Debugging faults in complex networks often requires capturing and analyzing traffic at the packet level. In this task, datacenter networks (DCNs) present unique challenges with their scale, traffic volume, and diversity of faults. To troubleshoot faults in a timely manner, DCN administrators must a) identify affected packets inside large volume of traffic; b) track them across multiple network components; c) analyze traffic traces for fault patterns; and d) test or confirm potential causes. To our knowledge, no tool today can achieve both the specificity and scale required for this task.

In a multi-tenant data center environment, the current paradigm for route control customization involves a labor-intensive ticketing process where tenants submit route control requests to the landlord. This results in tight coupling between tenants and the landlord, extensive human resource deployment, and long ticket resolution time. We propose Routing-as-a-Service (RaaS), a framework for tenant-directed route control in data centers. We show that RaaS-based implementation provides a route control platform where multiple tenants can perform route control independently with little administrative involvement, and the landlord can set the overall network policies. RaaS-based solutions can run on commercial off-the-shelf (COTS) hardware and leverage existing technologies, so it can be implemented in existing networks without major infrastructural overhaul. We present the design of RaaS, introduce its components, and evaluate a prototype based on RaaS.

Layer-4 load balancing is fundamental to creating scale-out web services. We designed and implemented Ananta, a scale-out layer-4 load balancer that runs on commodity hardware and meets the performance, reliability and operational requirements of multi-tenant cloud computing environments. Ananta combines existing techniques in routing and distributed systems in a unique way and splits the components of a load balancer into a consensus-based reliable control plane and a decentralized scale-out data plane. A key component of Ananta is an agent in every host that can take over the packet modification function from the load balancer, thereby enabling the load balancer to naturally scale with the size of the data center. Due to its distributed architecture, Ananta provides direct server return (DSR) and network address translation (NAT) capabilities across layer-2 boundaries. Multiple instances of Ananta have been deployed in the Windows Azure public cloud with combined bandwidth capacity exceeding 1Tbps. It is serving traffic needs of a diverse set of tenants, including the blob, table and relational storage services. With its scale-out data plane we can easily achieve more than 100Gbps throughput for a single public IP address. In this paper, we describe the requirements of a cloud-scale load balancer, the design of Ananta and lessons learnt from its implementation and operation in the Windows Azure public cloud.

To improve data availability and resilience MapReduce frameworks use file systems that replicate data uniformly. However, analysis of job logs from a large production cluster shows wide disparity in data popularity. Machines and racks storing popular content become bottlenecks; thereby increasing the completion times of jobs accessing this data even when there are machines with spare cycles in the cluster. To address this problem, we present Scarlett, a system that replicates blocks based on their popularity. By accurately predicting file popularity and working within hard bounds on additional storage, Scarlett causes minimal interference to running jobs. Trace driven simulations and experiments in two popular MapReduce frameworks (Hadoop, Dryad) show that Scarlett effectively alleviates hotspots and can speed up jobs by 20.2%.

In a multi-tenant data center environment, the current paradigm for route control customization involves a labor-intensive ticketing process, in which tenants submit route control requests to the landlord. This results in a tight coupling between tenants and landlord, extensive human resource deployment, and long ticket resolution time. We propose Routing-as-a-Service (RaaS), a framework for tenant-directed route control in data centers. We show that RaaS-based implementation provides a route control platform for multiple tenants to perform route control independently with little administrative involvement, and for the landlord to set the overall network policies. RaaS-based solutions can run on commercial off-the-shelf (COTS) hardware and leverage existing technologies, so it can be implemented in existing networks without major infrastructural overhaul. We present the design of RaaS, introduce its components, and evaluate a prototype based on RaaS.

Cloud service providers operate data centers around the world, and they depend on Global Traffic Management systems to direct requests from clients to the most appropriate data center to serve the requests. While GTM systems have been in-use for years, they are attracting re-newed interests due to the rapid expansion of cloud service providers' networks, the introduction of public DNS systems, as well as new proposals to alter how they should work and what information local DNS servers (LDNS) should make available to drive the GTM systems. This paper uses large-scale measurements conducted from more than 5M clients to establish properties of the current Internet that affect the design of the GTM systems, such as the stretch between a client's actual position and its LDNS from GTM's perspective, the impact of public DNS systems, and the granularity at which GTM decisions should be made. The results can inform the debate over how GTM systems should be designed.

Cloud data centers host diverse applications, mixing workloads that require small predictable latency with others requiring large sustained throughput. In this environment, today's state-of-the-art TCP protocol falls short. We present measurements of a 6000 server production cluster and reveal impairments that lead to high application latencies, rooted in TCP's demands on the limited buffer space available in data center switches. For example, bandwidth hungry "background" flows build up queues at the switches, and thus impact the performance of latency sensitive "foreground" traffic.

Networks cannot be managed without management plane communications among geographically distributed network devices and control agents. Unfortunately, the mechanisms used in commercial networks to support management plane communications are often hard to configure, insufficiently secured, and/or suboptimal in performance. This paper presents the design and implementation of the Meta-Management System (MMS), a network-layer subsystem that provides robust autonomic support for management plane communications. We demonstrate the practicality of the MMS via a fully functional implementation that runs on commodity hardware, and experimentally show that the MMS is efficient and scalable. The MMS software is freely available.

Internet backbone networks are under constant flux in order to keep up with demand and offer new features. The pace of change in technology often outstrips the pace of introduction of associated fault monitoring capabilities that are built into today's IP protocols and routers. Moreover, some of these new technologies cross networking layers, raising the potential for unanticipated interactions and service disruptions, which the individual layers' built-in monitoring capabilities may not detect. In these instances, operators typically employ higher layer monitoring techniques such as end-to-end liveness probing to detect lower or cross-layer failures, but lack tools to precisely determine where a detected failure may have occurred. In this paper, we evaluate the effectiveness of using risk modeling to translate high-level failure notifications into lower layer root causes in two specific scenarios in a tier-1 ISP. We show that a simple greedy heuristic works with accuracy exceeding 80 percent for many failure scenarios in simulation, while delivering extremely high precision (greater than 80 percent). We report our operational experience using risk modeling to isolate optical component and MPLS control plane failures in an ISP backbone.

The data centers used to create cloud services represent a significant investment in capital outlay and ongoing costs. We examine the costs of cloud service data centers today, and discuss challenges in optimizing work completed per dollar invested. To be agile and cost effective, data centers should allow agile resource allocation across large server pools. We discuss a practical network architecture that scales to support huge data centers with uniform high capacity between servers, performance isolation between services, and Ethernet layer-2 semantics. A working prototype, built using commodity switches, approaches in practice the high level of performance that the theory predicts.

We explore the nature of traffic in data centers, designed to support the mining of massive data sets. We instrument the servers to collect socket-level logs, with negligible performance impact. In a 1500 server operational cluster, we thus amass roughly a petabyte of measurements over two months, from which we obtain and report detailed views of traffic and congestion conditions and patterns. We further consider whether traffic matrices in the cluster might be obtained instead via tomographic inference from coarser-grained counter data.

To be agile and cost effective, data centers should allow dynamic resource allocation across large server pools. In particular, the data center network should enable any server to be assigned to any service. To meet these goals, we present VL2, a practical network architecture that scales to support huge data centers with uniform high capacity between servers, performance isolation between services, and Ethernet layer-2 semantics. VL2 uses (1) flat addressing to allow service instances to be placed anywhere in the network, (2) Valiant Load Balancing to spread traffic uniformly across network paths, and (3) end-system based address resolution to scale to large server pools, without introducing complexity to the network control plane. VL2's design is driven by detailed measurements of traffic and fault data from a large operational cloud service provider. VL2's implementation leverages proven network technologies, already available at low cost in high-speed hardware implementations, to build a scalable and reliable network architecture. As a result, VL2 networks can be deployed today, and we have built a working prototype. We evaluate the merits of the VL2 design using measurement, analysis, and experiments. Our VL2 prototype shuffles 2.7 TB of data among 75 servers in 395 seconds - sustaining a rate that is 94% of the maximum possible.

The development and maintenance of network device configurations is one of the central challenges faced by large network providers. Current network management systems fail to meet this challenge primarily because of their inability to adapt to rapidly evolving customer and provider-network needs, and because of mismatches between the conceptual models of the tools and the services they must support. In this paper, we present the Presto configuration management system that attempts to address these failings in a comprehensive and flexible way. Developed for and used during the last 5 years within a large ISP network, Presto constructs device-native configurations based on the composition of configlets representing different services or service options. Configlets are compiled by extracting and manipulating data from external systems as directed by the Presto configuration scripting and template language. We outline the configuration management needs of large-scale network providers, introduce the PRESTO system and configuration language, and reflect upon our experiences developing PRESTO configured VPN and VoIP services. In doing so, we describe how PRESTO promotes healthy configuration management practices.

A repository of router configuration files from production networks would provide the research community with a treasure trove of data about network topologies, routing designs, and security policies. However, configuration files have been largely unobtainable precisely because they provide detailed information that could be exploited by competitors and attackers. This paper describes a method for anonymizing router configuration files by removing all information that connects the data to the identity of the underlying network, while still preserving the structure of information that makes the data valuable to networking researchers. Anonymizing configuration files has unusual requirements, including preserving relationships between elements of data, anonymizing regular expressions, and robustly coping with more than 200 versions of the configuration language. Conventional tools and techniques are poorly suited to the problem. Our anonymization method has been validated with a major carrier, earning unprivileged researchers access to the configuration files of thousands of routers in hundreds of networks. Through example analysis, we demonstrate that the anonymized data retains the key properties of the network design. The paper sets out techniques that could be used in an attempt to break the anonymization, and it concludes our anonymization techniques are most applicable to enterprise networks, because the large number of enterprises and the difficulty of probing them from the outside make it hard to recognize an anonymized network based solely on publicly-available information about its topology or configuration. When applied to backbone networks, which are few in number and many of whose properties can be publicly measured, the anonymization might be broken by fingerprinting techniques described in this paper.

We present SGuard, a new fault-tolerance technique for distributed stream processing engines (SPEs) running in clusters of commodity servers. SGuard is less disruptive to normal stream processing and leaves more resources available for normal stream processing than previous proposals. Like several previous schemes, SGuard is based on rollback recovery [18]: it checkpoints the state of stream processing nodes periodically and restarts failed nodes from their most recent checkpoints. In contrast to previous proposals, however, SGuard performs checkpoints asynchronously: i.e. , operators continue processing streams during the checkpoint thus reducing the potential disruption due to the checkpointing activity. Additionally, SGuard saves the checkpointed state into a new type of distributed and replicated file system (DFS) such as GFS [22] or HDFS [9], leaving more memory resources available for normal stream processing. To manage resource contention due to simultaneous checkpoints by different SPE nodes, SGuard adds a scheduler to the DFS. This scheduler coordinates large batches of write requests in a manner that reduces individual checkpoint times while maintaining good overall resource utilization. We demonstrate the effectiveness of the approach through measurements of a prototype implementation in the Borealis [2] open-source SPE using HDFS [9] as the DFS.

Internet backbone networks are under constant flux, struggling to keep up with increasing demand. The pace of technology change often outstrips the deployment of associated fault monitoring capabilities that are built into today's IP protocols and routers. Moreover, some of these new technologies cross networking layers, raising the potential for unanticipated interactions and service disruptions that the built-in monitoring systems cannot detect. In such instances, failures may cause data packets to be silently dropped inside the network without triggering any alarms or responses (e.g., the failure is not routed around). So-called "silent failures" or "black holes" represent a critical threat to today's rapidly evolving networks. In this paper, we present a simple and effective method to detect and diagnose such silent failures. Our method uses active measurement between edge routers to raise alarms whenever end-to-end connectivity is disrupted, regardless of the cause. These alarms feed localization agents that employ spatial correlation techniques to isolate the root-cause of failure. Using data from two real systems deployed on sections of a tier-I ISP network, we successfully detect and localize three known black holes. Further, we present simulation results demonstrating that our system accurately and precisely (both greater than 80% according to our metrics) localizes a variety of failures classes.

Localizing the sources of performance problems in large enterprise networks is extremely challenging. Dependencies are numerous, complex and inherently multi-level, spanning hardware and software components across the network and the computing infrastructure. To exploit these dependencies for fast, accurate problem localization, we introduce an Inference Graph model, which is well-adapted to user-perceptible problems rooted in conditions giving rise to both partial service degradation and hard faults. Further, we introduce the Sherlock system to discover Inference Graphs in the operational enterprise, infer critical attributes, and then leverage the result to automatically detect and localize problems. To illuminate strengths and limitations of the approach, we provide results from a prototype deployment in a large enterprise network, as well as from testbed emulations and simulations. In particular, we find that taking into account multi-level structure leads to a 30% improvement in fault localization, as compared to two-level approaches.

Reliability is a critical requirement of the Internet. The availability and resilience of the Internet under failures can have significant global effects. However, in the current Internet routing architecture, achieving the high level of reliability demanded by many mission critical activities can be costly. In this paper, we first propose a novel solution framework called reliability as an interdomain service (REIN) that can be incrementally deployed in the Internet and may improve the redundancy of IP networks at low cost. We then present robust algorithms to efficiently utilize network redundancy to improve reliability. We use real IP network topologies and traffic traces to demonstrate the effectiveness of our framework and algorithms.

No abstract available.

The overall performance of a firewall is crucial in enforcing and administrating security, especially when the network is under attack. The continuous growth of the Internet, coupled with the increasing sophistication of the attacks, is placing stringent demands on firewall performance. In this paper, we describe a traffic-aware optimization framework to improve the operational cost of firewalls. Based on this framework, we design a set of tools that inspect and analyze both multidimensional firewall rules and traffic logs and construct the optimal equivalent firewall rules based on the observed traffic characteristics. To the best of our knowledge, this work is the first to use traffic characteristics in firewall optimization. Furthermore, we develop a novel adaptation mechanism that dynamically detects anomalous traffic behavior and adaptively alters the firewall rules to avoid serious performance degradation due to the traffic anomaly. To evaluate the performance of our approaches, we collected a large set of firewall rules and traffic logs at tens of enterprise networks managed by a Tier-1 service provider. Our evaluation results find these approaches very effective. In particular, we achieve more than 10 fold performance improvement by using the proposed traffic-aware firewall optimization.

Traffic engineering plays a critical role in determining the performance and reliability of a network. A major challenge in traffic engineering is how to cope with dynamic and unpredictable changes in traffic demand. In this paper, we propose COPE, a class of traffic engineering algorithms that optimize for the expected scenarios while providing a worst-case guarantee for unexpected scenarios. Using extensive evaluations based on real topologies and traffic traces, we show that COPE can achieve efficient resource utilization and avoid network congestion in a wide variety of scenarios.

The primary purpose of a network is to provide reachability between applications running on end hosts. In this paper, we describe how to compute the reachability a network provides from a snapshot of the configuration state from each of the routers. Our primary contribution is the precise definition of the potential reachability of a network and a substantial simplification of the problem through a unified modeling of packet filters and routing protocols. In the end, we reduce a complex, important practical problem to computing the transitive closure to set union and intersection operations on reachability set representations. We then extend our algorithm to model the influence of packet transformations (e.g., by NATs or ToS remapping) along the path. Our technique for static analysis of network reachability is valuable for verifying the intent of the network designer, troubleshooting reachability problems, and performing "what-if" analysis of failure scenarios.

A repository of router configuration files from production networks would provide the research community with a treasure trove of data about network topologies, routing designs, and security policies. However, configuration files have been largely unobtainable precisely because they provide detailed information that could be exploited by competitors and attackers. This paper describes a method for anonymizing router configuration files by removing all information that connects the data to the identity of the originating network, while still preserving the structure of information that makes the data valuable to networking researchers. Anonymizing configuration files has unusual requirements, including preserving relationships between elements of data, anonymizing regular expressions, and robustly coping with more than 200 versions of the configuration language, that mean conventional tools and techniques are poorly suited to the problem. Our anonymization method has been validated with a major carrier, earning unprivileged researchers access to the configuration files of more than 7600 routers in 31 networks. Through example analysis, we demonstrate that the anonymized data retains the key properties of the network design. We believe that applying our single-blind methodology to a large number of production networks from different sources would be of tremendous value to both the research and operations communities.

In any IP network, routing protocols provide the intelligence that takes a collection of physical links and transforms them into a network that enables packets to travel from one host to another. Though routing design is arguably the single most important design task for large IP networks, there has been very little systematic investigation into how routing protocols are actually used in production networks to implement the goals of network architects. We have developed a methodology for reverse engineering a coherent global view of a network's routing design from the static analysis of dumps of the local configuration state of each router. Starting with a set of 8,035 configuration files, we have applied this method to 31 production networks. In this paper we present a detailed examination of how routing protocols are used in operational networks. In particular, the results show the conventional model of interior and exterior gateway protocols is insufficient to describe the diverse set of mechanisms used by architects, and we provide examples of the more unusual designs and examine their trade-offs. We discuss the strengths and weaknesses of our methodology, and argue that it opens paths towards new understandings of network behavior and design.

IP forwarding anomalies, triggered by equipment failures, implementation bugs, or configuration errors, can significantly disrupt and degrade network service. Robust and reliable detection of such anomalies is essential to rapid problem diagnosis, problem mitigation, and repair. We propose a simple, robust method that integrates routing and traffic data streams to reliably detect forwarding anomalies. The overall method is scalable, automated and self-training. We find this technique effectively identifies forwarding anomalies, while avoiding the high false alarms rate that would otherwise result if either stream were used unilaterally.

A matrix giving the traffic volumes between origin and destination in a network has tremendously potential utility for network capacity planning and management. Unfortunately, traffic matrices are generally unavailable in large operational IP networks. On the other hand, link load measurements are readily available in IP networks. In this paper, we propose a new method for practical and rapid inference of traffic matrices in IP networks from link load measurements, augmented by readily available network and routing configuration information. We apply and validate the method by computing backbone-router to backbone-router traffic matrices on a large operational tier-1 IP network -- a problem an order of magnitude larger than any other comparable method has tackled. The results show that the method is remarkably fast and accurate, delivering the traffic matrix in under five seconds.

Understanding the variability of Internet traffic in backbone networks is essential to better plan and manage existing networks, as well as to design next generation networks. However, most traffic analyses that might be used to approach this problem are based on detailed packet or flow level measurements, which are usually not available throughout a large network. As a result there is a poor understanding of backbone traffic variability, and its impact on network operations (e.g. on capacity planning or traffic engineering).This paper introduces a metric for measuring backbone traffic variability that is grounded on simple but powerful traffic theory. What sets this metric apart, however, is that we present a method for making practical measurements of the metric using widely available SNMP traffic measurements. Furthermore, we use a novel method to overcome the major limitation of SNMP measurements -- that they only provide link statistics. The method, based on a "gravity model", derives an approximate traffic matrix from the SNMP data. In addition to simulations, we use more than 1 year's worth of SNMP data from an operational IP network of about 1000 nodes to test our methods. We also delve into the degree and sources of variability in real backbone traffic, providing insight into the true nature of traffic variability.

Open Shortest Path First (OSPF) is widely deployed in IP networks to manage intra-domain routing. OSPF is a link-state protocol, in which routers reliably flood "Link State Advertisements" (LSAs), enabling each to build a consistent, global view of the routing topology. Reliable performance hinges on routing stability, yet the behavior of large operational OSPF networks is not well understood. In this paper, we provide a case study on the eharacteristics and dynamics of LSA traffic for a large enterprise network. This network consists of several hundred routers, distributed in tens of OSPF areas, and connected by LANs and private lines. For this network, we focus on LSA traffic and analyze: (a) the class of LSAs triggered by OSPF's soft-state refresh, (b) the class of LSAs triggered by events that change the status of the network, and (c) a class of "duplicate" LSAs received due to redundancy in OSPF's reliable LSA flooding mechanism. We derive the baseline rate of refresh-triggered LSAs automatically from network configuration information. We also investigate finer time scale statistical properties of this traffic, including burstiness, periodicity, and synchronization. We discuss root causes of event-triggered and duplicate LSA traffic, as well as steps identified to reduce this traffic (e.g., localizing a failing router or changing the OSPF configuration).

In large scale, operational Internet protocol networks, creating timely, accurate and network-wide views of the intradomain topology is a fundamental problem. Topical network backbones consist of hundreds of routers, which establish routing adjacencies with one another through static configuration and dynamic routing protocols, such as open shortest path first (OSPF). We describe the design of an OSPF topology server which tracks intradomain topology, by passively and safely listening into OSPFs reliable flooding mechanism, or by pushing and pulling information from the routers via the simple network management protocol. We provide a detailed evaluation and comparison of the two approaches in terms of operational issues, reliability and timeliness of information.

As IP technologies providing both tremendous capacity and the ability to establish dynamic security associations between endpoints emerge, virtual private networks (VPNs) are going through dramatic growth. The number of endpoints per VPN is growing and the communication pattern between endpoints is becoming increasingly hard to predict. Consequently, users are demanding dependable, dynamic connectivity between endpoints, with the network expected to accommodate any traffic matrix, as long as the traffic to the endpoints does not overwhelm the capacity of the respective ingress and egress links. We propose a new service interface, termed a hose, to provide the appropriate performance abstraction. A hose is characterized by the aggregate traffic to and from one endpoint in the VPN to a set of other endpoints in the VPN, and by an associated performance guarantee. Hoses provide important advantages to a VPN customer: (1) flexibility to send traffic to a set of endpoints without having to specify the detailed traffic matrix, and (2) reduction in the size of access links through multiplexing gains obtained from the natural aggregation of the flows between endpoints. As compared with the conventional point-to-point (or customer pipe) model for managing quality of service (QoS), hoses provide reduction in the state information a customer must maintain. On the other hand, hoses would appear to increase the complexity of the already difficult problem of resource management to support QoS. To manage network resources in the face of this increased uncertainty, we consider both conventional statistical multiplexing techniques, and a new resizing technique based on online measurements. To study these performance issues, we run trace-driven simulations, using traffic derived from AT&T's voice network and from a large corporate data network. From the customer's perspective, we find that aggregation of traffic at the hose level provides significant multiplexing gains. From the provider's perspective, we find that the statistical multiplexing and resizing techniques deal effectively with uncertainties about the traffic, providing significant gains over the conventional alternative of a mesh of statically sized customer pipes between endpoints.

Engineering a large IP backbone network without an accurate network-wide view of the traffic demands is challenging. Shifts in user behavior, changes in routing policies, and failures of network elements can result in significant (and sudden) fluctuations in load. We present a model of traffic demands to support traffic engineering and performance debugging of large Internet service provider networks. By defining a traffic demand as a volume of load originating from an ingress link and destined to a set of egress links, we can capture and predict how routing affects the traffic traveling between domains. To infer the traffic demands, we propose a measurement methodology that combines flow-level measurements collected at all ingress links with reachability information about all egress links. We discuss how to cope with situations where practical considerations limit the amount and quality of the necessary data. Specifically, we show how to infer interdomain traffic demands using measurements collected at a smaller number of edge links-the peering links connecting to neighboring providers. We report on our experiences in deriving the traffic demands in the AT&T IP Backbone, by collecting, validating, and joining very large and diverse sets of usage, configuration, and routing data over extended periods of time. The paper concludes with a preliminary analysis of the observed dynamics of the traffic demands and a discussion of the practical implications for traffic engineering.

Engineering a large IP backbone network without an accurate, network-wide view of the traffic demands is challenging. Shifts in user behavior, changes in routing policies, and failures of network elements can result in significant (and sudden) fluctuations in load. In this paper, we present a model of traffic demands to support traffic engineering and performance debugging of large Internet Service Provider networks. By defining a traffic demand as a volume of load originating from an ingress link and destined to a set of egress links, we can capture and predict how routing affects the traffic traveling between domains. To infer the traffic demands, we propose a measurement methodology that combines flow-level measurements collected at all ingress links with reachability information about all egress links. We discuss how to cope with situations where practical considerations limit the amount and quality of the necessary data. Specifically, we show how to infer interdomain traffic demands using measurements collected at a smaller number of edge links --- the peering links connecting to neighboring providers. We report on our experiences in deriving the traffic demands in the AT&T IP Backbone, by collecting, validating, and joining very large and diverse sets of usage, configuration, and routing data over extended periods of time. The paper concludes with a preliminary analysis of the observed dynamics of the traffic demands and a discussion of the practical implications for traffic engineering.

As IP technologies providing both tremendous capacity and the ability to establish dynamic secure associations between endpoints emerge, Virtual Private Networks (VPNs) are going through dramatic growth. The number of endpoints per VPN is growing and the communication pattern between endpoints is becoming increasingly hard to forecast. Consequently, users are demanding dependable, dynamic connectivity between endpoints, with the network expected to accommodate any traffic matrix, as long as the traffic to the endpoints does not overwhelm the rates of the respective ingress and egress links. We propose a new service interface, termed a hose, to provide the appropriate performance abstraction. A hose is characterized by the aggregate traffic to and from one endpoint in the VPN to the set of other endpoints in the VPN, and by an associated performance guarantee.Hoses provide important advantages to a VPN customer: (i) flexibility to send traffic to a set of endpoints without having to specify the detailed traffic matrix, and (ii) reduction in the size of access links through multiplexing gains obtained from the natural aggregation of the flows between endpoints. As compared with the conventional point to point (or customer-pipe) model for managing QoS, hoses provide reduction in the state information a customer must maintain. On the other hand, hoses would appear to increase the complexity of the already difficult problem of resource management to support QoS. To manage network resources in the face of this increased uncertainty, we consider both conventional statistical multiplexing techniques, and a new resizing technique based on online measurements.To study these performance issues, we run trace driven simulations, using traffic derived from AT&T's voice network, and from a large corporate data network. From the customer's perspective, we find that aggregation of traffic at the hose level provides significant multiplexing gains. From the provider's perspective, we find that the statistical multiplexing and resizing techniques deal effectively with uncertainties about the traffic, providing significant gains over the conventional alternative of a mesh of statically sized customer-pipes between endpoints.

In order to provide an adequate quality of service to large-bandwidth calls, such as video conference calls, service providers of integrated services networks may want to allow some customers to book their calls ahead, i.e., make advance reservations. We propose a scheme for sharing resources among book-ahead (BA) calls (that announce their call holding times as well as their call initiation times upon arrival) and non-BA calls (that do not announce their holding times). It is possible to share resources without allowing any calls in progress to be interrupted, but in order to achieve a more efficient use of resources, we think that it may be desirable to occasionally allow a call in progress to be interrupted. (In practice, it may be possible to substitute service degradation, such as bit dropping or coarser encoding of video, for interruption.) Thus, we propose an admission control algorithm in which a call is admitted if an approximate interrupt probability (computed in real time) is below a threshold. Simulation experiments show that the proposed admission control algorithm can be better (i.e., yield higher total utilization or higher revenue) than alternative schemes that do not allow interruption, such as a strict partitioning of resources.

Calls that make large, persistent demands for network resources will be denied consistent service, unless the network employs adequate control mechanisms. Calls of this type include video conferences. Although overprovisioning network capacity would increase the likelihood of accepting these calls, it is a very expensive option to apply uniformly in a large network, especially as the calls require high bandwidth and low blocking probabilities. Such large calls typically require coordination of geographically distributed facilities and people at the end systems. So it is natural to book the network requirements ahead of their actual use. We present a new, effective admission control algorithm for booking ahead network services. The admission control is based on a novel application of effective bandwidth theory to the time domain. Systematic and comprehensive simulation experiments provide an understanding of how booking ahead affects call blocking and network utilization, considering call duration, number of links, bandwidth, routing, and the mix of book ahead versus immediate arrival traffic. Allowing some calls to book ahead radically reduces their chance of service denial, while allowing flexible and efficient sharing of network resources with normal calls that do not book ahead.

This paper presents a shaper architecture that scales to a large number of connections with diverse burstiness and bandwidth parameters. The architecture arbitrates fairly between connections with conforming cells by carefully integrating leaky-bucket traffic shaping with rate-based scheduling algorithms. Through a careful combination of per-connection queueing and approximate sorting, the shaper performs a small, bounded number of operations in response to each arrival and departure, independent of the number of connections and cells. To handle a wider range of rate parameters, a hierarchical arbitration scheme can reduce the implementation overheads and the interference between competing connections. Simulation experiments demonstrate that the architecture limits shaping delay and traffic distortions, even under heavy congestion.

Emerging broad-band switches must accommodate the diverse traffic parameters and quality-of-service requirements of voice, data, and video applications. End-to-end performance guarantees depend on connections complying with traffic contracts as their cells travel through the network. This paper presents a leaky-bucket shaper architecture that scales to a large number of connections with diverse burstiness and bandwidth parameters. In contrast to existing designs, the proposed architecture arbitrates fairly between connections with conforming cells by carefully integrating leaky-bucket traffic shaping with rate-based scheduling algorithms. Through a careful combination of per-connection queueing and approximate sorting, the shaper performs a small, bounded number of operations in response to each arrival and departure, independent of the number of connections and cells. When the shaper must handle a wide range of rate parameters, a hierarchical arbitration scheme can reduce the implementation overheads and further limit interference between competing connections. Through simulation experiments, we demonstrate that the architecture limits cell-shaping delay and traffic distortions, even in periods of heavy congestion. The efficient combination of traffic shaping and link scheduling results in an effective architecture for managing buffer and bandwidth resources in large, high-speed ATM switches.

Computational techniques are presented for the connection-level performance evaluation of communication networks, with stochastic multirate traffic, state-dependent admission control, alternate routing, and general topology-all characteristics of emerging integrated service networks. The techniques involve solutions of systems of fixed-point equations, which estimate equilibrium network behaviour. Although similar techniques have been applied with success to single-rate fully connected networks, the curse of dimensionality arises when the techniques are extended to multirate, multihop networks, and the cost of solving the fixed point equations exactly is exponential. This exponential barrier is skirted by exploiting, in particular, a close relationship with the network reliability problem, and by borrowing effective heuristics from the reliability domain. A series of experiments are reported on, comparing the estimates from the new techniques to the results of discrete-event simulations.

In emerging communication networks (B-ISDN based on asynchronous transfer mode (ATM) technology), a single link may carry traffic for thousands of connections with different traffic parameters and quality-of-service requirements. High-speed links, coupled with small packet/cell sizes, require efficient switch architectures that can handle cell arrivals and departures every few microseconds, or faster. This paper presents a collection of self-clocked fair queueing (SCFQ) architectures amenable to efficient hardware implementation in network switches. Exact and approximate implementations of SCFQ efficiently handle a moderate range of connection bandwidth parameters, while hierarchical arbitration schemes scale to a large range of throughput requirements. Simulation experiments demonstrate that these architectures divide link bandwidth fairly on a small time scale, preserving connection bandwidth and burstiness properties.

Lubachevsky [5] introduced a new parallel simulation technique intended for systems with limited interactions between their many components or sites. Each site has a local simulation time, and the states of the sites are updated asynchronously. This asynchronous updating appears to allow the simulation to achieve a high degree of parallelism, with very low overhead in processor synchronization. The key issue for this asynchronous updating technique is: how fast do the local times make progress in the large system limit? We show that in a simple K-random interaction model the local times progress at a rate 1/(K + 1). More importantly, we find that the asymptotic distribution of local times is described by a traveling wave solution with exponentially decaying tails. In terms of the parallel simulation, though the interactions are local, a very high degree of global synchronization results, and this synchronization is succinctly described by the traveling wave solution. Moreover, we report on experiments that suggest that the traveling wave solution is universal; i.e., it holds in realistic scenarios (out of reach of our analysis) where interactions among sites are not random.

Computational techniques are presented for connection-level performance evaluation of communication networks, with stochastic multirate traffic, state dependent admission control, alternate routing, and general topology --- all characteristics of emerging integrated service networks. The techniques involve solutions of systems of fixed point equations, which estimate equilibrium network behavior. Though similar techniques have been applied with success to single-rate fully connected networks, the curse of dimensionality arises when the techniques are extended to multirate, multihop networks, and the cost of solving the fixed point equations exactly is exponential. This exponential barrier is skirted by exploiting, in particular, a close relationship with the network reliability problem, and by borrowing effective heuristics from the reliability domain. A series of experiments are reported on, comparing the estimates from the new techniques to the results of discrete event simulations.

Considers the use of massively parallel architectures to execute a trace-driven simulation of a single cache set. A method is presented for the least-recently-used (LRU) policy, which, regardless of the set size C, runs in time O(log N) using N processors on the EREW (exclusive read, exclusive write) parallel model. A simpler LRU simulation algorithm is given that runs in O(C log N) time using N/log N processors. We present timings of this algorithm's implementation on the MasPar MP-1, a machine with 16384 processors. A broad class of reference-based line replacement policies are considered, which includes LRU as well as the least-frequently-used (LFU) and random replacement policies. A simulation method is presented for any such policy that, on any trace of length N directed to a C line set, runs in O(C log N) time with high probability using N processors on the EREW model. The algorithms are simple, have very little space overhead, and are well suited for SIMD implementation.>

Article Free Access Share on Experience in massively parallel discrete event simulation Authors: Albert G. Greenberg View Profile , Boris D. Lubachevsky View Profile , Li-C. Wang View Profile Authors Info & Claims SPAA '93: Proceedings of the fifth annual ACM symposium on Parallel Algorithms and ArchitecturesAugust 1993 Pages 193–202https://doi.org/10.1145/165231.165256Published:01 August 1993Publication History 1citation218DownloadsMetricsTotal Citations1Total Downloads218Last 12 Months7Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Deflection routing is a simple, decentralized, and adaptive method for routing data packets in communication networks. The focus of this work is on deflection routing in the Manhattan street network (a two-dimensional directed mesh), although the analytic approach should apply to any regular network. Two approximate performance models that give sharp estimates of the steady-state throughput and the average packet delay for packets admitted to the network are presented. The results of extensive simulation experiments are reported, which corroborate the models' predictions. The results show that deflection routing is very effective. Two measures of the merit of a network for deflection routing are its diameter and its deflection index. Networks are presented whose diameter and deflection index are near the optimal values.>

Abstract. Fair Queuing is a novel queuing discipline with important applications to data networks that support variable-size packets and to systems where the cost of preempting jobs from service is high. The disciphne controls a single server shared by N job arrival streams with each stream allotted a separate queue. After every job completion, the server is assigned to serve, without possibihty of interruption, the job at the head of one of the queues (as soon as at least one job appears in the system). Fair Queuing is designed to handle arbitrary job arrival sequences with essentially no a priori knowledge of their attributes. such that each stream receives its ‘Lfam share ” of serwce. In this paper, we consider two variants of the fair queuing discipline, and rigorously establish their fairness wa sample path comparisons with the head-of-line processor sharing disclphne, a mathematical idealization that prowdes a fairness paradigm. An efficient Implementation of one of the fair queuing disciplines is presented, In passing, a new, fast method for simulating processor sharing is derived. Simulation results are presented to further explore the comparison between fair queuing and processor sharing.

An approximate analysis of the transient and steady state behavior of deflection routing in hypercube networks is presented, under a uniform traffic model. In deflection routing congestion causes packets admitted to the network to be temporarily misrouted rather than buffered or dropped. The approximations show that deflection routing performs remarkably well in hypercube networks, for small as well as large networks and for the whole range from light to heavy load. Simulations suggest that the approximations are quite accurate.>

A simple model of master/slave processors is presented, along with two simple, practical scheduling algorithms. An approximate analysis of the model yields simple formulas for performance measures in terms of the hardware and workload parameters, and gives insight into the power and the limitations of master/slave systems. In particular, formulae are obtained for the maximal processing power (throughput) of the system, a quantity that remains bounded as the number of slave processors increases. This analysis is applicable to symmetric multiprocessors, where performance considerations such as cache performance may dictate asymmetric assignment of system tasks to the processors.>

New methods are presented for parallel simulation of discrete event systems that, when applicable, can usefully employ a number of processors much larger than the number of objects in the system being simulated, Abandoning the distributed event list approach, the simulation problem is posed using recurrence relations.We bring three algorithmic ideas to bear on parallel simulation: parallel prefix computation, parallel merging, and iterative folding.Efficient parallel simulations are given for (in turn) the G/G/l queue, a variety of queueing networks having a global first come first served structure (e.g., a series of queues with finite buffers), acyclic networks of queues, and networks of queues with feedbacks and cycles.In particular, the problem of simulating the arrival and departure times for the first N jobs to a single G/G/l queue is solved in time proportional to N/P + log P using P processors.

New methods are presented for parallel simulation of discrete event systems that, when applicable, can usefully employ a number of processors much larger than the number of objects in the system being simulated. Abandoning the distributed event list approach, the simulation problem is posed using recurrence relations. We bring three algorithmic ideas to bear on parallel simulation: parallel prefix computation, parallel merging, and iterative folding. Efficient parallel simulations are given for (in turn) the G/G/1 queue, a variety of queueing networks having a global first come first served structure (e.g., a series of queues with finite buffers), acyclic networks of queues, and networks of queues with feedbacks and cycles. In particular, the problem of simulating the arrival and departure times for the first N jobs to a single G/G/1 queue is solved in time proportional to N/P + log P using P processors.

RECAL is a new recurrence relation for calculating the partition function and various queue length moments for closed, product form networks. In this paper we discuss a number of the issues involved in the software implementation of RECAL on both sequential computers and parallel, shared memory computers. After a brief description of RECAL, we describe software implementing RECAL on a sequential computer. In particular, we discuss the problems involved in indexing and data storage. Next we describe code implementing RECAL on a parallel, shared memory computer. Special attention is given to designing a special buffer for temporary data storage and several other important features of the parallel code. Finally, we touch on software for serial and parallel implementations of a tree algorithm for RECAL.

Binary exponential backoff is a randomized protocol for regulating transmissions on a multiple-access broadcast channel. Ethernet, a local-area network, is built upon this protocol. The fundamental theoretical issue is stability: Does the backlog of packets awaiting transmission remain bounded in time, provided the rates of new packet arrivals are small enough? It is assumedn≥ 2 stations share the channel, each having an infinite buffer where packets accumulate while the station attempts to transmit the first from the buffer. Here, it is established that binary exponential backoff is stable if the sum of the arrival rates is sufficiently small. Detailed results are obtained on which rates lead to stability whenn= 2 stations share the channel. In passing, several other results are derived bearing on the efficiency of the conflict resolution process. Simulation results are reported that, in particular, indicate alternative retransmission protocols can significantly improve performance.

The problem of computing the maximum of n inputs on an asynchronous parallel computer is considered. In general, the inputs may arrive staggered in time, the number of processors available to the maximization algorithm may vary during its execution, and the number of inputs, n , may be initially unknown. Two simple, efficient algorithms to compute the maximum are presented. Each algorithm may be invoked asynchronously, as new inputs and processors arrive. Performance measures that account for the response times of the invocations are introduced, and the algorithms are analyzed under these measures.

New, improved algorithms are proposed for regulating access to a multiple-access channel, a common channel shared by many geographically distributed computing stations. A conflict of multiplicity n occurs when n stations transmit simultaneously to the channel. As a result, all stations receive feedback indicating whether n is 0, 1, or ≥2. If n = 1, the transmission succeeds; whereas if n ≥ 2, all the transmissions fail. Algorithms are presented and analyzed that allow the conflicting stations to compute a stochastic estimate n * of n , cooperatively, at small cost, as a function of the feedback elicited during its execution. An algorithm to resolve a conflict among two or more stations controls the retransmissions of the conflicting stations so that each eventually transmits singly to the channel. Combining one of our estimation algorithms with a tree algorithm (of Capetanakis, Hayes, and Tsybakov and Mikhailov) then leads to a hybrid algorithm for conflict resolution. Several efficient combinations are possible, the most efficient of which resolves conflicts about 20 percent faster on average than any of the comparable algorithms reported to date.

We consider the problem of selecting the kth largest of n inputs, where initially the inputs are stored in the n leaf processors of the 2n − 1 processor tree machine. A probabilistic algorithm is presented that implements a type of pipelining to solve the problem in a simple data driven fashion, with each processor maintaining just a constant amount of state information. On any problem instance, the algorithm's running time is2n]/[loglog n], for some constant c > 0, with overwhelming probability.

Article Simple, efficient asynchronous parallel algorithms for maximization Share on Authors: Albert G. Greenberg AT&T Bell Laboratories, 600 Mountain Avenue, Murray Hill, NJ AT&T Bell Laboratories, 600 Mountain Avenue, Murray Hill, NJView Profile , Boris D. Lubachevsky AT&T Bell Laboratories, 600 Mountain Avenue, Murray Hill, NJ AT&T Bell Laboratories, 600 Mountain Avenue, Murray Hill, NJView Profile , Andrew M. Odlyzko AT&T Bell Laboratories, 600 Mountain Avenue, Murray Hill, NJ AT&T Bell Laboratories, 600 Mountain Avenue, Murray Hill, NJView Profile Authors Info & Claims PODC '85: Proceedings of the fourth annual ACM symposium on Principles of distributed computingAugust 1985 Pages 300–308https://doi.org/10.1145/323596.323625Online:01 August 1985Publication History 1citation122DownloadsMetricsTotal Citations1Total Downloads122Last 12 Months2Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

We consider the stochastic behavior of binary exponential backoff, a probabilistic algorithm for regulating transmissions on a multiple access channel. Ethernet, a local area network, is built upon this algorithm. The fundamental theoretical issue is stability: does the backlog of packets awaiting transmission remain bounded in time, provided the rates of new packet arrivals are small enough?

A problem related to the decentralized control of a multiple access channel is considered: Suppose k stations from an ensemble of n simultaneously transmit to a multiple access channel that provides the feedback 0, 1, or 2+, denoting k = 0, k = 1, or k ≥ 2, respectively. If k = 1, then the transmission succeeds. But if k ≥ 2, as a result of the conflict, none of the transmissions succeed. An algorithm to resolve a conflict determines how to schedule retransmissions so that each of the conflicting stations eventually transmits singly to the channel. In this paper, a general model of deterministic algorithms to resolve conflicts is introduced, and it is established that, for all k and n (2 ≤ k ≤ n ), Ω( k (log n )/(log k )) time must elapse in the worst case before all k transmissions succeed.

A basic problem in the decentralized control of a multiple access channel is to resolve the conflicts that arise when several stations transmit simultaneously to the channel. Capetanakis, Hayes, and Tsybakov and Mikhailov found a deterministic {\em tree algorithm} that resolves conflicts amongkstations from an ensemble ofnin time\Theta (k + k \log (n / k))in the worst case. In this algorithm, at each step, the choice of which stations to enable to transmit depends crucially on feedback information provided by the channel. We show that ifkis given {\em a priori} then such conflicts can be resolved in time\Theta (k + k \log (n / k))using an algorithm in which the corresponding choices do not depend on feedback.

Freivalds recently reported a construction of a 2-way probabilistic finite automaton M that recognizes the set {a/sup m/b /sup m/ : m /spl ges/ 1} with arbitrarily small probability of error. This result implies that probabilistic machines of this type are more powerful than their deterministic, nondeterministic, and alternating counterparts. Freivalds' construction has a negative feature: the automation M runs in /spl Omega/ (2/sup n/2/n) expected time in the worst case on inputs of length n. We show that it is impossible to do significantly better. Specifically, no 2-way probabilistic finite automaton that runs in n/sup O (1)/ expected time recognizes {a/sup m/b/sup m/ : m /spl ges/ 1} with probability of error bounded away from 1/2. In passing we derive results on the densities of regular sets, the fine structure of Freivalds' construction, and the behavior of random walks controlled by Markov chains.

A conflict of multiplicity k occurs when k stations transmit simultaneously to a multiple access channel. As a result, all stations receive feedback indicating whether k is 0, 1, or is ≥ 2. If k = 1 the transmission succeeds, whereas if k ≥ 2 all the transmissions fail. In general, no a priori information about k is available. We present and analyze an algorithm that enables the conflicting stations to cooperatively compute a statistical estimate of k, at small cost, as a function of the feedback elicited during its execution. An algorithm to resolve a conflict among two or more stations controls the retransmissions of the conflicting stations so that each eventually transmits singly to the channel. Combining our estimation algorithm with a binary tree algorithm leads to a hybrid algorithm that resolves conflicts faster on average than any other reported to date.

In this paper, we investigate the power of such broadcast in solving a paradigmatic problem in distributed computing. Imagine a network in which each node machine Ni (1≤i≤n) keeps a Boolean value vi in local memory. The vi 's determine a set S={i: vi=1}. The non-emptiness problem on n nodes is to find some i in S, or else find that S is empty. In practice, a problem of this type arises in two ways: