Problem Of Resource Provisioning Research Articles

Cloud Computing: Empowering the Education and Global Learning

Over the past decades, the education has been never refurbished to be delivered on a platform like cloud. An application like a cloud, creates an illusion of a live classroom with the help of technologies like virtualization which creates an instance of a real component like a server, operating system, data, storage etc. Education industry has taken a leap towards advancement by adopting certain technologies in order to improve the quality of teaching and to enhance the ease of understanding of the concepts. Teaching now days is not only confined to the chalk and boards rather it has new trends like 3-D animations to make the things more crystal clear. Hence, empowers the students with the real-world scenarios and career ready skills. Technology has paved the way for this disruptive change. Traditionally, it was really hard to believe that teaching will ever be able to cross the geographical barriers. But the technological advancements it is now possible to reach to each and every student and a teacher. Moreover, technology have removed all the constraints in learning by providing ease of access and enhanced the learning more. But with the increased reach ability and ease of access to any resource, comes the problem of resource provisioning. One of the advancements that drive development for this need is distributed computing. Distributed computing is a mind-boggling innovation that makes ready for progressions with the assistance of virtualization.

Dynamic QoS-Aware Resource Assignment in Cloud-Based Content-Delivery Networks

The problem of resource provisioning and content placement in cloud-based content delivery networks is studied, and a two-stage resource provisioning and cloud assignment is proposed based on dynamic large and small-scale fluctuations of user demand rates as well as considering a constrained minimum lease time for resources. In the first stage, we perform resource provisioning while costs of resources, quality of service (QoS) violation, and cloud-based root server redirection are included in the optimization, and constraints of QoS and limited resource of cloud sites are taken into account. In the second stage, cloud site assignment is conducted where for fixed allocated resources, QoS violation and cloud-based root server redirection costs are minimized or reduced using three different proposed schemes. We further show that reassignment of cloud sites during rising demand rates within a lease time period improves revenue, while reassignment is not very effective for falling demand rates.

Optimized Service Level Agreement Establishment in Cloud Computing

Nowadays, the access to a cloud computing environment is provided on-demand, offering transparent services to clients. Although the cloud allows an abstraction of the behavior of the infrastructure in the service providers (involving logical and physical resources), the Service Level Agreements (SLAs) fulfilment remains a challenge, because depending on the service demand and the system configuration, the providers may not be able to meet the clients requirements. In this way, mechanisms that take account of load balancing and resource provisioning algorithms to provide an efficient load distribution in the available resources are necessary. However, the studies in the literature do not effectively address the problem of the resource provisioning to meet clients requirements using optimization techniques, restricting the analysis to a limited set of objectives. This paper proposes algorithms to address the computational resource provisioning problem using optimization techniques on-the-fly. The techniques optimize the use of the resources available in the cloud infrastructure, aiming to fulfill the clients requirements defined in the SLAs, and ensuring the efficient use of resources.

An Efficient Resource Provisioning Technique in Inter-Cloud using Peer-to-Peer Approach

Resource discovery is the process of computing systems; it supports resource management and scheduling of applications. It searches for the apposite variety of resource that satisfies the user's requirements. Resource discovery in computing system is perplexing because of conventional centralized approaches and resilient availability of resources. This scenario becomes more complex in inter-clouds owing to the dynamism and heterogeneity of clouds involved. The inspiration driving this paper is to utilize peer-to-peer approach to circumvent the problem of resource provisioning and resource discovery that are predominantly associated with centralized approach. This paper introduces a system model of Resource discovery in Inter-Cloud and mechanism to joining Remote Resource Manager using reputation based algorithm. We attempt to minimize the data traffic and balance the load partly by using the reputation based algorithm.

Optimizing Cloud-Service Performance: Efficient Resource Provisioning via Optimal Workload Allocation

Cloud computing is being widely accepted and utilized in the business world. From the perspective of businesses utilizing the cloud, it is critical to meet their customers’ requirements by achieving service-level-objectives. Hence, the ability to accurately characterize and optimize cloud-service performance is of great importance. In this paper a stochastic multi-tenant framework is proposed to model the service of customer requests in a cloud infrastructure composed of heterogeneous virtual machines. Two cloud-service performance metrics are mathematically characterized, namely the percentile and the mean of the stochastic response time of a customer request, in closed form. Based upon the proposed multi-tenant framework, a workload allocation algorithm, termed max-min-cloud algorithm, is then devised to optimize the performance of the cloud service. A rigorous optimality proof of the max-min-cloud algorithm is also given. Furthermore, the resource-provisioning problem in the cloud is also studied in light of the max-min-cloud algorithm. In particular, an efficient resource-provisioning strategy is proposed for serving dynamically arriving customer requests. These findings can be used by businesses to build a better understanding of how much virtual resource in the cloud they may need to meet customers’ expectations subject to cost constraints.

Approximation Modeling for the Online Performance Management of Distributed Computing Systems

A promising method of automating management tasks in computing systems is to formulate them as control or optimization problems in terms of performance metrics. For an online optimization scheme to be of practical value in a distributed setting, however, it must successfully tackle the curses of dimensionality and modeling. This paper develops a hierarchical control framework to solve performance management problems in distributed computing systems operating in a data center. Concepts from approximation theory are used to reduce the computational burden of controlling such large-scale systems. The relevant approximations are made in the construction of the dynamical models to predict system behavior and in the solution of the associated control equations. Using a dynamic resource-provisioning problem as a case study, we show that a computing system managed by the proposed control framework with approximation models realizes profit gains that are, in the best case, within 1% of a controller using an explicit model of the system.