HTTP Adaptive Streaming(HAS)of video content is becoming an undivided part of the Internet and accounts for most of today’s network traffic.Video compression technology plays a vital role in efficiently utilizing net...HTTP Adaptive Streaming(HAS)of video content is becoming an undivided part of the Internet and accounts for most of today’s network traffic.Video compression technology plays a vital role in efficiently utilizing network channels,but encoding videos into multiple representations with selected encoding parameters is a significant challenge.However,video encoding is a computationally intensive and time-consuming operation that requires high-performance resources provided by on-premise infrastructures or public clouds.In turn,the public clouds,such as Amazon elastic compute cloud(EC2),provide hundreds of computing instances optimized for different purposes and clients’budgets.Thus,there is a need for algorithms and methods for optimized computing instance selection for specific tasks such as video encoding and transcoding operations.Additionally,the encoding speed directly depends on the selected encoding parameters and the complexity characteristics of video content.In this paper,we first benchmarked the video encoding performance of Amazon EC2 spot instances using multiple×264 codec encoding parameters and video sequences of varying complexity.Then,we proposed a novel fast approach to optimize Amazon EC2 spot instances and minimize video encoding costs.Furthermore,we evaluated how the optimized selection of EC2 spot instances can affect the encoding cost.The results show that our approach,on average,can reduce the encoding costs by at least 15.8%and up to 47.8%when compared to a random selection of EC2 spot instances.展开更多
When deploying workflows in cloud environments,the use of Spot Instances(SIs)is intriguing as they are much cheaper than on-demand ones.However,Sls are volatile and may be revoked at any time,which results in a more c...When deploying workflows in cloud environments,the use of Spot Instances(SIs)is intriguing as they are much cheaper than on-demand ones.However,Sls are volatile and may be revoked at any time,which results in a more challenging scheduling problem involving execution interruption and hence hinders the successful handling of conventional cloud workflow scheduling techniques.Although some scheduling methods for Sls have been proposed,most of them are no more applicable to the latest Sls,as they have evolved by eliminating bidding and simplifying the pricing model.This study focuses on how to minimize the execution cost with a deadline constraint when deploying a workflow on volatile Sls in cloud environments.Based on Monte Carlo simulation and list scheduling,a stochastic scheduling method called MCLS is devised to optimize a utility function introduced for this problem.With the Monte Carlo simulation framework,MCLS employs sampled task execution time to build solutions via deadline distribution and list scheduling,and then returns the most robust solution from all the candidates with a specific evaluation mechanism and selection criteria.Experimental results show that the performance of MCLS is more competitive comparedwithtraditionalalgorithms.展开更多
Infrastructure-as-a-Service(IaaS)cloud platforms offer resources with diverse buying options.Users can run an instance on the on-demand market which is stable but expensive or on the spot market with a significant dis...Infrastructure-as-a-Service(IaaS)cloud platforms offer resources with diverse buying options.Users can run an instance on the on-demand market which is stable but expensive or on the spot market with a significant discount.However,users have to carefully weigh the low cost of spot instances against their poor availability.Spot instances will be revoked when the revocation event occurs.Thus,an important problem that an IaaS user faces now is how to use spot in-stances in a cost-effective and low-risk way.Based on the replication-based fault tolerance mechanism,we propose an on-line termination algorithm that optimizes the cost of using spot instances while ensuring operational stability.We prove that in most cases,the cost of our proposed online algorithm will not exceed twice the minimum cost of the optimal of-fline algorithm that knows the exact future a priori.Through a large number of experiments,we verify that our algorithm in most cases has a competitive ratio of no more than 2,and in other cases it can also reach the guaranteed competitive ratio.展开更多
基金This work has been supported in part by the Austrian Research Promotion Agency(FFG)under the APOLLO and Karnten Fog project.
文摘HTTP Adaptive Streaming(HAS)of video content is becoming an undivided part of the Internet and accounts for most of today’s network traffic.Video compression technology plays a vital role in efficiently utilizing network channels,but encoding videos into multiple representations with selected encoding parameters is a significant challenge.However,video encoding is a computationally intensive and time-consuming operation that requires high-performance resources provided by on-premise infrastructures or public clouds.In turn,the public clouds,such as Amazon elastic compute cloud(EC2),provide hundreds of computing instances optimized for different purposes and clients’budgets.Thus,there is a need for algorithms and methods for optimized computing instance selection for specific tasks such as video encoding and transcoding operations.Additionally,the encoding speed directly depends on the selected encoding parameters and the complexity characteristics of video content.In this paper,we first benchmarked the video encoding performance of Amazon EC2 spot instances using multiple×264 codec encoding parameters and video sequences of varying complexity.Then,we proposed a novel fast approach to optimize Amazon EC2 spot instances and minimize video encoding costs.Furthermore,we evaluated how the optimized selection of EC2 spot instances can affect the encoding cost.The results show that our approach,on average,can reduce the encoding costs by at least 15.8%and up to 47.8%when compared to a random selection of EC2 spot instances.
基金This work was supported by the National Natural Science Foundation of China(Nos.62172065 and 62072060)the Natural Science Foundation of Chongqing(No.cstc2020jcyj-msxmX0137).
文摘When deploying workflows in cloud environments,the use of Spot Instances(SIs)is intriguing as they are much cheaper than on-demand ones.However,Sls are volatile and may be revoked at any time,which results in a more challenging scheduling problem involving execution interruption and hence hinders the successful handling of conventional cloud workflow scheduling techniques.Although some scheduling methods for Sls have been proposed,most of them are no more applicable to the latest Sls,as they have evolved by eliminating bidding and simplifying the pricing model.This study focuses on how to minimize the execution cost with a deadline constraint when deploying a workflow on volatile Sls in cloud environments.Based on Monte Carlo simulation and list scheduling,a stochastic scheduling method called MCLS is devised to optimize a utility function introduced for this problem.With the Monte Carlo simulation framework,MCLS employs sampled task execution time to build solutions via deadline distribution and list scheduling,and then returns the most robust solution from all the candidates with a specific evaluation mechanism and selection criteria.Experimental results show that the performance of MCLS is more competitive comparedwithtraditionalalgorithms.
基金This work was supported by the National Key Research and Development Program of China under Grant No.2018YFB14-04501。
文摘Infrastructure-as-a-Service(IaaS)cloud platforms offer resources with diverse buying options.Users can run an instance on the on-demand market which is stable but expensive or on the spot market with a significant discount.However,users have to carefully weigh the low cost of spot instances against their poor availability.Spot instances will be revoked when the revocation event occurs.Thus,an important problem that an IaaS user faces now is how to use spot in-stances in a cost-effective and low-risk way.Based on the replication-based fault tolerance mechanism,we propose an on-line termination algorithm that optimizes the cost of using spot instances while ensuring operational stability.We prove that in most cases,the cost of our proposed online algorithm will not exceed twice the minimum cost of the optimal of-fline algorithm that knows the exact future a priori.Through a large number of experiments,we verify that our algorithm in most cases has a competitive ratio of no more than 2,and in other cases it can also reach the guaranteed competitive ratio.
