Minimizing data access latency in data grids by neighborhood‐based data replication and job scheduling

In Data Grid systems, quick data access is a challenging issue due to the high latency. The failure of requests is one of the most common matters in these systems that has an impact on performance and access delay. Job scheduling and data replication are two main techniques in reducing access latency. In this paper, we propose two new neighborhood‐based job scheduling strategies and a novel neighborhood‐based dynamic data replication algorithm (NDDR). The proposed algorithms reduce the access latency by considering a variety of practical parameters for decision making and the access delay by considering the failure probability of a node in job scheduling, replica selection, and replica placement. The proposed neighborhood concept in job scheduling includes all the nodes with low data transmission costs. Therefore, we can select the best computational node and reduce the search time by running a hierarchical and parallel search. NDDR reduces the access latency through selecting the best replica by performing a hierarchical search established based on the access time, storage queue workload, storage speed, and failure probability. NDDR improves the load balancing and data locality by selecting the best replication place considering the workload, temporal locality, geographical locality, and spatial locality. We evaluate our proposed algorithms by using Optorsim Simulator in two scenarios. The simulations confirm that the proposed algorithms improve the results compared with similar existing algorithms by 11%, 15%, 12%, and 10% in terms of mean job time, replication frequency, mean data access latency, and effective network usage, respectively.     

Placement problems for transparent data replication proxy services

Transparent data replication has been considered a promising technique for improving system performance for a large distributed network. In this paper, a hybrid transparent replication model is presented. We address the problems of replication proxy placement in the network and data replica placement on the installed proxies given that a maximum of M proxies are allowed. Both reads and writes are considered in these problems. The performance objective is to minimize the total data transfer cost. To address the placement problems, we first present the optimal solutions for a single object in a tree network without/with constraint on the number of replicas. Based on that, two schemes, namely, aggregate access (AGGA) and weighted popularity (WPOP), are proposed for the replication proxy placement problem. An optimal solution is described for the replica placement problem. The performance of the proposed placement schemes is evaluated with a set of carefully designed simulation experiments over a wide range of system parameters. The results give us several helpful intuitions in deploying transparent replication proxies in a practical system.     

异构无线网络中基于模糊逻辑的分级垂直切换算法

在异构无线网络中,针对综合考虑网络端和用户端参数的垂直切换算法,参数权重难以确定,同时基于模糊逻辑的垂直切换算法存在复杂度高的问题,该文提出一种基于模糊逻辑的分级垂直切换算法。首先,将接收信号强度(RSS)、带宽、时延输入到1级模糊逻辑系统,结合规则自适应匹配,推理出QoS模糊值,并通过QoS模糊值对网络进行初步筛选得到候选网络集;然后通过触发机制触发2级模糊逻辑系统,并将候选网络的QoS模糊值、网络负载率、用户接入费用输入2级模糊逻辑系统,同时结合规则自适应匹配,得到输出判决值,从而选择最佳接入网络。最后,实验结果表明,该算法能保证网络性能的同时,降低系统的时间开销。     

Data replication mechanisms in the peer‐to‐peer networks

Peer‐to‐peer (P2P) network is a distributed network in which nodes with similar capabilities exchange information with each other. Due to the nature of the P2P network distribution, numerous network message transformation is required to exchange the data between nodes over the network, which may increase access latency. Because of the high amount of the stored data in the P2P networks, the replication of data is very important. A large amount of data is handled using data replication to increase data access, reduce access latency, and increase data availability. This issue has an important role in the P2P networks, but there is no complete and systematic research in this field. Therefore, this paper aims to provide a comprehensive study of data replication mechanisms in the P2P networks. The 1027 papers have been identified and have been reduced to 213 main studies using the paper selection process. Moreover, in this paper, the major advances are reviewed in four main groups (structured, unstructured, super‐peer, and hybrid networks), and the new challenges are also highlighted. Moreover, the open issues and guidelines for future studies are presented.     

Joint Transmitter Power Control and Mobile Cache Management in Wireless Computing

We investigate efficient schemes for data communication from a server (base station and access point) to a mobile terminal over a wireless channel of randomly fluctuating quality. The terminal user generates requests for data items. If the buffer (cache) of the terminal contains the requested data, no access delay/latency is incurred. If not, the data is downloaded from the server, and until becoming available locally at the terminal, the user incurs a delay cost. Moreover, a transmission/power cost is incurred to transmit the data over the wireless link at a dynamically selected power level. To lower both the access delay and transmission costs, the system may prefetch data predictively and cache them on the terminal (especially during high-link-quality periods), anticipating future user requests. The goal is to jointly minimize the overall latency and power costs by dynamically choosing what data to (pre)fetch, what power level to use, and when to use it. We develop a modeling framework (based on dynamic programming and controlled Markov chains) that captures essential performance trade-offs. It allows for the computation of optimal decisions regarding what data to (pre)fetch and what power levels to use. To cope with emerging complexities, we then design efficient online heuristics whose simulation analysis demonstrates substantial performance gains over standard approaches.     

QoS-aware replica placement for data intensive applications

In recent years, with the rapid development of data intensive applications, data replication has become an enabling technology for the data grid to improve data availability, and reduce file transfer time and bandwidth consumption. The placement of replicas has been proven to be the most difficult problem that must be solved to realize the process of data replication. This paper addresses the quality of service （QoS） aware replica placement problem in data grid, and proposes a dynamic programming based replica placement algorithm that not only has a QoS requirement guarantee, but also can minimize the overall replication cost, including storage cost and communication cost. By simulation, experiments show that the replica placement algorithm outperforms an existing popular replica placement technique in data grid.     

Data Replication in Cooperative Mobile Ad-Hoc Networks

The mobile computing environment provides many benefits such as ubiquitous access to computing but includes constraints on resources such as available bandwidth and battery life. Replication is a widely recognized method for balancing the demands of storage space with bandwidth and battery life. We propose a novel scheme that seeks to strategically balance these constrained resources through a cooperative game-theory approach for replication in a mobile environment. Our replication strategy relies on the cooperation of the nodes within the network to make replica caching decisions which are spatiotemporally local-optimal for the network from an energy and bandwidth conservation standpoint. In cooperative altruistic data replication, each node calculates the net global benefit, for caching a replica of the requested data, as the result data is returned from the responding node to the requesting node, where it determines the spatiotemporally local-optimal node for replicating the data item. Performance results from our research indicate that our scheme, CADR, improves the query response time by 25 and 45 %, mean hop count is improved by 26 and 46 %, query error is reduced by 30 and 48 %, while energy utilization is reduced 30 and 57 % when compared with both another game theoretic replication approach and standard cooperative caching respectively.     

An Efficient Delay Tolerance and Cost-Effective Secure NFV Enabled Multi-casting in Mobile Edge Cloud Networks

The mobile edge cloud has developed as the main platform to offer low latency network services from the edge of networks for stringent delay necessities of mobile applications. In mobile edge cloud networks, network functions virtualization (NFV) creates the frameworks for building up a new dynamic resource management framework structure to effectively utilize network resources. Delay tolerance NFV-enabled multicast request admissions in a mobile edge-cloud network are explored in this paper to limit request admission delays or maximizing system performance for a group of requests arriving individually. At first, for the cost reduction issue of a single NFV-empowered multicast request admission, the admission cost of each multicast request is assessed, and the Support based graph is constructed. Here, the multicast requests are prioritized dependent on their admission cost. Subsequently, trust and the delay-based local gradient are assessed for the prioritized multicast requests. At long last, delay tolerance NFV multicasting is accomplished by successful (First Come First Serve) FCFS queuing reliant on the assessed local gradient of requests. When compared to existing approaches, the exploratory results show that the proposed methodology is superior in terms of throughput, admission cost, and running time.

     

云存储系统中数据副本服务的可靠性保障研究

以数据节点与网络链路的可靠性因素分析为基础,提出了云存储系统的数据副本服务可靠性模型。根据访问可靠性与数据副本数量、用户访问量之间的关系,设计数据服务可靠性、副本生成时机、存储节点选择的确定方法,实现了副本分布、删除算法,并在云存储系统ERS-Cloud上进行一系列实验,结果表明该方法能够有效保障数据服务的可靠性,进一步降低副本的冗余存储数量。     

A scalable network-based mobility management framework in heterogeneous IP-based networks

This paper proposes a mobility management scheme to provide a mobile node with high-quality handovers among heterogeneous wireless access networks. The proposed scheme employs a signaling architecture to support fast and reliable delivery of control messages by separating a control plane from a data transport plane in the core network. The proposed scheme is based on the network-based mobility management framework which requires the minimum modifications on terminal devices. With interaction between Layers 2 and 3, the proposed scheme accelerates the handover control procedures. It also enables a mobile subscriber to select a target network for a vertical handover with consideration of not only wireless signal strength but also user preference and quality-of-service status. The proposed scheme addresses the well-known problems of the Mobile IP-based approaches, triangular routing and bottleneck at the home agent, since it establishes a data tunnel for a mobile node along the shortest path between two different access networks. The simulation and experimental results indicate that our scheme provisions more efficient performance than the existing approaches in terms of handover latency, data packet loss, data delivery latency and load balancing.     

A fully distributed replica allocation scheme for an opportunistic network

An opportunistic network (OPPNET) consists of diverse mobile nodes with various mobility patterns. Numerous mobility patterns and the resource constraints of mobile nodes lead to network partitioning that result in system performance degradation including low data accessibility. In a traditional mobile ad hoc network (MANET) which is similar to an OPPNET, replica allocation schemes have been proposed to increase data accessibility. Although the schemes are efficient in a MANET, they may not be directly applicable to an OPPNET because the schemes are based on a grouping of mobile nodes. It is very difficult to build groups based on network topology in an OPPNET because a node in an OPPNET does not keep its network topology information. In this paper, we propose a novel replica allocation scheme for an opportunistic network called the Snooping-based Fully Distributed replica allocation scheme. The proposed scheme allocates replicas in a fully distributed manner without grouping to reduce the communication cost, and fetches allocated replicas utilizing a novel candidate list concept to achieve high data accessibility. In the proposed scheme, a node can fetch replicas opportunistically based on the candidate list. Consequently, the proposed replica allocation scheme achieves high data accessibility while reducing the communication cost significantly. Extensive simulation results demonstrate that the proposed scheme reduces the communication cost and improves data accessibility over traditional schemes.     

Optimal memory management strategies for a mobile user in abroadcast data delivery system

Data broadcasting has been considered as a promising way of disseminating information to a massive number of users in a wireless communication environment. In a broadcast data delivery system, there is a server which is broadcasting data to a user community. Due to the lack of communication from the users to the server, the server cannot know what a user needs. In order to access a certain item, a user has to wait until the item appears in the broadcast. The waiting time will be considerably long if the server's broadcast schedule does not match the user's access needs. If a user has a local memory, it can alleviate its access latency by selectively prefetching the items from the broadcast and storing them in the memory. A good memory management strategy can substantially reduce the user's access latency, which is a major concern in a broadcast data delivery system. An optimal memory management policy is identified that minimizes the expected aggregate latency. We present optimal memory update strategies with limited look ahead as implementable approximations of the optimal policy. Some interesting special cases are given for which the limited look-ahead policies are optimal. We also show that the same formulation can be used to find the optimal memory management policy which minimizes the number of deadline misses when users generate information requests which have to be satisfied within some given deadlines     

雾计算中细粒度属性更新的外包计算访问控制方案

针对基于密文策略的属性加密(CP-ABE)在低时延需求较高的雾计算环境中,存在加解密开销大、属性更新效率低的问题,提出了一种雾计算中细粒度属性更新的外包计算访问控制方案,使用模加法一致性秘密(密钥)分享技术构建访问控制树,将加解密计算操作外包给雾节点,降低用户加解密开销;结合重加密机制,在雾节点建立组密钥二叉树对密文进...     

Using optorsim to efficiently simulate replica placement strategies

Optorsim provides simulations of file replication strategies such as replica placement,replication scheduling,replica consistency maintenance,etc.However,to assess a replica strategy a researcher must write network configuration files and repeatedly modify parameters,which is inefficient.In this article,a scale-free algorithm is developed to generate network topology which is loaded into Optorsim,and a graphical user interface(GUI) is implemented to set all configuration parameters and algorithm parameters....     

Achieving distributed user access control in sensor networks

User access control in sensor networks defines a process of granting user an access right to the stored information. It is essential for future real sensor network deployment in which sensors may provide users with different services in terms of data and resource accesses. A centralized access control mechanism requires the base station to be involved whenever a user requests to get authenticated and access the information stored in the sensor node, which is inefficient, not scalable, and is exposed to many potential attacks along long communication paths. In this paper, we propose a distributed user access control under a realistic adversary model in which sensors can be compromised and user may collude. We split the access control into local authentication conducted by a group of sensors physically close to a user, and a light remote authentication based on the endorsement of the local sensors. We implement the access control protocols on a testbed of TelosB motes. Our analysis and experimental results show that our schemes are feasible for real access control requirements.     

Shortest path planning of a data mule in wireless sensor networks

Data mules are extensively used for data collection in wireless sensor networks (WSNs), which significantly reduces energy consumption at sensor nodes but increases the data delivery latency. In this paper, we focus on minimizing the length of the traveling path to reduce the data delivery latency. We first model the shortest path planning of a data mule as an optimization problem, and propose an optimal model and corresponding solving algorithm. The optimal model solution has high time complexity, mainly due to the parallel optimization of node visit arrangements and data access point (DAP) settings during the solution process, which is to obtain the shortest path result. In order to improve the computational efficiency, we next give the approximate model and its solving algorithm, which is mainly to decompose the path planning problem into the Traveling Salesman Problem (TSP) and nonlinear optimization problem, and optimize the two parts separately. The proposed approach is capable of expressing the influence of the communication range of each sensor node, which is suitable for more general application scenarios than the existing methods. Theoretical analysis and simulation results show that the solution has good performances in terms of path length and computational efforts.

     

数据网格中一种填空式副本分配算法

在数据网格应用中,数据会由于性能和可用性等原因进行复制.如何使数据复本合理分布以降低通信开销是数据网格系统需要解决的重要问题.本文针对一种简化的数据网格环境,考虑存储资源代理对数据的访问频率和代理间的网络性能,提出一种填空式数据副本分配算法(CDRDA).通过该算法得到的数据副本分配,构成多级虚拟存储架构.数据副本根据被存储资源代理访问的频率分布在访问开销小的节点上,从而使系统的整体访问效率达到近似最优.     

A Cost-Efficient Scheduling Algorithm of On-Demand Broadcasts

In mobile wireless systems data on air can be accessed by a large number of mobile users. Many of these applications including wireless internets and traffic information systems are pull-based, that is, they respond to on-demand user requests. In this paper, we study the scheduling problems of on-demand broadcast environments. Traditionally, the response time of the requests has been used as a performance measure. In this paper we consider the performance as the average cost of request composed of three kinds of costs – access time cost, tuning time cost, and cost of handling failure request. Our main contribution is a self-adaptive scheduling algorithm named LDFC, which computes the delay cost of data item as the priority of broadcast. It costs less compared with some previous algorithms in this context, and shows good adaptability as well even in pure push-based broadcasts.     

Towards a Sustainable Green Design for Next-Generation Networks

Recently distributed real-time database systems are intended to manage large volumes of dispersed data. To develop distributed real-time data processing, a reality and stay competitive well defined protocols and algorithms must be required to access and manipulate the data. An admission control policy is a major task to access real-time data which has become a challenging task due to random arrival of user requests and transaction timing constraints. This paper proposes an optimal admission control policy based on deep reinforcement algorithm and memetic algorithm which can efficiently handle the load balancing problem without affecting the Quality of Service (QoS) parameters. A Markov decision process (MDP) is formulated for admission control problem, which provides an optimized solution for dynamic resource sharing. The possible solutions for MDP problem are obtained by using reinforcement learning and linear programming with an average reward. The deep reinforcement learning algorithm reformulates the arrived requests from different users and admits only the needed request, which improves the number of sessions of the system. Then we frame the load balancing problem as a dynamic and stochastic assignment problem and obtain optimal control policies using memetic algorithm. Therefore proposed admission control problem is changed to memetic logic in such a way that session corresponds to individual elements of the initial chromosome. The performance of proposed optimal admission control policy is compared with other approaches through simulation and it depicts that the proposed system outperforms the other techniques in terms of throughput, execution time and miss ratio which leads to better QoS.

     

Secure Data Access and Sharing Scheme for Cloud Storage

Cloud storage is a new storage mode emerged along with the development of cloud computing paradigm. By migrating the data to cloud storage, the consumers can be liberated from building and maintaining the private storage infrastructure, and they can enjoy the data storage service at anywhere and anytime with high reliability and a relatively low cost. However, the security and privacy risks, especially the confidentiality and integrity of data seem to be the biggest hurdle to the adoption of the cloud storage applications. In this paper, we consider the secure data access and sharing issues for cloud storage services. Based on the intractability of the discrete logarithm problem, we design a secure data access and data sharing scheme for cloud storage, where we utilize the user authentication scheme to deal with the data access problem. According to our analysis, through our scheme, only valid user with the correct password and biometric can access to the cloud storage provider. Besides, the authorized users can access the rightful resources and verify the validity of the shared data, but cannot transfer the permission to any other party. At the same time, the confidentiality and integrity of data can be guaranteed.