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In mixed-model assembly lines, smooth operation of the assembly line depends on adherence to the scheduled sequence. However, during production process, this sequence is altered both intentionally and unintentionally. A major source of unintentional sequence alteration in automobile plants is the paint defects. A post-paint resequencing buffer, located before the final assembly is used to restore the altered sequence. Restoring the altered sequence back to the scheduled sequence requires three distinct operations in this buffer: Changing the positions (i.e. resequencing) of vehicles, inserting spare vehicles in between difficult models and replacing spare vehicles with paint defective vehicles. We develop a twostage stochastic model to determine the optimal number of spare vehicles from each model-colour type to be placed into the Automated Storage and Retrieval System resequencing buffer that maximises the scheduled sequence achievement ratio (SSAR). The model contributes to the literature by explicitly considering above three distinct operations and random nature of paint defect occurrences. We use sample average approximation algorithm to solve the model. We provide managerial insights on how paint entrance sequence, defect rate and buffer size affect the SSAR. A value of stochastic solution shows that the model significantly outperforms its deterministic counterpart. [ABSTRACT FROM AUTHOR]

This paper proposed an Integrated Random Early Detection (IRED) method that aims to resolve the problems of the queue-based AQM and loadbased AQM and gain the benefits of both using indicators from both types. The arrival factor (e.g., arrival rate, queue and capacity) and the departure factors are used to estimate the congestion through two integrated indicators. The utilized indicators are mathematically calculated and integrated to gain unified and coherent congestion indicators. Besides, IRED is built based on a new dropping calculation approach that fits the utilized congestion indicators while maintaining the intended buffer management criteria, avoiding global synchronization and enhancing the performance. The results showed that IRED, compared to RED, BLUE, ERED, FLRED, EnRED and DcRED, decreased packet delay and loss under various network status. Specifically, the results showed that in heavy and moderate traffic, the proposed IRED method outperformed the state-of-the-art methods in loss and delay by 18% and 10.6%, respectively. [ABSTRACT FROM AUTHOR]

In this work, dynamic modeling of Power-to-Gas work is presented. The created model was developed with Aspen Plus Dynamics™ tool. Required control strategies and additional Balance of Plant components were implemented to investigate Power-to-Gas transient behavior. Firstly, the developed model for Proton Exchange Membrane electrolysis went through a validation phase to highlight its performance regarding its ability to accurately describe the different phenomena observed in the real system. The created model was then scaled up to meet Electrolysis, Methanation and Oxy-combustion unit requirements. Given its ability to represent the entire system, its operation and its different dynamics, the developed model lends itself to different types of applications. In particular, a case study of coupling with wind farm entirely dedicated to synthetic natural gas production was analysed. The results of this integration have shown the ability of the developed concept to absorb electrical source intermittency. The observed limitations of this kind of application were underlined especially in the case of periods of low electricity production where a solution of buffer storage of excess hydrogen should be considered. [ABSTRACT FROM AUTHOR]

One-dimensional range queries, as one of the most basic type of queries in databases, have been studied extensively in the literature. For large databases, the goal is to build an external index that is optimized for disk block accesses (or I/Os). The problem is well understood in the static case. Theoretically, there exists an index of linear size that can answer a range query in O( Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed ) I/Os, where K is the output size and B is the disk block size, but it is highly impractical. In practice, the standard solution is the B-tree, which answers a query in O(log Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed ) I/Os on a data set of size N, where M is the main memory size. For typical values of N,M, and B, log Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed can be considered a constant. However, the problem is still wide open in the dynamic setting, when insertions and deletions of records are to be supported. With smart buffering, it is possible to speed up updates significantly to o(1) I/Os amortized. Indeed, several dynamic B-trees have been proposed, but they all cause certain levels of degradation in the query performance, with the most interesting tradeoff point at O(Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed ) I/Os for updates and O(Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed ) I/Os for queries. In this article, we prove that the query-update tradeoffs of all the known dynamic B-trees are optimal, when log Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed is a constant. This implies that one should not hope for substantially better solutions for all practical values of the parameters. Our lower bounds hold in a dynamic version of the indexability model, which is of independent interests. Dynamic indexability is a clean yet powerful model for studying dynamic indexing problems, and can potentially lead to more interesting lower bound results. [ABSTRACT FROM AUTHOR]

This research considers a two-stage hybrid flowshop scheduling problem with challenging characteristics substantiated by the complexity of the problem in a collaborating company. Multiple (three) parallel machines are involved in the first stage and only one machine in the second stage. Stage 1 parallel machines are able to process multiple jobs simultaneously but the jobs must be sequentially setup one after another with the loading time depending on the processing time of the stage 2 machine. A blocking environment exists between the two stages with no intermediate buffer storage. In order to reduce the complexity, multiple simultaneous jobs are grouped into batches according to their similarities. Batches can then be considered as basic units for scheduling to determine which stage 1 machine and in what sequence batches are loaded. A genetic algorithm is used to obtain near-optimal schedules mainly by minimising the makespan. The proposed model and solution algorithm are applied to solve the problem in the collaborating company under a set of complicated rules and constraints. Comprehensive studies are conducted with real-life data. The results are consistent with the company's operational principles and are superior compared with the manual schedules. [ABSTRACT FROM AUTHOR]

Extensive research can be found in coping with uncertainty under Material Requirements Planning (MRP)-planned manufacture, but the under-performance of many manufacturing enterprises is still reported. To examine the reasons, a review of the literature on uncertainty under MRP-planned manufacture has been carried out. This paper presents a comprehensive review and categorisation of such research. It aims to provide a structure within which directions can be given for future research and the results of past research work are summarized to give an indication to practitioners of how to cope with uncertainty. An uncertainty categorization structure has been developed using systems theory to categorize uncertainty into input and process, and simultaneously to highlight the uncertainty that occurs in the supply and demand chain of the manufacturing process. Buffering and dampening (BAD) approaches are proposed and the experimental methods used are incorporated in the structure. A range of research gaps is identified: the lack of a detailed structure to enable the significant uncertainty to be diagnosed optimally, as most of the past research studied uncertainty discretely and only some in specific combinations; the sub-optimal approach in coping with uncertainty; and little research has examined the interactions between uncertainties. Several directions for future research are proposed: the development of a cause-and-effect structure for diagnosing uncertainty, a holistic approach to deduce to the underlying causes of uncertainty, generalization of the cause-and-effect structure, examination of the BAD approaches applied in industry and consideration of interactions between uncertainties by first diagnosing the significant uncertainty. It is found that safety stock, the use of appropriate lot-sizing rules and rescheduling are the most robust approaches to cope with uncertainty, while safety capacity is not used at all for buffering process uncertainty. Hedging/over-planning is useful for dampening process uncertainty. It can be concluded that input uncertainty has been maturely researched with external demand uncertainty dominating others, but with a range of suggested BAD approaches available. Scheduling heuristics, simulation modelling and mathematical modelling are the most applicable experimental methods in this research area. This review and categorization have identified that a structured and systematic approach is required to cope with uncertainty holistically within MRP-planned manufacture. [ABSTRACT FROM AUTHOR]

This paper investigates the smallest level of buffering (LB), necessary to ensure the desired production rate in serial lines with unreliable machines. The reliability of machines is assumed to obey either exponential, or Erlang, or Rayleigh models. The LB is measured in units of the average downtime, T[sub down]. The dependence of LB on the reliability model, the number of machines, M, the average uptime, T [sub up], and the efficiency, e = T[sub up]/(T[sub up] + T[sub down]) is analysed. It is shown that reliability models with larger coefficient of variation require larger LB, and an empirical law that connects LB of the exponential model with those for other reliability models is established. It is shown that LB is an increasing function of M, but with an exponentially decreasing rate, saturating at around M = 10. Also, it is shown that LB does not depend explicitly on T[sub up] and is a decreasing function of e . Based on these results, rules-of-thumb are provided for selecting buffer capacity, which guarantee sufficiently high line efficiency. [ABSTRACT FROM AUTHOR]

The buffering in Mobile Ad hoc Networks (MANETs) will depend on end-to-end delay of every user's information from the arrival time of the resource node which is waiting for the receiving time-span of the destination node. Normally each base station will act as relay node or repeater for different mobile nodes as well as for different base stations. Deployment of base station in the MANET is also a critical research. Each base station contain large number of nodes which is connected based on peer-to- peer communications. Dynamic queuing method is used to share the traffic load to various paths which are selected according to the least buffer size; that increases the packet delivery of the network. Dynamic queuing method is used to reduce the overall waiting time of the packets in the network. In this paper, we propose Buffer Enabled Ad hoc On-demand Multipath Distance Vector (BE-AOMDV) algorithm, that generates the routing protocol with Bernoulli model and find that the packet information minimizes the average end-to-end delay. By using periodic updates on the buffer information of the nodes in the paths, the nodes can take dynamic decisions regarding the help of better routes in the data transmission which can lead to even better use of resources of the network. Buffer Enable Multipath Routing is compared with other multipath routing techniques. Furthermore, using perceptive simplifications, we find that the effect of buffers in networks increases the throughput although incongruously considering average end-to-end delay. During widespread simulations, our simulation results proved that the network with the appearance of data stream circumstance improves using Bernoulli function and the proposed method has the increased throughput compared to other related methods. [ABSTRACT FROM AUTHOR]

Buffering techniques have been commonly used in mobile video streaming systems to handle the bandwidth fluctuation and to mitigate the impact of the stochastic characteristic of wireless channels on a mobile user’s quality of experience. However, it has been shown by measurement study that users tend to abort when watching videos with mobile devices, which results in a significant wastage of the video data in the buffer. Therefore, one important problem in mobile video streaming is how to manage the buffer at each mobile user. On the other hand, mobile users generally share the wireless media to download the video data, i.e., mobile users compete with each other for the bandwidth to download the video data. Thus, another important problem in mobile video streaming is how to allocate bandwidth among mobile users. In this paper, we propose to optimize the resource management, i.e., to design buffer management strategy at each mobile user and bandwidth allocation strategy among mobile users, for the multiuser mobile video streaming systems. Specifically, we optimize the long-term average total cost of data wastage and quality of experience of mobile users with certain constraints. By introducing virtual queues and employing the Lyapunov optimization theory, we transform the original optimization problem into the drift-plus-penalty minimization problem. Then, we adopt the primal decomposition to decouple the relationship among different mobile users, which decomposes the problem into a master problem with multiple subproblems. A one-dimension full search algorithm is applied to find the global optimal solution to each subproblem, and the subgradient descent algorithm is utilized to update the solution to the master problem. Finally, simulations are conducted to show that the proposed algorithm is effective for the buffer management and bandwidth allocation in a multiuser mobile video streaming system. [ABSTRACT FROM AUTHOR]

Small satellite networks (SSNs) have attracted intensive research interest recently and have been regarded as an emerging architecture to accommodate the ever-increasing space data transmission demand. However, the limited number of on-board transceivers restricts the number of feasible contacts (i.e., an opportunity to transmit data over a communication link), which can be established concurrently by a satellite for data scheduling. Furthermore, limited battery space, storage space, and stochastic data arrivals can further exacerbate the difficulty of the efficient data scheduling design to well match the limited network resources and random data demands, so as to the long-term payoff. Based on the above motivation and specific characteristics of SSNs, in this paper, we extend the traditional dynamic programming algorithms and propose a finite-embedded-infinite two-level dynamic programming framework for optimal data scheduling under a stochastic data arrival SSN environment with joint consideration of contact selection, battery management, and buffer management while taking into account the impact of current decisions on the infinite future. We further formulate this stochastic data scheduling optimization problem as an infinite-horizon discrete Markov decision process (MDP) and propose a joint forward and backward induction algorithm framework to achieve the optimal solution of the infinite MDP. Simulations have been conducted to demonstrate the significant gains of the proposed algorithms in the amount of downloaded data and to evaluate the impact of various network parameters on the algorithm performance. [ABSTRACT FROM AUTHOR]

This paper presents two main contributions toward efficient very large scale integration circuits mapped with simple cells: 1) a complete synthesis flow to provide good-quality circuits mapped only with simple cells; and 2) an area-oriented, level-aware buffering algorithm based on inverter trees to fix cell fanout violations. An effective pattern-based algorithm to identify XORs/XNORs on and-inverter graphs is also presented. We show that efficient implementations in terms of inverter count, transistor count, area, power, and delay can be generated from circuits with a reduced number of both simple cells and inverters, combined with XOR/XNOR-based optimizations. The proposed buffering algorithm can handle all unfeasible fanout occurrences, while: 1) optimizing the number of added inverters and 2) assigning cells to the inverter tree based on their level criticality. When comparing with academic and commercial approaches, we are able to simultaneously reduce the average number of inverters, transistors, area, power dissipation, and delay up to 48%, 4%, 8%, 14%, and 16%, respectively. [ABSTRACT FROM AUTHOR]

This paper focuses on the enhancement of multimedia streaming services for passengers travelling in vehicles. Online media streaming and sharing is popular and has increased tremendously these days whether it is used at home, in the office or while travelling. The millions of internet users accessing media contents consumes a huge bandwidth and can create Internet bottlenecks or traffic congestion. Media traffic like videos flowing from such congested links can introduce even higher delays increasing buffering time. This can bring a bad Quality of Service (QoS) and bad Quality of Experience (QoE) to users. Such degradation is seen even more when streaming requests are sent by clients within mobile nodes like vehicles. To tackle this issue this paper proposes a hierarchical fog computing based multimedia streaming that reduces latency and minimizes Internet bandwidth consumption. A simulation was conducted for the performance evaluation of the proposed architecture and video streaming service was considered for evaluation. The result acquired from the simulation showed that proposed architecture enhances the QoS and brings better QoE to users. [ABSTRACT FROM AUTHOR]

In this paper, storage efficient caching based on time domain buffer sharing is considered. The caching policy allows a user’s device to determine whether and how long it should cache a content item according to the prediction of the user’s random request time, also referred to as the request delay information (RDI). In particular, the aim is to maximize the caching gain for communications while limiting its storage cost. To achieve this goal, a queueing theoretic model for caching with infinite buffers is first formulated, in which Little’s law is adopted to obtain the tradeoff between the hit ratio and the average buffer consumption. When there are multiple content classes with different RDIs, the storage efficiency is further optimized by carefully allocating the storage cost. For more practical finite-buffer caching, a $G/GI/L/0$ queue model is formulated, in which a diffusion approximation and the Erlang-B formula are adopted to determine the buffer overflow probability and the corresponding hit ratio. The optimal hit ratio is shown to be limited by the demand probability and buffer size for large and small buffers respectively. In practice, a user may exploit probabilistic caching with random maximum caching time and arithmetic caching without any need for content arrival statistics to efficiently harvest content files from the air. [ABSTRACT FROM AUTHOR]

Despite the wealth of research showing that psychological contract breach (PCB) has negative outcomes for individuals, knowledge about the influence of the social context in which breaches are experienced is still scarce. This is surprising, as scholars have argued that work climates, such as when unit members are generally highly committed, could buffer an individual's negative experiences at work. Yet, to date, the unit climate and PCB literatures have largely remained separated and our main goal is to integrate these fields. This is especially timely and relevant, because recent work in the unit climate literature indicates that merely looking at the average climate level might not be enough, because the climate's strength (i.e., the agreement or homogeneity within the unit) could also provide important social cues. Building on these recent advances, we develop and test a theoretical framework which links both climate concepts to PCB. More specifically, we hypothesized that especially when all unit members are highly and homogeneously committed, an employee would reframe their PCB in such a way that it would less adversely affect work engagement and turnover intentions. Using data from 1,272 employees across 36 healthcare units, multilevel structural path analyses supported this three-way interaction. By answering recent calls for more "social PCB research" and integrating the unit climate and PCB literatures, we aim to provide guidance to scholars and practitioners who want to understand in more depth the social context's influence on PCB. [ABSTRACT FROM AUTHOR]

We consider the problem of allocating buffer storage to unbalanced serial production lines so as to maximize steady-state throughput. We focus on the three-station line whose processing time probability distributions differ in either or both mean and variance. We develop rules of thumb for the optimal placement of buffers, and demonstrate the counterintuitive result that a balanced or nearly balanced buffer allocation is optimal for many highly unbalanced lines. We also demonstrate that an imbalance in means has a greater impact on buffer allocation than an imbalance in variances. Finally, we explore the extension of these results to the general serial line. [ABSTRACT FROM AUTHOR]

One of the problems in the design of multi-station lines is the allocation of different work elements to various workstations. This problem is called Assembly Line Balancing (ALB). The failure of workstations and other unforeseen circumstances can result in unnecessary idling of the production line. In order to improve the production rate of such systems, buffer storage of certain capacities can be allocated between each pair of workstations. In this paper, ALB with buffers is formulated as a Single Criterion Decision Making as well as a Multiple Criteria Decision Making problem. In the single objective problem, the cycle time is given and the optimal number of workstations and buffer sizes is obtained to minimize the total cost. In the Multiple Criteria problem, several criteria (objectives) are defined. These objectives are the number of workstations, their buffer sizes, the cycle time, and the total cost of operation with buffers. We demonstrate how the set of efficient (nondominated) alternatives for the multiple criteria problem can be generated. The paper also describes how the best alternative can be selected through the use of existing interactive multiple criteria methods. Several examples are solved and the results of computational experiments are provided. [ABSTRACT FROM AUTHOR]

Sequencing problems and especially flowshop sequencing problems have been broadly discussed in literature. But usually the effect of limited buffer storage is ignored, although in industrial production or computer architecture the amount of available buffer storage has an important influence on the performance of the system. This paper presents a systematic overview of how to formulate flowshop problems with limited buffer storage as well as several heuristics to compete with this class of problems. Numerical experiences are also described. [ABSTRACT FROM AUTHOR]

The planning of an AGV-based flexible assembly line is studied. The conflicting objectives of the minimization of the AGVs wait along the line and of the maximization of the system productivity are investigated. To improve the system performances the late start policy to launch and transfer the AGVs is introduced and a heuristic sequencing algorithm is proposed. The developed computer-aided planning procedures make it possible to consider different AGV launch policies, buffer capacities and management goals; moreover, with the combined use of effective sequences a better utilization of the different resources is reached. [ABSTRACT FROM AUTHOR]

When similar products are mix-produced through manufacturing operations where some of the processes are in common and some arc different, split automatic transfer lines are installed. In these line:3, a split interference caused by a split of the in-process job flow occurs, and line productivity decreases more than series lines. So buffer storages need to be allocated between successive stages. In this paper we study the behaviour of split lines using a Markov model, and compare two allocation methods for the buffer: one is the allocation of local buffers and the other is the allocation of a common buffer. [ABSTRACT FROM AUTHOR]

Presents a multi-stage flow lines with intermediate buffers approximated by two-stage lines using repeated aggregation. Characteristics of the aggregation method; Problems associated with the analysis and design of production lines.

Discusses design problems of allocating buffers and backup machines in automatic transfer lines. Sensitivity to machine failure; Production systems; Storage allocation by simulation.

In this paper, we present a complete and novel solution to the well known buffer storage design problem in a serial production line. The key ingredient of the solution is the efficient calculation of the gradient vector of the throughput with respect to the various buffer sizes. Analytical and experimental results are presented. [ABSTRACT FROM AUTHOR]

This paper considers the properties of the interstage buffer storage capacity as a 'decoupling agent' in multi-station non-powered assembly lines, where the economics of production are achieved by balancing delay costs against stock-carrying costs and storage space costs. A simplified mathematical model for exponential service time distribution based on the 'cyclic queue' concept has been developed, taking into account the three cost parameters, to optimize the buffer storage capacity. A numerical example and sensitivity analysis of the model are presented. [ABSTRACT FROM AUTHOR]

Order overplanning is an uncertainty management method practised by companies that face a very sporadic and irregular demand. It applies a basic buffering principle: to anticipate future demand by collecting early information on customer requirements. Us peculiarity is that, because of lumpiness of demand, this early information is captured by focusing on the purchasing process of each single customer. Order overplanning therefore uses two distinct units in the Master Production Scheduling (MPS) and forecasting procedures: while the MPS unit is an end item or a module, the forecasting unit is a single customer order. Up to now, order overplanning has been implemented by resorting to empirical rules and intuition. This paper investigates its demand management mechanisms by means of quantitative analyses with the aim of taking a first step towards a theory of order overplanning. In particular, the purpose is to assess its ability to cope with lumpiness. understand how its performance may be controlled and balanced, and identify its domain of applicability. Since the quantitative analyses are a first attempt to develop a theory of order overplanning, they take advantage of some simplifying hypotheses. Their implications for the robustness of the models, as well as directions for future research are also discussed. [ABSTRACT FROM AUTHOR]

This paper is the second of two papers that deal with the problem of scheduling in the permutation flowshop and flowline-based manufacturing cell with the consideration of different buffer-space requirements (or different in-process inventory levels) for jobs and the constraints on buffer-storage capacity between machines. We consider two sets of twin objectives of scheduling: one set consists of minimizing the idle-time of machines and waiting-time of jobs, and another set consists of minimizing idle-time of machines and weighted waiting-time of jobs. We present a bicriterion heuristic, with a variant, to obtain a sequence that minimizes the twin objectives under consideration. The heuristic works in two phases. The first phase, i.e., 'initial seed-sequence generation phase', deals with the development of a good seed sequence with respect to twin objectives under consideration. The second phase is the 'improvement phase' in which the seed sequence, obtained from the first phase, is improved by using a new perturbation technique called 'multiple random insertion technique'. An extensive computational experimentation has been carried out to compare the performance of the proposed heuristic and its variant with those of the existing heuristics for scheduling in the buffer-constrained and flowline-based manufacturing systems under study. The results of the experimentation reveal that the proposed heuristic and its variant emerge to be better than the existing heuristics. [ABSTRACT FROM AUTHOR]

Two models of steady-state production line systems are analysed using simulations to estimate the coefficients for an optimal solution to the problem of determining the buffer storage capacity for lines of varying length. The non-steady-state case is then considered and the possibility of using a time-dependent control on buffer capacity during the transient period is investigated. [ABSTRACT FROM AUTHOR]

Internet protocol television (IPTV), which is quickly growing in popularity, can provide hundreds of channels; however, finding a desired IPTV channel among hundreds of channels is a difficult and time consuming issue. Since the very big number of channels, and the delay in switching between them due to network bandwidth limitations, can make it difficult for viewers to find the content they want. To solve this problem, we propose a novel method to reduce channel surfing period that is called Pre-Buffering join with Program Driven (PBPD) method. It is noted that the number of channel switches has a main effect on the channel surfing period. Our proposed method is based on programs; it means that instead of choosing channels, users select their desired programs by which they can reach the channels playing the programs. In addition we send all channels playing the selected program type plus one channel (most popular) from each group of channels playing other types of program in time slots to the STB(set-top box) including minimum one I-frame and finally we use popularity to select the channel in the STB. Simulation results show that the proposed method can reduce the number of channel switches, waiting time for the delivery of the earliest key-frame after selecting a channel and average time to join a broadcasting group; therefore, the proposed method has a good performance in comparison to other methods. [ABSTRACT FROM AUTHOR]

Improvement in the monitoring and control of the efficiency of project scheduling is a challenge for project management research. Classic static buffer monitoring methods cannot be adapted to a complex project environment with a high-degree of uncertainties. In order to overcome this challenge, this paper suggests the use of a dynamic buffer monitoring model based on the phase attributes of the project. The proposed method allocates a project buffer to each phase based on the duration rate and the network complexity of the phases, sets buffer monitoring parameters and monitors the implementation of each phase dynamically. Buffer monitoring trigger points are determined and adjusted dynamically based on the attribution of each phase. Thus, dynamic rolling monitoring and control are conducted through the implementation of these phases. The empirical analysis through a Monte Carlo simulation shows that the duration and cost determined by the proposed method are more reasonable, thus signifying that it can optimise both project duration and cost. These findings indicate that, as opposed to traditional buffer monitoring methods, the proposed approach can effectively overcome student syndrome, monitor project scheduling and avoid unnecessary cost caused by excessive measures. [ABSTRACT FROM AUTHOR]

We study a single server queueing model with admission control and retrials. In the heavy traffic limit, the main queue and retrial queue lengths jointly converge to a degenerate two-dimensional diffusion process. When this model is considered with holding and rejection costs, formal limits lead to a free boundary curve that determines a threshold on the main queue length as a function of the retrial queue length, above which arrivals must be rejected. However, it is known to be a notoriously difficult problem to characterize this curve. We aim instead at optimizing the threshold on the main queue length independently of the retrial queue length. Our main result shows that in the small and large retrial rate limits, this problem is governed by the Harrison-Taksar free boundary problem, which is a Bellman equation in which the free boundary consists of a single point. We derive the asymptotically optimal buffer size in these two extreme cases, as the scaling parameter and the retrial rate approach their limits. [ABSTRACT FROM AUTHOR]

Delay tolerant network is a type of network where the end-to-end path is not available from source to destination due to the node mobility, dynamic topology and network partitioning or such a path is highly unstable and may split almost immediately after it has been explored. In this background, existing ad hoc routing protocols would be unsuccessful. Therefore, the concept of store-carry-forward mechanism is utilized by introducing a new layer on the top of transport layer called as bundle layer. The bundle layer store message(s) in a finite size buffer for the long duration of time. As a result, node buffer runs out of space and drop messages to overcome congestion. In this paper, we aim to schedule the node buffer by using local knowledge-based buffer management policies called as best message-size selections buffer management policy. We have utilized the local information available in message header such as message-size to control the drop of messages. The evaluation of proposed policy with the existing DOA, DLA, LIFO, MOFO, N-drop and SHLI have been analyzed in ONE simulator in terms of reducing message drop, overhead, latency and raising the delivery ratio. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the buffer-aided two-way wireless energy harvesting (EH) relay network comprising two users and one relay, where two users exchange information with the help of the relay via three phases ofEH, multiple-access, and broadcast. By transforming the opportunistic scheduling design problem into an equivalent convex problem, we present the adaptive design for the buffer-aided two-way EH relay network to maximize the long-term achievable rate region. To fulfill the delay sensitive transmission requirements, a delay-aware adaptive transmission (DAAT) scheduling scheme is proposed to guarantee the average end-to-end delivery delay by employing the Lyapunov optimization framework. Our analysis discloses that the average achievable rate region of the two-way wireless EH relay network can be improved when fully considering the potentials by deploying data buffer and energy storage at the relay. There exists an inherent tradeoff among the achievable sum rate, the delivery delay, and the power consumption. It is shown that, when a certain time delay is tolerable, the DAAT scheduling scheme is able to realize a rate region arbitrarily close to the achievable rate region of the buffer-aided two-way wireless EH relay network. [ABSTRACT FROM AUTHOR]

Field programmable gate array (FPGA) has been one of the most popular platforms to implement convolutional neural networks (CNNs) due to its high performance and cost efficiency; however, limited by the on-chip resources, the existing single-FPGA architectures cannot fully exploit the parallelism in CNNs. In this paper, we explore heterogeneous FPGA-based designs to effectively leverage both task and data parallelism, such that the resultant system can achieve the minimum cost while satisfying timing constraints. In order to maximize the task parallelism, we investigate two critical problems: 1) buffer placement, where to place buffers to partition CNNs into pipeline stages and 2) task assignment, what type of FPGA to implement different CNN layers. We first formulate the system-level optimization problem with a mixed integer linear programming model. Then, we propose an efficient dynamic programming algorithm to obtain the optimal solutions. On top of that, we devise an efficient algorithm that exploits data parallelism within CNN layers to further improve cost efficiency. Evaluations on well-known CNNs demonstrate that the proposed techniques can obtain an average of 30.82% reduction in system cost under the same timing constraint, and an average of 1.5 times speedup in performance under the same cost budget, compared with the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Adaptive Media Playout (AMP) controls adapt playout rate to prevent buffer outage and to reduce delay in playout. Most AMP techniques use buffer fullness or its variation as indicator to adapt to playout rate. Nonetheless, selecting a convenient buffer threshold of particular design is a great challenge. Based on this principle, Dynamic Fuzzy Logic (DFL) controls principle fused with AMP technique to propose a new technique, known as DFLAMP. The technique is implemented to reduce playout delay and prevent buffer outage. Moreover, DFLAMP is also utilized to adjust playout rate based on arrival rate variation and adapt playout rate to keep the buffer fullness around the desired level. Simulation results have indicated that DFLAMP technique efficiently outperforms other AMP approaches. DFLAMP shows 14.5% less buffer underflow, 25% less buffer overflow, and 26.4% less distortion of variance as compared to APTA. Moreover, it depicts 7.5% decreased total playout delay as compared to DPTA-APTA. [ABSTRACT FROM AUTHOR]

In this paper, we aim to minimize the average file transmission delay via bandwidth allocation and cache placement in two-tier heterogeneous networks with limited storage capacity, which consists of cache capacity and buffer capacity. For average delay minimization problem with fixed bandwidth allocation, although this problem is nonconvex, the optimal solution is obtained in closed form by comparing all locally optimal solutions calculated from solving the Karush–Kuhn–Tucker conditions. To jointly optimize bandwidth allocation and cache placement, the optimal bandwidth allocation is first derived and then substituted into the original problem. The structure of the optimal caching strategy is presented, which shows that it is optimal to cache the files with high popularity instead of the files with big size. Based on this optimal structure, we propose an iterative algorithm with low complexity to obtain a suboptimal solution, where the closed-from expression is obtained in each step. Numerical results show the superiority of our solution compared with the conventional cache strategy without considering cache and buffer tradeoff in terms of delay. [ABSTRACT FROM AUTHOR]

Fuzzing is an effective technique to discover vulnerabilities that involves testing applications by constructing invalid input data. However, for applications with checksum mechanism, fuzzing can only achieve low coverage because samples generated by the fuzzer are possibly incapable of passing the checksum verification. To solve this problem, most current fuzzers advise the user to comment out the checksum verification code manually, but it requires considerable time to audit the source code to identify the checksum point corresponding to checksum verification. In this paper, we present a novel approach based on taint analysis to identify the checksum point automatically. To implement this approach, the checksum-aware fuzzing assistant tool (CAFA) is designed. After the checksum point is identified, the application is statically patched in an antilogical manner at the checksum point. The fuzzing tool then tests the patched program to bypass the checksum verification. To evaluate CAFA, we use it to assist the American Fuzzy Lop (AFL) tool in fuzzing eight real-world applications with known input specification. The experimental results show that CAFA can accurately and quickly identify the checksum points and greatly improve the coverage of AFL. With the help of CAFA, multiple buffer overflow vulnerabilities have been discovered in the newest ImageMagick and RAR applications. [ABSTRACT FROM AUTHOR]

The manufacturers of NAND flash-based solid-state drives (SSDs) are increasing capacity and throughput by enlarging their page size, which is the minimum I/O unit in the NAND flash chips. Because the host and NAND flash chips have different I/O granularity units, the number of subpage requests increases. However, these subpage requests, especially writes, can cause internal fragmentation and endurance problems. Furthermore, subpage write requests inevitably involve read-modify-write (RMW) operations that increase the write response time because of the out-place-update feature in the NAND flash chips. In this paper, we propose a subpage-aware SSD to increase the lifetime and performance by reducing the number of NAND writes and eliminating unnecessary RMW operations. Our scheme attempts to merge subpage write requests to full page write requests in the write buffer to reduce the number of NAND writes and adds size information to the mapping table to detect unnecessary RMW operations. Our proposed scheme reduces the number of NAND writes by up to 30 and 19 percent on average and the write response time by up to 22 and 13 percent on average. [ABSTRACT FROM AUTHOR]

Adaptive streaming allows for dynamic adaptation of the bitrate to varying network conditions, to guarantee the best user experience. Adaptive bitrate algorithms face a significant challenge in correctly estimating the throughput, as the throughput varies widely over time. The current throughput estimation methods cannot distinguish between throughput fluctuations of different amplitude and frequency. In this paper, we propose a throughput estimation method that accurately estimates the throughput based on previous throughput samples. It is robust to short term and small fluctuations, and sensitive to large fluctuations in throughput. Furthermore, we propose a rate adaptive algorithm for video on demand (VoD) services that selects the quality of the video based on the estimated throughput and playback buffer occupancy. The objective of the rate adaptive algorithms is to guarantee high video quality to improve user experience. The proposed algorithm dynamically adjusts the quality level of the video stream. The proposed method selects high quality video segments, while minimizing the risk of playback interruption. Furthermore, the proposed method minimizes the frequency of video rate changes. We show that the algorithm smoothly switches the video rate to improve user experience. Furthermore, we determine that it efficiently utilizes network resources to achieve a high video rate; competing HTTP clients achieve equitable video rates. We also confirm that variations in the playback buffer size and segment duration do not affect the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

A method for evaluation of the performance of the disk subsystem of a data buffering and transmission system and results of experimental studies of the performance of the disk subsystem according to the developed method are presented. A conclusion is drawn on the reasonability of the use of hard-disk drives that provide the required read/write rates with a sufficiently high level of reliability in prospective development of the modern high-capacity NearLine SAS (NL-SAS). [ABSTRACT FROM AUTHOR]