Resource Allocation Policies in Cloud Computing Environment

Resource Allocation Policies in Cloud Computing Environment

Suvendu Chandan Nayak (C. V. Raman College of Engineering, India), Sasmita Parida (C.V. Raman College of Engineering, India), Chitaranjan Tripathy (Veer Surendra Sai University of Technology, India) and Prasant Kumar Pattnaik (KIIT University (Deemed), India)
DOI: 10.4018/978-1-5225-2013-9.ch005
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The basic concept of cloud computing is based on “Pay per Use”. The user can use the remote resources on demand for computing on payment basis. The on-demand resources of the user are provided according to a Service Level Agreement (SLA). In real time, the tasks are associated with a time constraint for which they are called deadline based tasks. The huge number of deadline based task coming to a cloud datacenter should be scheduled. The scheduling of this task with an efficient algorithm provides better resource utilization without violating SLA. In this chapter, we discussed the backfilling algorithm and its different types. Moreover, the backfilling algorithm was proposed for scheduling tasks in parallel. Whenever the application environment is changed the performance of the backfilling algorithm is changed. The chapter aims implementation of different types of backfilling algorithms. Finally, the reader can be able to get some idea about the different backfilling scheduling algorithms that are used for scheduling deadline based task in cloud computing environment at the end.
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Cloud computing is the leading booming technology for delivery of reliable, secure, fault-tolerant, sustainable, and scalable computational services to the end-users. Cloud computing is nothing but taking services that are “cloud services” and moving them outside an organizations firewall on shared systems (Mell & Grance, 2009). These applications and services are accessed via the web, instead of your hard drive. Cloud service providers offer elastic, on-demand, and measured infrastructure, platforms and software services. On-demand facility of virtualized resources as service is offered using virtualization in cloud computing without any delay (Kumbhare et al., 2015).

Cloud computing is typically classified based on either their deployment or service models represents cloud models based on the NIST definition framework (Mell & Grance, 2009). Cloud deployment models can be classified as private, public, community, and hybrid cloud. According to IDC (Calheiros & Buyya, 2014), the most beneficial aspects of using cloud include fast and easy deployment, the pay-per-use model, and reduction of in-house IT costs. In the public cloud, tenants have control over the OS, storage and deployed applications. Resources are provisioned in different geographic regions (Calheiros & Buyya, 2014). In the public cloud deployment model, the performance of an application deployed in multiple regions is a matter of concern for organizations. Proof of concepts in the public cloud environment gives a better understanding, but cost a lot in terms of capacity building and resource usage even in the pay-per-use model.

For implementation of such characteristics on cloud systems is under consideration, but it is required timely, iteratively, and controllable methodologies for the evaluation of newly developed cloud applications and policies before actual development of cloud products (Venkatesan et al., 2013). As the utilization of real test beds samples limit the experiments to the scale of the test bed performances and their results are an extremely difficult to undertake, simulate which may be used in future. The service model of cloud computing is shown in Figure 1. The service model mainly includes three types of services such as: Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS). Basically, for SaaS belongs to software. PaaS deals with a platform for the user application which is cost effective for the user without cloud computing and IaaS include both software and hardware resources (Kumbhare et al., 2015).

Figure 1.

Different service models in cloud computing


Cloud computing infrastructures and application services, allowing its users to focus on specific system design issues that they want to investigate, without getting concerned about the low level details related to cloud-based infrastructures and services. Simulation of cloud environments and applications to evaluate performance can provide useful insights to explore such dynamic, massively distributed, and scalable environments cloud to host applications (Parida & Nayak, 2013). The cloud creates complex provisioning, deployment, and configuration requirements. Mainly cloud computing is the collection of datacenters which provides virtual machines(VM) and each data center has a collection of physical machines (Nayak & Tripathy, 2016). Here, each physical machine has its own resources with a fixed or scalable amount such as CPU, main memory, storage area, I/O and bandwidth. When a request is sent by the end user to the datacenter, then the virtual machine (VM) is created according to the request of the physical machine (Nayak & Tripathy, 2016). The main challenging issue is the creation of different of virtual machines in a physical machine to execute the user’s request as showed in Figure 2.

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