Quality adaptive end-to-end packet scheduling to avoid playout interruptions in Internet video streaming systems

In Internet multimedia streaming, the quality of the delivered media can be adapted to the Quality of Service provided by the underlying network, thanks to encoding algorithms. These allow a fine grained enhancement of a low quality base layer at streaming time. The main objective that should be satisfied in such systems is to avoid the starvation of the decoding process and consequent playout interruptions. In this work, we tackle the problem using a control theoretic approach. In particular, we design and implement the novel end-to-end Quality Adaptive Scheduler for properly distributing the network available bandwidth among base and enhancement layers. The developed solution can be adopted in many contexts given that it has been designed without assumptions on the delivered media nor on the protocol stack. Anyway, to test its effectiveness, we have casted it in a H.264/AVC SVC based video streaming architecture for unicast Internet applications. The performance of the scheduler has been experimentally evaluated in both a controlled testbed and several “wild” Internet scenarios, including also UMTS and satellite radio links. Results have clearly demonstrated that our Quality Adaptive Scheduler is able to significantly improve the performance of the video streaming system in all operative conditions.     

认知无线电系统中授权频段机会可用价值研究

认知无线电技术能利用暂时空闲的授权频段,被认为是解决当前无线电频谱资源稀缺的有效技术。为提高搜索授权频段的效率,提出了一种简单而有效的方法。此种方法,根据认知无线电系统业务模型确定可用授权频段的加权系数,联合授权频段的占用状况来计算授权频段的机会可用价值。授权信道的可用价值反映了信道为认知无线电系统提供空白频谱的能力。     

Optimizing utilization of resource pools in web application servers

Among the web application server resources, the most critical for their performance are those that are held exclusively by a service request for the duration of its execution (or some significant part of it). Such exclusively held server resources become performance bottleneck points, with failures to obtain such a resource constituting a major portion of request rejections under server overload conditions. In this paper, we propose a methodology that computes the optimal pool sizes for two such critical resources: web server threads and database connections. Our methodology uses information about incoming request flow and about fine‐grained server resource utilization by service requests of different types, obtained through offline and online request profiling. In our methodology, we advocate (and show its benefits) the use of a database connection pooling mechanism that caches database connections for the duration of a service request execution (so‐called request‐wide database connection caching). We evaluate our methodology by testing it on the TPC‐W web application. Our method is able to accurately compute the optimal number of server threads and database connections, and the value of sustainable request throughput computed by the method always lies within a 5% margin of the actual value determined experimentally. Copyright © 2010 John Wiley & Sons, Ltd.     

Optimal interval scheduling with a resource constraint

We consider a scheduling problem where n jobs have to be carried out by m parallel identical machines. The attributes of a job j are a fixed start time s_j, a fixed finish time f_j, a resource requirement r_j, and a value v_j. Every machine owns R units of a renewable resource necessary to carry out jobs. A machine can process more than one job at a time, provided the resource consumption does not exceed R. The jobs must be processed in a non-preemptive way. Within this setting, we ask for a subset of jobs that can be feasibly scheduled with the maximum total value. For this strongly NP-hard problem, we first discuss an approximation result. Then, we propose a column generation scheme for the exact solution. Finally, we suggest some greedy heuristics and a restricted enumeration heuristic. All proposed algorithms are implemented and tested on a large set of randomly generated instances. It turns out that the column generation technique clearly outperforms the direct resolution of a natural compact formulation; the greedy algorithms produce good quality solutions in negligible time, whereas the restricted enumeration averages the performance of the greedy methods and the exact technique.     

A gradual noisy chaotic neural network for solving the broadcast scheduling problem in packet radio networks

In this paper, we propose a gradual noisy chaotic neural network (G-NCNN) to solve the NP-complete broadcast scheduling problem (BSP) in packet radio networks. The objective of the BSP is to design an optimal time-division multiple-access (TDMA) frame structure with minimal TDMA frame length and maximal channel utilization. A two-phase optimization is adopted to achieve the two objectives with two different energy functions, so that the G-NCNN not only finds the minimum TDMA frame length but also maximizes the total node transmissions. In the first phase, we propose a G-NCNN which combines the noisy chaotic neural network (NCNN) and the gradual expansion scheme to find a minimal TDMA frame length. In the second phase, the NCNN is used to find maximal node transmissions in the TDMA frame obtained in the first phase. The performance is evaluated through several benchmark examples and 600 randomly generated instances. The results show that the G-NCNN outperforms previous approaches, such as mean field annealing, a hybrid Hopfield network-genetic algorithm, the sequential vertex coloring algorithm, and the gradual neural network.     

On the complexity of interval scheduling with a resource constraint

We consider a scheduling problem where jobs have to be carried out by parallel identical machines. The attributes of a job j are: a fixed start time s_j, a fixed finish time f_j, and a resource requirement r_j. Every machine owns R units of a renewable resource necessary to carry out jobs. A machine can process more than one job at a time, provided the resource consumption does not exceed R. The jobs must be processed in a non-preemptive way. Within this setting, the problem is to decide whether a feasible schedule for all jobs exists or not.We discuss such a decision problem and prove that it is strongly NP-complete even when the number of resources are fixed to any value R≥2. Moreover, we suggest an implicit enumeration algorithm which has O(nlogn) time complexity in the number n of jobs when the number m of machines and the number R of resources per machine are fixed.The role of storage layout and preemption are also discussed.     

A sharing-oriented service selection and scheduling approach for the optimization of resource utilization

We present a sharing-oriented service selection and scheduling approach capable of finding a trade-off between requirement satisfaction degree, service utilization rate and service sharing cost for limited quantities and capacities of available services. In traditional service selection approaches, each customer requirement is independently satisfied by optimally selecting a set of candidate service resources. However, in real-life service scenarios, it is usual for multiple customers to raise their requirements simultaneously, and available services need to be allocated between them. Especially, when available services are limited in both quantity and capacity, a traditional “first-come-first-serve” strategy would lead to a low service utilization rate, and some requirements cannot be satisfied at all (i.e., a low requirement satisfaction degree). Our approach makes use of the feature that some services can be shared by several customer requirements. Specifically, a virtualized service resource consisting of multiple candidate services is constructed and scheduled to satisfy multiple customer requirements simultaneously. Our approach searches for the global optimization on requirement satisfaction degree, service utilization rate, and service sharing cost. We build a mathematical model for this multi-objective optimization problem and propose a nested genetic algorithm mixed with a greedy strategy. Experiments in an ocean transportation service setting are conducted and our approach is compared with traditional approaches to validate its effectiveness.     

DVB-RCS卫星系统无线资源管理体系架构

基于DVB-RCS（Digital Video Broadcasting and Return Channel via Satellite）卫星无线接入技术标准的宽带卫星通信技术已成为目前研究的热点。现有的DVB-RCS标准以物理层规范为主,其中的无线资源管理技术等重要的宽带无线接入功能的定义缺乏系统性,缺少具有可实现性的无线资源管理体系的描述,尚不能构成一个较完整的卫星宽带无线接入体系结构。针对宽带卫星通信的无线资源管理技术,提出一种基于DVB-RCS的宽带卫星无线资源管理体系结构,给出其主要功能模块设计及关键技术分析,并就可能的实现方法和进一步研究方向进行讨论。     

Performance of GSM networks with general packet radio services

Recently, most of the mobile network providers start to introduce general packet radio service (GPRS) in their existing GSM networks. GPRS is the technology that will enable more efficient Internet applications to run on mobile networks even before the installation of 3G systems. However, it is not yet clearly understood, how the new data services will affect the overall network performance. This paper provides a framework for analytical performance evaluation of a single GSM/GPRS cell based on a multidimensional Markov chain model. Important performance measures like new call and handover blocking probabilities, moments of the blocking period distributions, achievable average data rates and resource utilization are determined. Introducing a new connection admission control (CAC) algorithm, different partitioning strategies between GSM and GPRS resources are investigated. Finally, the influence of typical GPRS applications like Internet browsing on traditional GSM services has been studied.     

Stability of adaptive feedback-based resource managers for systems with execution time variations

Today’s embedded systems are exposed to variations in load demand due to complex software applications, dynamic hardware platforms, and the impact of the run-time environment. When these variations are large, and efficiency is required, adaptive on-line resource managers may be deployed on the system to control its resource usage. An often neglected problem is whether these resource managers are stable, meaning that the resource usage is controlled under all possible scenarios. In this paper we develop mathematical models for real-time embedded systems and we derive conditions which, if satisfied, lead to stable systems. For the developed system models, we also determine bounds on the worst case response times of tasks. We also give an intuition of what stability means in a real-time context and we show how it can be applied for several resource managers. We also discuss how our results can be extended in various ways.     

Performance analysis of the general packet radio service

This paper presents an efficient and accurate analytical model for the radio interface of the general packet radio service (GPRS) in a GSM network. The model is utilized for investigating how many packet data channels should be allocated for GPRS under a given amount of traffic in order to guarantee appropriate quality of service. The presented model constitutes a continuous-time Markov chain. The Markov model represents the sharing of radio channels by circuit switched GSM connections and packet switched GPRS sessions under a dynamic channel allocation scheme. In contrast to previous work, the Markov model explicitly represents the mobility of users by taking into account arrivals of new GSM and GPRS users as well as handovers from neighboring cells. Furthermore, we take into account TCP flow control for the GPRS data packets. To validate the simplifications necessary for making the Markov model amenable to numerical solution, we provide a comparison of the results of the Markov model with a detailed simulator on the network level.     

On scheduling a single machine with resource dependent release times

We consider a single machine scheduling problem with resource dependent release times that can be controlled by a non-increasing convex resource consumption function. The objective is to minimize the weighted total resource consumption and sum of job completion times with an initial release time greater than the total processing times. It is known that the problem is polynomially solvable in O(n⁴) with n the number of jobs.     

A tight approximation ratio of a list scheduling algorithm for a single-machine scheduling problem with a non-renewable resource

Journal of Scheduling - In this paper, we investigate an open problem by Györgyi and Kis for a single-machine scheduling problem with a non-renewable resource (NR-SSP) and total weighted...     

A two-stage adaptive fruit fly optimization algorithm for unrelated parallel machine scheduling problem with additional resource constraints

In this paper, an unrelated parallel machine scheduling problem with additional resource constraints (UPMSP_RC) from the real world manufacturing systems is studied. With the objective of minimizing the makespan, a mixed integer linear programming model is presented and several properties are analyzed. Furthermore, a two-stage adaptive fruit fly optimization algorithm (TAFOA) is proposed to solve the UPMSP_RC. At the first stage, a heuristic is proposed to generate an initial solution with high quality. At the second stage, the initial solution is adopted as the initial swarm center for further evolution. During the evolution, the search manners are selected adaptively with the guidance of the problem-specific knowledge, which is a sufficient condition of the best schedule under a given job-to-machine assignment. Moreover, the effect of parameters on the performance of the TAFOA is investigated by using the two-factor analysis of variance (ANOVA). Finally, extensive numerical comparisons are carried out to show the effectiveness of the TAFOA in solving the UPMSP_RC.     

Dynamic scheduling of design activities with resource constraints

A design process can be represented as a network of design activities. A number of design projects may be undertaken simultaneously. This paper deals with the problem of scheduling design activities of multiple design projects competing for the limited available resources. The problem of determining a schedule subject to precedence and resource constraints is difficult to solve. It becomes even more complex when unforeseen changes are considered, for example, in the level of resources. Therefore, the scheduling problem is decomposed into a series of multidimensional (multiresource) knapsack problems. Due to high computational complexity of the multidimensional knapsack problem, two solution procedures are proposed     

Measuring the impact of adversarial errors on packet scheduling strategies

In this paper, we explore the problem of achieving efficient packet transmission over unreliable links with worst-case occurrence of errors. In such a setup, even an omniscient offline scheduling strategy cannot achieve stability of the packet queue, nor is it able to use up all the available bandwidth. Hence, an important first step is to identify an appropriate metric to measure the efficiency of scheduling strategies in such a setting. To this end, we propose an asymptotic throughput metric which corresponds to the long-term competitive ratio of the algorithm with respect to the optimal. We then explore the impact of the error detection mechanism and feedback delay on our measure. We compare instantaneous with deferred error feedback, which requires a faulty packet to be fully received in order to detect the error. We propose algorithms for worst-case adversarial and stochastic packet arrival models, and formally analyze their performance. The asymptotic throughput achieved by these algorithms is shown to be close to optimal by deriving lower bounds on the metric and almost matching upper bounds for any algorithm in the considered settings. Our collection of results demonstrate the potential of using instantaneous feedback to improve the performance of communication systems in adverse environments.     

A note on optimization in deteriorating systems using scheduling problems with the aging effect and resource allocation models

This paper concerns scheduling problems with the aging effect and additional resource allocation. A measurable result of the aging phenomenon is that the time required to perform a job increases whereas the additional resource allocation allows one to decrease it. As an example of a deteriorating system that can be described and optimized by the application of the models and algorithms considered, we choose the pickling process, where cleaning of metal items decreases the efficiency of the pickling (cleaning) bath (i.e., one containing an active substance), whereas heating it up can improve the efficiency. In particular, we focus on the optimization problems for such systems and model them as single-machine scheduling problems with job processing times dependent on the fatigue of a machine and on the allocation of additional resources. The objectives considered are the minimization of time criteria (the maximum completion time and the maximum lateness) under a given resource consumption as well as the minimization of the resource consumption under given time criteria. The computational complexity of the problems is determined and solution properties are proved. On the basis of these, we construct optimal polynomial time algorithms for some cases of the problems considered.     

An adaptive scheme for fault-tolerant scheduling of soft real-time tasks in multiprocessor systems

The scheduling of real-time tasks with primary-backup-based fault-tolerant requirements has been an important problem for several years. Most of the known scheduling schemes are non-adaptive in nature meaning that they do not adapt to the dynamics of faults and task's parameters in the system. In this paper, we propose an adaptive fault-tolerant scheduling scheme that has a mechanism to control the overlap interval between the primary and backup versions of tasks such that the overall performance of the system is improved. The overlap interval is determined based on the observed fault rate and task's soft laxity. We also propose a new performance index, called SR index, that integrates schedulability (S) and reliability (R) into a single metric. To evaluate the proposed scheme, we have conducted analytical and simulation studies under different fault and deadline scenarios, and found that the proposed adaptive scheme adapts to system dynamics and offers better SR index than that of the non-adaptive schemes.