You Zhou, Youlin Zhang, Chaoyi Ma, Shigang Chen, Olufemi O. Odegbile
Traffic measurement provides critical information for network management, resource allocation, traffic engineering, and attack detection. Most prior art has been geared towards specific application needs with specific performance objectives. To support diverse requirements with efficient and future-proof implementation, this paper takes a new approach to establish common frameworks, each for a family of traffic measurement solutions that share the same implementation structure, providing a high level of generality, for both size and spread measurements and for all flows. The designs support many options of performance-overhead tradeoff with as few as one memory update per packet and as little space as several bits per flow on average. Such a family-based approach will unify implementation by removing redundancy from different measurement tasks and support reconfigurability in a plug-n-play manner. We demonstrate the connection and difference in the design of these traffic measurement families and perform experimental comparisons on hardware/software platforms to find their tradeoff, which provide practical guidance for which solutions to use under given performance goals.
{"title":"Generalized Sketch Families for Network Traffic Measurement","authors":"You Zhou, Youlin Zhang, Chaoyi Ma, Shigang Chen, Olufemi O. Odegbile","doi":"10.1145/3393691.3394191","DOIUrl":"https://doi.org/10.1145/3393691.3394191","url":null,"abstract":"Traffic measurement provides critical information for network management, resource allocation, traffic engineering, and attack detection. Most prior art has been geared towards specific application needs with specific performance objectives. To support diverse requirements with efficient and future-proof implementation, this paper takes a new approach to establish common frameworks, each for a family of traffic measurement solutions that share the same implementation structure, providing a high level of generality, for both size and spread measurements and for all flows. The designs support many options of performance-overhead tradeoff with as few as one memory update per packet and as little space as several bits per flow on average. Such a family-based approach will unify implementation by removing redundancy from different measurement tasks and support reconfigurability in a plug-n-play manner. We demonstrate the connection and difference in the design of these traffic measurement families and perform experimental comparisons on hardware/software platforms to find their tradeoff, which provide practical guidance for which solutions to use under given performance goals.","PeriodicalId":188517,"journal":{"name":"Abstracts of the 2020 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125326069","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}
Volume-based network denial-of-service (DoS) attacks refer to a class of cyber attacks where an adversary seeks to block user traffic from service by sending adversarial traffic that reduces the available user capacity. In this paper, we explore the fundamental limits of volume-based network DoS attacks by studying the minimum required rate of adversarial traffic and investigating optimal attack strategies. We start our analysis with single-hop networks where user traffic is routed to servers following the Join-the-Shortest-Queue (JSQ) rule. Given the service rates of servers and arrival rates of user traffic, we first characterize the feasibility region of the attack and show that the attack is feasible if and only if the rate of the adversarial traffic lies in the region. We then design an attack strategy that is (i).optimal: it guarantees the success of the attack whenever the adversarial traffic rate lies in the feasibility region and (ii).oblivious: it does not rely on knowledge of service rates or user traffic rates. Finally, we extend our results on the feasibility region of the attack and the optimal attack strategy to multi-hop networks that employ Back-pressure (Max-Weight) routing. At a higher level, this paper addresses a class of dual problems of stochastic network stability, i.e., how to optimally de-stabilize a network.
基于容量的网络拒绝服务(DoS)攻击是一类网络攻击，攻击者通过发送减少可用用户容量的对抗性流量来阻止用户访问服务。在本文中，我们通过研究对抗流量的最小要求率和研究最优攻击策略来探索基于容量的网络DoS攻击的基本限制。我们从单跳网络开始分析，其中用户流量按照最短队列连接(join -the- short - queue, JSQ)规则路由到服务器。给定服务器的服务率和用户流量的到达率，我们首先描述了攻击的可行性区域，并证明了当且仅当敌对流量的速率位于该区域时，攻击是可行的。然后，我们设计了一种攻击策略，它是(i).最优的:它保证攻击的成功，无论敌对流量率在可行性区域和(ii).无关的:它不依赖于服务费率或用户流量率的知识。最后，我们将攻击的可行性区域和最优攻击策略扩展到采用背压(Max-Weight)路由的多跳网络。在更高的层次上，本文讨论了一类随机网络稳定性的对偶问题，即如何最优解稳网络。
{"title":"Fundamental Limits of Volume-based Network DoS Attacks","authors":"Xinzhe Fu, E. Modiano","doi":"10.1145/3393691.3394190","DOIUrl":"https://doi.org/10.1145/3393691.3394190","url":null,"abstract":"Volume-based network denial-of-service (DoS) attacks refer to a class of cyber attacks where an adversary seeks to block user traffic from service by sending adversarial traffic that reduces the available user capacity. In this paper, we explore the fundamental limits of volume-based network DoS attacks by studying the minimum required rate of adversarial traffic and investigating optimal attack strategies. We start our analysis with single-hop networks where user traffic is routed to servers following the Join-the-Shortest-Queue (JSQ) rule. Given the service rates of servers and arrival rates of user traffic, we first characterize the feasibility region of the attack and show that the attack is feasible if and only if the rate of the adversarial traffic lies in the region. We then design an attack strategy that is (i).optimal: it guarantees the success of the attack whenever the adversarial traffic rate lies in the feasibility region and (ii).oblivious: it does not rely on knowledge of service rates or user traffic rates. Finally, we extend our results on the feasibility region of the attack and the optimal attack strategy to multi-hop networks that employ Back-pressure (Max-Weight) routing. At a higher level, this paper addresses a class of dual problems of stochastic network stability, i.e., how to optimally de-stabilize a network.","PeriodicalId":188517,"journal":{"name":"Abstracts of the 2020 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133037638","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}
Key-value lookup engines running in fast memory are crucial components of many networked and distributed systems such as packet forwarding, virtual network functions, content distribution networks, distributed storage, and cloud/edge computing. These lookup engines must be memory-efficient because fast memory is small and expensive. This work presents a new key-value lookup design, called Ludo Hashing, which costs the least space (3.76 + 1.05 ι bits per key-value item for ι-bit values) among known compact lookup solutions including the recently proposed partial-key Cuckoo and Bloomier perfect hashing. In addition to its space efficiency, Ludo Hashing works well with most practical systems by supporting fast lookups, fast updates, and concurrent writing/reading. We implement Ludo Hashing and evaluate it with both micro-benchmark and two network systems deployed in CloudLab. The results show that in practice Ludo Hashing saves 40% to 80%+ memory cost compared to existing dynamic solutions. It costs only a few GB memory for 1 billion key-value items and achieves high lookup throughput: over 65 million queries per second on a single node with multiple threads.
{"title":"Ludo Hashing: Compact, Fast, and Dynamic Key-value Lookups for Practical Network Systems","authors":"Shouqian Shi, Chen Qian","doi":"10.1145/3393691.3394198","DOIUrl":"https://doi.org/10.1145/3393691.3394198","url":null,"abstract":"Key-value lookup engines running in fast memory are crucial components of many networked and distributed systems such as packet forwarding, virtual network functions, content distribution networks, distributed storage, and cloud/edge computing. These lookup engines must be memory-efficient because fast memory is small and expensive. This work presents a new key-value lookup design, called Ludo Hashing, which costs the least space (3.76 + 1.05 ι bits per key-value item for ι-bit values) among known compact lookup solutions including the recently proposed partial-key Cuckoo and Bloomier perfect hashing. In addition to its space efficiency, Ludo Hashing works well with most practical systems by supporting fast lookups, fast updates, and concurrent writing/reading. We implement Ludo Hashing and evaluate it with both micro-benchmark and two network systems deployed in CloudLab. The results show that in practice Ludo Hashing saves 40% to 80%+ memory cost compared to existing dynamic solutions. It costs only a few GB memory for 1 billion key-value items and achieves high lookup throughput: over 65 million queries per second on a single node with multiple threads.","PeriodicalId":188517,"journal":{"name":"Abstracts of the 2020 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116572677","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}
Classically, the design of multi-agent systems is approached using techniques from distributed optimization such as dual descent and consensus algorithms. Such algorithms depend on convergence to global consensus before any individual agent can determine its local action. This leads to challenges with respect to communication overhead and robustness, and improving algorithms with respect to these measures has been a focus of the community for decades. This paper presents a new approach for multi-agent system design based on ideas from the emerging field of local computation algorithms. The framework we develop, LOcal Convex Optimization (LOCO), is the first local computation algorithm for convex optimization problems and can be applied in a wide-variety of settings. We demonstrate the generality of the framework via applications to Network Utility Maximization (NUM) and the distributed training of Support Vector Machines (SVMs), providing numerical results illustrating the improvement compared to classical distributed optimization approaches in each case.
{"title":"Logarithmic Communication for Distributed Optimization in Multi-Agent Systems","authors":"Palma London, Shai Vardi, A. Wierman","doi":"10.1145/3393691.3394197","DOIUrl":"https://doi.org/10.1145/3393691.3394197","url":null,"abstract":"Classically, the design of multi-agent systems is approached using techniques from distributed optimization such as dual descent and consensus algorithms. Such algorithms depend on convergence to global consensus before any individual agent can determine its local action. This leads to challenges with respect to communication overhead and robustness, and improving algorithms with respect to these measures has been a focus of the community for decades. This paper presents a new approach for multi-agent system design based on ideas from the emerging field of local computation algorithms. The framework we develop, LOcal Convex Optimization (LOCO), is the first local computation algorithm for convex optimization problems and can be applied in a wide-variety of settings. We demonstrate the generality of the framework via applications to Network Utility Maximization (NUM) and the distributed training of Support Vector Machines (SVMs), providing numerical results illustrating the improvement compared to classical distributed optimization approaches in each case.","PeriodicalId":188517,"journal":{"name":"Abstracts of the 2020 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127206719","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 introduce a novel decentralized, multi agent version of the classical Multi-Arm Bandit (MAB) problem, consisting of n agents, that collaboratively and simultaneously solve the same instance of K armed MAB to minimize individual regret. The agents can communicate and collaborate among each other only through a pairwise asynchronous gossip based protocol that exchange a limited number of bits. In our model, agents at each point decide on (i) which arm to play, (ii) whether to, and if so (iii) what and whom to communicate with. We develop a novel algorithm in which agents, whenever they choose, communicate only arm-ids and not samples, with another agent chosen uniformly and independently at random. The peragent regret achieved by our algorithm is O(⌈K/n⌉ + log(n)/Δ log(T)), where Δ is the difference between the mean of the best and second best arm. Furthermore, any agent in our algorithm communicates (arm-ids to an uniformly and independently chosen agent) only a total of Θ(log(T)) times over a time interval of T. We compare our results to two benchmarks - one where there is no communication among agents and one corresponding to complete interaction, where an agent has access to the entire system history of arms played and rewards obtained of all agents. We show both theoretically and empirically, that our algorithm experiences a significant reduction both in per-agent regret when compared to the case when agents do not collaborate and each agent is playing the standard MAB problem (where regret would scale linearly in K), and in communication complexity when compared to the full interaction setting which requires T communication attempts by an agent over T arm pulls. Our result thus demonstrates that even a minimal level of collaboration among the different agents enables a significant reduction in per-agent regret.
{"title":"Social Learning in Multi Agent Multi Armed Bandits","authors":"Abishek Sankararaman, A. Ganesh, S. Shakkottai","doi":"10.1145/3393691.3394217","DOIUrl":"https://doi.org/10.1145/3393691.3394217","url":null,"abstract":"We introduce a novel decentralized, multi agent version of the classical Multi-Arm Bandit (MAB) problem, consisting of n agents, that collaboratively and simultaneously solve the same instance of K armed MAB to minimize individual regret. The agents can communicate and collaborate among each other only through a pairwise asynchronous gossip based protocol that exchange a limited number of bits. In our model, agents at each point decide on (i) which arm to play, (ii) whether to, and if so (iii) what and whom to communicate with. We develop a novel algorithm in which agents, whenever they choose, communicate only arm-ids and not samples, with another agent chosen uniformly and independently at random. The peragent regret achieved by our algorithm is O(⌈K/n⌉ + log(n)/Δ log(T)), where Δ is the difference between the mean of the best and second best arm. Furthermore, any agent in our algorithm communicates (arm-ids to an uniformly and independently chosen agent) only a total of Θ(log(T)) times over a time interval of T. We compare our results to two benchmarks - one where there is no communication among agents and one corresponding to complete interaction, where an agent has access to the entire system history of arms played and rewards obtained of all agents. We show both theoretically and empirically, that our algorithm experiences a significant reduction both in per-agent regret when compared to the case when agents do not collaborate and each agent is playing the standard MAB problem (where regret would scale linearly in K), and in communication complexity when compared to the full interaction setting which requires T communication attempts by an agent over T arm pulls. Our result thus demonstrates that even a minimal level of collaboration among the different agents enables a significant reduction in per-agent regret.","PeriodicalId":188517,"journal":{"name":"Abstracts of the 2020 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131672359","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}
Alexander Marder, M. Luckie, B. Huffaker, K. Claffy
Current methods to analyze the Internet's router-level topology with paths collected using traceroute assume that the source address for each router in the path is either an inbound or off-path address on each router. In this work, we show that outbound addresses are common in our Internet-wide traceroute dataset collected by CAIDA's Ark vantage points in January 2020, accounting for 1.7% - 5.8% of the addresses seen at some point before the end of a traceroute. This phenomenon can lead to mistakes in Internet topology analysis, such as inferring router ownership and identifying interdomain links. We hypothesize that the primary contributor to outbound addresses is Layer 3 Virtual Private Networks (L3VPNs), and propose vrfinder, a technique for identifying L3VPN outbound addresses in traceroute collections. We validate vrfinder against ground truth from two large research and education networks, demonstrating high precision (100.0%) and recall (82.1% - 95.3%). We also show the benefit of accounting for L3VPNs in traceroute analysis through extensions to bdrmapIT, increasing the accuracy of its router ownership inferences for L3VPN outbound addresses from 61.5% - 79.4% to 88.9% - 95.5%.
{"title":"vrfinder: Finding Outbound Addresses in Traceroute","authors":"Alexander Marder, M. Luckie, B. Huffaker, K. Claffy","doi":"10.1145/3393691.3394227","DOIUrl":"https://doi.org/10.1145/3393691.3394227","url":null,"abstract":"Current methods to analyze the Internet's router-level topology with paths collected using traceroute assume that the source address for each router in the path is either an inbound or off-path address on each router. In this work, we show that outbound addresses are common in our Internet-wide traceroute dataset collected by CAIDA's Ark vantage points in January 2020, accounting for 1.7% - 5.8% of the addresses seen at some point before the end of a traceroute. This phenomenon can lead to mistakes in Internet topology analysis, such as inferring router ownership and identifying interdomain links. We hypothesize that the primary contributor to outbound addresses is Layer 3 Virtual Private Networks (L3VPNs), and propose vrfinder, a technique for identifying L3VPN outbound addresses in traceroute collections. We validate vrfinder against ground truth from two large research and education networks, demonstrating high precision (100.0%) and recall (82.1% - 95.3%). We also show the benefit of accounting for L3VPNs in traceroute analysis through extensions to bdrmapIT, increasing the accuracy of its router ownership inferences for L3VPN outbound addresses from 61.5% - 79.4% to 88.9% - 95.5%.","PeriodicalId":188517,"journal":{"name":"Abstracts of the 2020 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116187014","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}
A new generation of cyber-physical systems has emerged with a large number of devices that continuously generate and consume massive amounts of data in a distributed and mobile manner. Accurate and near real-time decisions based on such streaming data are in high demand in many areas of optimization for such systems. Edge data analytics bring processing power in the proximity of data sources, reduce the network delay for data transmission, allow large-scale distributed training, and consequently help meeting real-time requirements. Nevertheless, the multiplicity of data sources leads to multiple distributed machine learning models that may suffer from sub-optimal performance due to the inconsistency in their states. In this work, we tackle the insularity, concept drift, and connectivity issues in edge data analytics to minimize its accuracy handicap without losing its timeliness benefits. Thus, we propose an efficient model synchronization mechanism for distributed and stateful data analytics. Staleness Control for Edge Data Analytics (SCEDA) ensures the high adaptability of synchronization frequency in the face of an unpredictable environment by addressing the trade-off between the generality and timeliness of the model.
{"title":"Staleness Control for Edge Data Analytics","authors":"Atakan Aral, M. Erol-Kantarci, I. Brandić","doi":"10.1145/3393691.3394219","DOIUrl":"https://doi.org/10.1145/3393691.3394219","url":null,"abstract":"A new generation of cyber-physical systems has emerged with a large number of devices that continuously generate and consume massive amounts of data in a distributed and mobile manner. Accurate and near real-time decisions based on such streaming data are in high demand in many areas of optimization for such systems. Edge data analytics bring processing power in the proximity of data sources, reduce the network delay for data transmission, allow large-scale distributed training, and consequently help meeting real-time requirements. Nevertheless, the multiplicity of data sources leads to multiple distributed machine learning models that may suffer from sub-optimal performance due to the inconsistency in their states. In this work, we tackle the insularity, concept drift, and connectivity issues in edge data analytics to minimize its accuracy handicap without losing its timeliness benefits. Thus, we propose an efficient model synchronization mechanism for distributed and stateful data analytics. Staleness Control for Edge Data Analytics (SCEDA) ensures the high adaptability of synchronization frequency in the face of an unpredictable environment by addressing the trade-off between the generality and timeliness of the model.","PeriodicalId":188517,"journal":{"name":"Abstracts of the 2020 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115844459","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}
Pengxiong Zhu, Keyu Man, Zhongjie Wang, Zhiyun Qian, Roya Ensafi, J. A. Halderman, Haixin Duan
Transnational Internet performance is an important indication of a country's level of infrastructure investment, globalization, and openness. We conduct a large-scale measurement study of transnational Internet performance in and out of 29 countries and regions,and find six countries that have surprisingly low performance. Five of them are African countries and the last is mainland China, a significant outlier with major discrepancies between down stream and upstream performance. We then conduct a comprehensive investigation of the unusual transnational Internet performance of mainland China, which we refer to as the "Great Bottleneck of China". Our results show that this bottleneck is widespread, affecting 79% of the receiver-sender pairs we measured. More than 70%of the pairs suffer from extremely slow speed (less than 1 Mbps)for more than 5 hours every day. In most tests the bottleneck appeared to be located deep inside China, suggesting poor network infrastructure to handle transnational traffic. The phenomenon has far-reaching implications for Chinese users' browsing habits as well as for the ability of foreign Internet services to reach Chinese customers.
{"title":"Characterizing Transnational Internet Performance and the Great Bottleneck of China","authors":"Pengxiong Zhu, Keyu Man, Zhongjie Wang, Zhiyun Qian, Roya Ensafi, J. A. Halderman, Haixin Duan","doi":"10.1145/3393691.3394180","DOIUrl":"https://doi.org/10.1145/3393691.3394180","url":null,"abstract":"Transnational Internet performance is an important indication of a country's level of infrastructure investment, globalization, and openness. We conduct a large-scale measurement study of transnational Internet performance in and out of 29 countries and regions,and find six countries that have surprisingly low performance. Five of them are African countries and the last is mainland China, a significant outlier with major discrepancies between down stream and upstream performance. We then conduct a comprehensive investigation of the unusual transnational Internet performance of mainland China, which we refer to as the \"Great Bottleneck of China\". Our results show that this bottleneck is widespread, affecting 79% of the receiver-sender pairs we measured. More than 70%of the pairs suffer from extremely slow speed (less than 1 Mbps)for more than 5 hours every day. In most tests the bottleneck appeared to be located deep inside China, suggesting poor network infrastructure to handle transnational traffic. The phenomenon has far-reaching implications for Chinese users' browsing habits as well as for the ability of foreign Internet services to reach Chinese customers.","PeriodicalId":188517,"journal":{"name":"Abstracts of the 2020 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124746148","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 plays a crucial role in many large scale computer systems. Much prior work has focused on systems with First-Come-First-Served (FCFS) servers. However, servers in practical systems are more complicated. They serve multiple jobs at once, and their service rate can depend on the number of jobs in service. Motivated by this, we study load balancing for systems using Limited-Processor-Sharing (LPS). Our model has heterogeneous servers, meaning the service rate curve and multiprogramming level (limit on the number of jobs sharing the processor) differs between servers. We focus on a specific load balancing policy: Join-Below-Threshold (JBT), which associates a threshold with each server and, whenever possible, dispatches to a server which has fewer jobs than its threshold. Given this setup, we ask: how should we configure the system to optimize objectives such as mean response time? Configuring the system means choosing both a load balancing threshold and a multiprogramming level for each server. To make this question tractable, we study the many-server mean field regime. In this paper we provide a comprehensive study of JBT in the mean field regime. We begin by developing a mean field model for the case of exponentially distributed job sizes. The evolution of our model is described by a differential inclusion, which complicates its analysis. We prove that the sequence of stationary measures of the finite systems converges to the fixed point of the differential inclusion, provided a unique fixed point exists. We derive simple conditions on the service rate curves to guarantee the existence of a unique fixed point. We demonstrate that when these conditions are not satisfied, there may be multiple fixed points, meaning metastability may occur. Finally, we give a simple method for determining the optimal system configuration to minimize the mean response time and related metrics. While our theoretical results are proven for the special case of exponentially distributed job sizes, we provide evidence from simulation that the system becomes insensitive to the job size distribution in the mean field regime, suggesting our results are more generally applicable.
{"title":"Mean Field Analysis of Join-Below-Threshold Load Balancing for Resource Sharing Servers","authors":"I. Horváth, Ziv Scully, B. Van Houdt","doi":"10.1145/3393691.3394199","DOIUrl":"https://doi.org/10.1145/3393691.3394199","url":null,"abstract":"Load balancing plays a crucial role in many large scale computer systems. Much prior work has focused on systems with First-Come-First-Served (FCFS) servers. However, servers in practical systems are more complicated. They serve multiple jobs at once, and their service rate can depend on the number of jobs in service. Motivated by this, we study load balancing for systems using Limited-Processor-Sharing (LPS). Our model has heterogeneous servers, meaning the service rate curve and multiprogramming level (limit on the number of jobs sharing the processor) differs between servers. We focus on a specific load balancing policy: Join-Below-Threshold (JBT), which associates a threshold with each server and, whenever possible, dispatches to a server which has fewer jobs than its threshold. Given this setup, we ask: how should we configure the system to optimize objectives such as mean response time? Configuring the system means choosing both a load balancing threshold and a multiprogramming level for each server. To make this question tractable, we study the many-server mean field regime. In this paper we provide a comprehensive study of JBT in the mean field regime. We begin by developing a mean field model for the case of exponentially distributed job sizes. The evolution of our model is described by a differential inclusion, which complicates its analysis. We prove that the sequence of stationary measures of the finite systems converges to the fixed point of the differential inclusion, provided a unique fixed point exists. We derive simple conditions on the service rate curves to guarantee the existence of a unique fixed point. We demonstrate that when these conditions are not satisfied, there may be multiple fixed points, meaning metastability may occur. Finally, we give a simple method for determining the optimal system configuration to minimize the mean response time and related metrics. While our theoretical results are proven for the special case of exponentially distributed job sizes, we provide evidence from simulation that the system becomes insensitive to the job size distribution in the mean field regime, suggesting our results are more generally applicable.","PeriodicalId":188517,"journal":{"name":"Abstracts of the 2020 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems","volume":"235 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121870246","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 study a quantum switch that distributes maximally entangled multipartite states to sets of users. The entanglement switching process requires two steps: first, each user attempts to generate bipartite entanglement between itself and the switch; and second, the switch performs local operations and a measurement to create multipartite entanglement for a set of users. In this work, we study a simple variant of this system, wherein the switch has infinite memory and the links that connect the users to the switch are identical. Further, we assume that all quantum states, if generated successfully, have perfect fidelity and that decoherence is negligible. This problem formulation is of interest to several distributed quantum applications, while the technical aspects of this work result in new contributions within queueing theory. Via extensive use of Lyapunov functions, we derive necessary and sufficient conditions for the stability of the system and closed-form expressions for the switch capacity and the expected number of qubits in memory.
{"title":"On the Analysis of a Multipartite Entanglement Distribution Switch","authors":"P. Nain, Gayane Vardoyan, S. Guha, D. Towsley","doi":"10.1145/3393691.3394203","DOIUrl":"https://doi.org/10.1145/3393691.3394203","url":null,"abstract":"We study a quantum switch that distributes maximally entangled multipartite states to sets of users. The entanglement switching process requires two steps: first, each user attempts to generate bipartite entanglement between itself and the switch; and second, the switch performs local operations and a measurement to create multipartite entanglement for a set of users. In this work, we study a simple variant of this system, wherein the switch has infinite memory and the links that connect the users to the switch are identical. Further, we assume that all quantum states, if generated successfully, have perfect fidelity and that decoherence is negligible. This problem formulation is of interest to several distributed quantum applications, while the technical aspects of this work result in new contributions within queueing theory. Via extensive use of Lyapunov functions, we derive necessary and sufficient conditions for the stability of the system and closed-form expressions for the switch capacity and the expected number of qubits in memory.","PeriodicalId":188517,"journal":{"name":"Abstracts of the 2020 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132303032","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}
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