In this chapter,we discuss the basis for queueing theory and performance and capacity analysis, look at the economics of cloud computing, introduce key volume indicators for more accurate forecasting, and present the tools of capacity management needed to ensure adequate performance without overpaying.
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Cloud computing source: en.wikipedia.org/ wiki/Cloud_computing Cloud computing metaphor: For a user, the network elements representing the provider-rendered services are invisible, as if obscured by a cloud. Cloud computing is a kind of Internet-based computing that provides shared processing resources and data to computers and other devices on demand. It is a model for enabling ubiquitous, on-demand access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications and services), [1][2] which can be rapidly provisioned and released with minimal management effort. Cloud computing and storage solutions provide users and enterprises with various capabilities to store and process their data in third-party data centers. [3] It relies on sharing of resources to achieve coherence and economy of scale, similar to a utility (like the electricity grid) over a network. Advocates claim that cloud computing allows companies to avoid upfront infrastructure costs, and focus on projects that differentiate their businesses instead of on infrastructure. [4] Proponents also claim that cloud computing allows enterprises to get their applications up and running faster, with improved manageability and less maintenance, and enables IT to more rapidly adjust resources to meet fluctuating and unpredictable business demand. [4][5][6] Cloud providers typically use a "pay as you go" model. This will lead to unexpectedly high charges if administrators do not adapt to the cloud pricing model. [7] The present availability of high-capacity networks, low-cost computers and storage devices as well as the widespread adoption of hardware virtualization, service-oriented architecture, and autonomic and utility computing have led to a growth in cloud computing. [8][9][10] Companies can scale up as computing needs increase and then scale down again as demands decrease. Cloud computing has become a highly demanded service or utility due to the advantages of high computing power, cheap cost of services, high performance, scalability, accessibility as well as availability. Some cloud vendors are experiencing growth rates of 50% per year, [11] but being still in a stage of infancy, it has pitfalls that need to be addressed to make cloud computing services more reliable and user friendly. [12][13]
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Cloud computing is becoming increasingly widespread and sophisticated. A key feature of cloud computing is elasticity, which allows the provi-sioning and de-provisioning of computing resources on demand, via auto-scaling. Auto-scaling techniques are diverse, and involve various components at the infrastructure, platform and software levels. Auto-scaling also overlaps with other quality attributes, thereby contributing to service level agreements, and often applies modeling and control techniques to make the auto-scaling process adaptive. A study of auto-scaling architec-tures, existing techniques and open issues provides a comprehensive understanding to identify future research solutions. In this paper, we present a survey that explores definitions of related concepts of auto-scaling and a taxonomy of auto-scaling techniques. Based on the survey results , we then outline open issues and future research directions for this important subject in cloud computing.
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Relying on a technology that pools the resources of central servers across remote locations, and the internet, cloud computing as we know has opened up a new vista in how we look at application deployment and their uses. Cloud computing, in essence, gives the power to consumers and businesses to access their personal files, and in some cases even applications, on any compatible computer connected to the internet. Combining data storage, data and information processing, and bandwidth allows for a much more efficient computing. Data center carbon emissions have become a leading concern for service providers who offer cloud computing services. In order to control carbon emissions, it is understood that distribution of computing loads among various nodes of a distributed system can have significant impact. Load balancing, in other words, of resources utilization coupled with job response times, if done proactively can help avoid situations where some of the nodes are heavily loaded while other nodes are either lying idle or doing very little by way of productive operations. Load balancing is a continuous operation that tries to share resource demands on all processors in the system, or every node in the network, that each is burdened with approximately an equal amount resource at any given point of time. In our paper, we have proposed an electronic machine named Billboard Manager, which aims to achieve a balance of load across several virtual machines to maximize throughput. This proposed method balances the priorities of task order in the machines in a way so as to ensure that the waiting time of the tasks in the queue is at a minimum. In our study, we have compared our proposed algorithm with existing load balancing and scheduling algorithms. Results from our experiments show that our proposed algorithm compares favourably to existing ones. Our approach using the Billboard Manager clarifies that there is a marked change in average execution time and significant reduction of waiting time of queued tasks.
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The biggest challenge in cloud computing environment is resource allocation, which in turn should be managed effectively in order to optimize the task execution. The cloud providers let their customers to access the resources in the form of virtual machines in their data centers and charge them over a period. Resource allocation must ensure effective utilisation and meeting the customer needs. Also, resources need to be reallocated in case of failures or load maximization problems. Usually utmost care should be taken in maintaining the capacity of total number of virtual machines without exceeding the capacity of the physical machines. Therefore, the load of resources that exceeds the capacity decides the VM migration. A practical online bin packing algorithm called the Variable Item Size Bin Packing allocates data center resources dynamically through live VM migration. However, the Service Level Agreement parameters are not considered while migrating the VMs to the other PMs. To overcome this, Enhanced Variable Item Size Bin Packing technique is proposed in our work. Here, the CPU usage is considered as the SLA parameter. The experimental result proves that the proposed methodology provides better result than the existing methodology.
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The Cloud Computing Infrastructure-as-a-Service (IaaS) layer offers a service for on demand virtual machine images (VMIs) operation. This service offers a elastic platform for cloud users to build up, install, and test their applications. Virtual machine environments are becoming more universal due to the augmented performance of service hardware and the materialization of cloud computing for huge scale applications. Because of the reason that virtual machines continues to grow, performance critical applications will need proficient methods to realize their tasks. The operation of a VMI classically engages booting the image, installing and configuring the software packages. In the conventional approach, when a cloud user requests a new platform, the cloud provider chooses a suitable template image for cloning and deploying on the cloud nodes. The template image encloses pre-installed software packages. If it does not fit the requirements, then it will be tailored or the new one will be formed from scratch to fit the request. In the context of cloud service management, the customary approach features the difficult issues of handling the complexity of interdependency between software packages, scaling and maintaining the deployed image at runtime. The cloud providers would like to automate this process to improve the performance of the VMIs provisioning process, and to give the cloud users more flexibility for selecting or creating the appropriate images while maximizing the benefits for provider's intern of time, resources and operational cost. The increasing demand for storage and computation has driven the growth of large data centers–the massive server farms that run many of today's Internet and business applications. A data center can comprise many thousands of servers and can use as much energy as a small city. The massive amounts of computation power contained in these systems results in many interesting distributed systems and resource management problems. In this paper we focus to investigate challenges related to data centers, with a particular emphasis on how new virtualization technologies can be used to simplify deployment, improve resource efficiency, and reduce the cost of reliability, all in application agnostic ways. We first study problems that relate to the initial capacity planning required when deploying applications into a virtualized data center, issues related to memory utilization among virtual machines and performance metrics for memory virtualization..
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The objective the work is intend to highlight the key features and afford finest future directions in the research community of Resource Allocation, Resource Scheduling and Resource management from 2009 to 2016. Exemplifying how research on Resource Allocation, Resource Scheduling and Resource management has progressively increased in the past decade by inspecting articles, papers from scientific and standard publications. Survey materialized in three fold process. Firstly, investigate on the amalgamation of Resource Allocation, Resource Scheduling and then proceeded with Resource management. Secondly, we performed a structural analysis on different author's prominent contributions in the form of tabulation by categories and graphical representation. Thirdly, huddle with conceptual similarity in the field and also impart a summary on all resource allocations. In cloud computing environments, there are two players: cloud providers and cloud users. On one hand, providers hold massive computing resources in their large datacenters and rent resources out to users on a per-usage basis. On the other hand, there are users who have applications with fluctuating loads and lease resources from providers to run their applications. Further, delivers conclusions by conferring future research directions in the field of cloud computing, such as reduce clouds early in the Internet, combining Resource Allocation, Resource Scheduling and Resource management rather than a Cloud model for providing high quality results, etc.
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Cloud computing is emerging as a new paradigm of large-scale distributed computing. It is a framework for enabling convenient, on-demand network access to a shared pool of computing resources. Load balancing is one of the main challenges in cloud computing which is required to distribute the dynamic workload across multiple nodes to ensure that no single node is overwhelmed. It helps in optimal utilization of resources and hence in enhancing the performance of the system. The goal of load balancing is to minimize the resource consumption which will further reduce energy consumption and carbon emission rate that is the dire need of cloud computing. This determines the need of new metrics, energy consumption and carbon emission for energy-efficient load balancing in cloud computing. This paper discusses the existing load balancing techniques in cloud computing and further compares them based on various parameters like performance, scalability, associated overhead etc. that are conside.
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Cloud computing is an improving area in research and in industry, which consists of distributed computing, internet, web services and virtualization.One of the most important technologies to load forecasting in the cloud computing is to ensure the maximize utilization of the system resource. Under the premise that the load is known in the next level, the cloud computing node can assign the physical machines in advance, and hence reduces the waiting time of the task, which also reduces the cloud computing node's resource consumption. A neural fuzzy technique called Adaptive network based fuzzy inference system (ANFIS) has been used as a prime tool in the present work. Using this hybrid method, at first an initial fuzzy model along with its input variables are derived with the help of the rules extracted from the input output data of the system that is being represented. Next the neural network is used to fine tune the rules of the initial fuzzy model to produce the final ANFIS model of the system. In this proposed work ANFIS is used as the backbone for the load balancing in the cloud computing. I. INTRODUCTION Adaptive network based fuzzy inference system (ANFIS) is a neuro fuzzy technique where the fusion is made between the neural network and the fuzzy inference system. In ANFIS the parameters can be estimated in such a way that both the Sugeno and Tsukamoto fuzzy models are represented by the ANFIS architecture. Again with minor constraints the ANFIS model resembles the Radial basis function network (RBFN) functionally. This ANFIS methodology comprises of a hybrid system of fuzzy logic and neural network technique. The fuzzy logic takes into account the imprecision and uncertainty of the system that is being modeled while the neural network gives it a sense of adaptability. Cloud computing is a new pattern of large-scale distributed computing. It has stimulated computing and data away from desktop and manageable PCs, into large data centers [1]. It has the ability to connect the power of Internet and wide area network (WAN) to make use of resources that are available remotely, thereby providing cost-effective solution to most of the real life requirements [2]. It gives the scalable IT resources such as applications and services, in addition to the infrastructure on which they control, over the Internet, on pay-per-use basis to regulate the capacity rapidly and easily. It helps to occupy changes in demand and helps any organization in stay away from the capital costs of software and hardware [3] [4]. Therefore, cloud computing is a structure for enabling an appropriate, on-demand network access to a common pool of computing resources.Cloud service is divided into three models. They are,as shown in Fig. 1. Cloud Software as a service (Saas): The competence provided to the consumer is to make use of the provider's applications consecutively running on a cloud communications. The applications are easy to get from several client devices throughout a thin client interface such as a web browser. The consumer does not deal with the fundamental cloud infrastructure. Cloud Platform as a Service (Paas): The capability provided to the consumer is to arrange on the cloud communications consumer formed or obtained applications created by means of programming languages and tools sustained by the provider. The consumer does not supervise or control the fundamental cloud structure, but has control over the applications and perhaps application hosting environment configurations.
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