Tenant-based resource allocation model for cost-effective scaling Software-as-a-Service applications over cloud computing infrastructure

Tenant-based resource allocation model for cost-effective scaling Software-as-a- Service applications over cloud computing infrastructureDissertation DefensePh. D. in Information Technology and Communications Program
Javier MijailEspadasPech
Asesor: Dr. Arturo Molina

6/9/2011
2
Tecnológico de Monterrey –DTC -2011
Agenda
•Concepts: Cloud computing, SaaS, multi-tenancy
•Background
•Premise, hypothesis and objectives
•Research methodology
•Problem measurement
•Proposed solution
•Results analysis
•Conclusions, contributions and future work
•Thesis’ outcomes

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Tecnológico de Monterrey – DTC - 2011
The Cloud

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Cloud Computing
•Computing resources are being transformed into a model consisting of services that are commoditized and delivered in a similar way to traditional utilities such as water, electricity or gas.
•One of the computing paradigms that have promised to deliver this utility computing vision is known as Cloud Computing.
•Cloud Computing provides on-demand access to computational resources (commonly virtual machines) which together with pay-per-use business models, enables application providers seamlessly scaling their services.

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Cloud computing layers
Applications
(Software as a Service)
Abstract Services
(Platform as a Service)
Flexible infrastructure
(Infrastructure as a
Service)
Service
Service
Service
Infrastructure
Interface
Development
Deployment
Data Storage
Virtualization
OS
Hardware
Cloud Actors
Service Users
Service Providers
Cloud Layers
Salesforce.com
Microsoft Live Office
Cisco
Webex
Google Apps
Cloud Services
Amazon S3
Microsoft Azure
Google SDK
Amazon Elastic Cloud
Masso
Hosting Cloud
Virtualization
OS
Hardware
Virtualization
OS
Hardware

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Software-as-a-Service
•SaaS is a software delivery paradigm where the software is hosted off-premise, developed by service providers and delivered via Internet and the payment mode follows a subscription model.
•IDC estimates customers spending on SaaS solutions to increase to $14.8 billion by 2011.
•In order to achieve higher profit margin by leveraging the economics of scale, an important requirement for SaaS applications is the support of multiple tenants.

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SaaS multi-tenancy
•A tenant is a customerthat uses or provides a SaaS application.
•SaaS provider offers the same instance of a SaaS application to multiple tenants.
•Multi-tenancy means that each tenant can interact with the application as if it were the unique owner of the application.
•In particular a tenant cannot access or view the data of another tenant.

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Background: Large-scale
•Applications deployed on the Internet (i.e. SaaS) are immediately accessible to a vast population of potential users.
•They tend to experience fluctuating and unpredictable load, especially due to events such as breaking news (e.g., 9/11) and sudden popularity spikes (e.g., the "Slashdot Effect").
•Facebook dynamically generates more than two billion pages per day resulting in over 23,000 page views a second .
•Image-processing Animoto web application experimented viral growth that caused to go from ten to more than 3500 servers in only three days.
Worker servers on Amazon EC2

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Background: Resources
•For Internet software providers, having the power to scale up or down an application to only consume and pay for the resources that are required at certain point in time is an attractive capability and if done correctly it will be less expensive than running on traditional hosting.
•However, cost-effective scalability is not achieved just by deploying large-scale applications over pay-per-use cloud infrastructures, and idle processes and not used resources are wasted but charged to application providers.

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Background: Problem
•Real elasticity in pay-per-use cloud models is not achieved yet.
•Over provisioning effect happens due the resource underutilization: even if peak loads are successfully anticipated, resources are wasted (light gray areas).
•Under provisioning occurs when potential revenue from customers (dark gray areas) is lost due poor performance (saturation).
Pay-per-use resourcesReal demandExpected demandUnder provisioning (saturation) Over provisioning (underutilization)

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Premise
•SaaS applications give an opportunity to tackle the former problem due their multi-tenancy architecture.
•Due this multi-tenant nature of SaaS applications it is possible to create on-demand resources and distribute workload for each tenant within pay-per-use cloud infrastructures.
•SaaS platforms can take advantage of tenant- aware parallelism and work distribution by using cloud computing platforms.

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Hypothesis
•Is possible to reduce over and underutilization of virtualized resources in order to obtain cost-effective scalability for Software-as-a-Service applications within a cloud computing pay-per-use infrastructure by implementing tenant-based resource allocation mechanisms such as isolation, virtual machine creation and load balancing.

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Objectives
•To design a resource allocation model for scaling SaaS platforms and their applications over cloud computing platforms in order to create cost-effective scalability.
–Analyze SaaS and cloud architectures.
–Define workload mechanisms.
–Develop monitoring mechanisms.
–Define mechanisms for tenant-level isolation.
–Create algorithms for virtual machine allocation.
–Design a load balancer.
–Gather, consolidate and analyze results.

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Research Methodology
•Action-Research (AR) to implement actions and learning cycles from previous results.
•Two AR cycles, starting diagnosing the initial problem and finishing with the measuring of such problem within a test bed infrastructure for the first cycle.
•Second cycle starts by designing the solution (resource allocation model) and it outcomes an evaluation of this solution against former results through a statistical analysis.

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Research Methodology
DiagnosingAction PlanningAction TakingEvaluationAction PlanningAction TakingEvaluationLearningBackground (Chapter 1) Problem Statement (Chapter 1) Literature Review & State-of-art (Chapter 3) Over and Underutilization Measurements (Chapter 7) Cloud Resources: Concept & Measurement (Chapter 4) Proposed Model (Chapter 8)SaaS Platform: Design andImplementation (Chapter 5) Test Bed Architecture (Chapter 6) Tenant-based Isolation (Chapter 8) Tenant-based VM Allocation (Chapter 9) Tenant-based Load Balancing (Chapter 10) Result Analysis(Chapter 11) Conclusions and Future Work(Chapter 12) Phase 2Phase 1

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LiteratureReview
Researcharea
Section
Review
CloudComputing
3.2Cloudcomputing
Conceptsaboutcloudcomputing,itslayersanddefinitions.
Software-as-a-Service
3.3Software-as-a-Service
Software-as-a-Service(SaaS),itsimpact,characteristicsandimplementations.
SaaSplatformsfortestbed
3.4SaaSMulti-tenancy
Howmulti-tenancyisimplementedcurrentlyandtheexistenttenant-basedpatterns.
Cloudplatformsfortestbed
3.5EucalyptusCloudPlatform
Eucalyptusplatformisanalyzedanddetailed.
Virtualizationforcloudcomputing
3.6Virtualization
3.7VirtualizationModelsforCloudComputing
Willbedetailedhowvirtualizationmodelsfitswithinacloudenvironment.
Monitoringandmeasuringincloudcomputing
3.8.1Cloudresourcemonitoringandmeasuring
Measuringtheproblemisanimportantstepintheresearchmethodology,thereforeisanalyzedrelatedworktomonitoringcloudenvironments.
Isolationmodelsforcloudcomputing
3.8.2ToolkitforSaaSgranularityandisolation
SaaSisolationestablishesthebasemechanismtoseparateexecutionfromtenants.Thissectionwillpresentarelatedworkonthisproblem.
Virtualmachineallocationforcloudcomputing
3.8.3VirtualMachineAssignment
Relevantresearchandworkistheallocationofvirtualmachinesasthesearebasicunitsforpay-per-useresource.
Balancingandscaling
3.8.4Profile-basedjust-in-timescalability
3.8.5AutoScalingfromAmazonWebServices
Finally,itispresentedinSection3.7relatedworkforjust-in- timescalabilityandprofile-basedworkloadbalancing.

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Problem measurement
•How cloud computing resources are defined and measured?
•What is under and over resource provisioning?
•How they can be gathered and measured in virtualized environments such as cloud computing?
•Diagnosing: is important to measure resources within virtual machines (CPU, Java heap memory).
•Action planning: to deploy traditional scaling within cloud computing

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Traditional scaling: Load balancing
•The higher level of granularity for cloud computing resources is the virtual machine.
•Each virtual machine or cloud instance has its own capabilities (CPU, memory, storage, etc.).
•When running parallel servers such as Apache Tomcat, a Web Server is required on top to distribute the workload over the underlying server instances.

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Traditional scaling in cloud computing
•For the application server instances (Tomcat) is recommended to use large VM instances.
VMTomcatDatabaseCloud Platform (IaaS) VMTomcatVMTomcatVMTomcatVM = Virtual MachineSaaSPlatform

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Distributed metering
•JConsoleaccesses several core monitoring and management functionalities provided by the Java platform.
•Java Management Extension (JMX) Remote framework connects to remote Tomcat processes and gather all related resource consumption information about them.
•SIGAR (stands for System Information Gatherer And Reporter) API provides a portable interface for gathering system information.

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Distributed monitoring
•Resource Consumption State (RCS) is composed by the metering of CPU and heap memory in different time periods, not only for individual application servers, instead the measuring represents the status of the whole deployed cluster.
VMTomcatVMTomcatVMTomcatVMTomcatVMTomcatRCSMonitor

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Overutilization (saturation)
• For overutilization definition is
used the term of point of
exhaustion.
• Point of exhaustion for cloud
computing can be defined by the
maximum useful work
(throughput) that can be executed
on a single virtual machine.
• When resource utilization
skyrockets, throughput generally
declines.
• throughput = (number of requests)
/ (total time)
M1 M2 M3
Resource utilization
Throughput
T1
R1
R2
T2
T3
R3

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Underutilization (resource wasting)
•The quantity of underutilized resources for a set of VMs is the number of virtual machines that can be released by allocating their resources within another VM.
•Each VM is evaluated against the rest at certain point time.
•In this dissertation, this number is obtained through a knapsack (bin packing) approach.
•The knapsackfunction returns the maximum number of VMs that can be released because it maximizes resource availability and gives low weight to VMs with low usage.
40 % usedTotal VM1 memoryThreshold or maximum allowed (70%) 10 % usedTotal VM2 memory20 % usedTotal VM3 memory65 % usedTotal VM4 memory

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SaaS platform
•Diagnosing: is important to measure resources within virtual machines (CPU, Java heap memory).
•Action planning: to develop and deploy a SaaS platform with traditional scaling within cloud computing
•(1) multitenant-supporting large number of tenants/subscribers.
•(2) single version-single version per application and this one is available and shared among all the application users.
•(3) logical data separation-with just one data domain for each tenant but all the data is stored in a single database.
•SaaS platform enables a simple development, deployment and management of software applications delivered as services.
SaaSPlatformSubscription ServiceMetering ServiceCommunications Service(Mail, SMS) Integrations Service(Google Apps) SaaSApplicationSaaSApplicationSaaS SDKSaaS TagLibsJava LibrariesXML Configuration FilesMulti-tentantSaaSDB SaaS Core Web App SaaSApplication Deployment Manager Infrastructure ManagementSOAAuth, Log, Account

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Test bed architecture
itesm-cloud
Databases
Apache HTTP
VM
SaaS platform
(Tomcat instance)
VM VM
…
Workload Generation
JMeter machines
Requests
Resource Consumption State
(RCS) Monitor
SaaS platform
(Tomcat instance)
VM
SaaS platform
(Tomcat instance)
VM
itesm-cloud platform
Databases
Apache HTTP
VM
SaaS platform
(Tomcat instance)
VM
VM
… SaaS platform
(Tomcat instance)
VM
SaaS platform
(Tomcat instance)
VM
SaaS platform
(Tomcat instance)
VM
VM = Virtual Machine instance

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Generating workload
• Apache JMeter tool was selected to create
heavy workload to the Tomcat cluster installed
in a cloud environment and where the SaaS
platform is deployed.
JMeter
Site / Server
JMeter
JMeter
JMeter
Network
Host 1
Host 2
1
2

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Generating workload
• Incremental. For each time period, workload starts from
the peak of the previous time period and increases until to
reach the maximum peak of established users at the end of
current period (solid line).
• Peak load. For each time period, workload starts from zero
users and increments until to reach the maximum peak of
users at the middle of the period. Then, the workload starts
to decrease until zero at the end of the period (dotted line).
0
200
400
600
800
1000
1200
1400
1600
1800
1 2 3 4 5 6 7 8 9 10 11 12
Concurrent Users per Month
Concurrent Users
Each Tomcat server
(VM) can handle 100
users as top

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Problem measurement: Results
•% UU (Underutilization) = ( Combined UU / Measurements per hour) / Server-hours
•% OU (Overutilization) = Combined OU / (Measurements per month * Number of virtual machines)

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Resource allocation model
•Tenant-based isolation: creates tenant-level granularity and separates execution contexts for different tenants
•Tenant-based VM allocation: implements mechanisms to calculate the number of VM instances needed, given a set of tenants.
•Tenant-based load balancing: implements a distribution mechanism to process and dispatch workload requests concerning to each tenant.
Cloud Communication LayerVM FactoryVM Status HandlerSaaSCore Web AppTenant-based Load BalancerTenant Context ManagerRequestsTenant-Based VM Calculator Databases SaaSplatform(Tomcat instance) VMVM… SaaSplatform(Tomcat instance) VMSaaSplatform(Tomcat instance) VMitesm-cloudEucalyptus APITenant Context Interceptor

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Tenant-based isolation
•Each user belongs to one or more subscriber or tenant. Once the user has been authenticated through an email and password, SaaS platform links the user to its subscriber ID.
•The tenant-based information is stored in the context session and the rest of the applications can access it through the aspect-oriented mechanisms.
SaaSCore Web App Look for subscriberInternetGet subscriber (tenant) ID Set session cookieUserUserUserUser

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•Excepting the login request, each subsequent request done to the platform has an associated subscriber / tenant.
•This isolation mechanism has to be dependent of each request done and it represents the portion of the resources used by each subscriber.
Tenant Context CPU, Memory, StorageCPU, Memory, StoragePersistence Layer# loaded objectsUser.subscriberId= 101Subscriber 101# logged users# apps usedSubscriber 102# logged users# apps usedApp Service 1App Service 2App Service 3User.subscriberId= 101User.subscriberId= 101User.subscriberId= 102User.subscriberId= 102# database connections# database connectionsPersistence Layer# loaded objects

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// TenantContextInterceptor.java
public class TenantContextInterceptor extends AbstractInterceptor implements
StrutsStatics {
...
public String intercept(ActionInvocation invocation) throws Exception {
ActionContext context = invocation.getInvocationContext ();
HttpServletRequest request = (HttpServletRequest)
context.get(HTTP_REQUEST);
...
UserVO user = SidelAuth.getAuthenticatedUser(request);
String username = user.getEmail();
long subscriberId = user.getSubscriberId();
TenantContextManager tenantContextManager =
TenantContextManager.getInstance();
tenantContextManager.updateTenantContext(subscriberId,usern
ame);
...
return invocation.invoke ();
}
class Class Model
TenantContextInterceptor
+ intercept(ActionInvocation) : String
TenantContext
- activeUsers: int
- tenantId: long
- users: List<String>
TenantContextManager
- contexts: Map<Long, TenantContext>
+ getInstance() : TenantContextManager
+ updateTenantContext(long, String) : void
updates
uses
• TenantContextInterceptor class
implements an AbstractInterceptor
interface in order to catch the
request invocation performed by the
user.
• When the request is intercepted, it
is retrieved the
TenantContextManager instance
which implements a Singleton design
pattern to have only one single
instance of such class.
• TenantContextManager holds a
map where the TenantContext
objects are stored.

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Tenant-based VM allocation
•Virtual Machine Assignment Problem (VMAP): find an assignment which distributes the load on the VMs such that all have access to adequate resources and the number of servers used is minimized.
•Thesis adaptation calculates the minimum number of virtual machine instances with specific and homogeneous capacity that need to be run when certain workload is received in the cluster.

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•Tenant Context weight= Active users * ( heap size / maxThreads)
•VM capacity= Heap size -( ( heap size / maxThreads) * platform threads)

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intVMs = 0;
public intcalculateVMs(intcapacity,int[]tenantWeights){
VMs = preProcessWeights(capacity, tenantWeights); //STEP_1 Initial VMs for overweight tenants
tenantWeights= processWeightArray( capacity,tenantWeights); //STEP_2 Eliminate overweight values
calculateVmsAllocation(capacity, tenantWeights); //STEP_3 Calculate VMs
return VMs;
}//end of function
private void calculateVmsAllocation(intcapacity,int[]tenantWeights){
if (tenantWeights.length==0) return;
else{
//EVERY ITERATION INCREMENTS VM INSTANCES NUMBER
VMs++;
//SOLVING KNAPSACK
Knapsack KS = new Knapsack(capacity, tenantWeights, tenantWeights);KS.search(0,0,0);
int[] take = KS.getBestSolution();
//FILTERING NOT-TAKEN ELEMENTS
List<Integer> newWeightList= new ArrayList<Integer>();
for (intc=0;c<take.length;c++){
if (take[c]==0){ newWeightList.add(tenantWeights[c]); } //not taken
}
//REDUCING ARRAY
tenantWeights= convertToIntArray(newWeightList);
//RECURSIVE TO NEXT ITERATION
calculateVmsAllocation(capacity,tenantWeights);
}
} //end of function

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Cloud Communication Layer
•The Cloud Communicator Layercontains the components for monitoring and accessing the itesm-cloudprivate infrastructure through SOAP interfaces.
•It uses jCloudslibrary which is a Java open source framework that implements portable abstractions to get connected to public clouds.
//initialize credentials
String accesskeyid= "...";
String secretkey= "...";
//connect to the Eucalyptus itesm-cloud
Properties overrides = new Properties();
overrides.setProperty("eucalyptus.endpoint","http://itesm-cloud:8773/services/Eucalyptus");
ComputeServiceContextcontext = new ComputeServiceContextFactory().createContext("eucalyptus", accesskeyid,secretkey,ImmutableSet.<Module> of(new Log4JLoggingModule(), new JschSshClientModule()), overrides);
//create a VM template with SaaS platform image and small VM type
Template template=
context.getComputeService().templateBuilder().hardwareId("m1.small").imageId("Eucalyptus/emi-9ACB1363").build();
//set security params
template.getOptions().as(EC2TemplateOptions.class).securityGroups("default");
template.getOptions().as(EC2TemplateOptions.class).noKeyPair();
//creating N instances
Set<? extends NodeMetadata> nodes = context.getComputeService().runNodesWithTag("saasplatform", N, template);
...
//or shutdown an instance by ID
context.getComputeService().destroyNode("Eucalyptus/i-3FB706CB");

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Tenant-based load balancing
•Load balancing is an effective solution scheme which purpose is to make the clustered servers equalizing sharing the workload through mutual cooperation and work distribution.
•Tenant-based load balancing is proposed to take advantage of isolated parallelism in Web applications, meaning that requests from users are totally independent each other.

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•Tenant-based load balancer is part of the SaaS platform and it uses the Tenant Context Managercomponent to retrieve information about tenant statuses.
•The tenant-based load balancer tries to allocate requests of the same tenant to an unique virtual machine, depending of the VM capacity.
SaaSCore Web AppTenant-based Load BalancerServer PreparerCookie ManagerResponse Parser Request ProcessorTenant Request Scheduler Tenant Context ManagerWorkloadRequests Databases SaaSplatform(Tomcat instance) VMVM… SaaSplatform(Tomcat instance) VMSaaSplatform(Tomcat instance) VMCloud Communication Layeritesm-cloud

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•Request Processorreceives requests from HTTP or HTTPS ports and interprets the request method (GET, POST, etc).
•Server Preparerinstantiates a new HTTP client, set the request headers and establishes timeouts.
•Cookie Managercopies cookie information into the request.
•Response Parserreturns the server answer meta data to the client.
•Tenant Request Schedulercommunicates with Tenant Context Manager component in order to determine to which VM dispatch the request.

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private void readTenantInformation(long tenantID, Request schedRequest) {
String VM_IP_ADDRESS = DEFAULT_IP_ADDRESS;
TenantRequestSchedulertenantRequestScheduler= TenantRequestScheduler.getInstance(); TenantContextManagertenantContextManager= TenantContextManager.getInstance();
intactiveUsers= 0; //initialize users
Map<Long, TenantContext> tenantContextsMap= tenantContextManager.getTenantContextMap();
TenantContexttenantContext= tenantContextsMap.get(tenantID);
VM_IP_ADDRESS = tenantRequestScheduler.calculateVMIPAddress(tenantContext);
activeUsers= tenantContext.getActiveUsers();
TenantHostInfotenantHostInfo= new TenantHostInfo();
tenantHostInfo.setTenantId(tenantContext.getTenantId());
tenantHostInfo.setActiveUsers(activeUsers+1);
hostMap.put(VM_IP_ADDRESS, tenantHostInfo);
// UPDATING THE LOAD INDEX
loadIndex+= tenantHostInfo.getActiveUsers();
prepareServerRequest(schedRequest, VM_IP_ADDRESS)
}

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Solution measurements
Cloud Communication Layer
VM Factory VM Status Handler
SaaSCore Web App
Tenant-based Load Balancer
Tenant Context Manager
Requests
Tenant-Based VM Calculator
Databases
SaaS platform
(Tomcat instance)
VM VM
…
SaaS platform
(Tomcat instance)
VM
SaaS platform
(Tomcat instance)
VM
itesm-cloud
Eucalyptus API
Tenant Context Interceptor
After setting up the tenant-based components and deploying
them over the test bed, all the simulations and tests were run
again. Over and underutilization measurements where
performed against workload tests (incremental and peak-based).
itesm-cloud
Databases
Apache HTTP
VM
SaaS platform
(Tomcat instance)
VM VM
…
Workload Generation
JMeter machines
Requests
Resource Consumption State
(RCS) Monitor
SaaS platform
(Tomcat instance)
VM
SaaS platform
(Tomcat instance)
VM
vs.

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Result Analysisvs.
Control group
Experimental group

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Result Analysis
•t-student test allows to determine if two averages are significantly different.
•(N1+N2-2) = (12 + 12 -2 ) = 22 degrees of freedom = 99.5 accuracy
•t-student distribution table produces a value of t= 2.8188as base parameter.
•If t >t, we can say with a 99.5%of certainty that one average (in Table 11.2) is statistically less than another column (in Table 11.1).
12221212XXtSSNN   

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Result Analysis
Controlgroup
ExperimentalGroup
t-student value
>2.8188 t parameter
Incremental UU
18.42%
8.21%
3.2437
Yes
Incremental OU
13.26%
13.25%
1.0282
No
Peak-based UU
29.12%
8.77%
4.7208
Yes
Peak-based OU
12.22%
9.9%
0.7485
No
•Averages for underutilization before tenant-based components have been statistically improved.
•On the other hand, both t-student values corresponding to overutilization (OU) are not higher than t, even if the averages were reduced.

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Conclusions
•It was designed and deployed a SaaS platform architecture and applications that run within a cloud computing environment.
•It was defined the workload mechanisms and tools to generate request for the test beds.
•It was established a formal measure for under and over provisioning of virtualized resources (CPU and Heap memory) in cloud infrastructures.
•It was developed monitors for resource utilization (CPU and memory) within virtual machines.
•It was defined, designed and developed a mechanism for tenant-level isolation in order separate execution and gather information about tenant' resource usage.
•It was created the mechanisms and algorithms for virtual machine allocation based on tenant information and virtual machine capacity.
•It was designed and implemented a load balancer in order to distribute requests based on virtual machine statuses and tenant workload distribution.

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Conclusions
•This doctoral thesis demonstrates that resource allocation models have important impact in how the virtualized resources are created and instantiated within cloud environments.
•The stated hypothesis has been validated, it was reduced the over and underutilization of virtualized resources in order to obtain cost-effective scalability for Software-as-a-Service applications within a cloud computing pay-per-use infrastructure by implementing tenant-based resource allocation mechanisms such as isolation, virtual machine creation and load balancing.

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Main contributions
•Establishment of formal measurements for under and over provisioning of virtualized resources in cloud infrastructures.
•Presentation of an architecture based on open source components for developing, deploying and managing SaaS applications.
•This work provides a detailed deployment design about how to setup a cloud infrastructure over commodity facilities and the use of open technologies.
•This thesis contributes to the resource allocation topic specifically in cloud computing infrastructures, through new mechanisms for distribute workload in virtualized environments and dynamic load balancing proposal based on global statuses of tenant information.

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Future Work
•Different software platforms
•Measuring other resources
•Database clustering
•Multiple database in SaaS applications
•Different workload generation
•SaaS architecture and cloud platform
•Analytical testing
•Performance improvement

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Thesis outcomes
•2 conference papers.
•2 journal papers.
•Research internship at Microsoft Research with Cloud Computing Futures group. Redmond, WA. (2009).
•Business incubation cell about Software-as-a- Service platforms and applications. (2010).
•National conferences and collaborations.

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Publications
•Espadas, J.; Concha, D.; Molina, A. “Application Development over Software-as-a-Service Platforms” (2008). The Third International Conference on Software Engineering Advances ICSEA 2008 -Sliema, Malta.
•Espadas, J.;Concha, D.; Romero, D.; Molina, A. (2010). "Open Architecture for Developing Multitenant Software-as-a-Service Applications“. The First International Conference on Cloud Computing, GRIDs, and Virtualization. Published by XPS. November 21-26, 2010 -Lisbon, Portugal.
•Concha, D.; Espadas, J.; Romero, D. and Molina, A. (2010). "The e-HUB Evolution: From a Custom Software Architecture to a Software-as-a-Service Implementation" in Journal Computers in Industry, Volume 61, Issue 2, Elsevier, ISSN: 0166-3615, Impact Factor 2.014.
•Javier Espadas,Arturo Molina, Guillermo Jiménez, Martín Molina, David Concha, Raúl Ramírez. “Tenant-based resource allocation model for scaling Software-as-a-Service applications over cloud computing infrastructures”. Journal of Future Generation Computer Systems, Special Issue on Clouds and Service Oriented Architectures. Impact Factor 2.229 (In review process with minor changes).

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The end
•Questions

Tenant-based resource allocation model for cost-effective scaling Software-as-a-Service applications over cloud computing infrastructure

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