Most present day switching systems, in Internet routers and data-center switches, employ a single input-queued crossbar to interconnect input ports with output ports. Such switches need to compute a matching, between input and output ports, for each switching cycle (time slot). The main challenge in designing such matching algorithms is to deal with the unfortunate tradeoff between the quality of the computed matching and the computational complexity of the algorithm. In this paper, we propose a general approach that can significantly boost the performance of both SERENA and iSLIP, yet incurs only O(1) additional computational complexity at each input/output port. Our approach is a novel proposing strategy, called Queue-Proportional Sampling (QPS), that generates an excellent starter matching. We show, through rigorous simulations, that when starting with this starter matching, iSLIP and SERENA can output much better final matching decisions, as measured by the resulting throughput and delay performance, than they otherwise can.
{"title":"Queue-Proportional Sampling: A Better Approach to Crossbar Scheduling for Input-Queued Switches","authors":"Long Gong, Paul Tune, Liang Liu, Sen Yang, Jun Xu","doi":"10.1145/3078505.3078509","DOIUrl":"https://doi.org/10.1145/3078505.3078509","url":null,"abstract":"Most present day switching systems, in Internet routers and data-center switches, employ a single input-queued crossbar to interconnect input ports with output ports. Such switches need to compute a matching, between input and output ports, for each switching cycle (time slot). The main challenge in designing such matching algorithms is to deal with the unfortunate tradeoff between the quality of the computed matching and the computational complexity of the algorithm. In this paper, we propose a general approach that can significantly boost the performance of both SERENA and iSLIP, yet incurs only O(1) additional computational complexity at each input/output port. Our approach is a novel proposing strategy, called Queue-Proportional Sampling (QPS), that generates an excellent starter matching. We show, through rigorous simulations, that when starting with this starter matching, iSLIP and SERENA can output much better final matching decisions, as measured by the resulting throughput and delay performance, than they otherwise can.","PeriodicalId":133673,"journal":{"name":"Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129432514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Session details: SIGMETRICS Keynote Talk: Vahab Mirrokni","authors":"V. Misra","doi":"10.1145/3248538","DOIUrl":"https://doi.org/10.1145/3248538","url":null,"abstract":"","PeriodicalId":133673,"journal":{"name":"Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130375959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent years have seen a rapid growth of interest in exploiting monitoring data collected from enterprise applications for automated management and performance analysis. In spite of this trend, even simple performance inference problems involving queueing theoretic formulas often incur computational bottlenecks, for example upon computing likelihoods in models of batch systems. Motivated by this issue, we revisit the solution of multiclass closed queueing networks, which are popular models used to describe batch and distributed applications with parallelism constraints. We first prove that the normalizing constant of the equilibrium state probabilities of a closed model can be reformulated exactly as a multidimensional integral over the unit simplex. This gives as a by-product novel explicit expressions for the multiclass normalizing constant. We then derive a method based on cubature rules to efficiently evaluate the proposed integral form in small and medium-sized models. For large models, we propose novel asymptotic expansions and Monte Carlo sampling methods to efficiently and accurately approximate normalizing constants and likelihoods. We illustrate the resulting accuracy gains in problems involving optimization-based inference.
{"title":"Accelerating Performance Inference over Closed Systems by Asymptotic Methods","authors":"G. Casale","doi":"10.1145/3078505.3078514","DOIUrl":"https://doi.org/10.1145/3078505.3078514","url":null,"abstract":"Recent years have seen a rapid growth of interest in exploiting monitoring data collected from enterprise applications for automated management and performance analysis. In spite of this trend, even simple performance inference problems involving queueing theoretic formulas often incur computational bottlenecks, for example upon computing likelihoods in models of batch systems. Motivated by this issue, we revisit the solution of multiclass closed queueing networks, which are popular models used to describe batch and distributed applications with parallelism constraints. We first prove that the normalizing constant of the equilibrium state probabilities of a closed model can be reformulated exactly as a multidimensional integral over the unit simplex. This gives as a by-product novel explicit expressions for the multiclass normalizing constant. We then derive a method based on cubature rules to efficiently evaluate the proposed integral form in small and medium-sized models. For large models, we propose novel asymptotic expansions and Monte Carlo sampling methods to efficiently and accurately approximate normalizing constants and likelihoods. We illustrate the resulting accuracy gains in problems involving optimization-based inference.","PeriodicalId":133673,"journal":{"name":"Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115027312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes online offering strategies for a storage-assisted renewable power producer that participates in hour-ahead electricity market. The online strategy determines the offering price and volume, while no exact or stochastic future information is available in a time-coupled setting in the presence of the storage. The proposed online strategy achieves the best possible competitive ratio of O(log θ), where θ is the ratio between the maximum and minimum clearing prices. Trace-driven experiments demonstrate that the proposed strategy achieves close-to-optimal performance.
{"title":"Hour-Ahead Offering Strategies in Electricity Market for Power Producers with Storage and Intermittent Supply","authors":"L. Yang, M. Hajiesmaili, Hanling Yi, Minghua Chen","doi":"10.1145/3078505.3078543","DOIUrl":"https://doi.org/10.1145/3078505.3078543","url":null,"abstract":"This paper proposes online offering strategies for a storage-assisted renewable power producer that participates in hour-ahead electricity market. The online strategy determines the offering price and volume, while no exact or stochastic future information is available in a time-coupled setting in the presence of the storage. The proposed online strategy achieves the best possible competitive ratio of O(log θ), where θ is the ratio between the maximum and minimum clearing prices. Trace-driven experiments demonstrate that the proposed strategy achieves close-to-optimal performance.","PeriodicalId":133673,"journal":{"name":"Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125673956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Load balancing mechanisms and scheduling algorithms play a critical role in achieving efficient server utilization and providing robust delay performance in a wide range of networked systems. We will review some celebrated schemes and optimality results which typically assume that detailed state information, e.g. exact knowledge of queue lengths, is available in assigning jobs to queues or allocating a shared resource among competing users. In practice, however, obtaining such state information is non-trivial, and usually involves a significant communication overhead or delay, which is particularly a concern in large-scale networked systems with massive numbers of queues. These scalability issues have prompted increasing attention for the implementation complexity of load balancing and scheduling algorithms as a crucial design criterion, besides the traditional performance metrics. In this talk we examine the delay performance in such networks for various load balancing and scheduling algorithms, in conjunction with the associated implementation overhead. In the first part of the talk we focus on a scenario with a single dispatcher where jobs arrive that need to be assigned to one of several parallel queues. In the second part of the talk we turn to a system with a single resource, e.g. a shared wireless transmission medium, which is to be allocated among several nodes. We will specifically explore the delay scaling properties in a mean-field framework where the total load and service capacity grow large in proportion. The mean-field regime not only offers analytical tractability, but is also highly relevant given the immense numbers of servers in data centers and cloud networks, and dense populations of wireless devices and sensors in Internet-of-Things (IoT) applications. Time permitting, we will also discuss the impact of the underlying network structure and a few open research challenges.
{"title":"Delay Scalings and Mean-Field Limits in Networked Systems","authors":"S. Borst","doi":"10.1145/3078505.3080572","DOIUrl":"https://doi.org/10.1145/3078505.3080572","url":null,"abstract":"Load balancing mechanisms and scheduling algorithms play a critical role in achieving efficient server utilization and providing robust delay performance in a wide range of networked systems. We will review some celebrated schemes and optimality results which typically assume that detailed state information, e.g. exact knowledge of queue lengths, is available in assigning jobs to queues or allocating a shared resource among competing users. In practice, however, obtaining such state information is non-trivial, and usually involves a significant communication overhead or delay, which is particularly a concern in large-scale networked systems with massive numbers of queues. These scalability issues have prompted increasing attention for the implementation complexity of load balancing and scheduling algorithms as a crucial design criterion, besides the traditional performance metrics. In this talk we examine the delay performance in such networks for various load balancing and scheduling algorithms, in conjunction with the associated implementation overhead. In the first part of the talk we focus on a scenario with a single dispatcher where jobs arrive that need to be assigned to one of several parallel queues. In the second part of the talk we turn to a system with a single resource, e.g. a shared wireless transmission medium, which is to be allocated among several nodes. We will specifically explore the delay scaling properties in a mean-field framework where the total load and service capacity grow large in proportion. The mean-field regime not only offers analytical tractability, but is also highly relevant given the immense numbers of servers in data centers and cloud networks, and dense populations of wireless devices and sensors in Internet-of-Things (IoT) applications. Time permitting, we will also discuss the impact of the underlying network structure and a few open research challenges.","PeriodicalId":133673,"journal":{"name":"Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132015793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Concise is a Forwarding information base (FIB) design that uses very little memory to support fast query of a large number of dynamic network names or flow IDs. Concise makes use of minimal perfect hashing and the SDN framework to design and implement the data structure, protocols, and system. Experimental results show that Concise uses significantly smaller memory to achieve faster query speed compared to existing FIB solutions and it can be updated very efficiently.
简明是一种转发信息库(Forwarding information base, FIB)设计，它使用很少的内存来支持对大量动态网络名称或流id的快速查询。简洁利用最小完美哈希和SDN框架来设计和实现数据结构、协议和系统。实验结果表明，与现有的FIB解决方案相比，该方法使用了更小的内存，实现了更快的查询速度，并且可以非常有效地更新。
{"title":"A Fast, Small, and Dynamic Forwarding Information Base","authors":"Ye Yu, D. Belazzougui, Chen Qian, Qin Zhang","doi":"10.1145/3078505.3078556","DOIUrl":"https://doi.org/10.1145/3078505.3078556","url":null,"abstract":"Concise is a Forwarding information base (FIB) design that uses very little memory to support fast query of a large number of dynamic network names or flow IDs. Concise makes use of minimal perfect hashing and the SDN framework to design and implement the data structure, protocols, and system. Experimental results show that Concise uses significantly smaller memory to achieve faster query speed compared to existing FIB solutions and it can be updated very efficiently.","PeriodicalId":133673,"journal":{"name":"Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127275513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
is tutorial is in the broad area of Internet Economics, specifically applying ideas from game theory, both Cooperative and Non-Cooperative. We consider the origins of the Internet architecture, and the evolution of the Internet ecosystem from a protocol and application standpoint. We next look at the evolution of the pricing structure on the Internet along three different dimensions: (a) between ISPs, (b) between ISPs and content providers, and (c) between ISPs and end users. We present mathematical models describing the pricing structures in each dimension, the interaction between the three and competition amongst the entities leading to the notion of Network Neutrality. We look at various definitions of Network Neutrality and analyze the the impact of mechanisms like paid peering, zero rating and differential pricing on the principle of Network Neutrality.
{"title":"Routing Money, Not Packets: A Tutorial on Internet Economics","authors":"Richard T. B. Ma, V. Misra","doi":"10.1145/3078505.3083764","DOIUrl":"https://doi.org/10.1145/3078505.3083764","url":null,"abstract":"is tutorial is in the broad area of Internet Economics, specifically applying ideas from game theory, both Cooperative and Non-Cooperative. We consider the origins of the Internet architecture, and the evolution of the Internet ecosystem from a protocol and application standpoint. We next look at the evolution of the pricing structure on the Internet along three different dimensions: (a) between ISPs, (b) between ISPs and content providers, and (c) between ISPs and end users. We present mathematical models describing the pricing structures in each dimension, the interaction between the three and competition amongst the entities leading to the notion of Network Neutrality. We look at various definitions of Network Neutrality and analyze the the impact of mechanisms like paid peering, zero rating and differential pricing on the principle of Network Neutrality.","PeriodicalId":133673,"journal":{"name":"Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114375025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We consider a closed queueing network model of ridesharing systems such as Didi Chuxing, Lyft, and Uber. We focus on empty-car routing, a mechanism by which we control car flow in the network to optimize system-wide utility functions, e.g. the availability of empty cars when a passenger arrives. We establish both process-level and steady-state convergence of the queueing network to a fluid limit in a large market regime where demand for rides and supply of cars tend to infinity, and use this limit to study a fluid-based optimization problem. We prove that the optimal network utility obtained from the fluid-based optimization is an upper bound on the utility in the finite car system for any routing policy, both static and dynamic, under which the closed queueing network has a stationary distribution. This upper bound is achieved asymptotically under the fluid-based optimal routing policy. Simulation results with real-word data released by Didi Chuxing demonstrate that the utility under the fluid-based optimal routing policy converges to the upper bound with a rate of 1/√N, where N is the number of cars in the network.
{"title":"Fluid-Model-Based Car Routing for Modern Ridesharing Systems","authors":"Anton Braverman, J. Dai, Xin Liu, Lei Ying","doi":"10.1145/3078505.3078595","DOIUrl":"https://doi.org/10.1145/3078505.3078595","url":null,"abstract":"We consider a closed queueing network model of ridesharing systems such as Didi Chuxing, Lyft, and Uber. We focus on empty-car routing, a mechanism by which we control car flow in the network to optimize system-wide utility functions, e.g. the availability of empty cars when a passenger arrives. We establish both process-level and steady-state convergence of the queueing network to a fluid limit in a large market regime where demand for rides and supply of cars tend to infinity, and use this limit to study a fluid-based optimization problem. We prove that the optimal network utility obtained from the fluid-based optimization is an upper bound on the utility in the finite car system for any routing policy, both static and dynamic, under which the closed queueing network has a stationary distribution. This upper bound is achieved asymptotically under the fluid-based optimal routing policy. Simulation results with real-word data released by Didi Chuxing demonstrate that the utility under the fluid-based optimal routing policy converges to the upper bound with a rate of 1/√N, where N is the number of cars in the network.","PeriodicalId":133673,"journal":{"name":"Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122878288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Nguyen, Tri P. Nguyen, Tam N. Vu, Thang N. Dinh
Estimating cascade size and nodes' influence is a fundamental task in social, technological, and biological networks. Yet this task is extremely challenging due to the sheer size and the structural heterogeneity of networks. We investigate a new influence measure, termed outward influence (OI), defined as the (expected) number of nodes that a subset of nodes S will activate, excluding the nodes in S. Thus, OI equals, the de facto standard measure, influence spread of S minus |S|. OI is not only more informative for nodes with small influence, but also, critical in designing new effective sampling and statistical estimation methods. Based on OI, we propose SIEA/SOIEA, novel methods to estimate influence spread/outward influence at scale and with rigorous theoretical guarantees. The proposed methods are built on two novel components 1) IICP an important sampling method for outward influence; and 2) RSA, a robust mean estimation method that minimize the number of samples through analyzing variance and range of random variables. Compared to the state-of-the art for influence estimation, SIEA is Ω(log4 n) times faster in theory and up to several orders of magnitude faster in practice. For the first time, influence of nodes in the networks of billions of edges can be estimated with high accuracy within a few minutes. Our comprehensive experiments on real-world networks also give evidence against the popular practice of using a fixed number, e.g. 10K or 20K, of samples to compute the ``ground truth'' for influence spread.
{"title":"Outward Influence and Cascade Size Estimation in Billion-scale Networks","authors":"H. Nguyen, Tri P. Nguyen, Tam N. Vu, Thang N. Dinh","doi":"10.1145/3078505.3078526","DOIUrl":"https://doi.org/10.1145/3078505.3078526","url":null,"abstract":"Estimating cascade size and nodes' influence is a fundamental task in social, technological, and biological networks. Yet this task is extremely challenging due to the sheer size and the structural heterogeneity of networks. We investigate a new influence measure, termed outward influence (OI), defined as the (expected) number of nodes that a subset of nodes S will activate, excluding the nodes in S. Thus, OI equals, the de facto standard measure, influence spread of S minus |S|. OI is not only more informative for nodes with small influence, but also, critical in designing new effective sampling and statistical estimation methods. Based on OI, we propose SIEA/SOIEA, novel methods to estimate influence spread/outward influence at scale and with rigorous theoretical guarantees. The proposed methods are built on two novel components 1) IICP an important sampling method for outward influence; and 2) RSA, a robust mean estimation method that minimize the number of samples through analyzing variance and range of random variables. Compared to the state-of-the art for influence estimation, SIEA is Ω(log4 n) times faster in theory and up to several orders of magnitude faster in practice. For the first time, influence of nodes in the networks of billions of edges can be estimated with high accuracy within a few minutes. Our comprehensive experiments on real-world networks also give evidence against the popular practice of using a fixed number, e.g. 10K or 20K, of samples to compute the ``ground truth'' for influence spread.","PeriodicalId":133673,"journal":{"name":"Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems","volume":"80 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120819580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Internet applications provide interesting dynamic environments for online optimization techniques. In this talk, I will discuss a number of such problems in the context of online markets, and in serving cloud services. For online markets, I discuss problems in online advertising. Online ads are delivered in a real-time fashion under uncertainty in an environment with strategic agents. Making such real-time (or online) decisions without knowing the future is challenging for repeated auctions. In this context, I will first highlight the practical importance of considering "hybrid" models that can take advantage of forecasting, and at the same time, are robust against adversarial changes in the input. In particular, I discuss our recent results combining stochastic and adversarial input models. Then I will present more recent results concerning online bundling schemes that can be applied to repeated auction environments. In this part, I discuss ideas from our recent papers about online bundling, stateful pricing, bank account mechanisms, and Martingale auctions. For problems on the cloud, I will touch upon two online load balancing problems: one in the context of consistent hashing with bounded loads for dynamic environments, and one in the context of multi-dimensional load balancing. Other than presenting theoretical results on these topics, we show how some of our new algorithmic techniques have been applied by Google and other companies, and confirm their significance in practice.
{"title":"Online Optimization for Markets and the Cloud: Theory and Practice","authors":"V. Mirrokni","doi":"10.1145/3078505.3078507","DOIUrl":"https://doi.org/10.1145/3078505.3078507","url":null,"abstract":"Internet applications provide interesting dynamic environments for online optimization techniques. In this talk, I will discuss a number of such problems in the context of online markets, and in serving cloud services. For online markets, I discuss problems in online advertising. Online ads are delivered in a real-time fashion under uncertainty in an environment with strategic agents. Making such real-time (or online) decisions without knowing the future is challenging for repeated auctions. In this context, I will first highlight the practical importance of considering \"hybrid\" models that can take advantage of forecasting, and at the same time, are robust against adversarial changes in the input. In particular, I discuss our recent results combining stochastic and adversarial input models. Then I will present more recent results concerning online bundling schemes that can be applied to repeated auction environments. In this part, I discuss ideas from our recent papers about online bundling, stateful pricing, bank account mechanisms, and Martingale auctions. For problems on the cloud, I will touch upon two online load balancing problems: one in the context of consistent hashing with bounded loads for dynamic environments, and one in the context of multi-dimensional load balancing. Other than presenting theoretical results on these topics, we show how some of our new algorithmic techniques have been applied by Google and other companies, and confirm their significance in practice.","PeriodicalId":133673,"journal":{"name":"Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127106528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：