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Distributed network monitoring and multicommodity flows: a primaldual approach
, 2007
"... A canonical distributed optimization problem is solving a Covering/Packing Linear Program in a distributed environment with fast convergence and low communication and space overheads. In this paper, we consider the following covering and packing problems, which are the dual of each other: • Passive ..."
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Cited by 6 (2 self)
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A canonical distributed optimization problem is solving a Covering/Packing Linear Program in a distributed environment with fast convergence and low communication and space overheads. In this paper, we consider the following covering and packing problems, which are the dual of each other: • Passive Commodity Monitoring: minimize the total cost of monitoring devices used to measure the network traffic on all paths. • Maximum Throughput Multicommodity flow: maximize the total value of the flow with bounded edge capacities. We present the first known distributed algorithms for both of these problems that converge to (1 + ɛ)approximate solutions in polylogarithmic time with communication and space overheads that depend on the maximal path length but are almost independent of the size of the entire network. Previous distributed solutions achieving similar approximations required convergence time, communication, or space overheads that depend polynomially on the size of the entire network. The sequential simulation of our algorithm is more efficient than the fastest known approximation algorithms for multicommodity flows, e.g., GargKönemann [14], when the maximal path length is small.
Distributed Strategies for Channel Allocation and Scheduling in SoftwareDefined Radio Networks
"... Abstract—Equipping wireless nodes with multiple radios can significantly increase the capacity of wireless networks, by making these radios simultaneously transmit over multiple nonoverlapping channels. However, due to the limited number of radios and available orthogonal channels, designing efficie ..."
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Abstract—Equipping wireless nodes with multiple radios can significantly increase the capacity of wireless networks, by making these radios simultaneously transmit over multiple nonoverlapping channels. However, due to the limited number of radios and available orthogonal channels, designing efficient channel assignment and scheduling algorithms in such networks is a major challenge. In this paper, we present provablygood distributed algorithms for simultaneous channel allocation of individual links and packetscheduling, in SoftwareDefined Radio (SDR) wireless networks. Our distributed algorithms are very simple to implement, and do not require any coordination even among neighboring nodes. A novel access hash function or random oracle methodology is one of the key drivers of our results. With this access hash function, each radio can know the transmitters ’ decisions for links in its interference set for each time slot without introducing any extra communication overhead between them. Further, by utilizing the inductivescheduling technique, each radio can also backoff appropriately to avoid collisions. Extensive simulations demonstrate that our bounds are valid in practice. I.
Fast load balancing via bounded best response
, 2008
"... It is known that the dynamics of best response in an environment of noncooperative users may converge to a good solution when users play sequentially, but may cycle far away from the global optimum solution when users play concurrently. We introduce the notion of bounded best response where users r ..."
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Cited by 5 (2 self)
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It is known that the dynamics of best response in an environment of noncooperative users may converge to a good solution when users play sequentially, but may cycle far away from the global optimum solution when users play concurrently. We introduce the notion of bounded best response where users react with best response subject to rules that are forced locally by the system. We investigate the problem of load balancing tasks on machines in a bipartite graph model and show that the dynamics of concurrent bounded best response converges to a nearoptimum solution quickly, i.e., with polylogarithmic number of rounds. This is in contrast to the concurrent best response dynamics which cycles far away from the optimum and to any sequential dynamics which requires at least a linear number of rounds to get to a reasonable solution.
Approximating Wardrop Equilibria with Finitely Many Agents
"... We present efficient algorithms for computing approximate Wardrop equilibria in a distributed and concurrent fashion. Our algorithms are exexuted by a finite number of agents each of which controls the flow of one commodity striving to balance the induced latency over all utilised paths. The set of ..."
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Cited by 5 (2 self)
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We present efficient algorithms for computing approximate Wardrop equilibria in a distributed and concurrent fashion. Our algorithms are exexuted by a finite number of agents each of which controls the flow of one commodity striving to balance the induced latency over all utilised paths. The set of allowed paths is represented by a DAG. Our algorithms are based on previous work on policies for infinite populations of agents. These policies achieve a convergence time which is independent of the underlying network and depends mildly on the latency functions. These policies can neither be applied to a finite set of agents nor can they be simulated directly due to the exponential number of paths. Our algorithms circumvent these problems by computing a randomised path decomposition in every communication round. Based on this decomposition, flow is shifted from overloaded to underloaded paths. This way, our algorithm can handle exponentially large path collections in polynomial time. Our algorithms are stateless, and the number of communication rounds depends polynomially on the approximation quality and is independent of the topology and size of the network.
Stateless Distributed Gradient Descent for Positive Linear Programs
 STOC'08
, 2008
"... We develop a framework of distributed and stateless solutions for packing and covering linear programs, which are solved by multiple agents operating in a cooperative but uncoordinated manner. Our model has a separate “agent ” controlling each variable and an agent is allowed to readoff the current ..."
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Cited by 4 (1 self)
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We develop a framework of distributed and stateless solutions for packing and covering linear programs, which are solved by multiple agents operating in a cooperative but uncoordinated manner. Our model has a separate “agent ” controlling each variable and an agent is allowed to readoff the current values only of those constraints in which it has nonzero coefficients. This is a natural model for many distributed applications like flow control, maximum bipartite matching, and dominating sets. The most appealing feature of our algorithms is their simplicity and polylogarithmic convergence. For the packing LP max{c · x  Ax ≤ b, x ≥ 0}, the algorithm associates a dual variable yi = exp [ 1 ɛ ( Aix − 1)] for each constraint i and bi each agent j iteratively increases (resp. decreases) xj multiplicatively
Brief Announcement: Bridging the TheoryPractice Gap in MultiCommodity Flow Routing
"... In the concurrent multicommodity flow problem, we are given a capacitated network G = (V, E) of switches V connected by links E, and a set of commodities K = {(si, ti, di)}. The objective is to maximize the minimum fraction λ of any demand di that is routed from source si to target ti. This problem ..."
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In the concurrent multicommodity flow problem, we are given a capacitated network G = (V, E) of switches V connected by links E, and a set of commodities K = {(si, ti, di)}. The objective is to maximize the minimum fraction λ of any demand di that is routed from source si to target ti. This problem has been studied extensively
Scalable, Optimal Flow Routing in Datacenters via Local Link Balancing
"... Datacenter networks should support high network utilization. Yet today’s routing is typically load agnostic, so large flows can starve other flows if routed through overutilized links. Recent proposals for datacenter routing, such as centralized scheduling or endhost multipathing, do not offer opt ..."
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Datacenter networks should support high network utilization. Yet today’s routing is typically load agnostic, so large flows can starve other flows if routed through overutilized links. Recent proposals for datacenter routing, such as centralized scheduling or endhost multipathing, do not offer optimal throughput, and they suffer from scalability concerns and other limitations. We observe that most datacenter networks have a symmetry property that admits a better solution. We develop a simple, switchlocal algorithm called LocalFlow that routes the maximum multicommodity flow in these networks, while tolerating failures and asymmetry. LocalFlow evades existing hardness results by allowing flows to be fractionally split, but it minimizes the number of split flows by considering the aggregate flow per destination and allowing slack in the splitting. Through an optimization decomposition, we show that LocalFlow, in conjunction with unmodifed end hosts’ TCP, achieves an optimal solution. Splitting flows presents several new technical challenges that must be overcome in order to achieve high accuracy, interact properly with TCP, and be implementable on emerging standards for programmable, commodity switches. LocalFlow acts independently on each switch. This makes it highly scalable, allows it to adapt quickly to dynamic workloads, and enables flexibility in the deployment of its controlplane scheduling logic. We present detailed packetlevel simulations that demonstrate LocalFlow’s practicality and optimality, comparing it to a variety of alternative schemes and configurations, using distributions and traces from real datacenter workloads. 1.
Capacity of Wireless Networks under SINR Interference Constraints
, 2011
"... A fundamental problem in wireless networks is to estimate their throughput capacity given a set of wireless nodes and a set of connections, what is the maximum rate at which data can be sent on these connections. Most of the research in this direction has focused either on random distributions of p ..."
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A fundamental problem in wireless networks is to estimate their throughput capacity given a set of wireless nodes and a set of connections, what is the maximum rate at which data can be sent on these connections. Most of the research in this direction has focused either on random distributions of points, or has assumed simple graphbased models for wireless interference. In this paper, we study the capacity estimation problem using a realistic Signal to Interference Plus Noise Ratio (SINR) model for interference, on arbitrary wireless networks without any assumptions on node distributions. The problem becomes much more challenging for this setting, because of the nonlocality of the SINR model. Recent work by Moscibroda et al. (IEEE INFOCOM 2006, ACM MobiHoc 2006) has shown that the throughput achieved by using SINR models can differ significantly from that obtained by using graphbased models. In this work, we develop polynomial time algorithms to provably approximate the throughput capacity of wireless network under the SINR model. 1
Stateless Near Optimal Flow Control with Polylogarithmic Convergence
"... We design completely local, stateless, and selfstabilizing flow control mechanism to be executed by “greedy” agents associated with individual flow paths. Our mechanism is very natural and can be described in a single line: If a path has many “congested ” edges, decrease the flow on the path by a s ..."
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We design completely local, stateless, and selfstabilizing flow control mechanism to be executed by “greedy” agents associated with individual flow paths. Our mechanism is very natural and can be described in a single line: If a path has many “congested ” edges, decrease the flow on the path by a small multiplicative factor, otherwise increase its flow by a small multiplicative factor. The mechanism does not require any initialization or coordination between the agents. We show that starting from an arbitrary feasible flow, the mechanism always maintains feasibility and reaches, after polylogarithmic number of rounds, a 1 + ɛ approximation of the maximum throughput multicommodity flow. Moreover, the total number of rounds in which the solution is not 1 + ɛ approximate is also polylogarithmic. Previous distributed solutions in our model either required a state since they used a primaldual approach or had very slow (polynomial) convergence.
Jetway: Minimizing Costs on InterDatacenter Video Traffic
"... It is typical for video streaming service providers (such as NetFlix) to rely on services from cloud providers (such as Amazon), in order to build a scalable video streaming platform with high availability. The trend is largely driven by the fact that cloud providers deploy a number of datacenters i ..."
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It is typical for video streaming service providers (such as NetFlix) to rely on services from cloud providers (such as Amazon), in order to build a scalable video streaming platform with high availability. The trend is largely driven by the fact that cloud providers deploy a number of datacenters interconnected by highcapacity links, spanning different geographical regions. Video traffic across datacenters, such as video replication and transit servertocustomer video serving, constitutes a large portion of a cloud provider’s interdatacenter traffic. Charged by ISPs, such interdatacenter video traffic incurs substantial operational costs to a cloud provider. In this paper, we argue that costs incurred by such interdatacenter video traffic can be reduced or even minimized by carefully choosing paths, and by assigning flow rates on each interdatacenter link along every path. We present Jetway, a new set of algorithms designed to minimize cloud providers ’ operational costs on interdatacenter video traffic, by optimally routing video flows in an online fashion. Algorithms in Jetway are designed by following a methodical approach based on an indepth theoretical analysis. As a highlight of this paper, we have built a realworld system framework to implement and deploy Jetway in the Amazon EC2 datacenters. With both simulations and realworld experiments using our implementation, we show that Jetway effectively helps transmitting videos across datacenters with reduced costs to cloud providers and satisfactory realworld performance.