Autonomic Virtual Resource Management for Service Hosting Platforms

Proceedings of the Workshop on Software Engineering Challenges in Cloud Computing 05/2009; DOI: 10.1109/CLOUD.2009.5071526
Source: OAI


Cloud platforms host several independent applications on a shared resource pool with the ability to allocate com- puting power to applications on a per-demand basis. The use of server virtualization techniques for such platforms provide great flexibility with the ability to consolidate sev- eral virtual machines on the same physical server, to resize a virtual machine capacity and to migrate virtual machine across physical servers. A key challenge for cloud providers is to automate the management of virtual servers while taking into account both high-level QoS requirements of hosted applications and resource management costs. This paper proposes an autonomic resource manager to con- trol the virtualized environment which decouples the provi- sioning of resources from the dynamic placement of virtual machines. This manager aims to optimize a global utility function which integrates both the degree of SLA fulfillment and the operating costs. We resort to a Constraint Pro- gramming approach to formulate and solve the optimization problem. Results obtained through simulations validate our approach.

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    • "Buyya et al. [23] delivered a modeling which contains procedures for simulating large scale infrastructure and network connections in cloud computing environments using cloudsim. Nguyen et al. [24] proposed an elaborated architecture to operate the allocation of virtual machines in dynamic manner. Allocation of virtual machine based on structural constraint aware mechanism and algorithm were presented by Jayasinghe et al. [25] to enhance availability and performance on IaaS environment. "
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    ABSTRACT: Allocation and schedule of virtual machines based on the requisite of cloud users is a challenging crucial chore in cloud services especially in IaaS (Infrastructure as a Service). Whenever the virtual machines requests are increased or decreased, the resources have to be balanced to attain optimal resource utilization. In this paper, we propose an approach namely Effective Cloud Resource Allocation Using Improvised Genetic Approach, which directs to accomplish better virtual machine allocation across cloud servers for maintaining vertical elasticity and minimizing response time. The proposed approach is focused on elasticity and Scheduling to improve resource allocation mechanism in cloud computing. This paper not only focuses the resource utilization problem, but also discusses our innovative algorithm called Enhanced Genetic Algorithm (EGA) using Multipurpose Mutation Operator. The proposed algorithm makes the effectual use of mutation operator to avoid local optimum problem. It repairs infeasible solutions and handles local search efficiently. The result shows that the EGA provides an optimal solution and proves better performance compared to the existing algorithms. Our method exemplifies that there is a substantial improvement in response time and also reduction in VM (Virtual Machine) migration count.
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    • "Internet-based manufacturing or distributed manufacturing virtual enterprise and distributed manufacturing A comprehensive data warehouse is utilized to store CNC manufacturing information with STEP-NC data model utilized as the basis for representing manufacturing knowledge that is augmented with XML schema [38] [39] [40] [41] [42].Cost effectiveness of commercial computing clouds. A costeffective cloud computing framework for accelerating multimedia communication simulations the performance of the proposed systems has to be evaluated in many different network scenarios and for several values of all the key parameters[43] [44] [45] [46].This work focused on investigating the cost–performance tradeoff of a cloud computing approach to run simulations frequently encountered during the research and development phase of multimedia communication techniques, characterized by several develop–simulate–reconfigure cycles[47] [48] [49] [50] [51] [52] [53] [54] [55] [56].A framework for ranking of cloud computing services Cloud computing has emerged as a paradigm to deliver on demand resources, platform, software to customers similar to other utilities[57] [58] [59] [60].The growths of public Cloud offerings, for Cloud customers it has become increasingly difficult to decide, which provider can fulfill their requirements[60] [61] [62] [63] [64] [65] [66] [67] [68] [69] [70] [71] [72]. Currently most of these existing methods focused on the optimization of allocating physical resources to their associated virtual resources and migrating virtual machines to achieve load balance and increase resource utilization. "
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    ABSTRACT: The manufacturing industry is undergoing a major transformation enabled by IT and related smart technologies. The main thrust of Cloud computing is to provide on-demand computing services with high reliability, scalability and availability in a distributed environment. This paper summarizes our taxonomy of the CC review direction. The goals of this taxonomy were (i) clarify the needs and the directions of the use of the CC, (ii) define the academic and practical issues involved in CC , (iii) learn the state of the directions on methodologies of the CC , (iv) identify future research directions, which benefit the short and long terms. The taxonomy has concluded that (i) CC is advantageous in dealing with changes and uncertainties in the every-changing environment. (ii)It has been found that few existing CC can achieve the objective of security. (iii) The obstacles of the development of CC include the difficulties to identify and generalize the requirement of CC security, the lake of effective technologies that can be used to support the clouding use, and no international origination that serves for standardizing the modular components for cloud computing processes. In this paper, we use the IVSL(The Iraq Virtual Science Library) to select the free, full-text access to papers from major publishers as well as a large collection of on-line educational materials.
    05/2015; 4(1):204. DOI:10.14419/jacst.v4i1.4564
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    • "Their algorithms take both optimisation and fairness into account and provide a relatively good compromise resource allocation. Van et al. (2009b) proposed an autonomic resource manager to control the virtualised environment, which decouples the provisioning of resources from the dynamic placement of virtual machines. The manager aims to optimise a global utility function which integrates both the degree of SLA fulfilment and the operating costs. "
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    ABSTRACT: In this paper, we model the optimisation of the resource allocation in cloud computing as a constraint satisfaction problem considering three types of resources (CPU, RAM and bandwidth) and design a Choco-Based algorithm (CB) for VM resource allocation in virtualised cloud data centres. We also propose an Improved First-Fit Decreasing Algorithm (IFFD) and an Improved Best-Fit Decreasing Algorithm (IBFD) and conduct performance evaluation experiments using Choco. The experimental results show that CB has better results, whereas its solution time is longer than IFFD and IBFD in resource allocation. Moreover, to reduce the complexity of solving the problem of CSP-based resource allocation, we propose an equivalence optimisation which can greatly reduce the search space for resource allocation by making tree pruning with resource equivalence. Then, a resource allocation algorithm based on Equivalent Optimisation (EO) is designed. Experimental results also show that compared with CB, EO greatly reduces the time of allocating resource of cloud computing.
    International Journal of Web and Grid Services 01/2015; 11(2):193. DOI:10.1504/IJWGS.2015.068899 · 0.76 Impact Factor
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