Millimeter wave (mmWave) communication has emerged as a key enabler of high-throughput, low-latency wireless connectivity for next-generation intelligent transportation systems. However, its deployment in vehicular environments presents significant challenges due to high mobility, rapidly changing topologies, and frequent transitions between line-of-sight and non-line-of-sight conditions. To address these challenges, this paper proposes an adaptive beamforming framework that integrates fuzzy logic with the grey wolf optimizer (GWO), termed fuzzy-enhanced GWO (FE-GWO), to jointly optimize beam steering angles, power allocation, and beamforming weights in real time. The proposed framework utilizes a 3D hybrid beamforming model with a uniform planar array, constructed from two orthogonal uniform linear arrays, to enable independent control over azimuth and elevation angles. To enhance beam quality, a Hamming window is applied to the array factor to suppress sidelobes and minimize inter-beam interference. Fuzzy logic is embedded in the optimization process to improve the algorithm’s ability to handle uncertainties arising from dynamic channel conditions, including signal-to-interference-plus-noise ratio, path loss, and vehicular mobility. Simulation results demonstrate that the proposed FE-GWO algorithm outperforms traditional and baseline optimization algorithms. Our algorithm achieves notable improvements in SINR, throughput, spectral efficiency, and packet delivery ratio, while effectively reducing path loss, energy consumption, and beam switching latency.

Packet classification is a key mechanism that classifies incoming packets into flows to enable software-defined networking as well as a variety of networking services. Currently, ternary content addressable memory (TCAM) has been widely used for high-speed and low-latency packet classification. However, both range expansion and update performance are the fundamental issues for TCAM-based packet classification. A rule containing ranges could be replicated to multiple rules after converting its ranges into prefixes or ternary strings to occupy more than one TCAM entry. Many range encoding algorithms have been proposed to alleviate or avoid the problem of range expansion. These algorithms can be classified into database-independent (DI) and database-dependent (DD). While database-independent algorithms can accommodate new ranges without re-encoding the existing ranges, they may still cause rule replication. In contrast, database-dependent algorithms could avoid rule replication by adaptively encoding ranges, but new ranges may result in updates of the existing ranges. Accordingly, both types of algorithms may multiply the cost of TCAM updates. In this paper, we propose a DD range-encoding algorithm, Longest Enclosure Prefix Range Encoding (LEPRE), which can ensure that any new range does not cause any rule replication and re-encoding of the existing ranges. LEPRE employs the original fields as a part of range encoding to significantly decrease the requirements of extra bits for range encoding. Our experiment results show that LEPRE can maximize the TCAM storage efficiency. LEPRE also fully supports incremental updates to minimize the latency of TCAM updates.

Due to the long latency of accessing services from cloud datacenters, mobile edge computing (MEC) has been proposed to provide the capability of cloud computing in close proximity to mobile users. Mobile devices can offload computation-intensive applications to MEC servers to achieve better performance and reduce power consumption. Owing to user mobility, workloads of MEC servers would vary severely to cause imbalanced loads and incur hotspot servers. Previous studies of power saving and hotspot management apply mechanisms for cloud datacenters to MEC servers, but these studies may not be suitable for MEC servers due to the limited computation and communication capacity. We are motivated to propose a scheme, complementary offloading for hotspot mitigation (COHM), for MEC. COHM consists of mechanisms for MEC servers with different states, namely busy, idle, and available. These mechanisms jointly consider the computation tasks for MEC servers with different states to mitigate hotspots. The simulation results show that COHM can decrease the number of hotspot servers without degrading the quality of service for applications.

OpenFlow discovery protocol (OFDP) is the standard protocol for discovering network topologies for OpenFlow. Each controller generates and receives OFDP messages for each switch to yield a network topology. However, for a network with a large number of switches, the controller may suffer from high overhead to perform OFDP even though the network topology remains unchanged. In this article, we improve the efficiency of network topology discovery by implementing a delegated function in OpenFlow switches. Our mechanism can reduce controller’s overhead for topology discovery to shorten the time of discovering a network topology. It also provides with the interoperability with switches which only perform OFDP. The experimental results demonstrate that the proposed scheme can improve the time for discovering a new network topology by decreasing the number of messages. Our scheme also eliminates unnecessary Packet-Out and Packet-In messages when the network topology remains unchanged. As a result, the controller overhead, including CPU usage and traffic load, is greatly reduced to shorten the response latency of controllers. Hence, the proposed scheme improves the feasibility of achieving scalable OpenFlow networking.

Multicast is a one-to-many transmission, which can efficiently save bandwidth resources. It heavily relies on the support of network routers to replicate a packet to multiple egress ports. Large routers usually store local multicast labels (LMLs) for ports of packet replication. Owing to the limited number of LML entries, LMLs could be compressed to cause bandwidth waste. In an OpenFlow-based software-defined network, a switch can obtain the traffic volume of each passing-through flow. Our scheme of LML compression uses the information of traffic volume to reduce bandwidth waste.

Cloud applications such as virtualized network functions usually rely on static or dynamic replication mechanisms to achieve high service reliability and satisfy the requirements of service level agreement. Because replicas are allocated in geographically distributed datacenters, their updates increase the difficulty of maintaining consistency among replicas in different datacenters. The consistency overheads among replicas increase bandwidth consumption and transmission latencies to degrade the quality of service (QoS) performance and service reliability. Although previous mechanisms attempt to shorten response time, reduce bandwidth consumption, or maintain high service reliability with minimized storage cost, they do not consider the overhead of data consistency. In this paper, we study the problem of replica placement with consistency overheads by analyzing the latency and reliability for different types of operations. A replication algorithm, write-aware replica placement (WARP), is then presented based on the analysis. The simulation results show that WARP can shorten response time and improve QoS without degrading reliability.

Mobile edge computing (MEC) has been proposed to complement the limitations of cloud computing. Allocating replicas in MEC servers can shorten transmission latency and improve the quality of service (QoS). However, cloud-based replication schemes may not be suitable for MEC servers due to their limited resources and the varied traffic in RANs. In this paper, we analyze the revenue, cost, and profit for the replicas in MEC servers. Accordingly, we present algorithms of adaptive replication for MEC. Our algorithms dynamically allocate replicas based on the number of read/write operations in each time interval. We also employ an algorithm that adaptively forwards requests to replicas in the neighboring MEC servers to balance the load of MEC servers. The simulation results show that our scheme can effectively shorten the average response time and improve the QoS of packets for better profit.

The root switch of an Ethernet spanning tree is elected based on the priority values and MAC addresses of the switches. When an Ethernet network consists of multiple VLANs, the spanning trees of different VLANs may elect the same root switch due to the lack of adaptability and overload the root switch by their traffic. In this paper, we propose a scheme, adaptive root election, to automatically assign different switches as root switches. The simulation results show that our scheme can effectively reduce the number of root switches undertaken by a switch.

Packet classification is an enabling technology for various network services. Fast single-match packet classification can be achieved by using ternary content addressable memory (TCAM) because of the superior speed performance. TCAM has some drawbacks including incapability to store arbitrary ranges, confined TCAM capacity and limited choices of entry lengths. Moreover, TCAM only reports the first matching entry to impose a limitation on supporting multi-match packet classification, which requires all matching rules. The existing algorithms deal with the issues of TCAM-based multi-match packet classification by burdening TCAM with extra entries and/or accesses. In this work, we offload the overhead of TCAM to static random access memory (SRAM) to achieve efficient multi-match packet classification. Our scheme synthesizes TCAM compatible entries by using binary decision trees and employs SRAM for further comparisons. Each synthesized entry can be stored in one TCAM entry to significantly reduce TCAM consumption and fulfill low power consumption. The experimental results show that our scheme can lower the demand of TCAM to improve both search latency and energy efficiency. The scalability of TCAM-based multi-match packet classification can thus be improved drastically.

Ternary content addressable memory (TCAM) has superior performance for single-match packet classification but not the case for multi-match packet classification. The limitation is caused by TCAM architecture that reports only the first matching rule. To cope with the limitation, previous algorithms use extra TCAM entries or accesses, or both, to fulfill multi-match packet classification. These algorithms also reorder rules; thus, a multi-match classifier based on these algorithms cannot maintain performance for single-match packet classification. In other words, all matching rules must be yielded to determine the highest priority matching rule. In this paper, we present a TCAM-based scheme for multi-match packet classification without single-match penalty. Our scheme partitions a rule set based on range layering, which can be applied to achieve range encoding. The rule partitioning generates rule subsets which satisfy that the rules in a subset are mutually disjoint. Each rule is then tagged a bitmap for subset identification to fulfill multi-match packet classification. Two approaches, loose coupling and tight coupling, are derived with different search procedures while incorporating range encoding. Both approaches can maintain original rule order, but with different performance tradeoff. We also present a refinement which uses all available TCAM entries to improve the performance of multi-match packet classification. The experimental results show that combining range encoding with multi-match packet classification has advantages of storage efficiency and speed superiority. The capability of supporting single-match packet classification also provides better flexibility of applying different packet actions.

Packet classification, which performs multidimensional point location upon fields in packet headers, categorizes incoming packets into multiple forwarding classes based on predefined filters. It fulfills the requirements of network applications by treating their incoming packets with consistent actions defined in the filters. In this work, we propose a new scheme to enhance the scalability of packet classification by using multiple decision trees, where each filter is stored in one of the decision trees. With the optimized filter assignment, our scheme can significantly improve the storage efficiency of decision trees while reducing the search latency. We evaluate the performance of our scheme with twelve different types of filter databases whose sizes vary from 16K to 100K. The experimental results demonstrate the feasibility and scalability of the scheme. Also, we show that our scheme takes less time and space as compared to the prominent existing schemes.

Binary search on levels (BSOL) is a decision-tree algorithm for packet classification with superior speed performance. However, the most decision-tree-based algorithms, like BSOL, may suffer from a memory explosion problem caused by filter replications. In this work, we improve the storage performance of BSOL by employing a scheme, replication control. Our scheme dynamically generates multiple decision trees to eliminate filter replications in BSOL. The experimental results show that the new scheme achieves better performance than the existing decision-tree-based algorithms.

The key challenge to a datacenter network is its scalability to handle many customers and their applications. In a datacenter network, packet classification plays an important role in supporting various network services. Previous algorithms store classification rules with the same length combinations in a hash table to simplify the search procedure. The search performance of hash-based algorithms is tied to the number of hash tables. To achieve fast and scalable packet classification, we propose an algorithm, encoded rule expansion, to transform rules into an equivalent set of rules with fewer distinct length combinations, without affecting the classification results. The new algorithm can minimize the storage penalty of transformation and achieve a short search time. In addition, the scheme supports fast incremental updates. Our simulation results show that more than 90% hash tables can be eliminated. The reduction of length combinations leads to an improvement on speed performance of packet classification by an order of magnitude. The results also show that the software implementation of our scheme without using any hardware parallelism can support up to one thousand customer VLANs and one million rules, where each rule consumes less than 60 bytes and each packet classification can be accomplished under 50 memory accesses.

In this paper, we proposed a parallel IP address lookup architecture, which is a novel concept based on graphics processing unit (GPU) via Compute Unified Device Architecture (CUDA). Device function in GPU only performs IP address lookup. Host function is exploited to construct and update the data structure of IP address lookup. Both host and device functions can be executed simultaneously to fully utilize computation resource. Accordingly, we propose an IPv6-capable data structure and implement the data structure with CUDA. One of experimental results shows that G92 GPU can achieve a throughput more than 1.3 billion packets per second (GPPS) on IPv4 routing tables with more than 350K prefixes, which signifies CUDA-based IP forwarding engine with the proposed approach has the capability of GPPS IP forwarding rate on a low-end CUDA device. By employing dual data structures, our implementation can support several hundred thousand updates per second. Furthermore, the proposed forwarding scheme is varied to be applied and compatible with other Internet schemes and devices.

In hierarchical tree-based routing for service discovery in a MANET, all routes form a tree infrastructure. Therefore, the transmitted packets from a node may go up to the tree root and down to the leaf node. The routing overhead of the tree-based routing algorithm cannot be avoided if the packet forwarding is based on parent-child relationships, even the destination node may be located near the source node. In order to improve such problem, each node should consider its neighbor nodes as next hop nodes in their routing tables. In this work, we present a shortcut any cast tree routing in MANETs. We minimize control overhead by establishing the routing tables only for certain nodes on an any cast tree. Our scheme can reduce the volume of query messages as well as the reply messages. It also reduces the transmission latency. We also improve the information accuracy of the routing tables by periodically transmitting update messages. The simulation results show that the performance of our shortcut any cast tree routing algorithm is fast and efficient for MANETs.

Patient monitoring systems are gaining their importance as the fast-growing global elderly population increases demands for caretaking. These systems use wireless technologies to transmit vital signs for medical evaluation. In a multihop ZigBee network, the existing systems usually use broadcast or multicast schemes to increase the reliability of signals transmission; however, both the schemes lead to significantly higher network traffic and end-to-end transmission delay. In this paper, we present a reliable transmission protocol based on anycast routing for wireless patient monitoring. Our scheme automatically selects the closest data receiver in an anycast group as a destination to reduce the transmission latency as well as the control overhead. The new protocol also shortens the latency of path recovery by initiating route recovery from the intermediate routers of the original path. On the basis of a reliable transmission scheme, we implement a ZigBee device for fall monitoring, which integrates fall detection, indoor positioning, and ECG monitoring. When the triaxial accelerometer of the device detects a fall, the current position of the patient is transmitted to an emergency center through a ZigBee network. In order to clarify the situation of the fallen patient, 4-s ECG signals are also transmitted. Our transmission scheme ensures the successful transmission of these critical messages. The experimental results show that our scheme is fast and reliable. We also demonstrate that our devices can seamlessly integrate with the next generation technology of wireless wide area network, worldwide interoperability for microwave access, to achieve real-time patient monitoring.

Anycasting is a network service that selects the best one of service providers in an anycast group as a destination. While anycasting offers better service flexibility in mobile ad hoc networks (MANETs), it also incurs new problems. In MANETs, every node can move arbitrarily, and the routes from mobile nodes to their service providers would vary. Therefore, anycast service discovery in MANETs usually relies on network-layer message broadcasting, which leads to large traffic overhead for the scarce bandwidth of MANETs. In this work, we present a traffic-control scheme for anycast service discovery in MANETs. Our scheme can reduce the volume of query messages and the reply messages. In addition to basic anycasting, our scheme also supports k-anycast service that requests for k anycast service providers in each service instance. With k-anycast service, the fault tolerance and service flexibility of our scheme can be improved. Experimental results demonstrate that our scheme is efficient and feasible for MANETs.

Nowadays, IP networks are rapidly evolving toward a QoS-enabled infrastructure. The need for packet classification is increasing in accordance with emerging differentiated services. While the new differentiated services could significantly increase the number of rules, it has been demonstrated that performing packet classification on a potentially large number of rules is difficult and has poor worst-case performance. In this work, we present an enhanced tuple pruning search algorithm called "tuple pruning plus" (TPP) for packet classification, which outperforms the existing schemes on the scalability. Our main idea is to simplify the lookup procedure and to avoid unnecessary tuple probing by maintaining the least-cost property of rule through precomputation and the proposed information marker. With extra rules added for information marker, only one tuple access is required in each packet classification. In our experiments, 70 MB DRAM is used to achieve 50 million packets per second (MPPS) for a 1 M-rule set, showing a performance improvement by a factor of 50. We also present a heuristic to further reduce the required storage to about 20 MB. These results demonstrate the effectiveness of the TPP scheme to achieve high speed packet classification

Ternary content addressable memory (TCAM) is favorable for high-speed search due to its parallel architecture and ability for searching arbitrary-length keys. However, the usage of TCAM is limited because of its high cost and power consumption. This paper introduces a TCAM-based Huffman decoding algorithm for single-side growing Huffman tree (SGH-tree), which has been proposed to reduce the sparsity of traditional Huffman tree. Our scheme is based on the property, which leaves in the SGH-tree are highly concentrated. By extracting and searching the common prefixes of the codewords, the power consumption and the required storage of TCAM can be significantly reduced as well as its cost. In our experiments based on twelve real images, the power consumption is reduced to twentieth as compared to the original implementation.

To reduce the memory size and fasten the process of searching for a symbol in a Huffman tree, we exploit the property of the encoded symbols and propose a memory-efficient data structure to represent the Huffman tree, which uses memory nd bits, where n is the number of source symbols and d is the depth of the Huffman tree. Based on the proposed data structure, we present an O(log n)-time Huffman decoding algorithm. An adaptive version for single-side growing Huffman tree is also addressed. This version could improve the average performance from log n to /spl Sigma//sub i//sup n//spl lceil/i/(h - 1)/spl rceil/ /spl times/ w/sub i/log h//spl Sigma/w/sub i/, where w/sub i/ is the frequency for i/sub th/ symbol and h is a pre-defined value.

To provide differentiated quality of service, packet classification is important for determining which flow an incoming packet belongs to so as to decide what service quality it should receive. Packet classification is essentially a problem of multidimensional range matching. Tuple space search is a well-known solution based on multiple hash accesses for various filter length combinations. Tuple pruning algorithm is a tuple-based algorithm which is able to achieve good performance in a practical environment; however, its worst-case speed is not guaranteed. We explore the relative property of filters and reorganize the filters through filter conversion. As compared with the tuple pruning algorithm, the proposed scheme can significantly improve the worst-case performance. Experimental results on both real-world and synthetic filter databases show that the worst-case lookup speed of the proposed scheme is 9 to 31 times faster than that of the tuple pruning algorithm.

In next-generation networks, packet classification is important in fulfilling the requirements of multimedia services, including VoIP and VoD. Using pre-defined filters, the incoming packets can be categorized that determines to which forwarding class a packet belongs. Packet classification is essentially a problem of multidimensional range matching. The tuple space search is a well-known solution based on multiple hash accesses for various filter length combinations. The tuple-based algorithm, a rectangle search, is highly scalable with respect to the number of filters; however, it suffers from the memory-explosion problem. Besides, the lookup performance of the rectangle search is not sufficiently fast to accomplish high-speed packet classification. This work proposes an improved scheme to reduce the required storage and realize OC-192 wire-speed forwarding. The scheme consists of two parts. The "Tuple Reduction Algorithm" drastically reduces the number of tuples by duplicating filters. Dynamic programming is used to optimize the tuple reduction and two heuristic approaches are introduced to simplify the optimization process. Furthermore, the "Look-ahead Caching" scheme is presented to improve the lookup performance. The basic idea is to prevent unnecessary tuple probing by filtering out the "un-matched" situation of the incoming packet. The experimental results show that combining the tuple reduction algorithm with look-ahead caching increases the lookup speed by a factor of six while requiring only around one third of the storage. Additionally, an extension of multiple fields to more general filters is addressed.

Hashing is a widely used method to perform fast lookup. Several schemes have been proposed to support Internet lookup that includes IP lookup and packet classification. Rectangular search is a well-known packet classification scheme based on multiple hash accesses for different filter length. It shows good scalability with respect to the number of filters; however, the lookup performance is not satisfactory. For example, through experiments, each packet classification takes about 40 hash accesses in a 100,000-filter database and each hash access may take more than one memory access. Obviously, this is insufficient to provide gigabits throughput. We proposed a novel "lookahead caching" which can significantly improve the performance of the hash-based algorithm. The basic idea is to find out the unmatched case for each incoming packet, thus it is different from the traditional caching mechanism. The experimental results indicate that the proposed scheme can improve the performance by a factor of two. The scheme can be further enhanced using parallel processing.

We present a novel solution to the problem of best matching prefix (BMP) which is required in the IP routing lookup. Our approach is based on the trie-based algorithm. The idea is to prune the trie so that the main-searchable portion of the trie can be fitted into the small on-chip SRAM. The algorithm consists of two parts: level smart-compression trie and trie pruning. In the first part, we present the new trie-based algorithm which can provide flexibility, storage efficiency and incremental update. Moreover, the fixed-size data structure eliminates the complexity of the memory management. Secondly, we define the "core" and present how to apply the concept "trie pruning" to achieve fast IP routing lookup. With the currently popular platform, the proposed scheme can provide 40 MPPS without any assumption. While considering route flaps, the performance degrades by only 0.01% with 4000 BGP updates per 30 seconds.

Nowadays, the commonly used table lookup scheme for IP routing is based on the so-called classless interdomain routing (CIDR). With CIDR, routers must find out the best matching prefix (BMP) for IP packet forwarding, which complicates the IP lookup. Since the IP lookup performance is a major design issue for the new generation routers, we investigate the properties of the routing table and present a new IP lookup scheme. By adopting the extreme compression, the size of the forwarding table can be compressed to 360 Kbytes for a large routing table with 58,000 routing entries. It can accomplish one IPv4 route lookup within maximum nine memory accesses. Consequently, the data structure for the incremental update is introduced. With the new data structure, the proposed scheme can accomplish one route update within 500 ns. Even under the circumstance with 4000 route updates per second, the performance degradation is as low as 0.2 %.

The construction of routing tables has been studied extensively. Although existing work has certain advantages, it either uses complicated data structures which result in large storage requirements and high complexity for updating/building the forwarding table, or it is not scalable to fit in IPv6. Lampson et al. (1999) proposed an IP lookup algorithm which performs binary search on prefixes (BSP). The algorithm is attractive, even for IPv6, because of its bounded worst-case memory requirement. For achieving fast forwarding, the cost is the slowing down of insertion. Although this can be justified, the performance of routing-table reconstruction in BGP is too time-consuming to handle frequent route updates. We propose a fast forwarding table construction algorithm, which can handle more than 4,000 route updates per second. Moreover, it is simple enough to fulfil the need of fast packet forwarding. By using the modified multiway search tree, we can further reduce the depth of the tree and eliminate storage for pointers. This reduces the forwarding table size and shortens the lookup time.

A major design issue for the next generation routers is the IP lookup mechanism. The router needs to perform a longest prefix matching on the address lookup for each incoming packet to determine the next hop. Currently, the process is done in software and has become a major performance bottleneck of the router. We propose a fast IP lookup mechanism in which the forwarding table is small enough to fit in an SRAM with very low cost. It also can be implemented in hardware using the pipeline technique. A large routing table with 45,000 routing prefixes can be compressed to a forwarding table with about 430 kbytes in sine by using our proposed method. In the worst case, the number of memory accesses for a lookup is three. When implemented in pipeline technique, the proposed mechanism can achieve one routing lookup per memory access. With current 10 ns SRAM, this mechanism provides approximately 100 million routing lookups per second. Furthermore, the lookup speed can be improved linearly through the speedup of the memory access.