Table Entries Research Articles

Using the complement of the cosine to compute trigonometric functions

The computation of the sine and cosine functions is required in devices ranging from application-specific signal processors to general purpose floating-point units. Even in the latter case, the required functionality can be reduced to computing the sine and/or cosine of multiples of a constant angle. The latency of a sine/cosine generator can be reduced by using look-up tables. However, a direct implementation with look-up tables may be unfeasible if the input space is huge. In such a case, look-up tables with a number of entries lower than the size of the input space can be used indirectly. In previously published methods, the reduction in the number of table entries is obtained at the expense of increasing the table width and the computational cost. This paper introduces an alternative technique that makes it possible to reduce the size of the look-up tables as well as the required multiplications. The proposed technique can be used to implement sine/cosine generators of huge input space. It has been used to implement several twiddle factor generators in reconfigurable hardware and has enabled the number of look-up tables to be reduced by between 6 and 26% with respect to previous table-based techniques. Also, these implementations are about 50% faster than those based on Volder’s algorithm.

PARD: Hybrid Proactive and Reactive Method Eliminating Flow Setup Latency in SDN

Advantages of Software Defined Networking are unquestionable and are widely described in numerous scientific papers, business white papers and press articles. However, to achieve full maturity, crucial impediments to this concept and its shortcomings must be overcame. One of the most important issues regards significant setup latency of a new flow. To address this issue we propose PARD: a hybrid proactive and reactive method to manage flow table entries. Additional advantages of the proposed solution are, among the others, its ability to preserve all capabilities of Software Defined Networking, utilization of multiple flow tables, a possibility to employ fine-grained traffic engineering and, finally, compatibility with existing protocol and hardware design. It is shown that the proposed solution is able to significantly reduce latency of first packets of a new flow, which directly impacts packet loss and perceived throughput. Thus, our solution is expected to enable a wide deployment of Software Defined Networking concept without any need for protocol changes or, what is extremely important, hardware modifications.

Rule Caching in SDN-Enabled Base Stations Supporting Massive IoT Devices With Bursty Traffic

Software-defined networking (SDN) is recognized as a promising solution for the efficient management of numerous devices in the Internet of Things (IoT). To appropriately forward the incoming packets, SDN-enabled devices request the controller for traffic rules resulting in a significant delay. To avoid frequent communication with the controller and reducing the delay, these devices cache the rules as match action pairs in flow tables for a certain amount of time. This is referred to as rule caching. Flow tables are made of ternary content-addressable memories (TCAMs) capable of high-speed parallel lookup. Nonetheless, due to the high expenses and power consumption of TCAMs, flow tables have limited capacity and cannot store rules of all the users. Therefore, the assignment of limited flow tables is a challenge in IoT networks with a large number of users. In this article, we consider an SDN-enabled base station serving a set of users in a cell and is equipped with a finite-capacity flow table. We assume users’ traffic obeys a bursty ON–OFF model and formulate the efficient allocation of flow table entries to users over time. It results in a mixed-integer nonlinear program. We introduce a change of variables that turns the problem into an integer linear program. This is still intractable and cannot support problems with large dimensions such as massive IoT networks. To handle its complexity, we design a time-efficient procedure with close-to-optimal performance. Finally, numerical results demonstrate the significant improvement achieved by the proposed scheme.

Hiding Process Memory Via Anti-Forensic Techniques

Nowadays, security practitioners typically use memory acquisition or live forensics to detect and analyze sophisticated malware samples. Subsequently, malware authors began to incorporate anti-forensic techniques that subvert the analysis process by hiding malicious memory areas. Those techniques typically modify characteristics, such as access permissions, or place malicious data near legitimate one, in order to prevent the memory from being identified by analysis tools while still remaining accessible. With this paper, we present three novel methods that prevent malicious user space memory from appearing in analysis tools and additionally making the memory inaccessible from a security analysts perspective. Two of these techniques manipulate kernel structures, namely Page Table Entries and the structures responsible for managing user space memory regions, while the third one utilizes shared memory and hence does not require elevated privileges. As a proof of concept, we implemented all techniques for the Windows and Linux operating systems, and subsequently evaluated these with both, memory forensics and live analysis techniques. Furthermore, we discuss and evaluate several approaches to detect our subversion techniques and introduce two Rekall plugins that automate the detection of hidden memory for the shared memory scenario.

Stream-Based Lossless Data Compression Applying Adaptive Entropy Coding for Hardware-Based Implementation

Toward strong demand for very high-speed I/O for processors, physical performance growth of hardware I/O speed was drastically increased in this decade. However, the recent Big Data applications still demand the larger I/O bandwidth and the lower latency for the speed. Because the current I/O performance does not improve so drastically, it is the time to consider another way to increase it. To overcome this challenge, we focus on lossless data compression technology to decrease the amount of data itself in the data communication path. The recent Big Data applications treat data stream that flows continuously and never allow stalling processing due to the high speed. Therefore, an elegant hardware-based data compression technology is demanded. This paper proposes a novel lossless data compression, called ASE coding. It encodes streaming data by applying the entropy coding approach. ASE coding instantly assigns the fewest bits to the corresponding compressed data according to the number of occupied entries in a look-up table. This paper describes the detailed mechanism of ASE coding. Furthermore, the paper demonstrates performance evaluations to promise that ASE coding adaptively shrinks streaming data and also works on a small amount of hardware resources without stalling or buffering any part of data stream.

Detection and Mitigation of DoS Attacks in Software Defined Networks

The introduction of software-defined networking (SDN) has emerged as a new network paradigm for network innovations. By decoupling the control plane from the data plane in traditional networks, SDN provides high programmability to control and manage networks. However, the communication between the two planes can be a bottleneck of the whole network. SDN-aimed DoS attacks can cause long packet delay and high packet loss rate by using massive table-miss packets to jam links between the two planes. To detect and mitigate SDN-aimed DoS attacks, this paper presents FloodDefender, an efficient and protocol-independent defense framework for SDN/OpenFlow networks. FloodDefender stands between the controller platform and other controller apps, and conforms to the OpenFlow policy without additional devices. The detection module in FloodDefender utilizes new frequency features to precisely identify SDN-aimed DoS attacks. The mitigation module uses three new techniques to efficiently mitigate attack traffic: table-miss engineering to prevent the communication bandwidth from being exhausted; packet filter to filter out attack traffic and save computational resources of the control plane; and flow rule management to eliminate most of useless flow entries in the switch flow table. Our evaluation on a prototype implementation of FloodDefender shows that the defense framework can precisely identify and efficiently mitigate the SDN-aimed DoS attacks with very little overhead.

Evaluation of DNA repair biology signatures to predict specific carboplatin (C) versus docetaxel (D) benefit in advanced triple-negative breast cancer (aTNBC).

1074 Background: In the Triple Negative Trial we observed no improved response rate (RR) to C over D in aTNBC [Tutt et al, Nat Med 2018], but we did in BRCA1/2 mutated (mut) patients (pts). We hypothesise tumors with other aberrant DNA damage response (DDR) characteristics having higher RR to DNA damage inducing C than D. Methods: We tested the predictive value of DDR process related gene expression signatures (PARPi7, chromosomal instability CIN70, TP53 & DDR Deficiency (DDRD)) on 192 treatment naïve primary tumours (PT) by total RNA-sequencing. Odds ratio (OR) for RR are reported. Paired PT & recurrent (REC) signature scores were compared. Results: Unexpectedly, high DDRD and PARPi7 were associated with higher RR to D than C ( p =0.01 & 0.06). No effect was observed for CIN70 or TP53 signature. To assess whether the unexpected results were due to biological changes 12 PT-REC pairs were available from pts who received chemotherapy (CT) between PT & REC. CIN70 increased from PT to REC, DDRD (non-significantly) & PARPi7 decreased. 4/5 TP53 wildtype classified PT samples classified as mut in REC. The BRCA1/2 & DDRD-treatment interactions only held in pts who received CT before trial entry (table). The PARPi7-treatment interaction only held in CT naïve pts. In CT naïve pts, high CIN70 tumors suggested higher C RR as hypothesized. Restricted to the 149 PAM50 basal-like pts, results were non-significant but similar trends seen. Conclusions: In this trial of aTNBC, DDRD high pts with prior CT had better RR to D than C. A possible explanation for this unexpected result is selective pressure of adjuvant DNA damaging CT and selection for relative taxane sensitivity in those who recur despite a high DDRD score. The hypothesised CIN70 treatment interaction was observed in CT naïve pts. Our results suggest care is required in application of signatures to initial diagnostic material when predicting response to DNA damaging agents at REC particularly in pts with prior CT. [Table: see text]

An Improved Logarithmic Multiplier for Energy-Efficient Neural Computing

Multiplication is the most resource-hungry operation in neural networks (NNs). Logarithmic multipliers (LMs) simplify multiplication to shift and addition operations and thus reduce the energy consumption. Since implementing the logarithm in a compact circuit often introduces approximation, some accuracy loss is inevitable in LMs. However, this inaccuracy accords with the inherent error tolerance of NNs and their associated applications. This article proposes an improved logarithmic multiplier (ILM) that, unlike existing designs, rounds both inputs to their nearest powers of two by using a proposed nearest-one detector (NOD) circuit. Considering that the output of the NOD uses a one-hot representation, some entries in the truth table of a conventional adder cannot occur. Hence, a compact adder is designed for the reduced truth table. The 8x8 ILM achieves up to 17.48 percent saving in power consumption compared to a recent LM in the literature while being almost 8 percent more accurate. Moreover, the evaluation of the ILM for two benchmark NN workloads shows up to 21.85 percent reduction in energy consumption compared to the NNs implemented with other LMs. Interestingly, using the ILM increases the classification accuracy of the considered NNs by up to 1.4 percent compared to a NN implementation that uses exact multipliers.

A Secure Shuffling Mechanism for White-Box Attack-Resistant Unmanned Vehicles

Unmanned Vehicles (UVs) have been being utilized for various applications, such as surveillance, search/rescue, and monitoring. The information they transmit is critical for decision-making. However, UVs are vulnerable to white-box attacks due to improvements in reverse engineering techniques and the openness of their software. Therefore, attackers with sufficient knowledge of a target UV can steal secret information stored in the UV by exploiting its vulnerabilities. Recently, several white-box cryptography techniques have been introduced to protect secret keys from being extracted by converting them into large look-up tables. However, none of them provide approaches to securely update the look-up tables. Thus, once a remote attacker succeeds in extracting the static look-up table from a UV, he/she can use it to decrypt past/future communications or to send false information to the control station by impersonating the UV. In this paper, we propose a look-up table shuffling mechanism that supports white-box cryptography with dynamics. The mechanism makes it hard for attackers to determine the positions of the table entries, and thus to decrypt/encrypt ciphertexts/plaintexts. To show the practicality of the block cipher with our mechanism, we implemented it on a board equipped with a GPU and show its GPU-accelerated performance.

A lightweight BLASTP and its implementation on CUDA GPUs

The BLAST server in the National Center for Biotechnology Information in the USA receives tens of thousands of queries per day on average. However, the service is always the same for every query even though query lengths vary significantly. In fact, the lengths of a large portion of protein sequences are less than 500. On the other hand, the hit detection process consumes the most of the execution time of BLAST and its core architecture is a lookup table. Following the above reasons, we propose a lightweight BLASTP for servicing not-too-long queries, where a hybrid query-index table is proposed accordingly. Each table entry consists of four bytes that can store up to three query positions. Therefore, a sequence word usually requires only one memory fetch to retrieve its hit information. Furthermore, additional dummy entries are embedded into the table and interleaved with original entries. The entries without any hits and dummy entries both can be used to buffer spilled query positions. The above features result in a much smaller lookup table with a higher utilization rate and a lower cache miss ratio. Experimental results show that the lightweight BLASTP outperforms CUDA-BLASTP with speedups ranging from 1.82 to 3.37 based on the first two critical phases.

PD10-10 IMPACT OF BASELINE DISEASE VOLUME AND PRIOR DOCETAXEL THERAPY ON PROSTATE-SPECIFIC ANTIGEN-RELATED OUTCOMES IN PATIENTS WITH METASTATIC HORMONE-SENSITIVE PROSTATE CANCER TREATED WITH ENZALUTAMIDE PLUS ANDROGEN DEPRIVATION THERAPY

You have accessJournal of UrologyProstate Cancer: Advanced (including Drug Therapy) I (PD10)1 Apr 2020PD10-10 IMPACT OF BASELINE DISEASE VOLUME AND PRIOR DOCETAXEL THERAPY ON PROSTATE-SPECIFIC ANTIGEN-RELATED OUTCOMES IN PATIENTS WITH METASTATIC HORMONE-SENSITIVE PROSTATE CANCER TREATED WITH ENZALUTAMIDE PLUS ANDROGEN DEPRIVATION THERAPY Neal D. Shore*, E. David Crawford, Russell Z. Szmulewitz, Daniel Petrylak, Jeffrey Holzbeierlein, Arnauld Villers, Arun Azad, Antonio Alcaraz, Boris Alekseev, Taro Iguchi, Francisco Gomez-Veiga, Brad Rosbrook, Benoit Baron, Gabriel P. Haas, Arnulf Stenzl, and Andrew J. Armstrong Neal D. Shore*Neal D. Shore* More articles by this author , E. David CrawfordE. David Crawford More articles by this author , Russell Z. SzmulewitzRussell Z. Szmulewitz More articles by this author , Daniel PetrylakDaniel Petrylak More articles by this author , Jeffrey HolzbeierleinJeffrey Holzbeierlein More articles by this author , Arnauld VillersArnauld Villers More articles by this author , Arun AzadArun Azad More articles by this author , Antonio AlcarazAntonio Alcaraz More articles by this author , Boris AlekseevBoris Alekseev More articles by this author , Taro IguchiTaro Iguchi More articles by this author , Francisco Gomez-VeigaFrancisco Gomez-Veiga More articles by this author , Brad RosbrookBrad Rosbrook More articles by this author , Benoit BaronBenoit Baron More articles by this author , Gabriel P. HaasGabriel P. Haas More articles by this author , Arnulf StenzlArnulf Stenzl More articles by this author , and Andrew J. ArmstrongAndrew J. Armstrong More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000844.010AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Enzalutamide (ENZA) + androgen deprivation therapy (ADT) significantly reduced the risk of radiographic progression or death in men with metastatic hormone-sensitive prostate cancer (mHSPC), regardless of baseline prostate-specific antigen (PSA) levels (ARCHES; NCT02677896). Here, we further assess PSA-related outcomes in patients enrolled in ARCHES by disease volume and prior docetaxel therapy at study entry. METHODS: Patients with mHSPC were randomized 1:1 to receive ENZA (160 mg/day) + ADT or placebo (PBO) + ADT. Primary endpoint was radiographic progression-free survival. Secondary endpoints included time to PSA progression and PSA undetectable rate. Post hoc analyses were performed based on disease volume and prior docetaxel at study entry, which were stratification factors, as well as on time to 50% PSA reduction and time to undetectable (<0.2 ng/mL) PSA. RESULTS: Of the overall population (n=1150), 423 (36.8%) patients had low-volume disease and 205 (17.8%) patients reported prior docetaxel use. ENZA + ADT significantly improved PSA-related outcomes versus PBO + ADT, regardless of disease volume or prior docetaxel use at study entry (Table). The proportion of patients with ≥50% PSA reduction from baseline ranged from 85–95% for ENZA + ADT and 18–66% for PBO + ADT across the different patient subgroups. CONCLUSIONS: ENZA + ADT provides improvements in all assessed PSA-related outcomes versus PBO + ADT in patients with mHSPC, irrespective of disease volume or prior docetaxel therapy, with the limitation that the true baseline PSA is unknown for some patients due to prior ADT use in this patient population. Source of Funding: This study was funded by Astellas Pharma Inc. and Pfizer Inc., the co-developers of enzalutamide. Medical writing and editorial assistance were provided by Lianne Young, BSc (Hons), and Jane Beck from Complete HealthVizion, funded by the study sponsors. © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e249-e250 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Neal D. Shore* More articles by this author E. David Crawford More articles by this author Russell Z. Szmulewitz More articles by this author Daniel Petrylak More articles by this author Jeffrey Holzbeierlein More articles by this author Arnauld Villers More articles by this author Arun Azad More articles by this author Antonio Alcaraz More articles by this author Boris Alekseev More articles by this author Taro Iguchi More articles by this author Francisco Gomez-Veiga More articles by this author Brad Rosbrook More articles by this author Benoit Baron More articles by this author Gabriel P. Haas More articles by this author Arnulf Stenzl More articles by this author Andrew J. Armstrong More articles by this author Expand All Advertisement PDF downloadLoading ...

BlockSDN: Blockchain-as-a-Service for Software Defined Networking in Smart City Applications

Smart cities have emerged as a hub of intelligent applications (e.g., intelligent transportation systems, smart parking, smart homes, and e-healthcare) to provide ambient-assisted living and quality of experience to wide communities of users. The smooth execution of these applications depends on reliable data transmission between various smart devices and machines. However, the exponential increase in data traffic due to the growing dependency of end users on smart city applications has created various bottlenecks (e.g., channel congestion, manual flow configurations, limited scalability, and low flexibility) on the conventional network backbone, which can degrade the performance of any designed solution in this environment. To mitigate these challenges, SDN emerges as a powerful new technology that provides global visibility of the network by decoupling the control logic from the forwarding devices. The abstraction of network services in SDN architecture provides more flexibility for network administrators to execute various applications. In SDN architecture, the decision making process is handled by a logically centralized controller, which may have a single point of failure. An adversary/ attacker can compromise the controller using different types of attacks (e.g., eavesdropping, man-in-the middle attack, and distributed denial of service) in order to gain total control of the network by updating the flow table entries at the data plane or hindering control plane operations. Therefore, to cope with the aforementioned challenges, new strategies and solutions are required for securing the SDN-enabled network architecture at different planes and their associated interconnections. In this article, various security issues and different attack vectors are discussed along with possible solutions. To mitigate various attacks, BlockSDN, a blockchain as a service framework, for SDN is proposed. The architecture of permissioned blockchain is presented followed by two attack scenarios, 1) a malware compromised switch at the data plane and 2) distributed denial of service attack at the control plane, to demonstrate the applicability of the BlockSDN framework for various future applications. Finally, the open issues and challenges with respect to the design of blockchain solutions for SDN in smart city applications are also discussed.

STEREOS: Smart Table EntRy Eviction for OpenFlow Switches

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

Design and analysis of a hybrid mobility management scheme for wireless mesh network to handle highly mobile mesh clients

Nowadays, the importance of continuous connectivity of wireless network has increased because of increase in the number of applications in handheld devices. For offering continuous connectivity, seamless mobility management is very much necessary. Many mobility management schemes have been proposed. The techniques can broadly classified as, tunnel based scheme and routing based scheme. The tunnel based techniques has the advantage of limited route management packet transmission. But disadvantage is that besides source and destination addresses each of the data packets carry an extra header for routing. In case of routing based scheme, advantage is that the data packets do not carry any extra header for routing. But the disadvantage is that during handoff lots of route management packets are transmitted. As the mesh client speed increases, these problems further increase. Hybrid mobility management technique has been proposed in this paper for minimizing the problems of both tunnel based scheme and routing based scheme. It has been designed to handle highly mobile mesh clients. The proposed scheme routes internet packets using routing table entries. On the other hand, it uses tunneling to route the intranet packets to the destination. The hybrid scheme and some of the existing schemes have been analyzed and compared. Performance comparison shows that the hybrid scheme outperforms these techniques.

Generation of a 3D error compensation grid from ISO 230-1 error parameters obtained by a SAMBA indirect calibration and validated by a ball-bar spherical test

Tool path deviation reduces machined parts quality. To enhance machine tool accuracy, compensation tables are provided in most controllers to automatically apply small corrections to axis commands. A model-based approach, considering the ISO 230-1 machine geometric error parameters, is proposed to generate the table entries. The error parameters are estimated using model-based indirect calibration results from a scale and master balls artifact probing (SAMBA) test. Two models are used, one with primarily the axis alignment errors and scale errors and the other including many error motions. The 3D grid error compensation is generated with a minimal optimum mesh grid dimension to achieve a preset precision considering the estimated model error parameters. The efficiency of the table is evaluated using a 3D ball-bar test consisting of various circular trajectories along several meridians and the equator before and after applying the table-based error compensation. It is shown that the volumetric errors due to out-of-squareness errors and linear axis linear positioning errors can be compensated using a 2 × 2 × 2, 8 nodes, grid. However, when including error motions, the optimum grid dimension depends on the specific error values of the machine. For the tested machine, a 19 × 19 × 19 for 6859 nodes grid was required, with which the out-of-sphericity of the tool trajectory relative to the workpiece frame is improved by over 82%.

Data meaning and knowledge discovery: Semantical aspects of information systems

Data tables provide the standard means of representation of qualitative or quantitative information about objects of interest. They also form a starting point for the task of information processing: an operation of passing from raw data or information to semantically processed knowledge. The fundamental issue here is the question about the meaning of data: What do entries in the data table actually tell us about objects? It entails another question: How should the meaning be further processed? The primary aim of the article is an attempt to answer these two questions. To this end we are going to employ conceptual scales from formal concept analysis, an important theory of data processing introduced by Rudolf Wille, and apply them to data tables so as to obtain multivalued information systems, which were introduced and developed within the conceptual framework of rough set theory by Zdzisław Pawlak and Ewa Orłowska. Our main idea is to regard multivalued information systems as the semantics or meaning of the original tables. This idea allows us to describe and combine classical rough set theory, dominance-based rough set approach, and formal concept analysis within the single framework of multivalued information systems, which is rich enough to cope with a number of semantical nuances that may occur during the process of data analysis.

MPLS-based reduction of flow table entries in SDN switches supporting multipath transmission

In this paper, the problem of resource utilisation improvement in software-defined networking (SDN) is addressed. The need for resource optimisation is understood here be twofold. First, bandwidth in links should be saved when congestion appears. Second, the internal resources represented by table entries of SDN switches should be minimised to ensure fast processing and flexibility. Here, both types of resources are optimised with a new mechanism for flow aggregation. The mechanism is accompanied with a multipath transmission supporting reaction when network conditions change.The proposed mechanism uses classical MPLS labelling, which enables flow aggregation together with multipath transmission; therefore, neither involves any definition of new protocols nor requires the application of legacy signalling protocols. Only simple yet powerful modifications of the existing solutions assured by flexibility of the OpenFlow protocol are necessary. Furthermore, the proposed solution can be incrementally deployed in legacy networks.The aggregation results in a low number of flow entries in core switches in comparison to legacy OpenFlow operation. The simulations show that the number of flow entries in core switches can be reduced by as much as 93%, while the overall network traffic is increased by around 171%. This type of scalability improvement of flow processing is obtained as a result of the introduction of a centrally managed MPLS label distribution performed by an SDN controller. Moreover, the proposed method of multipath transmission improves network resource utilisation. Additionally, independently of the traffic pattern, the proposed approach significantly reduces the communication overhead between the controller and the switches.

Network Traffic Sampling System Based on Storage Compression for Application Classification Detection

With the development of the Internet, numerous new applications have emerged, the features of which are constantly changing. It is necessary to perform application classification detection on the network traffic to monitor the changes in the applications. Using RelSamp to sample traffic can provide the sampled traffic with sufficient application features to support application classification. RelSamp separately assigns counters for each flow to record the statistical features and introduces a collision chain into the hash flow table to resolve hash conflicts in the table entries. However, in high-speed networks, owing to the number of concurrent flows and heavy-tailed nature of the traffic, the storage allocation method of RelSamp results in a significant waste of storage on the traffic sampling device. Moreover, the hash conflict resolution of RelSamp causes the collision chains of several hash table entries to be excessively deep, thereby reducing the search efficiency of the flow nodes. To overcome the shortcomings of RelSamp, this study presents a sampling model known as MiniSamp. Based on the RelSamp sampling mechanism, MiniSamp introduces shared counter trees to compress the storage space of the counters during the sampling process and integrates an efficient search tree into the hash table. The search tree structure is adjusted according to the network environment to improve the search efficiency of the flow nodes. The experimental results demonstrate that MiniSamp can effectively aid network operators to classify traffic in the high-speed network.

Packet Processing Architecture Using Last-Level-Cache Slices and Interleaved 3D-Stacked DRAM

Packet processing performance of Network Function Virtualization (NFV)-aware environment depends on the memory access performance of commercial-off-the-shelf (COTS) hardware systems. Table lookup is a typical example of packet processing, which has a significant dependence on memory access performance. Thus, the on-chip cache memories of the CPU are becoming more and more critical for many high-performance software routers or switches. Moreover, in the carrier network, multiple applications run on top of the same hardware system in parallel, which requires the capacity of cache memories. In this paper, we propose a packet processing architecture that enhances memory access parallelism by combining on-chip last-level-cache (LLC) slices and off-chip interleaved 3 Dimensional (3D)-stacked Dynamic Random Access Memory (DRAM) devices. Table entries are stored in the off-chip 3D-stacked DRAM, so that memory requests are processed in parallel by using bank interleaving and channel parallelism. Also, cached entries are distributed to on-chip LLC slices according to a memory address-based hash function so that each CPU core can access on-chip LLC in parallel. The evaluation results show that the proposed architecture reduces the memory access latency by 62 % and 12 % and increases the throughput by 108 % and 2 % with reducing blocking probability of memory requests 96 % and 50 %, compared to the architecture with on-chip shared LLC and that without on-chip LLC, respectively.

Multi-hop Deflection Routing Algorithm Based on Reinforcement Learning for Energy-Harvesting Nanonetworks

Nanonetworks are composed of interacting nano-nodes, whose size ranges from several hundred cubic nanometers to several cubic micrometers. The extremely constrained computational resources of nano-nodes, the fluctuations in their energy caused by energy harvesting processes, and their very limited transmission range at Terahertz (THz)-band frequencies (0.1-10 THz), make the design of routing protocols in nanonetworks very challenging. A multi-hop deflection routing algorithm based on reinforcement learning (MDR-RL) is proposed in this paper to dynamically and efficiently explore the routing paths during packet transmissions. First, new routing and deflection tables are implemented in nano-nodes, so that nano-nodes can deflect packets to other neighbors when route entries in the routing table are invalid. Second, one forward updating scheme and two feedback updating schemes based on reinforcement learning are designed to update the tables, namely, on-policy and off-policy updating schemes. Finally, extensive simulations in networks simulator-3 are conducted to analyze the performance of MDR-RL using different updating policies, as well as to compare the performance with other machine learning routing algorithms based on Neural Networks and Decision Tree. The results show that the MDR-RL can increase the packet delivery ratio and number of delivered packets, and can decrease the packet average hop count.