As mixing services are increasingly being exploited by malicious actors for illicit transactions, mixing address association has emerged as a critical research task. A range of approaches have been explored, with graph-based models standing out for their ability to capture structural patterns in transaction networks. However, these approaches face two main challenges: label noise and label scarcity, leading to suboptimal performance and limited generalization. To address these, we propose HiLoMix, a graph-based learning framework specifically designed for mixing address association. First, we construct the Heterogeneous Attributed Mixing Interaction Graph (HAMIG) to enrich the topological structure. Second, we introduce frequency-aware graph contrastive learning that captures complementary structural signals from high- and low-frequency graph views. Third, we employ weak supervised learning that assigns confidence-based weights to noisy labels. Then, we jointly train high-pass and low-pass GNNs using both unsupervised contrastive signals and confidence-based supervision to learn robust node representations. Finally, we adopt a stacking framework to fuse predictions from multiple heterogeneous models, further improving generalization and robustness. Experimental results demonstrate that HiLoMix outperforms existing methods in mixing address association.

Sharding is a promising solution to enhance blockchain scalability. While deploying more shards of smaller sizes for a given network scale can significantly boost performance, it also heightens the risk of shard failures. In many existing sharding systems, the failure of a single shard can compromise the entire system. Therefore, to ensure safety, current systems often require each shard to contain hundreds of consensus nodes to prevent crashes, adversely affecting scalability. In this paper, we propose Levee, a blockchain sharding system capable of tolerating shard failures. When a shard malfunctions, Levee can swiftly detect, isolate, and autonomously recover the faulty shard, allowing other shards to operate without interruption. This fault-tolerance feature enables Levee to reduce shard sizes by 73.7% and increase the number of shards by 3.25 times, all without sacrificing security. When tested in a scenario with 7000 nodes, Levee demonstrated a 14.34 times increase in throughput and a 65% decrease in transaction latency compared to traditional non-fault-tolerant sharding systems.

Cross-chain swaps are a crucial application that facilitates the transfer of digital assets across different blockchains, thereby enhancing the flexibility and availability of asset circulation. Ensuring atomicity is a fundamental objective for cross-chain swaps. The Hashed TimeLock Contract (HTLC) protocol is one of the primary solutions for cross-chain swaps. It ensures atomicity by statically partitioning execution actions for swap submission and asset refund along the time dimension. Such designs rely on a predictable upper bound on transaction confirmation time. However, this assumption does not hold in practical blockchain environments, leading to atomicity violations. To address this limitation, we propose Chuchu, a hashlock group protocol for cross-chain swaps. Chuchu abandons static partitioning along the time dimension and instead distinguishes swap submission and asset refund through explicitly defined asset locking states and their dynamic transitions. To cope with divergent execution progress across blockchains, Chuchu bounds execution progress divergence and introduces an exit-proof mechanism, ensuring that locked assets always have well-defined and consistent unlocking paths under all execution scenarios. The effectiveness and feasibility of Chuchu are demonstrated through theoretical analysis and formal verification using TLA+. Meanwhile, experiment results show that Chuchu reduces execution time by over 98% compared to the HTLC protocol in the case of swap rollbacks.

Ethereum 2.0 (ETH2) marks a pivotal shift in blockchain technology, transitioning from a Proof-of-Work (PoW) to a Proof-of-Stake (PoS) consensus mechanism, with Gasper at its core. While this evolution promises enhanced scalability and energy efficiency, the performance of its block proposal stage is highly sensitive to network latency and system parameters, such as slot length. This sensitivity introduces a critical trade-off between throughput and security, measured by the probability of blockchain forking. This paper reveals that network latency is not just a passive risk but an exploitable attack surface. We introduce the "adaptive latency-driven equivocation attack", a novel adversarial strategy where an attacker deliberately creates forks while mimicking the behavior of a high-latency node, thus achieving plausible deniability. To formally analyze and quantify the impact of this threat, we develop a comprehensive theoretical model by using Markov chains to analyze the fork probability and throughput of the Gasper's block proposal mechanism under both honest and adversarial conditions. Through extensive simulations, we validate the accuracy of our model in both normal and bursty traffic conditions. Our findings provide a systematic methodology for optimizing system parameters to achieve a robust balance between performance and security, offering a foundational guide for configuring ETH2 networks against sophisticated, latency-based threats.

Decentralized Finance (DeFi) is an emerging paradigm within the blockchain space that aims to revolutionize conventional financial systems by applying blockchain technology. The substantial value of digital assets managed by DeFi protocols makes it a lucrative target for attacks. Despite the human resources and the application of automated tools, frequent attacks still cause significant fund losses to DeFi participants. Existing tools primarily rely on oracles similar to those used in traditional software analysis, making it challenging for them to detect functional bugs specific to the DeFi domain. Since blockchain functions as a distributed ledger system, the foundation of any DeFi protocol is the accurate maintenance of key state variables representing user funds. If these variables are not properly updated or designed to reflect the intended flow of funds, attackers can exploit these flaws to steal assets. From the study of popular DeFi protocols, we observe that, in DeFi systems, to ensure a transaction does not misappropriate someone's fund, the direction of changes (increase or decrease) of values associated with the amount of asset or debt of a user has to adhere to some fairness properties. We propose a concept called fairness bug which allows attackers to gain profit without cost. We propose an inter-procedural and inter-contract static analysis technique that utilizes symbolic execution and an SMT solver to automatically detect fairness bugs in DeFi smart contracts. We have implemented our fairness-checking approach in our tool, named FairChecker. We evaluate our tool on a benchmark of 113 real-world DeFi protocols with 34 fairness bugs. The results show that our tool can detect 32 bugs with a recall of 94.1 % and a precision of 46.4 %, demonstrating its effectiveness.

Rollup stands out as one of the most effective techniques for blockchain Layer-2 scaling. By processing transactions off-chain, it significantly enhances the throughput. However, the most rollup implementations currently rely on centralized sequencers, exposing the system and users to censorship attacks and risking network paralysis. In contrast, fully decentralized sequencers encounter latency issues and reduced throughput during the consensus phase. We propose a multislot weighted leader election algorithm based on shared sequencers, apply the proposer–builder separation (PBS) model, and use the fuzzy cognitive map (FCM) to analyze and optimize the important influence parameters. With its low trust dependence and high functionality, the probability of selecting malicious nodes is reduced. The sequencing and consensus are separated, so that the transaction can quickly reach soft confirmation. We implement this algorithm in a shared sequencer prototype. The experimental results show that the proposed algorithm parameter settings are in line with the expectations, and the probability of electing malicious nodes is significantly reduced. The transactions per second (TPS) of the network can cope with the throughput requirements of the Layer-2.

The widespread adoption of WiFi has made throughput efficiency a critical concern in wireless networks. While Full-Duplex (FD) technology promises to double network capacity by enabling simultaneous transmission and reception, existing FD-WiFi designs primarily focus on the data transmission phase, leaving the fundamental inefficiencies in channel contention unaddressed. This paper presents CollFree, a novel WiFi protocol that exploits FD capabilities during both contention and data transmission phases. At its core, CollFree introduces a Slotwise Arbitration (SA) mechanism that enables each node to simultaneously transmit contention signals and sense channel status in each contention slot. This dual-mode operation significantly reduces contention time and facilitates collision-free data transmissions through a unique winner-determination process. We then develop theoretical models to analyze CollFree’s contention performance and throughput efficiency under both perfect and imperfect Clear Channel Assessment (CCA) conditions, providing guidelines for parameter optimization in practical deployments. Extensive simulations demonstrate that CollFree enhances throughput efficiency by over 20% compared to state-of-the-art FD-WiFi systems while maintaining distributed control and compatibility with current WiFi standards. These results suggest that CollFree represents a significant step toward realizing the full potential of FD technology in next-generation WiFi networks.

Conventional Byzantine fault tolerance (BFT) requires replicated state machines to execute deterministic operations only. In practice, numerous applications and scenarios, especially in the era of blockchains, contain various sources of non-determinism. Meanwhile, it is even sometimes desirable to support non-determinism, and replicas still agree on the execution results. Despite decades of research on BFT, we still lack an efficient and easy-to-deploy solution for BFT with non-determinism—BFT-ND, especially in the asynchronous setting. We revisit the problem of BFT-ND and provide a formal and asynchronous treatment of BFT-ND. In particular, we design and implement Block-ND that insightfully separates the task of agreeing on the order of transactions from the task of agreement on the state: Block-ND allows reusing existing BFT implementations; on top of BFT, we reduce the agreement on the state to multivalued Byzantine agreement (MBA), a somewhat neglected primitive by practical systems. Block-ND is completely asynchronous as long as the underlying BFT is asynchronous. We provide a new MBA construction that is significantly faster than existing MBA constructions. We instantiate Block-ND in both the partially synchronous setting (with PBFT, OSDI 1999) and the purely asynchronous setting (with PACE, CCS 2022). Via a 91-instance WAN deployment on Amazon EC2, we show that Block-ND has only marginal performance degradation compared to conventional BFT.

With the development of blockchain applications, demand for cross-chain technology has been increasing. Relay chain mode is the state-of-the-art and mainstream solution nowadays. However, the relay chain mode suffers from poor performance, which stems from its core facility – the relay chain. Therefore, guiding its improvement and parameters configuration is vital. Currently, there is no specialized performance model of the relay chain. The cross-chain scenario involves receiving transactions from blockchains and uniformly verifying them, while general blockchain models are not applicable for it. Relay chains are characterized by the following features: transaction arrival in batches with uncertain sizes, updating block headers for simplified payment verification (SPV), Byzantine fault tolerance (BFT) type protocol, and different packaging rules. This work first proposes an analytical framework for relay chain performance. It captures the mentioned features by constructing a batch-arrival and bulk-service model. We give a concrete calculation of the relay chain with practical BFT (PBFT) consensus and develop a method to arrive at the computational forms of two essential performance descriptors: system throughput and cross-chain transaction confirmation delay. Through this model, we can judge accurately whether the relay chain is overloaded, and eliminate the overload state by tuning the parameters; and we can evaluate the system performance under different traffic and design parameters. Finally, we verify the model through experiments. With our study, operators can configure the system parameters effectively and improve the relay chain to meet the requirements of practical use.

The account allocation mechanism is a crucial component affecting the performance of sharding blockchain systems. A well-designed account allocation mechanism must reduce the number of cross-shard transactions while balancing the workload across shards. State-of-the-art mechanisms, which are semi-static, typically adjust account partitions based on historical transactions at regular intervals. However, in real-world applications, unpredictable new scenarios in historical transactions or sudden workload changes can impact shard performance. These existing mechanisms can neither foresee such scenarios to avoid cross-shard transactions nor dynamically adjust account partitions to improve workload issues, leading to suboptimal performance until the next re-allocation. To this end, we propose Orbit, a dynamic account allocation mechanism based on the pending transactions in the pool. Orbit can promptly detect new situations and changes in pending transactions and provide updated allocation strategies. Moreover, through its off-chain scheduling mechanism, Orbit can deploy these strategies before transaction packaging to enhance shard performance. Experimental results show that compared to the state-of-the-art allocation mechanisms, Orbit improves throughput by 2.02 times, reduces cross-shard transactions to 11.9%, and achieves a more balanced shard workload. Additionally, Orbit excels in various other aspects, including latency, transaction queue size, and bandwidth overhead, outperforming the state-of-the-art mechanisms.

Mobile edge computing allows for high-performance and low-latency applications by delegating computation and data processing tasks to edge servers. However, ensuring the integrity of cached data on these servers can be challenging due to their limited resources. Current designs often use a per-edge multi-round approach, which necessitates multiple communication rounds between each edge server and the application vendor (AppVend). This approach results in high communication and computational costs, as well as the stragglers effect during batch verification. To address these inefficiencies, we propose a Hierarchical Signature Aggregation for Edge Data Integrity (HSA-EDI) verification design. Our design adopts a novel per-edge one-round approach, which significantly reduce the number of communication rounds to one for each edge server, while mitigating the impact of stragglers. Furthermore, it remarkably reduces computational costs through a hierarchical aggregation mechanism. This mechanism supports intra-edge signature aggregation at the edge server level, followed by inter-edge aggregation at the AppVend, which enhances overall efficiency. We then conduct a theoretical analysis of HSA-EDI’s correctness, security, and communication, computation, and storage efficiency. Experimental results validate its superior performance over state-of-the-art designs.

Sharding is one of the most promising technologies for significantly increasing blockchain transaction throughput. However, as the number of shards increases, the ratio of cross-shard transactions in existing blockchain sharding protocols gradually approaches 100%. Since cross-shard transactions consume many times more resources than intra-shard transactions, the processing overhead of cross-shard transactions already accounts for the majority of the total overhead of the sharding system. There is a very large gap between the transaction throughput of the sharding system and its theoretical upper limit. In this article, we propose Estuary, a novel low cross-shard blockchain sharding protocol. Taking the state model as an entry point, Estuary designs a multi-level state model and state splitting and aggregation mechanism. It decouples the identity and quantity of state units, enabling transactions between users to be completed within one shard. Only when the state quantity for all shards of a user is insufficient a small number of cross-shard transactions are required. On this basis, we propose a community overlap propagation algorithm for sharding. It defines the users’ belonging coefficients of each shard and optimizes the state distribution so that the state distribution can better match the transaction characteristics between users. Finally, we develop an analysis framework for the sharding protocol and experiment with real Bitcoin transactions. The evaluation results show that compared to the state-of-the-art sharding protocol, Estuary reduces the ratio of cross-shard transactions by 88.54% and achieves more than 1.85 times the throughput improvement (92.98% of the theoretical upper limit).

Sharding is a prevalent approach for addressing performance issues in blockchain. To reduce governance complexities and ensure system security, a common practice involves a relay chain to coordinate cross-shard transactions. However, with a growing number of shards and cross-shard transactions, the single relay chain usually first suffers from performance bottleneck and shows poor scalability, thus making the relay chain's scalability vital for sharding systems. To solve this, we proposeMitosis, the first multi-relay architecture to improve the relay chain's scalability by sharding the relay chain itself. Our proposed relay sharding algorithm dynamically adjusts the number of relays or optimizes the topology between relays and shards to adaptively scale up relay chain's performance. Furthermore, to guarantee the security of the multi-relay architecture, a new validator reconfiguration scheme is designed, accompanied by a comprehensive security analysis ofMitosis. Through simulation experiments on two mainstream relay chain paradigms, we demonstrate thatMitosiscan achieve high scalability and outperform state-of-the-art baselines in terms of workload of relays, relay chain throughput, and transaction latency.

Pointer operations are common in programs written in modern programming languages such as C/C++ and Java. While widely used, pointer operations often suffer from bugs like null pointer exceptions that make software systems vulnerable and unstable. However, precisely verifying the absence of null pointer exceptions is notoriously slow as we need to inspect a huge number of pointer-dereferencing operations one by one via expensive techniques like SMT solving. We observe that, among all pointer-dereferencing operations in a program, a large number can be proven to be safe by lightweight preprocessing. Thus, we can avoid employing costly techniques to verify their nullity. The impacts of lightweight preprocessing techniques are significantly less studied and ignored by recent works. In this paper, we propose a new technique, BONA, which leverages the synergistic effects of two classic preprocessing analyses. The synergistic effects between the two preprocessing analyses allow us to recognize a lot more safe pointer operations before a follow-up costly nullity verification, thus improving the scalability of the whole null exception analysis. We have implemented our synergistic preprocessing procedure in two state-of-the-art static analyzers, KLEE and Pinpoint. The evaluation results demonstrate that BONA itself is fast and can finish in a few seconds for programs that KLEE and Pinpoint may require several minutes or even hours to analyze. Compared to the vanilla versions of KLEE and Pinpoint, BONA respectively enables them to achieve up to 1.6x and 6.6x speedup (1.2x and 3.8x on average) with less than 0.5% overhead. Such a speedup is significant enough as it allows KLEE and Pinpoint to check more pointer-dereferencing operations in a given time budget and, thus, discover over a dozen previously unknown null pointer exceptions in open-source projects.

As the requirements of different application scenarios vary, numerous blockchains have been developed. Among them, chains using Byzantine Fault Tolerance consensus (BFT chains for short) occupy a dominant position. Performance modeling is the most common method for guiding chain design and configuration. However, many lack specific models due to the large number of BFT chains. Providing a general framework for them is promising and should demonstrate two important features that BFT chains may differ in: different consensus stages and different packaging rules. This paper is the first to propose a general framework applicable to various BFT chains. We use the bulk-service model in queuing theory to reflect the above two features of various BFT chains and provide calculation methods for them. We give an expression for the confirmation delay of transactions (TXs for short) in the chain and verify it through experiments on various BFT chains. We also use the model for a brief analysis to assist designers in configuring and designing BFT chains.

With the emergence of various application scenarios, various chains have been developed to meet their requirements. Among them, chains with fixed block intervals (fixed chains for short) occupy an increasingly significant position. Performance has always been a key bottleneck of blockchains and modeling for them is the most common method for performance analysis. But til now, few models for fixed chains exist and they are not precise and applicable enough. This paper proposes a model for fixed chains via the bulk-service queuing theory, which can reflect the real scenario more precisely and apply to the high load. We consider the continuous time and the transaction (TX for short) pool with limited capacity and reflect the chains’ features of fixed intervals and empty blocks to improve accuracy and applicability. We give an expression for three significant measurements: the average confirmation delay of TXs, the blockchain throughput, and the TX rejection rate. We use Ethereum to validate our model. And moreover, we use the model for analysis to assist designers in operating chains.

Enterprise-grade permissioned blockchain systems provide a promising infrastructure for data sharing and cooperation between different companies. However, performance bottlenecks seriously hinder the adoption of these systems in many industrial applications that process complex business logic and huge transaction volumes. Our research identifies two key factors that limit the system performance: 1) At the block level, the serial dependency of inter-block processing severely limits the system throughput. A new block must wait for the completion of all previous blocks. 2) At the transaction level, the lack of efficient intra-block transactions concurrency makes it difficult to achieve high performance, especially when dealing with multiple CPU-heavy contracts which are commonly used in industrial scenarios.

Consensus algorithm is the core component of consortium blockchains. Equality, Unpredictability and Scalability are three important demands for the consensus algorithms of consortium blockchain. Existing deterministic consensus algorithms (e.g. PBFT) can ensure Equality, but cannot meanwhile meet Unpredictability and Scalability; probabilistic consensus algorithms (e.g. PoW) can achieve Scalability and guarantee a decent Unpredictability, but cannot meet the Equality requirement. In this paper, we propose a new consensus algorithm, namely Themis, which takes the three properties into account. Themis independently adjusts the block-producing difficulty of each node through a self-adaptive node election mechanism, effectively reducing the correlation between the block-producing frequency and the invested computing power of each node. Besides, a GEOST main chain consensus rule is proposed to handle forks and further improve the performance of the algorithm. If a fork occurs, consensus nodes will choose the sub-chain with the highest Equality to join the main chain. Evaluations show that Themis achieves outstanding performance in Equality and Unpredictability while ensuring Scalability, compared with the existing algorithms.

Hashed Timelock Contract (HTLC) is a widely-used protocol for cross-chain asset swaps. However, it relies on serial asset-locking to guarantee atomicity, which causes high latency and poor fairness. Aiming at the drawbacks of HTLC, we propose Lilac, a cross-chain asset swap protocol that supports parallel asset-locking. Lilac replaces the unique asset-unlocking credential in HTLC with multiple sub-credentials generated by all participating users, and the sequence of sub-credentials is used as the complete asset-unlocking credential. Users obtain the complete credential only when all assets have been locked, and the credential construction process is independent of the order in which assets are locked, so atomicity can be guaranteed when users lock their assets in parallel. Experiments show when a swap involves 2 to 4 blockchains, Lilac reduces the swap latency by 36.75% to 62.20%. Moreover, Lilac reduces the waiting time gap between different users so the fairness of a swap is improved.

With the increasing demands for data, the subscription scheme came into being in the face of pricing for an extensive and unfixed number of data items. However, in the existing subscription scheme, a diversity of customers in the real market may lead to the lack of stability, which means risking the failure of pricing. Additionally, the study involves arbitrage-free, an essential economics concept, which is not reasonable on data items. To address these problems, this paper provides insights for designing an improved subscription scheme that includes two components: the calculation and the specific validity. On the one hand, the calculation improves the existing scheme by building a new structure that combines different customers' behaviours instead of the separated calculation in the existing scheme, and can steadily set prices for subscriptions to maximise the sellers' profit even in a real market. On the other hand, the specific validity shows the improvement towards arbitrage-free by taking the characteristics of data subscriptions into account. In other words, the specific validity endows the scheme with more rationality.

Broadcasting plays a vital role in the consensus mechanisms of blockchain systems, since the consensus of each block must wait until the previous block is received by (nearly) all the nodes in the blockchain systems. Therefore, optimizing the performance of broadcasting can significantly improve the performance of the blockchain system. However, compared with other traditional P2P applications such as file downloading or video delivery, the broadcasting in blockchain has two new requirements, namely low redundancy and low propagation latency, which all the existing mechanisms (e.g. flooding, structural DHT etc.) can not meet well. In this paper, we propose Swift, a new broadcasting mechanism for blockchain systems. It optimizes the P2P topology construction and broadcast algorithm in the structured network based on unsupervised learning and greedy algorithm, effectively reducing the propagation latency of the blockchain P2P network while avoiding the waste of redundant bandwidth. We implemented a prototype of Swift and evaluated its performance on a testbed network that consists of 1000 blockchain nodes. The experimental findings show that Swift can reduce propagation latency by 19.8% with similar bandwidth consumption, generating an 18% increase in the throughput performance of the blockchain. Finally, with the increase in connections, Swift can simultaneously achieve low latency and maintain a relatively stable redundant bandwidth waste, instead of linearly increasing in flooding.

Disassembling stripped binaries is a prominent challenge for binary analysis, due to the interleaving of code segments and data, and the difficulties of resolving control transfer targets of indirect calls and jumps. As a result, most existing disassemblers have both false positives (FP) and false negatives (FN). We observe that uncertainty is inevitable in disassembly due to the information loss during compilation and code generation. Therefore, we propose to model such uncertainty using probabilities and propose a novel disassembly technique, which computes a probability for each address in the code space, indicating its likelihood of being a true positive instruction. The probability is computed from a set of features that are reachable to an address, including control flow and data flow features. Our experiments with more than two thousands binaries show that our technique does not have any FN and has only 3.7% FP. In comparison, a state-of-the-art superset disassembly technique has 85% FP. A rewriter built on our disassembly can generate binaries that are only half of the size of those by superset disassembly and run 3% faster. While many widely-used disassemblers such as IDA and BAP suffer from missing function entries, our experiment also shows that even without any function entry information, our disassembler can still achieve 0 FN and 6.8% FP.

The advent of software defined networking enables flexible, reliable and feature-rich control planes for data center networks. However, the tight coupling of centralized control and complete visibility leads to a wide range of issues among which scalability has risen to prominence due to the excessive workload on the central controller. By analyzing the traffic patterns from a couple of production data centers, we observe that data center traffic is usually highly skewed and thus edge switches can be clustered into a set of communication-intensive groups according to traffic locality. Motivated by this observation, we present LazyCtrl, a novel hybrid control plane design for data center networks where network control is carried out by distributed control mechanisms inside independent groups of switches while complemented with a global controller. LazyCtrl aims at bringing laziness to the global controller by dynamically devolving most of the control tasks to independent switch groups to process frequent intra-group events near the datapath while handling rare inter-group or other specified events by the controller. We implement LazyCtrl and build a prototype based on Open vSwitch and Floodlight. Trace-driven experiments on our prototype show that an effective switch grouping is easy to maintain in multi-tenant clouds and the central controller can be significantly shielded by staying “lazy”, with its workload reduced by up to 82 percent.

Smartphones have become an important component of everyday's life. They store a large amount of users' private and sensitive information like contacts, GPS location, messages and interests. Privacy issues are a growing concern for the phone users. However, despite an existing rich literature in privacy leakage on mobile network measurement, our empirical knowledge of users' private leakage is relatively limited. In this work, we present a large scale and comprehensive investigation spanning over 9 months of users' private information leakage that consisted of monitoring 180K popular apps coming from 50+ Chinese AppStores. In order to do this, we used a customized platform that can monitor the execution of applications running over Android system to observe in vivo privacy leakage of applications. Our key findings are that: (1) Accessing users' private information is very common among mobile apps, i.e. over 90% of apps accesses some kind of user private information, and to our surprise, almost 95% apps claimed access to private information without concretely accessing them (2) We analyzed different category of Apps and observed slight differences in the pattern of access to private information among different categories (3) Downloading apps from big Appstores does not necessarily mean safer and more private apps. We observe that local Chinese shop and Google Play generate similar observations.

Resource isolation of the computation and storage in the cloud is relatively mature, but the network resource is still shared among tenants leading to variable and unpredictable network performance when bandwidth guarantees are not enforced. Currently most of the bandwidth guarantee approaches are based on the idea of single-path reservation without fully exploiting the multipath resource, which leads to poor network utilization. In this paper, we propose a multi-path bandwidth guarantee approach called MultiBand, which provides bandwidth guarantees by allocating bandwidth across multiple paths. We utilize label-based routing technique to explicitly control the packets' transmission paths, and design a MHTB rate limiter model to split and schedule the traffic over the multiple reserved paths. Besides, Our Multiband solution has the work-conserving property. We evaluated our approach through simulations with realistic topologies and typical traffic patterns. Our results show that MultiBand is able to provide multipath bandwidth guarantees and to achieve higher network utility and tenant throughput compared with those of current approaches.

Bitrate adaptation is critical in ensuring good users’ quality-of-experience (QoE) in Internet video delivery system. Several efforts have argued that accurate throughput prediction can dramatically improve (1) initial bitrate selection for low startup delay and high initial resolution; (2) midstream bitrate adaptation for high QoE. However, prior ef- forts did not systematically quantify real-world throughput predictability or develop good prediction algorithms. To bridge this gap, this paper makes three key technical contributions: First, we analyze the throughput characteristics in a dataset with 20M+ sessions. We find: (a) Sessions sharing similar key features (e.g., ISP, region) present similar initial values and dynamical patterns; (b) There is a natural “stateful” dynamical behavior within a given session. Second, building on these insights, we develop CS2P, a better throughput prediction system. CS2P leverages data-driven approach to learn (a) clusters of similar sessions, (b) an initial throughput predictor, and (c) a Hidden-Markov-Model based midstream predictor modeling the stateful evolution of throughput. Third, we develop a prototype system and show by trace-driven simulation and real-world experiments that CS2P outperforms state-of-art by 40% and 50% median pre- diction error respectively for initial and midstream through- put and improves QoE by 14% over buffer-based adaptation algorithm.

Resource competition and conflicts in datacenter networks (DCNs) are frequent and intense. They become inevitable when mixing elephant and mice flows on shared transmission paths, resulting in arbitration between throughput and latency and performance degradation. We propose a novel flow scheduling scheme, Freeway, that leverages on path diversity in the DCN topology to guarantee, simultaneously, mice flow completion within deadline and high network utilization. Freeway adaptively partitions the available paths into low latency and high throughput paths and provides different transmission services for each category. A M/G/1-based model is developed to theoretically obtain the highest value of average delay over the path that will guarantee for 99% of mice flows their completion time before the deadline. Based on this bound, Freeway proposes a dynamic path partitioning algorithm to adjust dynamically with varying traffic load the number of low latency and high throughput paths. While mice flows are transmitted over low latency paths using a simple equal cost multiple path (ECMP) scheduling, Freeway load balances elephant flows on different high-throughput paths. We evaluate Freeway in a series of simulation on a large scale topology and use real traces. Our evaluation results show that Freeway significantly reduces the mice flows completion time within deadlines, while achieving remarkable throughput compared with current schemes. It is remarkable that Freeway does not need any change of DCN switch fabrics or scheduling algorithms and can be deployed easily on any generic datacenter network with switches implementing VLANs and trunking.

The advent of software defined networking enables flexible, reliable and feature-rich control planes for data center networks. However, the tight coupling of centralized control and complete visibility leads to a wide range of issues among which scalability has risen to prominence. We observe that data center traffic is usually highly skewed and thus edge switches can be grouped according to traffic locality. As a result, the workload of the central controller could be highly reduced if we carry out distributed control inside those groups. Based on the above observation, we present LazyCtrl, a novel hybrid control plane design for data center networks. LazyCtrl aims at bringing laziness to the central controller by dynamically devolving most of the control tasks to independent switch groups to process frequent intra-group events using distributed control mechanisms, while handling rare inter-group or other specified events by the controller. We implement LazyCtrl and build a prototype based on Open vSwich and Floodlight. Trace-driven experiments on our prototype show that an effective switch grouping is easy to maintain in multi-tenant clouds and the central controller can be significantly shielded by staying lazy, with its workload reduced by up to 82%.

Even though a key driver for Information-Centric Networking (ICN) has been the rise in Internet video traffic, there has been surprisingly little work on analyzing the interplay between ICN and video ? which ICN caching strategies work well on video work- loads and how ICN helps improve video-centric quality of experience (QoE). In this work, we bridge this disconnect with a trace- driven study using 196M video requests from over 16M users on a country-wide topology with 80K routers. We evaluate a broad space of content replacement (e.g., LRU, LFU, FIFO) and content placement (e.g., leave a copy everywhere, probabilistic) strategies over a range of cache sizes. We highlight four key findings: (1) the best placement and re- placement strategies depend on the cache size and vary across improvement metrics; that said, LFU+probabilistic caching [37] is a close-to-optimal strategy overall; (2) video workloads show considerable caching-related benefits (e.g., -- 10% traffic reduction) only with very large cache sizes (≥ 100GB); (3) the improvement in video QoE is low (≥ 12%) if the content provider already has a substantial geographical presence; and (4) caches in the middle and the edge of the network, requests from highly populated regions and without content servers, and requests for popular content contribute most to the overall ICN-induced improvements in video QoE.

We envision a real-time network traffic map for the Internet, where each network link is annotated with its capacity and its current utilization, with an interface that networked applications can query to inform their control decisions. While this goal is simple to state, it has been out of our reach due to concerns over measurement overhead and coverage. Our insight is that the rise of Internet video and the availability of measurements from video players present an unprecedented opportunity to address these issues. We outline a preliminary roadmap to build on this opportunity to realize a global traffic map.

Information-centric networking (ICN) is designed to decouple contents from hosts at the network layer, using in-network caching as a key feature to improve the overall performance. However, the en-route caching strategy used in many ICN implementations generally yields redundancies in the cached contents across different routers. There are also some recent works focusing on cache optimization by respectively exploiting either application layer or network layer, which we think is not sufficient to increase cache hit rate and reduce traffic. In this paper, we propose a novel caching scheme (CRCache) that utilizes a cross-layer design to cache contents in a few selected routers based on the correlation of content popularity and the network topology. Specifically, through exploiting information available at both application and network layers, CRCache aims to improve the cache hit rate and reduce the overall network traffic. We conduct a large scale and real traces-driven simulation with an underlying real Internet topology in China, and show that by using CRCache, the overall cache hit rate is increased by 62.5% and network traffic reduction is improved by at least 42% compared with recent single layer schemes.

The network resource competition of today' data enters is extremely intense between long-lived elephant flows and latency-sensitive mice flows. Achieving both goals of high throughput and low latency respectively for the two types of flows requires compromise, which recent research has not successfully solved mainly due to the transfer of elephant and mice flows on shared links without any differentiation. However, current data enters usually adopt clos-based topology, e.g. Fat-tree/VL2, so there exist multiple shortest paths between any pair of source and destination. In this paper, we leverage on this observation to propose a flow scheduling scheme, Freeway, to adaptively partition the transmission paths into low latency paths and high throughput paths respectively for the two types of flows. An algorithm is proposed to dynamically adjust the number of the two types of paths according to the real-time traffic. And based on these separated transmission paths, we propose different flow type-specific scheduling and forwarding methods to make full utilization of the bandwidth. Our simulation results show that Freeway significantly reduces the delay of mice flow by 85.8% and achieves 9.2% higher throughput compared with Hedera.

Peer-to-peer (P2P) systems are imposing a heavy burden on internet services providers (ISPs). P2P caching is an effective way of easing this burden. We focus on the cache deployment problem as it has a significant impact on the effectiveness of caching. An ISP backbone network is usually abstracted to a graph comprising nodes representing core routers and links connecting adjacent core routers. While deploying P2P caches at nodes (NCD, node-based cache deployment) can reduce the amount of P2P traffic transmitted from access networks to the ISP backbone network, deploying P2P caches on links (LCD, link-based cache deployment) can directly reduce the amount of P2P traffic on the ISP backbone network. However, neither NCD nor LCD maximizes the performance of P2P caches. In this paper, we propose a node-link-based cache deployment method (NLCD), which optimally selects nodes or links as deployment locations during the cache deployment process. First, we propose an analysis model and define an optimal cache deployment problem for NLCD. Then, we prove that this problem is NP complete and develop a corresponding deployment algorithm. Experimental results show that the average link utilization of NLCD is 5–15% lower than that of LCD, and 7–30% lower than that of NCD.

In ad hoc networks, each node is required to forward packets for others. However, based on energy consumption considerations, a node may reject other nodes' forwarding requests to save the limited battery power for its own data transmission. Therefore, a lot of incentive schemes have been proposed to promote the cooperation of the nodes. Most of the existing research work assumes that all the nodes in the network should provide full cooperation in order to optimize the transmission efficiency. However, this assumption is too strict because the activities of the nodes in ad hoc networks have some inherent uncertainty. In this paper, we propose a power control mechanism in ad hoc networks under a dynamic repeated game-theoretic framework. A notion of nodes' evaluation levels for the future experiences is defined to take account of the non-cooperation due to the inherent uncertainty in the ad hoc network nodes' activities. Our scheme does not require all the nodes in the ad hoc network to absolutely cooperate with each other. The simulation results show that, compared with the existing schemes, our power control mechanism considering non-cooperative packet forwarding improves the average transmission efficiency by ∼25% and has good scalability.

P2P platforms have been criticized because of the heavy strain that they can inflict on costly inter-domain links of network operators. It is therefore mandatory to develop network optimization schemes for controlling the load generated by a P2P platform on an operator network. While many research efforts exist on centralized tracker-based systems, in recent years multiple DHT-based P2P platforms have been widely deployed and considered as commercial services due to their scalability and fault tolerance. Finding network optimization for DHT-based P2P applications has thereby potential large practical impacts. In this paper, we present THash, a simple scheme that implements a distributed and effective network optimization for DHT systems. THash uses standard DHT put/get semantics and utilizes a triple hash method to guide the DHT clients to choose their sharing peers in proper domains. We have implemented THash in a major commercial P2P system (PPLive), using the standard ALTO/P4P protocol as the network information source. We conducted experiments over this network in real operation and observed that compared with Native DHT, THash reduced respectively by 47.4% and 67.7% the inter-PID and inter-AS traffic, while reducing the average downloading time by 14.6% to 24.5%.

P2P platforms have been criticized because of the heavy strain that some P2P services can inflict on costly inter-domain links of network operators. It is therefore necessary to develop network optimization schemes for controlling the load generated by P2P platforms on an operator network. Previous focus on network optimization has been mostly on centralized tracker-based systems. However, in recent years multiple DHT-based P2P networks are widely deployed due to their scalability and fault tolerance, and these networks have even been considered as platforms for commercial services. Thereby, finding network optimization for DHT-based P2P applications has potentially large practical impacts. In this paper, we present THash, a simple scheme to implement an effective distributed network optimization for DHT systems. THash is based on standard DHT put/get semantics and utilizes a triple hash method to guide the DHT clients sharing resources with peers in proper domains. We have implemented THash in a major P2P application (PPLive) by using the standard ALTO/P4P protocol as the network information source. We conducted realistic experiments over the network and observed that compared with Native DHT, THash only generated 45.5% and 35.7% of inter-PID and inter-AS traffic, and at the same time shortened the average downloading time by 13.8% to 22.1%.

P2P live streaming systems have been widely adopted nowadays. However, the flash crowd still poses challenges in such P2P systems, which often occurs when an enormous number of users suddenly arrive to view a newly released live program. Facing so many new users, a P2P streaming system usually can not provide reasonable quality of service and these new users often suffer from a long startup delay and a high service rejection rate. In this paper, we propose a bandwidth-aware peer selection method to alleviate the flash crowd. To use the rare available bandwidths more effectively, we let new peers send more requests to the high-bandwidth parents and less requests to the low-bandwidth parents, aiming to make the upload rate of each parent match well with its upload capacity. Moreover, two analytical models are also constructed to evaluate our method and the traditional random peer selection method. Both model analysis and simulation experiment reveal the merits of our method in tackling the flash crowd, in terms of growth of system scale, average startup delay and rejection rate, compared with the random peer selection method.

Peer-to-Peer (P2P) systems are generating a large portion of the total Internet traffic and imposing a heavy burden on Internet Services Providers (ISPs). Proxy caching for P2P traffic is an effective means of reducing network usage, thereby reducing operation costs for ISPs. Proxy cache storage design has a significant impact on ISPs. While there are several works on how to optimally design cache locations and capacity allocation to each location given a total capacity, few works tell ISPs what is the optimal total P2P cache storage capacity. In this paper, we propose an analysis method to the problem of optimally determining P2P cache size. An analysis methodology is proposed to determine the optimal cache size by considering the monetary costs of cache storage and bandwidth. Guided by our model, a close-form expression is developed to guide an ISP in the cache capacity design. Numerical evaluation results show that ISPs can achieve significant cost saving by deploying P2P cache and by allocating the cache capacity optimally.

Peer-to-Peer (P2P) technology has been widely adopted by various live streaming systems recently, due to its better scalability and lower costs compared with the client-server architecture. However, P2P live streaming systems are still challenged by the flash crowd scenarios, which often occur when a great number of users suddenly arrive and compete for the limited upload bandwidth of a P2P system. In this case, users are usually subject to a long startup delay and are likely to retry multiple times before leave out of impatience. Current studies mainly focus on the measurement of practical systems and model analysis on flash crowd, but there are few specific approaches so far. In this paper, we develop a capacity-aware user access control algorithm to relieve the flash crowd problem. Firstly, we control the peers to enter the system at a proper rate, which avoids too high arrival rate slowing down the increase of system scale. Secondly, to increase the system service capacity as soon as possible, we let the peers with higher capacity enter the system ahead of the peers with lower capacity. Finally, we also consider the waiting time of peers with low capacity and let them in before they lose patience. To evaluate our algorithm, a new analysis model is also proposed. Simulation experiments and model analysis reveal that our algorithm is more effective to increase the system scale, and can achieve shorter user waiting time as well as lower reject rate.

Based on energy consumption considerations, an ad hoc network node may reject other nodes' forwarding requests to save the limited battery power for its own data transmission. Therefore, a lot of incentive schemes have been proposed to promote the cooperation of the nodes. The utilization of the incentive schemes makes the nodes willing to cooperate with each other, because their non-cooperation can be punished in the future. However, the activities of the nodes in ad hoc networks have some inherent uncertainty. For example, the batteries of some nodes are exhausted or some nodes move to other regions. Under these situations, the existing incentive schemes are no longer effective and the nodes have to terminate their cooperation and stop forwarding packets for others. In this paper, we propose a power control mechanism in ad hoc networks under a dynamic repeated game-theoretic framework. A notion of nodes' evaluation levels for the future experiences is defined to take account of the non-cooperation due to the inherent uncertainty in the ad hoc network nodes' activities. The nodes achieve their optimal transmission efficiency by using a two-step power control mechanism. The simulation results show that compared with the existing schemes our power control mechanism considering non-cooperative packet forwarding improves the average transmission efficiency by approximately 25%.

Hybrid content distribution network (HCDN) makes use of the highly complementary advantages of conventional CDN (content distribution network) and pure P2P (peer-to-peer). In HCDN, clients can concurrently retrieve content from both CDN and P2P networks. In this paper, service differentiation is formulated as a constraint optimization problem, in which two critical factors are taken into consideration: quality factor of a link and demand factor of a file. With the convex optimization theory, two bandwidth allocation algorithms, HBAA-P for source peer and HBAA-S for surrogate server, are proposed and proved for service differentiation in the hybrid architecture. Some experimental results are illustrated to make sense of the performance features of our approaches.

Adopting two independent models for functional verification and performance analysis respectively could not guarantee the performance models satisfying the functionality correctness. In this paper, a colored Petri nets (CP-nets) based method is proposed to integrate functional verification and performance analysis for network protocols. Firstly, a CP-nets based function model for the protocol is constructed and validated. Then, performance related temporal constrains are added into above model, and data monitor units are generated together to form a corresponding CP-nets based performance model. Finally, based on such performance model, simulation based performance evaluation is executed. Because such coessential CP-nets models are utilized where every occurrence sequence in the performance model corresponds to an occurrence sequence in the functional model, it is guaranteed that both models satisfy the functionality correctness requirements of that protocol. As a representative, an integrated analysis process of TRDP protocol is presented to illustrate the practical effectiveness of our proposed method.

Content distribution networks (CDNs) improve the performance of content delivery by replicating the popular content on surrogate servers deployed at the edge of the Internet. The CDN-P2P architecture, which combines the complementary advantages of both CDN and P2P networks, can improve the quality of service (QoS). In this paper, we propose a k-coordinated decentralized replica placement algorithm (DRPA) based on a gain formulation of the replica placement problem. Although the gain formulation is designed for different types of the CDN-P2P architecture, we focus on the robust ring-based architecture in this study. In our approach, each surrogate server makes the replica placement in terms of the content replicas on k closer surrogate servers, which enhances the system scalability compared to the centralized replica placement heuristics. In addition, according to the simulation results, the proposed algorithm is able to reduce the backbone traffic between the servers and the requesting peers compared to the traditional replica placement algorithms for the pure CDN.

In recent years, structured P2P communications is being widely used for its features of self-organization as well as good scalability and flexibility. To make sure that every node can participate in such a network whether it is behind a NAT or not, we must solve the NAT traversal problem. However, existing NAT traversal methods all need the support of a centralized server which will destroy the distributive characteristic of structured P2P. In this paper, we propose a distributed NAT traversal mechanism called SMBR (Selective-Message Buddy Relaying) for structured P2P. SMBR has two main advantages. The first one is that it does not need the support of a server and thus can maintain the characteristics of structured P2P. Secondly, SMBR uses different mechanisms for the control messages and data according to their size. For control messages, it uses the method of buddy's relay while for data direct connections can be built with the help of the buddy. Using this mechanism, SMBR can achieve a balance between the traversal time and the buddies' load.

P4P (Provider Portal for Applications) integrated P2P (peer-to-peer) network is one of the main trends of P2P networks. While P4P brings advantages to P2P, it also brings new challenges in solving trust problems in P2P networks. In this paper, we consider the P4P's characteristics, domain partition and strategy matrix guidance, and propose a novel domain-based trust model for P2P networks. In our model, we distinguish peer's intra-domain behavior and inter-domain behavior. Peer's intra-domain good behavior does not mean that the peer also behaves well when interacting with other domain's peers, due to P4P's inter-domain traffic control. In addition, we give each domain a trust value, using them to modify the strategy matrix provided by P4P. Simulation results show that our model can effectively distinguish good peers and malicious peers. Even when a peer behaves well in its own domain, our method can make other domains cooperate together to evaluate the peer's bad inter-domain behavior. Lastly, using the domain's trust value to modify the P4P's strategy matrix can reduce the impact of malicious peers' cheating behavior on forming the strategy matrix.

In ad hoc networks, reducing energy consumption and improving throughput are both important for high network performance. This paper presents a joint power and rate control adaptive algorithm to optimize the trade-off between power consumption and throughput in ad hoc networks. Each node chooses its own transmission power and rate based on limited environment information in order to achieve optimal transmission efficiency. In a fictitious game framework with strategy space transformation, our joint power and rate control adaptive algorithm can be viewed as a supermodular game. By interpreting the supermodular game using myopic best response updates, this algorithm can converge to the unique optimal transmission efficiency. Finally, the simulation results show that this supermodular game approach improves the average transmission efficiency by about 33%.

The convergence of IP network over broadcasting transmission systems is a feasible solution towards the next generation wireless broadband multimedia network for large numbers of users and Internet service providers. Taking the characteristics of IP over broadcasting network into account, this paper proposes a novel Dynamic Differentiated Service Management (DDSM) to provide QoS guarantee for delivering IP applications on the IP-Broadcasting Gateway equipment. In the DDSM model, the queue threshold is regulated dynamically according to the level of emergency and priority; the scheduling weight derives from the queue saturation during a regulating period. As shown in the performance evaluation and simulation analysis, DDSM significantly improves the resource utility of the IP over broadcasting system, and reduces the packet drop probability and the packet delay. Thus DDSM achieves the effective differentiated service of the diverse IP applications over broadcasting network.

With the development of heterogeneous networks, multimode terminals are becoming more and more popular. However, when there are several different kinds of sessions requiring transmission simultaneously, how to distribute these sessions among the available access networks according to the different features of the flows and the current link conditions of the candidate networks is a new challenge. In this paper, we propose a new solution to the flow distribution problem for multimode terminals. Our proposal, automatic flow distribution (AFD), includes a network selection algorithm located at the terminals and an admission control algorithm located at the access points of the networks. Consequently, the terminals and the networks can cooperate with each other and realize automatic flow distribution among the different available access networks. We utilize the notion of priority, ensuring that the more important sessions have preferential use of the network resources. In addition, in order not to excessively deteriorate the transmission performance of the lower priority flows, a probabilistic suspension scheme is introduced. Finally, the AFD method utilizes the concept of "entropy" to automatically compute the weights of different attributes which influence the flow distribution decision making, thus avoiding the users' difficulty to specify the weights manually.

This paper focuses on the issue of flow distribution for multimode mobile terminals in heterogeneous networks, and proposes a flow distribution algorithm based on priority and weight self-production mechanism. The algorithm utilizes the notion of "priority" , assigning higher priorities to more important sessions, therefore ensuring these sessions have the preferential utilization of the network resources. In addition, this paper proposes a new method to automatically determine the weights of the multiple attributes which influence the session flow distribution decision, thus avoiding the difficulty and irrationality when relying on the users to specify the weights directly. Simulation results show that our algorithm can efficiently distribute the session flows on a multimode mobile terminal to the appropriate networks for transmission based on the current conditions of different access networks, and on the QoS requirements of different types of sessions. The decisions made by our algorithm reduce the monetary cost and power consumption of the communication as well as guarantee the QoS of different types of sessions. Finally, our algorithm offers significant improvements in performance indicators such as adjacency to the optimal network and session completion rate.

In the IEEE 802.16e OFDMA systems, the data mapping algorithm maps the data to the appropriate rectangular regions in the two-dimensional matrix of time and frequency domain. Each region is described by an Information Element (IE) which is used for signaling and occupies a slot. The IEs as well as vacant slots in the allocated rectangular region result in a substantial amount of overhead. In order to minimize the overhead so as to increase system throughput, the paper proposes a "Mapping with Appropriate Truncation and Sort" (MATS) algorithm. Extensive simulations are conducted in terms of mapping efficiency, mapping cost and system throughput to evaluate the performance of MATS. The results show that compared with Raster, MATS can increase the mapping efficiency by up to 2.4% and reduce the mapping cost by up to 80% and 37% for constant bit rate traffic and variable bit rate traffic, respectively. Moreover, system throughput is increased by more than 3% in the 10 MHz bandwidth network. Consequently, MATS can substantially reduce the overhead and achieve high system throughput.

Trust relationship among multiple domains is the basis for inter-domain fast authentication. This paper proposes a fast authentication method combining inter-domain trust relationship for wireless mobile IPv6 networks. In order to implement inter-domain trust relationship, a dynamic trust maintenance mechanism is designed. Based on Combined Public Key (CPK) algorithm, a new signature and verification scheme is applied to accelerate the authentication process. This scheme supports the proposed trust-based mutual authentication between mobile node and the access network. Theoretical analysis and numerical results show that the proposed method is more effective in reducing authentication delay and signaling overhead. Additionally, the proposed method is proven to be tolerant of existential forgery and man-in-the-middle attacks.

In this paper, a singular value decomposition based signature matrix inversion (SVD-SI) scheme is proposed for downlink in multiuser MIMO systems, which is more efficient and able to reduce the feedback overhead by limited feedback. The wireless channels are decomposed into several eigenmodes with the right singular vectors as the spatial signatures, which are quantized and fed back to the base station. Then, the operating users are selected according to their signatures, and the multiuser interference is eliminated by the signature matrix inversion scheme. Finally, we characterize the performance of SVD-SI under limited feedback, and give the recommended feedback rate under different scenarios. Numerical results show that the proposed scheme achieves significant throughput improvement while reducing the feedback overhead.

In MIPv6, the home agent (HA) is the key entity to ensure a mobile nodepsilas (MN) reachability. A single HA on the home link will become a performance bottleneck. In order to enhance service availability and improve system performance, it is necessary to configure multiple HAs on the home link and efficiently balance load among these HAs. This paper proposes a dynamic multiple HA load balancing mechanism based on active overload prevention (DHALAOP). The solution actively prevents HA from overloading in advance, rather than just passively transferring excess load among HAs as previous load balancing mechanisms do. A novel dynamic weight load evaluation algorithm is introduced to provide the basis for optimal load balancing decision. In addition, DHALAOP utilizes single HA mirror image and load slicing scheme to achieve the transparency of load balancing and eliminate unnecessary additional overhead. The theoretical analysis results show that DHALAOP can be more efficient in enhancing service availability and improving system performance as compared with the previous mechanisms. At the same time it introduces a lower signaling cost.

We divide the predictive FMIPV6 handover procedure into 5 phases. Among these phases, the link layer prediction, the predictive tunneling and sender preparation phases are mainly concerned, the latencies and the reasons for the latencies in the 3 phases are analyzed in an IEEE 802.11 test-bed. Based on the analysis, a cross-layer fast handover management mechanism is proposed and proved to be efficient through experiments.

Single frequency networks (SFN) broadcast is an efficient method to provide broadcast services in cellular networks. How to select broadcast services by the SFN operation to trade off between the occupied bandwidth and the SFN performance including spectrum efficiency and broadcast service continuity is a new problem. To the best of our knowledge no solutions have been proposed to solve this problem so far in the literature. We define the problem of SFN broadcast services selection as a knapsack problem and solve it to minimize the occupied bandwidth while at the same time guaranteeing the SFN performance. Based on the solution, several SFN broadcast services selection algorithms are proposed which vary in the reselection policy. Numerical results show that our proposed algorithms are applicable to different cases with different system requirements and in particular, that the semi-dynamic-SFN broadcast services selection algorithm is an efficient solution in general.

In a wireless sensor network for data-gathering applications, if all network data congregate to a stationary sink node hop by hop, the sensor nodes near the sink have to consume more energy on forwarding data for other nodes, which probably causes the early function loss of the sensor network. Employing a mobile sink can alleviate the hotspot problem and balance the energy consumption among the sensor nodes. In this paper, we propose two autonomous moving schemes for the mobile sink. In our schemes, the sink makes moving decisions without complete knowledge of network topology and the energy distribution of all sensor nodes. We evaluated the performance of our moving schemes by simulation and the results show that both the two schemes can extend the network lifetime prominently.

This paper proposes a new cross layer scheme (Fast RSVP) to reserve resources in mobile IPv6 networks. Through the cooperation of mobile IP and RSVP modules, Fast RSVP includes a number of mechanisms such as advanced resource reservation on neighbor tunnels, resource reservation on optimized routes, resource reservation for handover sessions, path merge etc. Network simulation results show that our scheme, compared with other traditional ways to reserve resources in mobile environments, has the following advantages: (1) it allows a mobile node to realize fast handover with QoS guarantees; (2) it avoids resource wasting caused by triangular routes and duplicate reservations; (3) it distinguishes different types of reservation requests, greatly reducing the handover session forced termination rate while maintaining high performance of the network.

This paper proposes a new cross layer scheme (fast RSVP) to reserve resources for mobile IPv6. Through the cooperation of mobile IP and RSVP modules, fast RSVP includes a number of mechanisms such as advance resource reservation on neighbor tunnels, resource reservation on optimized routes, resource reservation for handover sessions (guard channel) etc. Network simulation results show that our scheme, compared with other traditional ways to reserve resources in mobile environments, has the following advantages: (1) it allows a mobile node to realize fast handover with QoS guarantees; (2) it avoids resource wasting caused by triangular routes and duplicate reservations; (3) it distinguishes different types of reservation requests, greatly reducing the handover session forced termination rate while maintaining high performance of the network.

In this paper, we propose a practically efficient Subcarrier Allocation scheme based on Lagrangian relaxation to solve the problem of subcarrier allocation in OFDMA wireless channels. The problem of subcarrier allocation is formulated into an Integer Programming (IP) problem, which is relaxed by replacing complicating constraints with Lagrange multipliers using Lagrangian Relaxation. A subgradient method is used to optimize the Lagrangian dual function and a heuristic is designed to obtain the feasible solution. Lagrangian Relaxation Subcarrier Allocation (LRSA) is proven to be of polynomial complexity and it provides bounds on the value of channel efficiency. Numerical results show that compared with other algorithms proposed in the literature, LRSA can result in a significant improvement in channel efficiency, while at the same time guaranteeing minimum data rates of users.

In this paper, we tackle the packet scheduling problem in IEEE 802.16e wireless metropolitan area network (WMAN), where the Sleep Mode is applied to save energy of mobile stations (MSs). Our objective is to design an energy efficient scheduling policy which works closely with the sleep mode mechanism so as to maximize battery lifetime in MSs. To the best of our knowledge no power saving scheduling algorithms based on sleep mode defined in IEEE 802.16e have been proposed so far in the literature. We propose a longest virtual burst first (LVBF) scheduling algorithm which schedules packets of MSs in a virtual burst mode where there is one primary MS and multiple secondary MSs sharing the wireless link resource. LVBF prolongs MSs' lifetime by reducing the average time when MSs stay in the idle state and the number of state transitions between the awake and sleep states. Simulation results show that, in comparison with the round robin scheduling scheme, LVBF can produce significant overall energy saving, while guaranteeing the QoS requirements of MSs in terms of their minimum data rates.

In this paper, the problem of scheduling multiple users sharing a time varying wireless channel is studied, in networks such as in 3G CDMA and IEEE 802.16. We propose a new generic wireless packet scheduling framework (WPSF), which takes into account not only the quality of service (QoS) requirements but also the wireless resource consumed. The framework is generic in the sense that it can be used with different resource constraints and QoS requirements depending on the traffic flow types. Subsequently, based on this framework a minimum rate and channel aware (MRCA) scheduling algorithm is presented. MRCA attempts to greedily enhance wireless channel efficiency by making use of multi-user channel quality diversity, while providing acceptable QoS in term of users' minimum rate constraints. Simulation results show the desirable properties identified in the algorithm.

We propose a new learning model called granular support vector machines for data classification problems. Granular support vector machines systematically and formally combines the principles from statistical learning theory and granular computing theory. It works by building a sequence of information granules and then building a support vector machine in each information granule. In this paper, we also give a simple but efficient implementation method for modeling a granular support vector machine by building just two information granules in the top-down way (that is, halving the whole feature space). The hyperplane used to halve the feature space is selected by extending statistical margin maximization principle. The experiment results on three medical binary classification problems show that finding the splitting hyperplane is not a trivial task. For some datasets and some kernel functions, granular support vector machines with two information granules could achieve some improvement on testing accuracy, but for some other datasets, building one single support vector machine in the whole feature space gets a little better performance. How to get the optimal information granules is still an open problem. The important issue is that granular support vector machines proposed in This work provides an interesting new mechanism to address complex classification problems, which are common in medical or biological information processing applications.