Response time driven multimedia data objects allocation forbrowsing documents in distributed environments

Distributed information processing, in many WWW applications, requires access to and the transfer and synchronization of large multimedia data objects (MDOs) across the communication network. Moreover, end users expect very fast response times and high QoS. Since the transfer of large MDOs across the communication network contributes to the response time observed by the end users, the problem of allocating these MDOs so as to minimize the response time is challenging. This problem becomes more complex in the context of hypermedia documents, in which the MDOs need to be synchronized during presentation to the end users. The basic problem of data allocation in distributed database environments is NP-complete. Therefore, there is a need to pursue and evaluate solutions based on heuristics which generate near-optimal MDO allocation. We address this problem by: (1) conceptualizing this problem by using a navigational model to represent hypermedia documents and their access behavior by end users, and by capturing the synchronization requirements on MDOs, (2) formulating the problem by developing a base case cost model for response time and generalizing it to incorporate user interaction and buffer memory constraints, (3) designing two algorithms to find near-optimal solutions for allocating MDOs of the hypermedia documents while adhering to the synchronization requirements, and (4) evaluating the trade-off between the time complexity to obtain the solution and the solution quality by comparing the solutions generated by the algorithms with the optimal solutions generated through an exhaustive search     

Storage allocation scheme for virtual instances of cloud computing

Cloud computing delivers resources and services through virtual machines on a pay-as-you-go basis. The allocation of storage space to users is usually determined by means of open allocation mechanisms that cannot guarantee an efficient allocation. Current allocation mechanisms do not consider user requests when making provisioning decisions. In other words, they assume that the storage spaces are fixed. In this study, we propose an algorithm for allocating storage spaces based on the requests of users. We present a unified storage allocation scheme (USAS) for cloud computing. USAS is a dynamic storage allocation framework for unlimited, limited, and free users. Our proposed approach is based on a storage partitioning policy, and we have compared our proposed scheme with open storage scheme and fixed storage scheme with common partition. We show through simulation study that USAS dynamically allocates space for different user requirements for all traffic loads.

     

Performance analysis of a threshold based distributed channel allocation algorithm for cellular networks

Rapid advances of the handheld devices and the emergence of the demanding wireless applications require the cellular networks to support the demanding user needs more effectively. The cellular networks are expected to provide these services under a limited bandwidth. Efficient management of the wireless channels by effective channel allocation algorithms is crucial for the performance of any cellular system. To provide a better channel usage performance, dynamic channel allocation schemes have been proposed. Among these schemes, distributed dynamic channel allocation approaches showed good performance results. The two important issues that must be carefully addressed in such algorithms are the efficient co-channel interference avoidance and messaging overhead reduction. In this paper, we focus on our new distributed channel allocation algorithm and evaluate its performance through extensive simulation studies. The performance evaluation results obtained under different traffic load and user mobility conditions, show that the proposed algorithm outperforms other algorithms recently proposed in the literature.     

基于比例公平的多用户MIMO-OFDM系统自适应资源分配算法*

针对传统多用户MIMO-OFDM系统中自适应资源分配算法计算复杂度较高、实时性不强、无法保证用户间公平性等问题,提出了一种低复杂度的自适应子载波、比特及功率分配算法。在子载波分配上,该算法能够在兼顾比例速率约束的前提下使系统发射功率达到最小化;在比特及功率分配上,该算法将非线性优化问题转换为线性优化问题,在保证系统性能的同时显著降低计算量。仿真结果表明,该算法具有良好的性能,能够有效降低计算量,并使系统容量在用户间分配得更加公平和合理。     

认知无线电网络中基于需求的频谱资源分配算法研究

认知无线电网络中,已有的分配算法未考虑用户的需求,这样会导致需求小的用户分配到更多的资源。为解决上述问题,提出基于需求以及联合比例公平两种频谱分配算法,这两种算法均将用户需求作为频谱分配时需要考虑的因素。仿真结果表明,基于需求以及联合比例公平算法比原算法更能满足各用户的需求。     

Sub-channel shared resource allocation for multi-user distributed MIMO-OFDM systems

Well-controlled resource allocation is crucial for promoting the performance of multiple input multiple output orthogonal frequency division multiplexing （MIMO-OFDM） systems. Recent studies have focused primarily on traditional centralized systems or distributed antenna systems （DASs）, and usually assumed that one sub-carrier or sub-channel is exclusively occupied by one user. To promote system performance, we propose a sub-channel shared resource allocation algorithm for multiuser distributed MIMO-OFDM systems. Each sub-channel can be shared by multiple users in the algorithm, which is different from previous algorithms. The algorithm assumes that each user communicates with only two best ports in the system. On each sub-carrier, it allocates a sub-channel in descending order, which means one sub-channel that can minimize signal to leakage plus noise ratio （SLNR） loss is deleted until the number of remaining sub-channels is equal to that of receiving antennas. If there are still sub-channels after all users are processed, these sub-channels will be allocated to users who can maximize the SLNR gain. Simulations show that compared to other algorithms, our proposed algorithm has better capacity performance and enables the system to provide service to more users under the same capacity constraints.     

基于优先级的TDMA动态时隙分配算法

根据帧结构的不同对现有的时隙分配算法进行分类,分析其特点及综合性能。根据二叉树块内均分法,提出一种改进的时分多址动态时隙分配算法,地面主控站可根据用户的紧急或优先级高低的预约请求进行实时分配,能满足用户占用时隙块发送较长报文的需求。仿真结果表明,该算法能减少报文的平均延误时间,适合突发应急报文的传输需要。     

分布式存储系统中用户磁盘空间分配策略

针对用户文件的分布式存放导致的磁盘空间管理问题,提出一种在分布式存储系统中动态分配磁盘空间的策略,在保持用户磁盘配额大小不变的情况下,根据各存储节点的数据量差异,按需分配用户实际所用的存储空间。与传统的磁盘分配机制相比,该方法简便灵活,更能适应网络存储数据的动态变化,有效提高磁盘空间资源的利用率。     

非理想条件下OFDMA系统物理层安全的资源分配算法

针对OFDMA系统中用户QoS需求的差异性,提出了一种非理想状态下不同用户的资源分配算法,即求取用户携带比特数和发射功率最优解问题。基于自适应功率分配增益较小的情况,通过进行功率的平均分配来降低算法复杂度,基于吞吐量最大化原则,将剩余未分配的子载波分配给能获得最大传输速率的用户,提升系统的整体吞吐量水平。仿真结果表明:相对于其他传统资源分配算法,这个算法能够保证不同混合用户的最小传输速率要求的同时,有效提升算法的公平性。     

宏-飞蜂窝双层网络中基于毫微微基站分组的资源分配

针对宏-飞蜂窝双层网络模型中宏小区（Macrocell）用户层和毫微微小区（Femtocell）用户层之间的跨层干扰和Femtocell之间的同层干扰,提出了一种基于毫微微基站分组的资源分配算法。该算法包括两个部分：一部分是宏基站先利用改进的差额法,设置虚拟的宏用户（MUE）,将之变为平衡的指派问题再为宏小区用户分配信道,然后用注水算法分配功率,保证宏小区用户的正常传输。另一部分是在保证宏小区用户的服务质量基础上,采用一种增强型的蚁群优化（EACO）算法,设定信息素浓度范围后对毫微微小区进行分组,避免了原始的蚁群算法有可能陷入局部最优的现象;再利用一种启发式算法和分布式功率分配算法分别对毫微微用户（FUE）进行信道和功率分配,在满足毫微微小区用户的数据速率需求下,最大化频谱效率。仿真结果表明,EACO有效地抑制了跨层干扰和同层干扰,既能保证用户的数据速率需求,又能有效提升网络频谱效率。     

Shared data allocation in a mobile computing system: exploring local and global optimization

In this paper, we devise data allocation algorithms that can utilize the knowledge of user moving patterns for proper allocation of shared data in a mobile computing system. By employing the data allocation algorithms devised, the occurrences of costly remote accesses can be minimized and the performance of a mobile computing system is thus improved. The data allocation algorithms for shared data, which are able to achieve local optimization and global optimization, are developed. Local optimization refers to the optimization that the likelihood of local data access by an individual mobile user is maximized whereas global optimization refers to the optimization that the likelihood of local data access by all mobile users is maximized. Specifically, by exploring the features of local optimization and global optimization, we devise algorithm SD-local and algorithm SD-global to achieve local optimization and global optimization, respectively. In general, the mobile users are divided into two types, namely, frequently moving users and infrequently moving users. A measurement, called closeness measure which corresponds to the amount of the intersection between the set of frequently moving user patterns and that of infrequently moving user patterns, is derived to assess the quality of solutions provided by SD-local and SD-global. Performance of these data allocation algorithms is comparatively analyzed. From the analysis of SD-local and SD-global, it is shown that SD-local favors infrequently moving users whereas SD-global is good for frequently moving users. The simulation results show that the knowledge obtained from the user moving patterns is very important in devising effective data allocation algorithms which can lead to prominent performance improvement in a mobile computing system.     

Iterative power and subcarrier allocation in rate-constrained orthogonal multicarrier downlink systems based on hybrid harmony search heuristics

This paper presents a novel iterative hybrid algorithm for subcarrier and power allocation in a cognitive orthogonal frequency division multiple access (OFDMA) downlink. In the considered setup a primary base station forwards information to K distant receivers by using a single OFDM waveform, whereas a secondary base station - subject to stringent per-user rate constraints - interferes with the former by sending information from users to the same set of destinations. Power and user allocation at both base stations is jointly performed by the proposed algorithm to maximize the overall throughput of the setup while satisfying, at the same time, the imposed rate constraints. Our proposal, which stems from an hybridization of the harmony search (HS) and differential evolution (DE) algorithms along with a greedy local repair method, is shown - through computer simulations over the extended vehicular A ITU channel model - to be an effective and practical resource allocation procedure for cognitive OFDMA downlinks.     

Multi economic agent interaction for optimizing the aggregate utility of grid users in computational grid

This paper investigates the interactions between agents representing grid users and the providers of grid resources to maximize the aggregate utilities of all grid users in computational grid. It proposes a price-based resource allocation model to achieve maximized utility of grid users and providers in computational grid. Existing distributed resource allocation schemes assume the resource provider to be capable of measuring user’s resource demand, calculating and communicating price, none of which actually exists in reality. This paper addresses these challenges as follows. First, the grid user utility is defined as a function of the grid user’s the resource units allocated. We formalize resource allocation using nonlinear optimization theory, which incorporates both grid resource capacity constraint and the job complete times. An optimal solution maximizes the aggregate utilities of all grid users. Second, this paper proposes a new optimization-based grid resource pricing algorithm for allocating resources to grid users while maximizing the revenue of grid providers. Simulation results show that our proposed algorithm is more efficient than compared allocation scheme. Li Chunlin received the ME in computer science from Wuhan Transportation University in 2000, and PhD degree in Computer Software and Theory from Huazhong University of Science and Technology in 2003. She now is an associate professor of Computer Science in Wuhan University of Technology. Her research interests include computational grid, distributed computing and mobile agent. She has published over 15 papers in international journals. Li Layuan received the BE degree in Communication Engineering from Harbin Institute of Military Engineering, China in 1970 and the ME degree in Communication and Electrical Systems from Huazhong University of Science and Technology, China in 1982. Since 1982, he has been with the Wuhan University of Technology, China, where he is currently a Professor and PhD tutor of Computer Science, and Editor in Chief of the Journal of WUT. He is Director of International Society of High-Technol and Paper Reviewer of IEEE INFOCOM, ICCC and ISRSDC. His research interests include high speed computer networks, protocol engineering and image processing. Professor Li has published over 150 technical papers and is the author of six books. He also was awarded the National Special Prize by the Chinese Government in 1993.     

基于经济模型的计算网格资源分配

提出了一种基于市场的计算网格资源分配框架,在这个模型中,用效用函数刻画用户对网格服务的满意程度,将计算网格资源分配问题转化为在传统的考虑负载平衡的基础上,求最大化所有用户的效用和,并给出了分布式、可扩展的算法。     

一种广域网中分布式数据库设计的数据分配方法

在广域网中，片段分配是分布式数据库设计中的一个主要问题，因为它关系到整个分布式数据库系统的性能。基于一个较简单、全面的分配模型提出了一个片段分配的启发式算法，旨在尽量减少通讯代价的基础上找到一个接近最优解。     

Adaptive joint bandwidth and power allocation in heterogeneous wireless access environment

Joint bandwidth and power allocation for a multi-radio access(MRA)system in a heterogeneous wireless access environment is studied.Since both the number of users being served by the system and the wireless channel state are time-varying,the optimal resource allocation is no longer a static optimum and will change with the varying network state.Moreover,distributed resource allocation algorithms that require iterative updating and signaling interactions cannot converge in negligible time.Thus,it is unrealistic to assume that the active user number and the wireless channel state remain unchanged during the iterations.In this paper,we propose an adaptive joint bandwidth and power allocation algorithm based on a novel iteration stepsize selection method,which can adapt to the varying network state and accelerate the convergence rate.A distributed solution is also designed for the adaptive joint resource allocation implementation.Numerical results show that the proposed algorithm can not only track the varying optimal resource allocation result much more quickly than a traditional algorithm with fixed iteration stepsize,but can also reduce the data transmission time for users and increase the system throughput.     

基于最大加权独立集的频谱分配算法

动态频谱接入技术允许认知用户接入未授权的频谱,可以有效地提高频谱资源的利用率。频谱分配算法的时间开销和公平性是算法优劣的主要评价标准。本文从图论着色模型出发,构建了着色算法的评价体系及优化目标。针对用户间的公平性与分配的时间开销问题,在极大独立集的基础上提出了基于加权最大独立集的着色算法,获得了接近于最优的用户公平性,且该算法的时间开销等于信道数,与认知用户的数目无关。仿真分析验证了算法的正确性。     

Register spilling via transformed interference equations for PAC DSP architecture

Digital signal processors (DSPs) with very long instruction word (VLIW) data‐path architectures are increasingly being deployed on embedded devices for multimedia processing applications. To reduce the power consumption and design cost of VLIW DSP processors, distributed register files and multibank register architectures are being adopted to reduce the number of read and write ports associated with register files, which presents new challenges for devising compiler optimization schemes. This paper addresses the issues of reducing the spill code for a VLIW DSP with distributed register files. Spill code produced by register allocation is traditionally handled by memory spills, but the multibank register‐file architecture provides the opportunity to spill‐out register values onto different register banks. We present a conceptual framework based on the universal and the proxy interference graphs to model the live ranges of registers for spilling codes to different register banks. Heuristic algorithms are then developed on the basis of this concept. By heuristically estimating the register pressure for each register file, we treat different register banks as optional spilling locations in addition to traditional spilling to memory. Experiments were performed on the parallel architecture core VLIW DSP with distributed register files by incorporating our proposed optimization schemes into an Open64‐based compiler. The experimental results show that our approach can improve the performances on average for DSPStone and MiBench benchmarks with spilling cases by 7.1% and 21.6%, respectively, compared with the one always handling spill code in memory. Copyright © 2013 John Wiley & Sons, Ltd.     

Solving the Register Allocation Problem for Embedded Systems Using a Hybrid Evolutionary Algorithm

Embedded systems are unique in the challenges they present to application programmers, such as power and memory space constraints. These characteristics make it imperative to design customized compiler passes. One of the important factors that shape runtime performance of a given embedded code is the register allocation phase of compilation. It is crucial to provide aggressive and sophisticated register allocators for embedded devices, where the excessive compilation time can be tolerated due to high demand on code quality. Failing to do a good job on allocating variables to registers (i.e., determining the set of variables to be stored in the limited number of registers) can have serious power, performance, and code size consequences. This paper explores the possibility of employing a hybrid evolutionary algorithm for register allocation problem in embedded systems. The proposed solution combines genetic algorithms with a local search technique. The algorithm exploits a novel, highly specialized crossover operator that takes into account domain-specific information. The results from our implementation based on synthetic benchmarks and routines that are extracted from well-known benchmark suites clearly show that the proposed approach is very successful in allocating registers to variables. In addition, our experimental evaluation also indicates that it outperforms a state-of-the-art register allocation heuristic based on graph coloring for most of the cases experimented.     

基于NOMA系统的用户分组和功率分配策略

针对非正交多址接入（NOMA）系统的最优用户分组和功率分配方案的高复杂度问题,提出一种改进的用户分组和功率分配策略。首先,对用户进行分组,由信道增益值确定每个子信道的第一个用户,再由贪婪匹配方法分配剩余用户;然后,对用户的功率进行分配,将该功率分配问题分成子信道间和子信道内两部分,对于子信道间采用线性注水算法分配功率,对于子信道内则采用所提的迭代功率分配算法分配功率;最后,构造拉格朗日函数以实现在最大发射功率以及保证每个用户最低数据速率的约束下系统吞吐量的最大化。仿真结果表明,在多用户的情况下,与线性注水的分数阶功率分配（LWF-FTPA）算法和等分数阶功率分配（EQ-FTPA）算法相比,系统吞吐量分别提高了8%和20%,所提策略优于传统算法。