comparison indices and algorithms on user interface,
5 VALIDATION OF FLEXCLOUD
the selected algorithms and indices would be added
toseparatelist,atthesametime,Iteratorforalgorithm To validate the accuracy of FlexCloud, we have de-
and index, Index Iterator and Algorithm Iterator, signed some test cases to compare the theoretical
would be generated. When outputting results, the results and simulation results. In this section, we use
Iterator would schedule algorithms in Iterator one LS (List Scheduling), LPT(Longest Processing Time
by one and output indices results with showIndex() First), EDF(End-time Decreasing First) algorithms to
method in order. To satisfy the Iterator, both algo- compare theoretical and simulation results.