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检索条件"任意字段=3rd International Workshop on Algorithmic Aspectsof Cloud Computing, ALGOCLOUD 2017"

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3rd international workshop on algorithmic Aspects of cloud computing, algocloud 2017

3rd International Workshop on Algorithmic Aspects of Cloud C...

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3rd international workshop on algorithmic aspectsof cloud computing, algocloud 2017

ISBN: (纸本)9783319748740

The proceedings contain 10 papers. The special focus in this conference is on algorithmic Aspects of cloud computing. The topics include: A walk in the clouds: Routing through vnfs on bidirected networks;Service chain placement in SDNs;tight approximability of the server allocation problem for real-time applications;risk aware stochastic placement of cloud services: The case of two data centers;towards an algebraic cost model for graph operators;computing probabilistic queries in the presence of uncertainty via probabilistic automata;improving rule-based elasticity control by adapting the sensitivity of the auto-scaling decision timeframe;risk aware stochastic placement of cloud services: The multiple data center case.

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A Walk in the clouds: Routing Through VNFs on Bidirected Networks 3rd

A Walk in the Clouds: Routing Through VNFs on Bidirected Net...

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3rd international workshop on algorithmic Aspects of cloud computing (algocloud)

作者： Foerster, Klaus-Tycho Parham, Mahmoud Schmid, Stefan Aalborg Univ Aalborg Denmark

ISBN: (纸本)9783319748757;9783319748740

The virtualization of network functions enables innovative new network services which can be deployed quickly and at low cost on (distributed) cloud computing infrastructure. This paper initiates the algorithmic study of the fundamental underlying problem of how to efficiently route traffic through a given set of Virtualized Network Functions (VNFs). We are given a link-capacitated network G = (V, E), a source-destination pair (s, t) is an element of V-2 and a set of waypoints W subset of V (the VNFs). In particular, we consider the practically relevant but rarely studied case of bidirected networks. The objective is to find a (shortest) route from s to t such that all waypoints are visited. We show that the problem features interesting connections to classic combinatorial problems, present different algorithms, and derive hardness results.

关键词： Distributed cloud

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Improving Rule-Based Elasticity Control by Adapting the Sensitivity of the Auto-Scaling Decision Timeframe 3rd

Improving Rule-Based Elasticity Control by Adapting the Sens...

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3rd international workshop on algorithmic Aspects of cloud computing (algocloud)

作者： Trihinas, Demetris Georgiou, Zacharias Pallis, George Dikaiakos, Marios D. Univ Cyprus Dept Comp Sci Nicosia Cyprus

ISBN: (纸本)9783319748757;9783319748740

cloud computing offers the opportunity to improve efficiency with cloud providers offering consumers the ability to automatically scale their applications to meet exact demands. However, "auto-scaling" is usually provided to consumers in the form of metric threshold rules which are not capable of determining whether a scaling alert is issued due to an actual change in the demand of the application or due to short-lived bursts evident in monitoring data. The latter, can lead to unjustified scaling actions and thus, significant costs. In this paper, we introduce AdaFrame, a novel library which supports the decision-making of rule-based elasticity controllers to timely detect actual runtime changes in the monitorable load of cloud services. Results on real-life testbeds deployed on AWS, show that AdaFrame is able to correctly identify scaling actions and in contrast to the AWS auto-scaler, is able to lower detection delay by at least 63%.

关键词： cloud computing Auto-scaling Elasticity cloud monitoring

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Risk Aware Stochastic Placement of cloud Services: The Multiple Data Center Case 3rd

Risk Aware Stochastic Placement of Cloud Services: The Multi...

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3rd international workshop on algorithmic Aspects of cloud computing (algocloud)

作者： Shabtai, Galia Raz, Danny Shavitt, Yuval Tel Aviv Univ Sch Elect Engn IL-69978 Tel Aviv Israel The Technion Fac Comp Sci IL-32000 Haifa Israel

ISBN: (纸本)9783319748757;9783319748740

Allocating the right amount of resources to each service in any of the data centers in a cloud environment is a very difficult task. In a previous work we considered the case where only two data centers are available and proposed a stochastic based placement algorithm to find a solution that minimizes the expected total cost of ownership. This approximation algorithm seems to work well for a very large family of overflow cost functions, which contains three functions that describe the most common practical situations. In this paper we generalize this work for arbitrary number of data centers and develop a generalized mechanism to assign services to data centers based on the available resources in each data center and the distribution of the demand for each service. We further show, using simulations based on synthetic data that the scheme performs very well on different service workloads.

关键词： Approximation algorithms

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Risk Aware Stochastic Placement of cloud Services: The Case of Two Data Centers 3rd

Risk Aware Stochastic Placement of Cloud Services: The Case ...

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3rd international workshop on algorithmic Aspects of cloud computing (algocloud)

作者： Shabtai, Galia Raz, Danny Shavitt, Yuval Tel Aviv Univ Sch Elect Engn IL-69978 Tel Aviv Israel The Technion Fac Comp Sci IL-32000 Haifa Israel

ISBN: (纸本)9783319748757;9783319748740

Allocating the right amount of resources to each service in any of the data centers in a cloud environment is a very difficult task. This task becomes much harder due to the dynamic nature of the workload and the fact that while long term statistics about the demand may be known, it is impossible to predict the exact demand in each point in time. As a result, service providers either over allocate resources and hurt the service cost efficiency, or run into situation where the allocated local resources are insufficient to support the current demand. In these cases, the service providers deploy overflow mechanisms such as redirecting traffic to a remote data center or temporarily leasing additional resources (at a higher price) from the cloud infrastructure owner. The additional cost is in many cases proportional to the amount of overflow demand. In this paper we propose a stochastic based placement algorithm to find a solution that minimizes the expected total cost of ownership in case of two data centers. Stochastic combinatorial optimization was studied in several different scenarios. In this paper we extend and generalize two seemingly different lines of work and arrive at a general approximation algorithm for stochastic service placement that works well for a very large family of overflow cost functions. In addition to the theoretical study and the rigorous correctness proof, we also show using simulation based on real data that the approximation algorithm performs very well on realistic service workloads.

关键词： Approximation algorithms

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