Explain market-Oriented Cloud computing architecture.

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 Market Oriented Cloud Computing (Mocc)

As customers rely on Cloud providers to cover all of their computing needs, they will expect certain Qe from their providers to fulfill their objectives and continue their operations. Cloud providers must examine and satisfy the various QoS standards of each unique consumer as stipulated in unique SLAs. To do this, cloud providers cannot continue to install traditional system-centric resource management architectures that do not give incentives for them to share their resources while still treating all service requests as equal insignificance. Instead, market-oriented resource management is required to maintain the supply and demand for Cloud resources at market equilibrium, provide feedback in the form of economic incentives for both Cloud consumers and providers, and promote QoS-based resource allocation mechanisms that differentiate service requests based on their utility. The diagram depicts a high-level architecture for enabling market-oriented resource allocation in Data Centers and Clouds. 

There are four main entities involved:



1. Users/Brokers: 

Operating Users or brokers send service requests to the Data Center and Cloud from anywhere in the world for processing.


2. SLA Resource Allocator:

The SLA Resource Allocator serves as a liaison between the Data Center/Cloud service provider and external users/brokers. To support SLA-oriented resource management, the following systems must interact:

-Service Request Examiner and Admission Control:  When a service request is initially made, the Service Request Examiner and Admission Control mechanism evaluates the supplied request for QoS criteria before deciding whether to accept or refuse the request. As a result, it prevents resource overload, in which numerous service requests are unable to be delivered properly due to a lack of available resources. It also requires up-to-date status information on resource availability from the VM Monitor and workload processing from the Service Request Monitor to make appropriate resource allocation choices. It then distributes requests to VMs and determines resource entitlements for assigned 

- VMs. Pricing:  The Pricing mechanism determines how service requests are billed. Requests, for example, might be charged depending on submission time (peak/off-peak), price rates (fixed/changing), or resource availability (supply/demand). Pricing serves as a foundation for regulating the supply and demand for computing resources inside the Data Center and aids in the proper prioritization of resource allocations.

-Accounting: The Accounting mechanism keeps track of the real utilization of resource requests so that the ultimate cost may be calculated and charged to the consumer Furthermore, the Service Request Examiner and Admission Control mechanism may use the stored previous usage data to optimize resource allocation choices.

-VM Monitor: The VM Monitor mechanism monitors the availability of VMs as well as resource entitlements.

- Dispatcher: The Dispatcher mechanism begins executing accepted service requests assigned VMs. Service Request Monitor. The Service Request Monitor mechanism monitors the status of service requests as they are being executed.


3. VMs:

 Multiple VMs may be started and terminated dynamically on a single physical system to satisfy accepted service requests, enabling maximum flexibility to design multiple partitions of resources on the same physical system to match particular service request needs. Furthermore, because each VM is separated from one another on the same physical computer, many VMs can execute applications based on various operating system environments on the same physical computer at the same time. 


4. Physical Machines:

 The Data Center is made up of several computer servers that supply resources to satisfy service demands.


Commercial offerings of MOCC must be able to:

  • assist with customer-driven service management based on customer profiles and specified service criteria
  •  establish computational risk management techniques for identifying, assessing, and managing risks associated with application execution in terms of service requirements and customer demands 
  • develop appropriate market-based resource management strategies that include both customers
  • driven service management and computational risk management to maintain SLA-oriented
  • resource allocation incorporates autonomic resource management models that effectively self-manage changes in service requirements to satisfy both new service demands and existing service obligations.
  •  make use of VM technology to dynamically assign resource shares based on service requirements.

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