A hybrid auto-scaling technique for clouds processing applications with service level agreements

Abstract This research focuses on the automatic provisioning of cloud resources by an intermediary enterprise. This enterprise provides a virtual private cloud for a single client enterprise by using resources from a public cloud. The intermediary cloud provider is controlled by a broker that uses techniques to dynamically control the number of resources used by the client enterprise. The research presents a hybrid auto-scaling technique based on a combination of a reactive approach and a proactive approach to scale resources based on user demand. The primary goal of this auto-scaling technique is to achieve a profit for the intermediary enterprise while maintaining a desired grade of service for the client enterprise. The second goal of the technique is to reduce costs for the single client enterprise. The technique supports both on-demand requests and requests with service level agreements (SLAs). This paper describes the auto-scaling algorithms associated with the hybrid technique and includes a discussion of system design and implementation experience for a prototype system. A detailed performance analysis based on simulations and measurements made based on the prototype is presented

Effect of Manganese (II) Oxide on microstructure and ionic transport properties of nanostructured cubic zirconia

Effect of MnO addition on microstructure and ionic transport properties of nanocrystalline cubic(c)-ZrO2 is reported. Monoclinic (m) ZrO2 powders with 10-30 mol% MnO powder are mechanically alloyed in a planetary ball mill at room temperature for 10 h and annealed at 550 degrees C for 6 h. In all compositions m-ZrO2 transforms completely to nanocrystalline c-ZrO2 phase and MnO is fully incorporated into c-ZrO2 lattice. Rietveld's refinement technique is employed for detailed microstructure analysis by analyzing XRD patterns. High resolution transmission electron microscopy (HRTEM) analysis confirms the complete formation of c-ZrO2 phase. Presence of stoichiometric Mn in c-ZrO2 powder is confirmed by Electron Probe Microscopy analysis. XPS analysis reveals that Mn is mostly in Mn2+ oxidation state. A correlation between lattice parameter and oxygen vacancy is established. A detailed ionic conductivity measurement in the 250 degrees-575 degrees C temperature range describes the effect of MnO on conductivity of c-ZrO2. The ionic conductivity (s) of 30 mol% MnO alloyed ZrO2 at 550 degrees C is 0.04 s cm(-1). Electrical relaxation studies are carried out by impedance and modulus spectroscopy. Relaxation frequency is found to increase with temperature and MnO mol fraction. Electrical characterization predicts that these compounds have potentials for use as solid oxide fuel cell electrolyte material. (C) 2015 Elsevier Ltd. All rights reserved

An auto-scaling framework for controlling enterprise resources on clouds

This paper presents a novel technique for auto-scaling cloud resources provided by an intermediary enterprise which services requests from a client enterprise. The intermediary enterprise acquires resources on demand from a public cloud. A broker is deployed by the intermediary enterprise to handle client requests with service level agreements (SLAs). A reactive auto-scaling algorithm is activated on request arrival and achieves auto-scaling by acquiring new resources for serving the recently arrived request. The technique ensures that a grade of service specified by the client enterprise is satisfied and is based on a profit analysis for the intermediary enterprise. A resources is released after the last request allocated on the resource has completed execution. The paper demonstrates that the proposed reactive auto-scaling technique can effectively lead to a profit for the intermediary enterprise as well as a reduction of cost for the client enterprise

Predictive Auto-scaling Techniques for Clouds Subjected to Requests with Service Level Agreements

This paper focuses research focuses on automatic provisioning of cloud resources performed by an intermediary enterprise that provides a virtual private cloud for a single client enterprise by using resources from a public cloud. This paper concerns auto-scaling techniques for dynamically controlling the number of resources used by the client enterprise. We focus on proactive auto-scaling that is based on predictions of future workload based on the past workload. The primary goal of the auto-scaling techniques is to achieve a profit for the intermediary enterprise while maintaining a desired grade of service for the client enterprise. The technique supports both on demand requests and requests with service level agreements (SLAs). This paper presents an auto-scaling algorithm and includes a discussion of system design and implementation experience for a prototype system that implements the technique. A detailed performance analysis based on measurements made on the prototype is presented

A framework for automatic resource provisioning for private clouds

A private cloud is maintained by an enterprise for its internal use. In such a scenario instead of buying the resources the enterprise can acquire the resources from a public clou