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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    • "One of the important criteria for appraising the superiority of modern computing systems is whether it satisfies the increasing demand for high performance and energy-saving [1] [2]. Due to the issue of increasing energy consumption in large-scale computing systems, many efficient techniques, such as dynamic voltage and frequency scaling [1] and virtual resource management [3], have been proposed to control energy consumption. On the other hand, distributed resource sharing technology [4] which effectively improves the performance of system has been more widely employed in computing systems, especially for cloud computing systems. "
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    ABSTRACT: The serious issue of energy consumption for high performance computing systems has attracted much attention. Performance and energy-saving have become important measures of a computing system. In the cloud computing environment, the systems usually allocate various resources (such as CPU, Memory, Storage, etc.) on multiple virtual machines (VMs) for executing tasks. Therefore, the problem of resource allocation for running VMs should have significant influence on both system performance and energy consumption. For different processor utilizations assigned to the VM, there exists the tradeoff between energy consumption and task completion time when a given task is executed by the VMs. Moreover, the hardware failure, software failure and restoration characteristics also have obvious influences on overall performance and energy. In this paper, a correlated model is built to analyze both performance and energy in the VM execution environment given the reliability restriction, and an optimization model is presented to derive the most effective solution of processor utilization for the VM. Then, the tradeoff between energy-saving and task completion time is studied and balanced when the VMs execute given tasks. Numerical examples are illustrated to build the performance-energy correlated model and evaluate the expected values of task completion time and consumed energy.
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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.
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    • "In some cases, this method cannot get a solution at all. Another way to get the optimal solution is using a Constraint Programming (CP) engine [8] [9] [13] [14]. Employing CP to optimize VM placement is a convenient technique of elegance and flexibility which simplifies the way of getting solutions of the problem. "
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