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Developing new mathematical frameworks such as distributed dynamic routing algorithms for constructing optimal incremental paths from a node to another node is an important challenge in data communication networks. These new algorithms can model network resources optimally and increase network performances. A bundle of single routes in a current communication path, which starts from a source node and ends to a destination node, can consist of several successive nodes and links. The Incremental term emphasizes that the number of routes (links and nodes) in a current path can change so that achieving more data rate and optimal efficiency in the network. In this paper, our problem is to add/omit some routes consisting of some nodes and links to/from the current unicast path dynamically and optimally. We call this problem the Optimal Dynamic Distributed Unicast Routing (ODDUR) problem and it is a NP-complete problem. This problem can be formulated as a new type of Linear Programming Problem (LPP) for finding a minimum cost multichannel unicast path, which this path will minimize end-to-end delay and bandwidth consumption along the routes from the source node to the destination node. In this paper, at first a new mathematical framework will be constructed and then this framework will propose the new optimal dynamic distributed unicast routing algorithm for solving our LPP problem. This algorithm will compute an optimal solution for our LPP based on the simplex method and postoptimality computations and will reduce computations and consumed time. Simulation results will show that our new algorithm is more efficient than other available algorithms in terms of utilization of bandwidths and data rate. [ABSTRACT FROM AUTHOR]

A huge amount of sensing data are generated by a large number of pervasive IoT devices. In order to find meaningful information from the big data, it is essential to perform pre-processing, in which many outlier data points need to be removed, because they deteriorate as time passes. Although pre-processing is essential in the big data field, there has been a significant lack of research works with case studies. In this paper, big data pre-processing methods are investigated and proposed. To evaluate the pre-processing methods for accurate analysis, we used a collection of digital tachograph (DTG) data. We obtained DTG sensing data of 6198 driving vehicles over a year. We studied five kinds of pre-processing methods: filtering ranges, excluding meaningless values, comparing filters from variables, applying statistical techniques, and finding driving patterns. In addition, we developed a MapReduce program using a Hadoop ecosystem and deployed big data to perform the pre-processing analysis. Through the pre-processing steps, we confirmed that the proportion of DTG sensing data points including any errors was up to 27.09%. Compared to the traditional brute-force way to detect, ours had 71.1% additional detection effect. In addition, we confirmed that outlier data points, which are difficult to detect through simple range error pre-processing, could be well detected. [ABSTRACT FROM AUTHOR]

High-performance processors give opportunities and challenges for development of real-time and embedded applications. New advances in hardware introduce new questions as alternatives to enable multiple applications to share a single processor and memory, so that the high-performance hardware that contains millions of transistors can be fully utilized, as also the way to keep system dependable and stable by making applications stay in spatial and temporal isolation inside same system. It is introduced in this paper XtratuM, a real-time hypervisor designed and implemented based on the concept of a partitioned system, by enabling partitions to execute simultaneously in spatial and temporal isolation without interfering with each other, but sharing the same hardware. Still in this paper, we provide a brief introduction on partitioned systems and its significance, also presenting the prototype implementation of XtratuM on PowerPC architecture including essential parts: hypercalls, timer, interrupt, and memory management implementations. Benchmark applications have been carried out to illustrate that the model implemented by XtratuM is suitable to offer the capability of spatial and temporal isolation under real-time requirements. [ABSTRACT FROM AUTHOR]

Modern microprocessors achieve high application performance at an acceptable level of power dissipation. Reorder buffer is used for out-of-order instructions to be committed in-order. The reorder buffer plays a key role in modern microprocessors because performance improvement techniques highly rely on aggressive speculation to feed wider issue, out-of-order, and deep pipelines. In terms of power to performance trade-off, reorder buffer is particularly important. This is because enlarging the reorder buffer size achieves high performance but naive scaling of the conventional reorder buffer architecture can severely increase the complexity and power consumption. In this paper, we propose low-power reorder buffer techniques for contemporary microprocessors. First, the separated reorder buffer reduces power dissipation by deferred allocation and early release. The deferred allocation delays the SROB allocation of instructions until all their data dependencies are resolved. Then, the instructions are executed in program order and they are released faster from the SROB. The result of the instruction is written into rename buffers immediately after the execution completes. Then, the result values in the rename buffer are written into the architectural register file at the commit state. The proposed approaches in this paper provide higher resource utilization and low power consumption. [ABSTRACT FROM AUTHOR]

Storage and retrieval of data in Wireless Sensor Networks (WSNs) have been found to be challenging issues in recent studies. The two principal approaches used in almost all proposed schemes in this field are Centralized and Decentralized. Many investigations have considered the security and communication issues in centralized schemes where a central node exists as the sink and all collected data are sent to it to be processed or the central node provides access to the possible users. In decentralized systems, all nodes are equally responsible for the duties of the central node in centralized approaches. In cases where sensors cannot transmit data immediately, which is usually the case in distributed approaches, in-network storage and retrieval is an alternative with its own security and communication issues stemming from the properties of energy sensitive sensors. In this paper, a semi-centralized scheme is introduced that is based on a number of traditional techniques, namely, clustering, symmetric and asymmetric key-management, and threshold secret sharing. The proposed scheme provides an energy-efficient and secure in-network storage and retrieval that could be applied to WSNs. A predictive method is proposed to adaptively determine the proper parameters for the threshold secret sharing technique. Confidentiality, dependability, and integrity of the sensed data are enhanced in a distributed manner with fairly low communication and computation costs. Simulations were utilized to illustrate the effect of several network parameters on energy consumption and to come up with optimization recommendations for the parameters of the proposed secret sharing scheme. [ABSTRACT FROM AUTHOR]

Generally, in mobile communication environments, Wireless Authentication and Key Establishment (WAKE) protocols are essential for secure information transmission. Key Recovery (KR) protocols, including WAKE protocols, can enable an authorized third party to gain access to encrypted message data under certain lawful circumstances. However, it is not easy to construct secure and strongly privacy-preserved KR protocols for mobile communication environments due to mobile hardware resources' limitation. In this paper, we propose a security-enhanced KR protocol and then we construct a privacy-enhanced KR protocol, which is based on the security-enhanced KR protocol, for protecting users' location privacy. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient parallel algorithm for computing Lagrange interpolation on k-ary n-cube networks. This is done using the fact that a k-ary n-cube can be decomposed into n link-disjoint Hamiltonian cycles. Using these n link-disjoint cycles, we interpolate Lagrange polynomial using full bandwidth of the employed network. Communication in the main phase of the algorithm is based on an all-to-all broadcast algorithm on the n link-disjoint Hamiltonian cycles exploiting all network channels, and thus, resulting in high-efficiency in using network resources. A performance evaluation of the proposed algorithm reveals an optimum speedup for a typical range of system parameters used in current state-of-the-art implementations. [ABSTRACT FROM AUTHOR]

Data gathering is a major function of many applications in wireless sensor networks (WSNs). The most important issue in designing a data gathering algorithm is how to save energy of sensor nodes while meeting the requirement of applications/users such as sensing area coverage. In this paper, we propose a novel hierarchical clustering protocol (DEEG) for long-lived sensor network. DEEG achieves a good performance in terms of lifetime by minimizing energy consumption for in-network communications and balancing the energy load among all the nodes, the proposed protocol achieves a good performance in terms of network lifetime. DEEG can also handle the energy hetergenous capacities and guarantee that out-network communications always occur in the subregion with high energy reserved. Furthermore, it introduces a simple but efficient approach to cope with the area coverage problem. We evaluate the performance of the proposed protocol using a simple temperature sensing application. Simulation results show that our protocol significantly outperforms LEACH and PEGASIS in terms of network lifetime and the amount of data gathered. [ABSTRACT FROM AUTHOR]

Distributed Denial-of-Service (DDoS) attacks pose a serious threat to Internet security. Most current research focuses on detection and prevention methods on the victim server or source side. To date, there has been no work on defenses using valuable information from the innocent client whose IP has been used in attacking packets. In this paper, we propose a novel cooperative system for producing warning of a DDoS attack. The system consists of a client detector and a server detector. The client detector is placed on the innocent client side and uses a Bloom filter-based detection scheme to generate accurate detection results yet consumes minimal storage and computational resources. The server detector can actively assist the warning process by sending requests to innocent hosts. Simulation results show that the cooperative technique presented in this paper can yield accurate DDoS alarms at an early stage. We theoretically show the false alarm probability of the detection scheme, which is insensitive to false alarms when using specially designed evaluation functions. [ABSTRACT FROM AUTHOR]

Articles posted on a forum often contain new Internet words related to opinion elements (feature words and opinion words). Consequently, existing Chinese opinion-mining systems may exhibit low recall and precision because they cannot recognize these new Internet words. Therefore, we propose a simple algorithm to elaborate on the opinion elements of such articles by extracting the opinion elements. Moreover, when an opinion word is combined with a specific word or concatenated with another opinion word, it may cause a change in the polarity or meaning of the opinion. This fact is prone to cause difficulties by changing the polarity or meaning of certain opinion elements, leading to errors in the analysis results of the Chinese system. We designed three algorithms with context dependency to address this problem. In this paper, we develop a semi-automatic Chinese opinion-mining system with these algorithms to extract these new opinion elements. Then, we determine whether the new word identified through manual judgment is a useful opinion element for a specific domain and add it to the thesaurus. In comparison with semi-automatic annotation methods, our approach can save considerable labor. After a 20-month follow-up analysis, the experimental data indicated that the precision, recall, and F1 of the system reached 84.0, 89.4 %, and 0.865, respectively. [ABSTRACT FROM AUTHOR]

Location-based services on GPS-enabled smartphones are undergoing strong growth. Capitalizing on the popularity of this geo-location social media, a mobile app called Foursquare is developed to recommend its users places where they may be interested in, to travel from their current proximities. Such location data, in the form of check-ins by Foursquare, have huge business potentials including marketing, advertising and consumers' behaviors analysis. Many researchers from both academia and industries are seriously looking into this location-based big data which comes in high velocity (with millions of users and frequent geo-tagging), and wide variety (with potentially many meta-data and associations), accumulating into a huge volume. One of the fundamental analytics in such big data is to guess which check-in locations a user would move to, as a prerequisite for sequential mining and other lifestyle pattern analysis. This paper reports a novel, but simple big data analytic by sampling a portion of location data for predicting the next check-in locations. This proposed analytic does not need every individual user's history path and ID to match the history path of the current user in the database in order to infer a prediction. We show by a simulation experiment based on a Foursquare dataset that a minimum of two pairs of coordinates are required to provide a prediction. Several variables such as segment lengths, number of check-ins, and time factors are investigated in the experiment in relation to the prediction accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we design a new software-defined wireless network architecture to enable high performance in the next generation of ship area networks (ShAN). We first introduce the current problem of end-to-end transmission delay in ship area networks, and then claim that both network and routing layers protocol will require improvement to guarantee data transmission. Hence, we propose a new network architecture, which combines SOTDMA and SDWN advantages to reduce end-to-end transmission delay to achieve high performance in ship area networks. We sketch out the changes and extensions of an SDWN network controller and switches to enable high performance. The complete design of an SDWN architecture using SOTDMA is an exciting avenue for future ship area networks. Using our proposed network architecture, our evaluation results show that for one wireless AP station, we can obtain 3 Mbps throughput. [ABSTRACT FROM AUTHOR]

Social network services (SNSs) have become one of the core Internet-based application services in recent years. Through SNSs, diverse kinds of private data are shared with users' friends and SNS plug-in applications. However, these data can be exposed via abnormal private data access. For example, the addition of fake friends to a user's account is one approach to gain access to a private user's data. Private user data can be protected from being accessed by using an automated method to assess information. This paper proposes a method that evaluates private data accesses for social network security. By defining normal private data access patterns in advance, abnormal private data access patterns can be exposed. Normal private data access patterns are generated by analyzing all of the consecutive private data accesses of users based on Bayesian probability. We have proven the effectiveness of our approach by conducting experiments where the private data access signals of Twitter accounts were collected and analyzed. [ABSTRACT FROM AUTHOR]

The performance of interconnection networks is a challenging issue for High-Performance Computing (HPC) systems, which becomes even more important when the number of interconnected endnodes grows. In that sense, Dragonfly interconnection patterns are a very popular option to configure the network topology, especially for large systems, as they are able to achieve a high scalability relying on high-radix switches. This kind of hierarchical topologies has two levels of interconnection (i.e., connections within the element of a group and connections among groups) and each one can be interconnected using different patterns. However, regardless of the Dragonfly interconnection pattern, the Head-of-Line (HoL) blocking effect derived from congestion situations may jeopardize the Dragonfly performance. This paper analyzes the dynamics of congestion in different Dragonfly fully-connected interconnection patterns. Also, we describe a queuing scheme called Hierarchical Two-Level Queuing (H2LQ), designed specially to reduce HoL blocking in any fully-connected Dragonfly network that uses minimal-path routing. Finally, we present experiment results which show that this scheme significantly boost Dragonfly performance, regardless the interconnection pattern, especially when congestion arises, while requiring fewer network resources than other techniques oriented to deal with the effects of congestion. [ABSTRACT FROM AUTHOR]

This paper presents an efficient algorithmic approach to the GPU-based parallel resolution of dense linear systems of extremely large size. A formal transformation of the code of Gauss method allows us to develop for matrix calculations the concept of stripe algorithm, as opposed to that of tile algorithm. Our stripe algorithm is based on the partitioning of the linear system's matrix into stripes of rows and is well suited for efficient implementation on a GPU, using cublasDgemm function of CUBLAS library as the main building block. It is also well adapted to storage of the linear system on an array of solid state devices, the PC memory being used as a cache between the SSDs and the GPU memory. We demonstrate experimentally that our code solves efficiently dense linear systems of size up to 400,000 (160 billion matrix elements) using an NIVDIA C2050 and six 240 GB SSDs. [ABSTRACT FROM AUTHOR]

In distributed systems, data may be correlated due to accesses from clients and the correlation has some impact on date placement, and existing research works focus on independent data objects. In this paper, we address both the scalability and the stability of the data placement solutions in internet environment. We first show that replica allocation decisions can be made locally for each replica site in a tree network, with data access knowledge of its neighbors. We then develop a new replication cost model for correlated data objects in Internet environment. Based on the cost model and the algorithms in previous research, we develop a distributed optimal replica allocation algorithm (DOPR) for correlated data in internet environment. A distributed heuristic algorithm (DHPR) is then developed to efficiently make replica placement decisions. The algorithm obtains sub-optimal solutions for the correlated data model and yields significant performance gains. Experimental studies show that the distributed heuristic allocation algorithm significantly outperforms the general frequency-based replication schemes (in which the replication decision of each data object is made based on the number of accesses on that data object). [ABSTRACT FROM AUTHOR]

We present a grid-based system that enables researchers in civil engineering to conduct hybrid remote experiments as well as to record and replay these experiments. The system has been designed for Real Time Hybrid Testing Facility in the Korea Construction Engineering Development (KOCED) project and has been implemented based on the Open Service Grid Architecture (OGSA) and the Open System for Earthquake Engineering Simulation (OpenSees) for modeling and computational simulation on structural systems. Users can access the system with a Java client for data visualization and video monitoring through an Internet portal using a web browser. The system runs on a network of distributed NaradaBrokering streaming servers and exploits RFT and GridFTP for reliable file transfer. The main contribution of this paper is that we propose an scalable grid-based recording and replay architecture for remote experiment using distributed streaming network that can scale to support a growing large number of clients simultaneously. We also evaluate the performance of the proposed extensible architecture of distributed servers with mathematical modeling as well as simulations to show how it scales to cope with a growing large number of clients at the same time. [ABSTRACT FROM AUTHOR]

A hybrid cloud integrates private clouds and public clouds into one unified environment. For the economy and the efficiency reasons, the hybrid cloud environment should be able to automatically maximize the utilization rate of the private cloud and minimize the cost of the public cloud when users submit their computing jobs to the environment. In this paper, we propose the Adaptive-Scheduling-with-QoS-Satisfaction algorithm, namely AsQ, for the hybrid cloud environment to raise the resource utilization rate of the private cloud and to diminish task response time as much as possible. We exploit runtime estimation and several fast scheduling strategies for near-optimal resource allocation, which results in high resource utilization rate and low execution time in the private cloud. Moreover, the near-optimal allocation in the private cloud can reduce the amount of tasks that need to be executed on the public cloud to satisfy their deadline. For the tasks that have to be dispatched to the public cloud, we choose the minimal cost strategy to reduce the cost of using public clouds based on the characteristics of tasks such as workload size and data size. Therefore, the AsQ can achieve a total optimization regarding cost and deadline constraints. Many experiments have been conducted to evaluate the performance of the proposed AsQ. The results show that the performance of the proposed AsQ is superior to recent similar algorithms in terms of task waiting time, task execution time and task finish time. The results also show that the proposed algorithm achieves a better QoS satisfaction rate than other similar studies. [ABSTRACT FROM AUTHOR]

Virtualization is a popular technology. Services and applications running on each virtual machine have to compete with each other for limited physical computer or network resources. Each virtual machine has different I/O requirement and special priority. Without proper scheduling resource management, a load surge in a virtual machine may inevitably degrade other's performance. In addition, each virtual machine may run different kinds of application, which have different disk bandwidth demands and service priorities. When assigning I/O resources, we should deal with each case on demand. In this paper, we propose a dynamic virtual machine disk bandwidth control mechanism in virtualization environment. A Disk Credit Algorithm is introduced to support a fine-gained disk bandwidth allocation mechanism among virtual machines. We can assign disk bandwidth according to each virtual machine's service priority/weight and its requirement. Related experiments show that the mechanism can improve the VMs' isolation and guarantee the performance of the specific virtual machine well. [ABSTRACT FROM AUTHOR]

Reversible data hiding has attracted considerable attention in recent years. Being reversible, the decoder can extract hidden data and recover the original image completely, and the difference expansion (DE) scheme can lead to a lossless pixel after secret data exacting. Furthermore, despite achieving pixel reversibility based on the concept of expanded differencing, the difference expansion scheme can cause enormous image distortion because of the size of the difference. The proposed scheme in this paper describes a novel prediction for achieving predictive error based reversible data hiding by considering the relation between a pixel and its neighboring pixel and using the predictor to identify the projected difference in pixel value. Experimental results show that the proposed scheme is capable of providing great embedding capacity without causing noticeable distortion by selecting the minimal predictor based on pixel expansion. In multilevel cases, this proposed method performs better than other existing methods. Moreover, the proposed scheme is able to pass the Chi-square test, a test used to find whether an image utilizes LSB for data hiding. [ABSTRACT FROM AUTHOR]

Cloud computing is a fast growing field, which is arguably a new computing paradigm. In cloud computing, computing resources are provided as services over the Internet and users can access resources based on their payments. The issue of access control is an important security scheme in the cloud computing. In this paper, a Contract RBAC model with continuous services for user to access various source services provided by different providers is proposed. The Contract RBAC model extending from the well-known RBAC model in cloud computing is shown. The extending definitions in the model could increase the ability to meet new challenges. The Contract RBAC model can provide continuous services with more flexible management in security to meet the application requirements including Intra-cross cloud service and Inter-cross cloud service. Finally, the performance analyses between the traditional manner and the scheme are given. Therefore, the proposed Contract RBAC model can achieve more efficient management for cloud computing environments. [ABSTRACT FROM AUTHOR]

smart object system encompasses the synergy between computationally augmented everyday objects and external applications. This paper presents a software framework for building smart object systems following a declarative programming approach centered around custom written documents that glue the smart objects together. More specifically, in the proposed framework, applications' requirements and smart objects' services are objectified through structured documents. A runtime infrastructure provides the spontaneous federation between smart objects and applications through structural type matching of these documents. There are three primary advantages of our approach: firstly, it allows developers to write applications in a generic way without prior knowledge of the smart objects that could be used by the applications. Secondly, smart object management (locating, accessing, etc.) issues are completely handled by the infrastructure; thus application development becomes rapid and simple. Finally, the programming abstraction used in the framework allows extension of functionalities of smart objects and applications very easily. We describe an implemented prototype of our framework and show examples of its use in a real life scenario to illustrate its feasibility. [ABSTRACT FROM AUTHOR]

In this paper, a distributed and scalable Grid service management architecture is presented. The proposed architecture is capable of monitoring task submission behaviour and deriving Grid service class characteristics, for use in performing automated computational, storage and network resource-to-service partitioning. This partitioning of Grid resources amongst service classes (each service class is assigned exclusive usage of a distinct subset of the available Grid resources), along with the dynamic deployment of Grid management components dedicated and tuned to the requirements of a particular service class introduces the concept of Virtual Private Grids. We present two distinct algorithmic approaches for the resource partitioning problem, the first based on Divisible Load Theory (DLT) and the second built on Genetic Algorithms (GA). The advantages and drawbacks of each approach are discussed and their performance is evaluated on a sample Grid topology using NSGrid, an ns-2 based Grid simulator. Results show that the use of this Service Management Architecture in combination with the proposed algorithms improves computational and network resource efficiency, simplifies schedule making decisions, reduces the overall complexity of managing the Grid system, and at the same time improves Grid QoS support (with regard to job response times) by automatically assigning Grid resources to the different service classes prior to scheduling. [ABSTRACT FROM AUTHOR]

Corporations are using computational GRIDs to improve their operations. Future GRIDs will allow an organisation to take advantage of computational GRIDs without having to develop a custom in-house solution. GRID Resource Providers (GRP) make resources available on the GRID so that others may use these resources. GRPs allow companies to make use of a range of resources such as processing power or mass storage. However simply providing resources is not enough to ensure the success of a computational GRID. Access to these resources must be controlled otherwise computational GRIDs will simply evolve to become a victim of their own success, unable to offer a suitable Quality of Service (QoS) to any user. The task of providing a standard querying mechanism for Computational GRID Environments (CGE) has already witnessed considerable work from groups such as the Globus project who have delivered the Metacomputing Directory Service (MDS) which provides a means to query devices attached to the GRID. This paper describes a monitoring component both capable of the dynamic discovery of available resources in a computing GRID environment and executing user jobs on the available resource at a given moment in time. Consideration has been paid to integrating with GRID accounting systems. [ABSTRACT FROM AUTHOR]

We propose two new authentication schemes for the cloud that support private attribute-based authentication services. The basic scheme is non-anonymous attribute-based authentication scheme. The extended scheme of the basic scheme is fully anonymous attribute-based authentication scheme to realize full anonymity and unlinkability services. In the proposed schemes, a user is authenticated by the remote server if the intersection of the set of his/her assigned attributes and the server’s required attributes exceeds a satisfactory predefined level. Unlike existing attribute-based encryption and signature schemes that require the user to perform significant amount of elliptic curve bilinear pairings and modular exponentiations, and require the user to hold a significantly long decryption/signature key, in our schemes the user is not required to perform any bilinear pairings. With a fixed length private key, independent of the number of attributes, the cloud user performs only few exponentiations by which he/she is able to authenticate himself/herself to the remote server and establish a session key with the server with the condition that he/she satisfies a predefined level of the server’s attributes requirement. Therefore, our schemes are suitable for implementation on devices with limited resources. We provide the rigorous security of the proposed schemes and complexity analysis of our schemes. Finally, the security and performance comparisons of our schemes with the existing related schemes show that our schemes outperform other existing schemes. [ABSTRACT FROM AUTHOR]

As social network services have been integrated with organizations' business, it is trying to apply them to a variety of organizations' tasks from normal enterprise processes such as tourism, fashion and distribution to public institutions' processes. Organizations need to build an environment not resistant to new environments by enhancing applications of the existing built ICT technologies for focusing on various utilizations of SNS and integrating with the existing enterprise systems within the organizations to use them. Accordingly, there is a need to develop the ICT use index and the ICT use development level for objectively diagnosing enterprises' ICT use state to integrate with SNS services. However, the studies so far have focused on only understanding enterprises' introduction effects of ICT technologies or fragmentary status of ICT use and external use of SNS. In addition, there are insufficient studies on the business processes that could show results of applying ICT technologies which are bases to be integrated with SNS. Therefore, this study would like to analyze characteristics and performance by organizations' ICT use levels so that SNS services could be applied in earnest from various perspectives based on the organizations' characteristics. [ABSTRACT FROM AUTHOR]

Internet protocol television viewers spend considerable time browsing through the many existing channels, which is inefficient and time consuming. Although the recommendation system can solve the channel-switching problem, its performance is slow unless it is adapted to read a large amount of data sets. This study proposes a novel cloud-assisted channel-recommendation system under a cloud computing environment, channel association rules (CARs), to speed up the performance of channel switching, thereby help users to find their favorite channels in less time. The CARs algorithm is compared with the conventional (COV) solution and the most frequently selected (MFS) algorithm based on MovieLens data sets. The experimental results indicate that the predictive accuracy of CARs is superior to that of the COV and MFS algorithms. In addition, CARs use parallel computing in MapReduce to distribute large amounts of user history logs across multiple computers for processing. The experimental results show that the proposed algorithm can be employed to efficiently handle big data in a finite time when a huge of cloud servers are rented from commercial cloud providers such as Amazon Elastic Compute Cloud (EC2), Microsoft HDinsight. [ABSTRACT FROM AUTHOR]

To model a layered video streaming system in super-peer overlay networks that faces with heterogeneity and volatility of peers, we formulate a layer scheduling problem from understanding some constraints such as layer dependency, transmission rule, and bandwidth heterogeneity. To solve this problem, we propose a new layer scheduling algorithm using a real-coded messy genetic algorithm, providing a feasible solution with low complexity in decision. We also propose a peer-utility-based promotion algorithm that selects the most qualified neighbor to guarantee the sustained quality of streaming despite high intensity of churn. Simulation results show that the proposed layer scheduling scheme can achieve the most near-optimal solutions compared to the four conventional scheduling heuristics in the average streaming ratio. It also highly outperforms those with different peer selection strategies in terms of the average bandwidth (6.9 % higher at least) and the variation of utilization (11.3 % lower at least). [ABSTRACT FROM AUTHOR]

Live Internet streaming can be regarded as a major current multimedia delivery mode. Efficient delivery under changing network conditions is a severe challenge in the design of live streaming systems. This study analyzes the key considerations and factors influencing live stream quality during system operations, and attempts to improve present P2P (peer-to-peer) live streaming systems by allowing users to enjoy high quality of service under the limitations of network resources. The proposed R-D (Rate-Distortion) optimized-dynamic-nodes-join algorithm is based on multipath streaming concept and receiver-driven approach. This distributed algorithm enables the system to evaluate the current network status in order to optimize the end-to-end distortion of P2P networks and stay in the optimal status. Experiment results of this study demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]