User requirements and Resource Management is more important in cloud computing. Automation of user requirements phase is still not available in a cloud scenario. User requirements layer specifies the capacity of virtual machine required by the user and applicatons to be placed within the virtual machine. Based on the history of user requirements in an academic institution they could be converted into packages and the user selects the appropriate package. User requirements are then mapped to both SMI and SLA for appropriate package selection. Both the user requirements and packages rely on the underling infrastructure resources. Hence effective resource management is must in academic institutions.

1. An Effective User Requirements and Resource Management in an
Academic Cloud-A Review
C.Madhumathi 1
, Gopinath Ganapathy2
School of Computer Science, Engineering and Applications
Bharathidasan University, Tiruchirapalli, TamilNadu 620 023, India.
1
madhu.csbdu54@gmail.com
2
gganapathy@gmail.com
Abstract:
User requirements and Resource Management is more important in cloud computing.
Automation of user requirements phase is still not available in a cloud scenario. User
requirements layer specifies the capacity of virtual machine required by the user and
applicatons to be placed within the virtual machine. Based on the history of user requirements
in an academic institution they could be converted into packages and the user selects the
appropriate package. User requirements are then mapped to both SMI and SLA for appropriate
package selection. Both the user requirements and packages rely on the underling infrastructure
resources. Hence effective resource management is must in academic institutions.
Keywords: Resource Management-User Requirements- Academic Cloud-SMI-Package
selection.
Introduction: Cloud Computing is a model that enables on-demand network access to a
shared pool of configurable computing resources that can be rapidly provisioned and released
with minimal management effort or service provider interaction. Cloud computing consists of
five different characteristics such as On-demand self service, Broad network access, Resource
Pooling, Rapid Elasticity and Measured Service. Cloud computing provides three types of
service models such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and
Software as a Service (SaaS). It has four types of deployment models such as Private Cloud,
Public Cloud, Hybrid Cloud and Community Cloud [1]. Virtualization plays a major role in
Cloud computing. It is the creation of virtual operating system, server, storage device and
network resources. There are two different approaches in virtualization. They are full and para
virtualization. Full virtualization consists of complete simulation of the actual hardware. It
cannot touch the source code of base or guest operating system. In para virtualization hardware
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2. is not simulated. It abstracts the base architecture. It works without VT technology enabled [2].
Effective cloud service can only be obtained by an effective virtual resource management
which is built up from the available physical resources. Cloud service could be effective only
when it meets the user requirement.
Related Works:
Sunil kumar et.al made a review about the Resource management of cloud at IaaS level. Issues,
Solutions, tools, techniques involved in resource management at IaaS level are explained in
detail and also specifies the necessity of management of resources in cloud perspective [2].
Saurabh Kumar et.al, specifies the framework for SMI for ranking cloud services. In his
framework, SMI cloud architecture specifies the service selection and ranking based on QoS
and previous user experiences. It also specifies the QoS parameters to be considered for service
selection [3]. Madhumathi et.al, specifies the SMI based Workload recommendation for
academic usage logs. In her work, Cloud package selection based on AHP (Analytic
Hierarchical Process) using MCDM (Multi Criteria Decision Making) technique narrates the
identification technique of academic user requirements [4].
Academic User Requirements:
In an academic scenario, the end users are either student/faculty. Their requirements could be
either a request for a virtual machine or a package. The requirements are first checked with
SLA (Service Level Agreements). It is defined as an official commitment that prevails between
a service provider and a user. Automation of user requirements mainly depends upon the SLA
and SMI of a cloud provider. Quality of Service is attained when it satisfies both SLA and SMI.
Service Level Agreement (SLA):
SLAs offered by service providers as service-based agreement. SLA management consist of
the SLA contract definition, SLA negotiation, SLA monitoring, SLA violation detection and
SLA enforcement [5].
Service Measurement Index (SMI):
The Service Measurement Index (SMI) is a set of business-relevant key performance indicators
(KPI’s) that provide standardized method for measuring the services. CSMIC (Cloud Services
Measurement Initiative Consortium) develops SMI for measuring services provided by
different cloud service providers [6]. Each Service provider has their own SLA. SMI consists
of six key metrics such as Accountability, Agility, Assurance of Service, Cost, Performance,
Security and Usability [6]. These are further divided into the following factors.
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3. SMI Key metrics Functions
Accountability Auditability, Compliance, data ownership,
provider ethicality and sustainability.
Agility Elasticity, portability, adaptability and
flexibility.
Performance Functionality, Service Response time and
Accuracy.
Assurance Reliability, Resiliency and Service stability.
Security Confidentiality, privacy, data integrity and
availability.
Usability Accessibility, Installability, Learnability,
and Operability.
Table.1 Functions of SMI key metrics [3][7]
Quality of Service (QoS):
Quality of Service is attained when a service provider satisfies the user’s requirement. In
order to provide an efficient quality of service effective resource management is needed.
There are 14 Quality of Service parameters to be considered for evaluation. In which first
nine parameters are quantitative and last five parameters are qualitative.
S.No QoS parameters
1. Bandwidth (Bw)
2. Computation Capability (CC)
3. Availability (Av)
4. Correctness (Cr)
5. Usability (Us)
6. Reliability (Re)
7. Variable computation load (Vc)
8. Serviceability (Se)
9. Latency (l)
10. Security (S)
11. Portability (P)
12. Reliable storage (Rs)
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4. 13. Data Backup (Db)
14. Customization (Cu)
Table.2 List of QoS parameters [8]
From the above table, the nine quantifiable attributes are calculated as follows [8] [9],
1. Bandwidth: Network bandwidth refers to the number of bits transferred (sent/
received) in a single workload per unit time (usually in seconds).
2. Computational Capacity: Computational capacity corresponds to the ratio between
the actual usage time and the expected usage time.
3. Availability: Availability refers to the recovery level of the system in case of failure.
4. Correctness: Correctness defines the degree of accuracy provided to the cloud
customers.
5. Usability: Usability refers to the ratio of successful workload operations exhibited by
the system.
6. Reliability: Reliability refers to the time taken for the system to recover and operate
successfully after a failure.
7. Variable computing load: It is the change in the load balance with respect to time.
Calculating the variance of the workload can be used to identify this parameter.
8. Serviceability: Serviceability is the probability of the service being up and running.
9. Latency: Latency is the difference between the workload input and output times.
Network Bandwidth=Bits/ second
(B/S)
Computing Capacity=Actual Usage time of the Resource/Expected Usage time of
the Resource.
Availability=mean time to failure/ (mean time to failure + mean time to repair)
Correctness=total number of failed transmissions/ (total number of failed
transmissions + total number of successful transmissions)
Usability=no of successful operations in a workload/ (total operations available
in the workload)
Reliability=mean time to failure + mean time to repair
Serviceability= Service Uptime/ (Service Uptime+ Service Downtime)
Latency = Time of output produced with respect to that Cloud workload - Time
of input in a Cloud workload
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5. The last five parameters which are qualitative are calculated based on the user assigned
weights of range scale. The user assigns points based on the quality of parameters within a
range scale for example user can assign a weight for security within the range from 1-5.
Resource Management
Management of both physical and virtual resources plays a major role in cloud
computing. In order to provide effective cloud services effective resource management is a
must. Hypervisor is used in the creation of virtual resources [2].
Hypervisor
Hypervisor or virtual machine monitor (VMM) is a computer software, firmware or
hardware that creates and runs virtual machines. Host machine is a computer on which a
hypervisor runs one or more virtual machines and each virtual machine is called a guest
machine. Operating system which runs on the host machine is called as host operating system
whereas which runs on the guest machine is called as guest operating system. Hypervisor
presents the guest operating systems with a virtual operating platform and manages the
execution of the guest operating system [10]. There are two types of hypervisor. They are,
Type 1 (or) Baremetal (or) Native Hypervisors
It runs directly on the host’s hardware. It runs mostly on the provider side server
machines. Eg: Xen, Oracle VM, VMware ESX/ESXi, Microsoft Hyper-V.
Figure.1 Type 1 Hypervisors
Type 2 (or) Hosted Hypervisors
It runs on an operating system just as other computer programs. It runs mostly on
the client side machines. Eg: VirtualBox, VMware Workstation, QEMU.
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6. Figure.2 Type 2 Hypervisors
Thus effective resource management deals with one or more of the following ten factors.
They are listed below.
Table.3 Issues in Effective Management of Resource [2]
Resource Provisioning- Allocation of Service provider’s resources to a user.
Resource Allocation- Distribution of resources among users or programs.
Resource Adaptation- Dynamic adjustment of resources to fulfil the requirements of the user.
Resource Mapping- Mapping of the resources available with the provider and required by the
user.
Resource Modelling- It illustrates the attributes such as states, transitions, inputs and outputs
in subsequent time intervals.
Resource Estimation- Prior planning of resources required either through guess or calculation.
Resource Discovery- Identification of required resource from available resources.
Effective
Management of
Resource
Provisioning
Allocation
Adaptation
Mapping
Modelling
Estimation
Discovery
Selection
Brokering
Scheduling
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7. Resource Selection- Selecting the best or optimum resource from a pool of available resource.
Resource Brokering- Negotiation between the cloud broker and user in order to attain the
necessary resource at expected time and within the specified objectives.
Resource Scheduling- Sharing resource at certain time bound and occurrence of events at
those time interval.
Conclusion:
Thus effective resource management is essential in a cloud scenario inorder to meet
the user’s requirements. In this paper, the terminologies related to cloud resources from the
provider and user perspective are narrated in a watertight way. The cloud packages which
satisfies the SMI, SLA and QoS parameters are provided as service when required by the user.
This paper illustrates the need for effective resource management and it lays a foundation for
various research work to be carried out in these criteria in an academic perspective.
References:
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September 2011
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[4] C.Madhumathi, Gopinath Ganapathy, “Cloud Package Selection for Academic
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[6] https://en.wikipedia.org/wiki/Service_Measurement_Index.
[7] C.Madhumathi, Gopinath Ganapathy, “ Effective User Requirements Identification and
SMI Parameter based WorkLoad Recommendation for Academic Clouds based on Usage
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