Software-defined networking (SDN) is fundamentally changing the way networks operate, enabling programmable and flexible network management and configuration. As the de facto standard southbound interface of SDN, OpenFlow defines how the control plane interacts with the data forwarding plane. In OpenFlow, flow tables play a significant role in packet forwarding. However, the size of the flow table is limited due to power, cost, and silicon area constraints and capacity-limited tables cannot hold all of the active flows in medium-to-large-scale SDN networks. Thus, when a flow table reaches capacity, an intelligent eviction strategy, which efficiently manages the limited flow table resource, is critical. In this paper, we propose Smart Table EntRy Eviction for OpenFlow Switches (STEREOS), which uses machine learning to classify flow entries as active or inactive and forms the basis for intelligent eviction. Trace-driven simulations demonstrate that STEREOS increases flow table usage by more than 50% and reduces incorrect flow entry evictions by up to 78%, compared with the dominant Least Recently Used eviction policy. Moreover, packet-level simulations of a datacenter network demonstrate that STEREOS can greatly reduce the control overhead, increase overall network throughput by 19%, and reduce packet loss rate by 70%.

Distributing high-quality 360 VR content to clients with modern networks using existing delivery techniques is economically challenging at scale. Despite the abundance of research in multihomed delivery and client-side adaptive bitrate (ABR) algorithms, current techniques continue to suffer from similar fundamental laws; namely the reliance on inflexible networks, inaccurate network models, and rigid control rules leading to suboptimal performance. This paper proposes a unique approach to address many of these limitations. First, through the implementation of a modified version of an MPEG initiative called Server and Network Assisted DASH (SAND) for use in multipath networks enabling opportunistic messaging between the client, server, and network. Secondly, through the optimization of ABR algorithms that were generated using reinforcement learning and finally, by active management of multipath enabled transport protocols. Testbed experimental results revealed that our technique outperformed many state-of-the- art ABR algorithms in multipath networks. The average multipath differential delay decreased by 30% or more and outperformed standard Multipath TCP (MPTCP) and Quick UDP Internet Connections (QUIC) in key quality of experience (QoE) metrics by up to 20%.

OpenFlow is the de facto southbound interface for Software Defined Networking (SDN), which defines the interactions between the control plane and data plane. In OpenFlow, flow table is significant in packet forwarding. However, the capacity of the flow table is limited due to power, cost, and silicon area constraints. In this case, it is extremely important to efficiently manage flow tables. In this paper, we focus on one of the flow table managing mechanisms defined in OpenFlow, proactive flow entry deletion. This mechanism enables the controller to proactively delete flow entries in flow tables by explicitly sending specific OpenFlow messages to switches. The key challenge for this mechanism is to determine which flow entries should be removed. To address this challenge, we propose a machine learning based proactive flow entry deletion which can learn from the historical data of flow entries and thus predict the time when a flow entry will be last referred to. Based on the predictions, the flow entry with smallest last refer time will be deleted. Our simulations show that our proposal can achieve up to 23% fewer capacity misses compared with the random deletion and First-In-First-Out (FIFO) deletion policies, as well as a slightly decreased (2% ~ 8%) overhead.

Software Defined Networking (SDN) is fundamentally changing the way networks work, which enables programmable and flexible network management and configuration. As the de facto southbound interface of SDN, OpenFlow defines how the control plane can directly interact with the forwarding plane. In OpenFlow, flow tables play a significant role in packet forwarding. However, the capacity of flow table is limited due to power, cost, and silicon area constraints. The capacity-limited flow table cannot hold the explosive flows generated by the fine- grained granularity control mechanism used in SDN. Thus the flow table is frequently overflowed. In the case of overflow, eviction strategy which replaces existing flow entries with the new ones is critical to guarantee the efficient usage of the flow table. In this paper, we present a machine learning based eviction approach which can identify whether a flow entry is active or inactive and thus timely evict the inactive flow entries when flow table overflow occurs. Our simulations based on real network packet traces show that the proposed method can increase the usage of flow table by more than 55% and reduce the number of capacity misses by up to 80%, compared with the Least Recently Used eviction policy.

Omnidirectional streaming requirements can be an impediment to delivering virtual reality (VR) quality con- tent at scale. Leveraging multihomed device network options becomes a viable approach to ease congestion. Multipath TCP (MPTCP) has the ability to utilize multiple networks simultaneously, but its performance can suffer from excessive overhead due to its use of large reordering buffers. In this paper, we propose a unique approach to removing this limitation with a software-defined networking architecture. Using Mininet with Floodlight controllers to manage available paths, we designed a framework that actively manages Quick UDP Internet Connections (QUIC) and/or MPTCP network usage based on network characteristics. Testbed experiments reveal that the start- up buffering delay is decreased by up to 60% or more when using our algorithm in networks with packet loss rates above 2%. These results are favorable even when compared against another modern multipath algorithm that attempts to remedy this issue. Our analysis shows our framework outperforms standard MPTCP in network usage efficiency by up to 50% at packet loss rates above 1%.

Software Defined Networking (SDN) is an emerging networking paradigm which separates the control plane from the data forwarding plane. To control large networks, designing a scalable control plane for SDN is one of the most significant challenges. In this paper, we focus on comparing the scalability performance of SDN control planes with different architectures. We first compare simulation and emulation approach in scalability evaluation for SDN, and conclude that simulation is the most appropriate approach for our study. To apply simulation approach, we identify two critical processes (flow setup and statistics collection) which restrict the scalability of the control plane. Based on these two processes, we abstract switches and controllers in the five existing SDN control plane architectures (i.e., centralized, P2P with local view, P2P with global view, hierarchical, and hybrid). These abstractions allow us to build simulations based on ns-3 network simulator for SDN networks with different control plane architectures. Our experiments show that the hierarchical control plane achieves the best scalability performance, while the centralized and P2P with global view control planes get the worst performance. Furthermore, our simulations demonstrate the significance of statistics collection speed in scaling the SDN network. We believe this study can give implications about control plane architecture selection for operators (developers) who want to deploy (develop) their own SDN networks (controllers).

Adaptive bitrate streaming is one of the most popular methods to deliver high definition video content for HTTP clients. Commonly used techniques rely primarily on the estimation of network bandwidth and/or buffer state client-side. Inaccurate estimation leads to performance degradation due to a lack of correct bitrate encoding per chunk, and poor network utilization as a result of inflexible transport protocols. In this paper, we propose a unique approach to removing the client-side estimation of network bandwidth and buffer state. Three modular HTTP/2 server push-based strategies leveraging Multipath TCP are designed to improve the delivery of the video content in lossy environments. The goal is to improve start-up delay and overall performance. Laboratory experiments reveal that the start-up buffering delay is decreased by 40% or more when using HTTP/2 over MPTCP in networks with packet loss rates above 2%.

When designing, implementing and executing large-scale distributed simulation codes it is well-known the so-called optimistic time synchronization methods often lead to better overall performance as compared to the so-called conservative methods. Particularly when the model being simulated suffers from small lookahead between two or more of the logical processes working on the overall simulation execution. However, the design and implementation of simulation model codes intended to be executed using optimistic approach is often daunting, especially when working with existing codes which were designed and implemented to be used in a conservative approach. When executing simulation events, optimistic simulation models must first save any existing state in the model prior to executing codes that change that state, and furthermore must implement rollback code which restores that state to the original value. The necessity for such extra software functionality is often a barrier to the complete implementation of an optimistic-based simulation model and the corresponding improved performance.

The ability to execute the original source code for network protocols and applications within a network simulation environment frees the simulation modeler from the time consuming task of having to create, test and debug models representing these applications. This work extends the functionality of the Direct Code Execution (DCE) framework of ns-3 by incorporating the ability to call NVIDIA CUDA kernels from within simulated ns-3 nodes. This new functionality allows researchers to simulate large scale GPU applications in the realm of new and more flexible paradigms such as software-defined networking. Along with presenting this new functionality, this paper examines the different options available within the framework for communicating between the simulated nodes and the GPU. Each implementation is tested with multiple example CUDA kernels to demonstrate how they perform.

As software-defined networking (SDN) grows beyond its original aim to simply separate the control and data network planes, it becomes useful both financially and analytically to provide adequate mechanisms for simulating this new paradigm. A number of simulation/emulation tools for modeling SDN, such as Mininet, are already available. A new, novel framework for providing SDN simulation has been provided in this work using the network simulator ns-3. The ns-3 module Direct Code Execution (DCE) allows real-world network applications to be run within a simulated network topology. This work employs DCE for running the SDN controller library POX and its applications on nodes in a simulated network topology. In this way, real-world controller applications can be completely portable between simulation and actual deployment. This work also describes a user-defined ns-3 application mimicking an SDN switch supporting OpenFlow 1.0 that can interact with real-world controllers. To evaluate its performance, this ns-3 DCE SDN framework is compared against Mininet as well as some other readily available SDN simulation/emulation tools. Metrics such as realtime performance, memory usage, and reliability in terms of packet loss are analyzed across the multiple simulation/emulation tools to gauge how they compare.

This paper presents Manifold, an open-source parallel simulation framework for multicore architectures. It consists of a parallel simulation kernel, a set of microarchitecture components, and an integrated library of power, thermal, reliability, and energy models. Using the components as building blocks, users can assemble multicore architecture simulation models and perform serial or parallel simulations to study the architectural and/or the physical characteristics of the models. Users can also create new components for Manifold or port existing models. Importantly, Manifold's component-based design provides the user with the ability to easily replace a component with another for efficient explorations of the design space. It also allows components to evolve independently and making it easy for simulators to incorporate new components as they become available. The distinguishing features of Manifold include i) transparent parallel execution, ii) integration of power, thermal, reliability, and energy models, iii) full system simulation, e.g., operating system and system binaries, and iv) component-based design. In this paper we provide a description of the software architecture of Manifold, and its main elements - a parallel multicore emulator front-end and a parallel component-based back-end timing model. We describe a few simulators that are built with Manifold components to illustrate its flexibility, and present test results of the scalability obtained on full-system simulation of coherent shared-memory multicore models with 16, 32, and 64 cores executing PARSEC and SPLASH-2 benchmarks.

In this paper, we consider the problem of building mesh-based multicast routing structures that account for the impact of interference in wireless multihop networks. Our analysis is based on the most accurate known interference model, namely the physical interference model. We first analyze interference-aware mesh structures that augment individual paths in a multicast tree. Based on this analysis, we propose two interference-aware multicast mesh routing structures, which extend an interference-aware Steiner multicast tree in two different ways to form interference-aware meshes. We evaluate the performances of our proposed interference-aware multicast mesh structures in wireless networks where wireless links are bursty and nodes can be faulty. Under these conditions, we show that our proposed algorithms provide up to 80% increase in goodput over existing tree-based multicast routing structures and up to 45% increase in goodput over existing mesh-based multicast routing structures.

No abstract available.

In this paper, we revisit the problem of optimal tree-based routing structures for multicast in wireless multihop networks, but accounting for the impact of interference. Our analysis is based on the most accurate known interference model, namely the SINR-based physical interference model. We first study the problem in a low-intensity multicast scenario where we derive optimal node selection strategies for different subtree structures. We then extend these analyses to account for interference between consecutive packets in higher-rate multicast scenarios. Based on these analyses, we propose and evaluate two new multicast routing structures: the interference-aware Steiner tree (IAST) algorithm, which requires global knowledge of node locations, and the fixed-distance tree merging (FTM) algorithm, which does not require global knowledge. We show that our proposed algorithms provide up to 57% reduction in schedule length and up to 41% increase in goodput over existing tree-based routing structures.

There has been little research that studies the effect of partitioning on parallel simulation of multicore systems. This paper presents our study of this important problem in the context of Null-message-based synchronization algorithm for parallel multicore simulation. This paper focuses on coarse grain parallel simulation where each core and its cache slices are modeled within a single logical process (LP) and different partitioning schemes are only applied to the interconnection network. In this paper we show that encapsulating the entire on-chip interconnection network into a single logical process is an impediment to scalable simulation. This baseline partitioning and two other schemes are investigated. Experiments are conducted on a subset of the PARSEC benchmarks with 16-, 32-, 64- and 128-core models. Results show that the partitioning scheme has a significant impact on simulation performance and parallel efficiency. Beyond a certain system scale, one scheme consistently outperforms the other two schemes, and the performance as well as efficiency gaps increases as the size of the model increases - with up to 4.1 times faster speed and 277% better efficiency for 128-core models. We explain the reasons for this behavior, which can be traced to the features of the Null-message-based synchronization algorithm. Because of this, we believe that, if a component has increasing number of inter-LP interactions with increasing system size, such components should be partitioned into several sub-components to achieve better performance.

There continues to be many advances in the theory and practice of Modeling and Simulation (M&S). However, some of these can be considered as Grand Challenges; issues whose solutions require significant focused effort across a community, sometimes with ground-breaking collaborations with new disciplines. In 2002, the first M&S Grand Challenges Workshop was held in Dagstuhl, Germany, in an attempt to focus efforts on key areas. In 2012, a new initiative was launched to continue these Grand Challenge efforts. Panel members of this third Grand Challenge present their views on M&S Grand Challenges. Themes presented in this panel include M&S Methodology; Agent-based M&S; M&S in Systems Engineering; Cyber Systems Modeling; and Network Simulation.

This paper presents two optimization techniques for the basic Null-message algorithm in the context of parallel simulation of multicore computer architectures. Unlike the general, application-independent optimization methods, these are application-specific optimizations that make use of system properties of the simulation application. We demonstrate in two aspects that the domain-specific knowledge offers great potential for optimization. First, it allows us to send Null-messages much less eagerly, thus greatly reducing the amount of Null-messages. Second, the internal state of the simulation application allows us to make conservative forecast of future outgoing events. This leads to the creation of an enhanced synchronization algorithm called Forecast Null-message algorithm, which, by combining the forecast from both sides of a link, can greatly improve the simulation look-ahead. Compared with the basic Null-message algorithm, our optimizations greatly reduce the number of Null-messages and increase simulation performance significantly as a result. On a subset of the PARSEC benchmarks, a maximum speedup of about 6 is achieved with 17 LPs.

An important aspect of any network simulation that models wireless networks is the design and implementation of the Propagation Loss Model. The propagation loss model is used to determine the wireless signal strength at the set of receivers for any packet being transmitted by a single transmitter. There are a number of different ways to model this phenomenon, and these vary both in terms of computational complexity and in the measured performance of the wireless network being modeled. In fact, the ns -- 3 simulator presently has 11 different loss models included in the simulator library. We performed a detailed study of these models, comparing their overall performance both in terms of the computational complexity of the algorithms, as well as the measured performance of the wireless network being simulated. The results of these simulation experiments are reported and discussed. Not surprisingly, we observed considerable variation in both metrics.

This paper presents a general fault-tolerant event detection scheme that allows nodes to detect erroneous local decisions by leveraging the local decisions reported by their neighbors. This detection scheme can handle cases where nodes have different accuracy levels. The derived fault-tolerant estimator is proven to be optimal under the maximum a posteriori (MAP) criterion. An equivalent weighted voting scheme is also derived. Further, two new error models are derived to take into account the neighbor distance and the geographical distributions of the two decision quorums. These models are particularly suitable for detection applications where the event under consideration is highly localized. The fault-tolerant estimator is simulated using a network of 1,024 nodes deployed randomly in a square region and assigned random probabilities of failure. Several estimation schemes that allow nodes to learn their error rates continuously are developed. These error rates are used in the distributed estimation schemes to assign appropriate weights to the nodes in the voting scheme.

We bridge a gap between Wireless Sensor Network (WSN) research and information theory research by utilizing information theoretic measures as top-level design metrics for WSNs. In particular, we use source coding theorems from information theory to determine a loose bound on the minimum number of sensors that must successfully report their measurements in order for the WSN to accurately estimate the features of some phenomenon. Although application of the information theoretic techniques is straightforward and this WSN problem has been well studied, to our knowledge this is the first time that these tools have been applied to the problem in a general setting.

We define the collection channel in a Wireless Sensor Network (WSN) and show how this collection channel can be used in the proofs of new types of coding theorems for WSNs. These coding theorems differ from traditional coding theorems in that they are used to describe aspects of the WSN other than source and channel codes. We also determine the capacity of the collection channel. This capacity is complementary to the transport capacity of a communication system and represents the WNS's ability to obtain information about a phenomenon that it is sensing.

Functional-level hardware simulation is a commonly used approach to validate various digital logic designs prior to fabrication. Discrete event simulation is particularly well suited for such modeling efforts, and is in widespread use throughout the computer architecture research community, as well as commercial entities that design and produce products based on digital logic. In prior work, we introduced a novel pull-model approach that resulted in a considerable improvement in execution time for these digital logic simulations. However, a significant shortcoming of our prior reported work using the pull-model was the lack of inclusion of timing delays between components in the model. The prior work assumed that any change in the output state of a component was immediately known to the corresponding inputs of directly connected devices. While this assumption is clearly unrealistic, such a model is still useful in determining the logical validity of digital designs, and allows quick-look analysis that the designs are logically correct. Here, we enhance the pull-model approach to include rise-time delays and speed-of-light delays in the component interconnects, and show that we still achieve considerable performance improvement over more traditional approaches. Additionally, we report performance results for several different test cases of varying sizes, and show performance improvements across the board.

We consider the problem of broadcasting video streams to a large number of users through P2P overlay, which is emerging as a killer application after P2P file sharing. Considerable research has been conducted in this area in recent years. But most of them are experimental or on a particular algorithm/implementation. The lacking of a general model prevents formal studies on P2P streaming. In this paper, we present some interesting and important results of our work on the theoretical aspect of P2P streaming. The main contribution of this paper includes (1) proposing a general probability model making the analytical analysis and evaluation of P2P streaming possible; (2) with the help of the proposed model, proving that the upper bound of the efficiency for any P2P streaming networks is 1-(1/2k), where k is the average number of neighbors each peer knows about; (3) obtaining the condition to reach that upper bound and showing how to use it to guide real design; (4) showing that P2P streaming is quite different from P2P file sharing. It is not always safe to apply the experience obtained in one context to the other. In particular, we demonstrate that unlike P2P file sharing, P2P streaming does not always benefit from a wide piece diversity (i.e., the total number of the available pieces across the entire network at a given time).

Historically, large-scale low-lookahead parallel simulation has been a difficult problem. As a solution, we have designed a global synchronization unit (GSU) that would reside centrally on a multi-core chip and asynchronously compute the lower bound on time stamps (LBTS), the minimum timestamp of all unprocessed events in the simulation, on demand to synchronize conservative parallel simulators. Our GSU also accounts for transient messages, messages that have been sent but not yet processed by their recipient, eliminating the need for the simulator to acknowledge received messages. In this paper we analyze the sensitivity of simulation performance to the time required to access the GSU. The sensitivity analysis revealed that with GSU access times as high as hundreds of cycles, there was still a significant performance advantage over the baseline shared-memory implementation.

Simulation methods have become an integral part of almost all aspects of networking research and particularly in the area of wireless networks. Any new protocol or methodology that is proposed for wireless networks almost always will be demonstrated using simulations. However, there is little evidence that existing models of physical layer signal propagation and signal strength calculations produce realistic or meaningful results. Indeed, there have been a number of empirical studies that have shown little predictability in measured signal strength and packet receipt probability for deployed networks. Common assumptions used in simulation models about the behavior of the wireless signal have been shown to often be invalid. These prior works motivated us to design a stochastic model for wireless signal propagation in our GTNetS simulator. Our model is based on a detailed empirical measurement study and uses random variations to produce more realistic behavior for wireless packet transmissions and reception modeling.

Quality of service (QoS) in wireless 802.11 networks cannot be achieved without the support for service differentiation at the medium access layer. IEEE802.11 suffers from QoS limitations that have often been summarized in the literature. We present simulation results to show the effectiveness of several proposed techniques of improving the QoS in the IEEE802.11 protocol. Specifically, we closely examine the sliding contention window (SCW) technique and present a performance study that augments the work done by the original authors, by considering the determinism of QoS achieved. We find that simple approaches such as priority queuing and differential inter-frame spacing provide service differentiation at a single station but are not successful in providing it across a number of stations. Further, we find that the SCW approach provides a high service differentiation and deterministic QoS guarantees. It achieves these by a strict separation of the contention window range of different priority classes. Further, it measures and reacts to losses suffered by high priority flows at the MAC layer and to the increase in network load. These cross layer design techniques help SCW maintain good performance even at high network load. However, this strict service differentiation comes at the cost of reduced performance of low priority flows at heavy load.

Network Simulation tools have played very significant role in wireless network research in the past decade. Compared to wired networks, there are a lot more parameters that effect the behavior of wireless networks. For reasons of simplicity and computational complexity, some details are abstracted out when implementing protocol models in network simulators. IEEE 802.11 has been the dominant protocol for the MAC and PHY layers of most wireless networks in practice. Subtle implementation variations in the 802.11 protocol result in largely divergent results when higher level protocols are simulated on top of the 802.11MAC. In this paperwe concentrate on these implementation details and show how these affect the behavior of the 802.11 MAC model in various simulators. We also identify the abstractions that result in the diverging behavior of the 802.11 MAC protocol model and try to compare those with reference to the IEEE specification.

The Intel Mote is a new sensor node platform with improved radio bandwidth and reliability due to the usage of Bluetooth radio. The connection-oriented nature of Bluetooth raises the issues of effective multi-hop network (scatternet) formation and maintenance that network and routing layer must address on top of the TinyOS abstractions. The hop distance and wireless link quality pose major challenges to multi-hop network performance, especially the connection-oriented networks such as Bluetooth scatternet. In this paper, we present a metric-based scatternet formation algorithm for the Intel Mote, which can optimize the Bluetooth network formation from the hop distance and link quality perspectives. In addition, a smart repair mechanism is proposed to deal with link/node failure and recover the network connectivity promptly with low overhead. The experiments with the Intel Mote platform demonstrate the effectiveness of the optimizations, which make the platform more powerful

We present a sensor network simulation environment that allows users to evaluate the effects of different architectural choices and strategies on the lifetime and performance of a sensor network. Our tool can also be used to evaluate new approaches (routing protocols, cooperation algorithms), and compare them with the ones already in place. This simulator is an extension of the Georgia Tech Network Simulator (GTNetS) and leverages its design choices for maximum performance. It incorporates models for the different functional units composing a sensor node and characterizes the energy consumption of each. It also has a model for a network base station and its interactions with the rest of the network. We report our results from a set of tests that demonstrate some of the capabilities of our simulator. In particular, we were able to simulate sensor networks of several hundred thousand nodes while using less than 2 GB of memory, which exceeds the capabilities of existing simulators by an order of magnitude. The simulator is also used to study the performance of an algorithm that assigns globally unique IDs to nodes in a sensor network.

As wireless devices become more and more popular, ad hoc networks grow in the number of nodes as well as the complexity of communication among the large number of nodes. However, due to the limitation of simulation technologies, it is either impossible or very hard to investigate the scalability of ad hoc routing protocols in very large-scale wireless networks. In this paper, a comprehensive simulation study is conducted of the performance of an on-demand routing protocol on a very large-scale, with as many as 50,000 nodes in the network. We address the scalability analysis based on various network sizes, traffic load, and mobility. The reasons for packet loss are analyzed and categorized at each layer. Based on the observations, we optimize the parameter selection and try to exhaust the scalability boundary of the on-demand routing protocol for wireless ad hoc networks.

A sensor network consists of a set of battery-powered nodes, which collaborate to perform sensing tasks in a given environment. It may contain one or more base stations to collect sensed data and possibly relay it to a central processing and storage system. These networks are characterized by scarcity of resources, in particular the available energy. We present a distributed algorithm to solve the unique ID assignment problem. The proposed solution starts by assigning long unique IDs and organizing nodes in a tree structure. This tree structure is used to compute the size of the network. Then, unique IDs are assigned using the minimum number of bytes. Globally unique IDs are useful in providing many network functions, e.g. configuration, monitoring of individual nodes, and various security mechanisms. Theoretical and simulation analysis of the proposed solution have been preformed. The results demonstrate that a high percentage of nodes (more than 99%) are assigned globally unique IDs at the termination of the algorithm when the algorithm parameters are set properly. Furthermore, the algorithm terminates in a relatively short time that scales well with the network size. For example, the algorithm terminates in about 5 minutes for a network of 1,000 nodes

Bluetooth is a promising short-range wireless communication technology with the characteristics of interference resilience and power efficiency which are required by wireless sensor networks. As an enhanced sensor node platform, the Intel Mote uses Bluetoth as its radio scheme and MAC protocol. However, most of the existing scatternet formation protocols intend to connect all the nodes within the networks regardless of traffic pattern. For wireless sensor network applications with traffic of low duty cycle, on-demand scatternet formation and routing achieve significant power saving by avoiding to maintain the entire network connectivity. We propose in this paper an on-demand scatternet and route formation protocol for Bluetooth-based wireless sensor networks. It is able to cope with multiple sources initiating traffic simultaneously in densely deployed wireless sensor networks. We also introduce a modified inquiry scheme using extended ID packet for power efficient propagation of route request messages. Furthermore, we propose a mechanism employing POLL packets in Page processes to transfer scatternet formation and route reply information without extra overhead. Simulation results show that our on-demand scatternet formation and routing protocol can provide multihop channels with reasonable delay for Bluetooth-base wireless sensor networks.

We present a new protocol for multi-hop routing in mobile ad hoc networks called dynamic nix-vector routing (DNVR). Our scheme is based on the nix-vector concept for efficient routing originally designed for wired networks. DNVR acquires a loop-free route and maintains it on a need basis as do other on-demand protocols. However, DNVR has several new features as compared to other existing reactive routing protocols which lead to more stable routes and better scalability. DNVR effectively validates the stored routes as well as efficiently senses the up-to-date network topology during a route discovery phase by sending a unicast probe packet. To accommodate networks with a high degree of mobility, the routing states are invalidated in a timely manner. DNVR adopts a conservative route discovery strategy by suppressing route requests in some cases, and thus only a few routes are maintained per destination. Moreover, it attains bandwidth efficiency by using a neighbor index and medium access control (MAC) addresses for routing purposes, which obviates the need for address resolution. We show via simulation that DNVR scales well to a large network with varying traffic load under diverse mobility scenarios. We compare DNVR to the well known dynamic source routing (DSR) protocol, which is believed to be one of the most efficient on-demand routing protocols. Simulation results reveal that DNVR is as efficient as DSR while at the same time providing higher packet delivery ratios (up to 46 % higher) and smaller delays (up to 23 % smaller) than DSR in most cases.

A novel load-balancing technique for ad hoc on-demand routing protocols is presented. Currently, ad hoc routing protocols lack load-balancing capabilities, and thus, they often fail to provide good performance especially in the presence of a large volume of traffic. We present a simple but very effective method to achieve load balance and congestion alleviation. The new scheme is motivated by the observation that ad hoc on-demand routing protocols flood route request (RREQ) messages to acquire routes, and only nodes that respond to those messages have a potential to serve as intermediate forwarding nodes. If a node ignores RREQ messages within a specific period, it can completely be excluded from the additional communications that might have occurred for that period otherwise. Thus, a node can decide not to serve a traffic flow by dropping the RREQ for that flow. In the new scheme, RREQ messages are forwarded selectively according to the load status of each node so that overloaded nodes can be excluded from the requested paths. Each node begins to allow additional traffic flows again whenever its overloaded status is dissolved. The new scheme utilizes interface queue occupancy and workload to control RREQ messages adaptively. The enhanced versions of protocols with this scheme are compared to the base protocols. Simulation results reveal that the new scheme greatly reduces packet latency as well as routing overhead without adversely affecting the network throughput, and it successfully balances the network load among nodes.

Worm detection systems have traditionally focused on global strategies. In the absence of a global worm detection system, we examine the effectiveness of local worm detection and response strategies. This paper makes three contributions: (1) we propose a simple two-phase local worm victim detection algorithm, DSC (Destination-Source Correlation), based on worm behavior in terms of both infection pattern and scanning pattern. DSC can detect zero-day scanning worms with a high detection rate and very low false positive rate. (2) We demonstrate the effectiveness of early worm warning based on local victim information. For example, warning occurs with 0.19% infection of all vulnerable hosts on Internet when using a /12 monitored network. (3) Based on local victim information, we investigate and evaluate the effectiveness of an automatic real-time local response in terms of slowing down the global Internet worms propagation. (2) and (3) are general results, not specific to certain detection algorithm like DSC. We demonstrate (2) and (3) with both analytical models and packet-level network simulator experiments.

Presents an approach for creating distributed, component-based simulations of communication networks by interconnecting models of sub-networks drawn from different network simulation packages. This approach supports the rapid construction of simulations for large networks by reusing existing models and software, and fast execution using parallel discrete event simulation techniques. A dynamic simulation backplane is proposed that provides a common format and protocol for message exchange, and services for transmitting data and synchronizing heterogeneous network simulation engines. In order to achieve plug-and-play interoperability, the backplane uses existing network communication standards and dynamically negotiates among the participant simulators to define a minimal subset of required information that each simulator must supply, as well as other optional information. The backplane then automatically creates a message format that can be understood by all participating simulators and dynamically creates the content of each message by using callbacks to the simulation engines. We describe our approach to interoperability as well as an implementation of the backplane. We present results that demonstrate the proper operation of the backplane by distributing a network simulation between two different simulation packages, ns2 and GloMoSim. Performance results show that the overhead for the creation of the dynamic messages is minimal. Although this work is specific to network simulations, we believe our methodology and approach can be used to achieve interoperability in other distributed computing applications as well.

The memory resources required by network simulations can grow quadratically with the size of the simulated network. In simulations that use routing tables at each node to perform per-hop packet forwarding, the storage required for the routing tables is O(N/sup 2/), where N is the number of simulated network nodes in the topology. Additionally, the CPU time required in the simulation environment to compute and populate these routing tables can be excessive and can dominate the overall simulation time. We propose a new routing technique, known as Neighbor-Index Vectors, or NIx-Vectors, which eliminates both the storage required for the routing tables and the CPU time required to compute them. We show experimental results using NIx-Vector routing in the popular network simulator ns (S. McCanne and S. Floyd, 1997). With our technique, we achieve a near order of magnitude increase in the maximum size of a simulated network running ns on a single workstation. Further, we demonstrate an increase of two orders of magnitude in topology size (networks as large as 250000 nodes) by using this technique and running the simulation in parallel on a network of workstations.

Discrete event simulation is widely used within the networking community for purposes such as demonstrating the validity of network protocols and architectures. Depending on the level of detail modeled within the simulation, the running time and memory requirements can be excessive. The goal of our research is to develop and demonstrate a practical, scalable approach to parallel and distributed simulation that will enable widespread reuse of sequential network simulation models and software. We focus on an approach to parallelization where an existing network simulator is used to build models of subnetworks that are composed to create simulations of larger networks. Changes to the original simulator care minimized, enabling the parallel simulator to easily track enhancements to the sequential version. We describe our lessons learned in applying this approach to the publicly available ns software package (McCanne and Floyd, 1997) and converting it to run in a parallel fashion on a network of workstations. This activity highlights a number of important problems, from the standpoint of how to parallelize an existing serial simulation model and achieving acceptable parallel performance.

In Internet multicast, the set of receivers can be dynamic with receivers joining and leaving a group asynchronously and without the knowledge of the source(s). The Internet today uses source-based scoping by which the source determines how far its multicast transmissions will propagate. This mechanism, however, does not allow a receiver to bound the number of hops added to a multicast routing tree as a result of its join request or its continued membership. In this paper we explore the use of receiver-based scoping, in which a receiver's join request is augmented with one or more scope values. By use of these values, the receiver specifies its wish to join and remain in the multicast group as long as the number of additional hops its membership incurs is within its declared scope. In environments where a multicast receiver incurs a cost in relation to its incremental resource usage, this scoping technique can be used to give the receiver direct control over this cost. The paper is concerned with discussion of the design of the receiver-based scoping mechanism and its possible use within multicast applications. We also consider the implementation of the mechanism as an extension to the existing Internet multicast infrastructure in a manner that is independent of the multicast routing protocol in use.