Many cloud providers offer IoT services that simplify the collection and processing of IoT information. However, the IoT infrastructure composed of sensors and actuators that produces this information remains outside the cloud; therefore, application developers must install, connect and manage the cloud. This requirement can be a market barrier, especially for small/medium software companies that cannot afford the infrastructural costs associated with it and would only prefer to focus on IoT application developments. Motivated by the wish to eliminate this barrier, this paper proposes a Cloud of Things platform, called VirIoT, which fully brings the Infrastructure as a service model typical of cloud computing to the world of Internet of Things. VirIoT provides users with virtual IoT infrastructures (Virtual Silos) composed of virtual things, with which users can interact through dedicated and standardized broker servers in which the technology can be chosen among those offered by the platform, such as oneM2M, NGSI and NGSI-LD. VirIoT allows developers to focus their efforts exclusively on IoT applications without worrying about infrastructure management and allows cloud providers to expand their IoT services portfolio. VirIoT uses external things and cloud/edge computing resources to deliver the IoT virtualization services. Its open-source architecture is microservice-based and runs on top of a distributed Kubernetes platform with nodes in central and edge data centers. The architecture is scalable, efficient and able to support the continuous integration of heterogeneous things and IoT standards, taking care of interoperability issues. Using a VirIoT deployment spanning data centers in Europe and Japan, we conducted a performance evaluation with a two-fold objective: showing the efficiency and scalability of the architecture; and leveraging VirIoT's ability to integrate different IoT standards in order to make a fair comparison of some open-source IoT Broker implementations, namely Mobius for oneM2M, Orion for NGSIv2, Orion-LD and Scorpio for NGSI-LD.</p>

Distributed Hash Table (DHT) has been widely applied to peer-to-peer (P2P) applications for efficient content lookup mechanisms. IPFS, one of the distributed systems for sharing files, websites, and data on a worldwide scale, adopts Kademlia as one of the DHTs, that is included in libp2p on the network layer of IFPS. Though DHT-based content lookup can scale in the number of peers, how to control lookup performance, e.g., lookup latency and lookup hop count, is one of the issues for a very large-scale network. Thus, it is necessary to guarantee both the lookup latency and the hop count even if the network scale becomes larger. In this paper, we propose a Kademlia alternative, called KadRTT, that reduces both the lookup latency and hop count. KadRTT has two functionalities, i.e., (i) RTT-based lookup target selection not to increase the maximum hop count, and (ii) uniform ID arrangement for each k-bucket to shorten the initial ID distance from content ID. Experimental results by the simulation show that KadRTT outperforms other Kademlia-based DHTs in terms of lookup latency and hop count.

In order to simplify deployment and management of IoT services, Network Function Virtualization (NFV) and Service Function Chaining (SFC) are promising solutions, and much researchers have conducted these topics. To enhance the reliability of former research efforts, in this paper, we propose an orchestration framework for IoT-centric SFC by using Docker and Kubernetes. The framework enables an automatic IoT service deployment by satisfying service requirements and computing and network resource constraints. In such deployment, we apply a Virtual Network Function (VNF)/Service Function (SF) placement problem to achieve efficient utilization of the resources. We set an objective function as minimizing both numbers of SF instances and communications and build a mathematical model based on Integer Linear Programming (ILP). To validate it, we implement a model for the framework and evaluate the performances by carrying out a numerical evaluation and a real experiment. From the evaluation results, we confirm that the proposed approach can reduce the number of SF placements and the number of communications among SF instances.

In this paper, we discuss how to implement a mechanism that combines function chaining and cache in Named Data Networking (NDN), an incarnation of information centric networking technology, for real-world IoT environments. We explain our new architecture, called NDN-FC+, for combining function chaining with cache over NDN, and how to extend existing NDN software to support function chaining and caching. The key features discussed in this paper are Interest and Data packet structure, forwarding methods, and naming schemes for a cached content. In particular, it is important to implement the cache, which is one of the major features of NDN. By using the cache, the network will be able to keep contents closer to the users and send them with low latency. Also, by combining function chaining and caching, and caching the content that has been processed by several functions in advance, it will be possible to communicate the processed content without processing. The feasibility of our proposed protocol for caching and forwarding methods is displayed through a prototype implementation. The performance evaluation was performed in a topology that executes the functions chained to the image data from the sensor, assuming use in the real world IoT environment.

In order to provide interoperability of cross-domain IoT applications involving different IoT platforms, the authors previously proposed a virtual IoT system called VirIoT. The proposed system is composed of two functionalities: ThingVisor and vSilo, and it aims at decoupling IoT device providers and IoT application developers. ThingVisor enables to produce virtual IoT devices, or Virtual Things, from physical IoT devices for sharing the physical devices among cross-domain IoT applications. In addition, vSilo enables to bridge between such Virtual Things and IoT applications for the interoperability of cross-domain IoT devices. In this paper, in order to enhance the VirIoT system, we propose ThingVisor Factory that helps to design ThingVisors in a user-friendly way and deploy them on demand autonomously by following container orchestration methodologies, such as Kubernetes. ThingVisor Factory is based on two concepts: Dataflow programming-based Graphical User Interface (GUI) and service function chaining-based ThingVisor development.

Abstract-Virtualized service and network functions are deployed on virtual machines (VMs) to realize essential processing to realize service function chaining (SFC). Issues on SFC is SF allocation to a VM and to minimize the response time and number of function instances. In this paper, we propose an SF clustering-based scheduling algorithm, called 'SF-clustering for utilizing virtual CPUs' (SFCUV), to solve the SF allocation and SF selection problems simultaneously. Experimental results show that SF-CUV can utilize vCPUs to minimize the response time.

To develop and interoperate smart city applications efficiently, smart city IoT platforms require efficient handling of various types of sensor devices, networking and computing resources, and different domain applications. To address this fact, the authors of this paper proposed a research project named 'Fed4IoT', which is an acronym for federation of IoT and cloud infrastructures, to provide scalable and interoperable smart city applications. The project primarily proposes two key technologies: IoT device virtualization and context-information sharing. In this paper, we introduce the IoT device virtualization (one of key technology in Fed4IoT) that enables efficient utilization of networking and computing resources by applying a micro-service sharing and dynamic resource scaling. In the performance evaluations, we implement three micro services, such as object detection, garbage detection and road damage detection, as smart city applications and dynamically and flexibly allocate computing resources to three micro services by using Docker and Kubernetes. Through the evaluations we validate that the proposed IoT device virtualization achieves the efficient computing resource utilization and reduction of networking resources.

To develop and interoperate smart city applications efficiently, smart city IoT platforms require efficient handling of various types of sensor devices, networking and computing resources, and different domain applications. To address this fact, in this paper, we introduce an IoT device virtualization that enables efficient utilization of computing resources. The proposal applies a micro-service sharing and dynamic resource scaling. In the performance validations, we implement an early prototype using Docker, Kubernetes, and Apache Kafka. Through the preliminary experiment, we confirm that the proposal can improve the application processing time by appropriately sharing and scaling micro services.

Edge computing can realize a data locality among a cloud and users, and it can be applied to task offloading, i.e., a part of workload on a mobile terminal is moved to an edge or a cloud system to minimize the response time with reducing energy consumption. Mobile workflow jobs have been widely used due to advance of computational power on a mobile terminal. Thus, how to offload or schedule each task in a mobile workflow is one of the current challenging issues. In this paper, we propose a task scheduling algorithm with task offloading, called priority-based continuous task selection for offloading (PCTSO), to minimize the schedule length with energy consumption at a mobile client being reduced. PCTSO tries to select dependent tasks such that many tasks are offloaded so as to utilize many vCPUs in the edge cloud
in this manner, the degree of parallelism can be maintained. Experimental results of the simulation demonstration that PCTSO outperforms other algorithms in the schedule length and satisfies the energy constraint.

Big data computing in the cloud needs faster processing and better resource provisioning. MapReduce is the framework for computing large scale datasets in cloud environments. Optimization of resource requirement for each job to satisfy a specific objective in MapReduce is an open problem. Many factors, e.g., system side information and requirements of each client must be considered to estimate the appropriate amount of resources. This paper presents a mathematical model for the optimal number of map tasks in MapReduce resource provisioning. This model is to estimate the optimal number of the mappers based on the resource specification and the size of the dataset.

Information-centric networking (ICN) is a new approach to networking contents rather than devices that hold the contents. It has recently attracted much attention of network research and standardization communities. National and multi-national funded research projects have progressed worldwide. International Telecommunication Union-Telecommunication Standardization Sector (ITU-T) started ICN standardization activities in 2012. In parallel, the standards-oriented research cooperation is progressing in the Information-Centric Networking Research Group (ICNRG) of the Internet Research Task Force (IRTF). All these global efforts have been collectively advancing the novel network architecture of ICN. However, there are very few surveys and discussions on the detailed ICN standardization status. To update the reader with information about the ICN research and standardization related activities, this paper starts with the history of global activities on ICN from 2010, giving references to various projects. It then describes the recent progress in the standardization of ICN component technologies in ITU-T and various documents produced by ICNRG. Lastly, it discusses the future directions for progressing ICN.

As mobile multimedia services have significantly evolved and diversified with the spread of smartphones and Internet of Things (IoT) devices, low-delay multimedia cloud computing is the need of the hour. To address this demand, in this study, we introduce an edge cloud system that equips a multimedia service function chaining capability. A prototype implementation of the proposed edge cloud system has three main features: 1) edge computing deployment by using OpenStack, 2) multimedia service slicing and chaining, and 3) efficient resource management in edge networks. Based on these features, the proposed system achieves lower multimedia processing delay compared to a conventional cloud computing platform. We deploy the proposed system in our laboratory and validate the system performance by using typical multimedia application, such as human detection in video surveillance.

Since the content delivery unit over Information-Centric Networking (ICN) has shifted from files to the segments of a file named chunks, solely either file-level or chunk-level request probability is insufficient for ICN cache management. In this paper, a Request Expectation Index (RXI) based cache replacement algorithm for streaming content delivery is proposed. In this algorithm, RXI is introduced to serve as a fine-grained and unified estimation criteria of possible future request probability for cached chunks. RXI is customized for streaming content delivery by adopting both file-level and chunk-level request probability and considering the dynamically varied request status at each route as well. Compared to prior work, the proposed algorithm evicts the chunk with the minimum expectation of future request to maintain a high cache utilization. Additionally, simulation results demonstrate that the RXI-based algorithm can remarkably enhance the streaming content delivery performance and can be deployed in complex network scenarios. The proposed results validate that, by taking fine-grained request probability and request status into consideration, the customized in-network caching algorithm can improve the ICN streaming content delivery performance by high cache utilization, fast content delivery, and lower network traffic.

Many workflow scheduling algorithms for heterogeneous systems have been developed to satisfy multiple requirements such as minimizing schedule length while maximizing throughput. In particular, in list-based scheduling approaches, the schedule length depends on the given nodes as well as the task allocation and ordering policies. This is because the scheduling priority is derived by averaging the execution time and communication time of the given nodes. If the set of nodes can be adjusted before the scheduling tasks, a small schedule length can be achieved. In this paper, we propose a prior node selection algorithm, called lower bound based candidate node selection (LBCNS) to select a subset of given nodes to minimize the schedule length while fairly scheduling each job. Our proposal has two approaches: (i) LBCNS_DEFAULT, which considers the job characteristics and each node's performance, and (ii) priority-based LBCNS, which additionally takes each scheduling priority into account for a dedicated task scheduling algorithm.
The experimental results of extensive simulations show that LBCNS_DEFAULT has the best fairness for scheduling multiple workflow jobs, while priority-based LBCNS achieves the minimum schedule length with the highest efficiency for a single workflow job and multiple workflow jobs. (C) 2017 Elsevier Inc. All rights reserved.

Named Data Networking (NDN) is a promising architecture for the future Internet and it is mainly designed for efficient content delivery and retrieval. However, producer mobility support is one of the challenging problems of NDN. This paper proposes a scheme which aims to optimize the tunneling-based producer mobility solution inNDN. It does not require NDN routers to change their routing tables (Forwarding Information Base) after a producer moves. Instead, the Interest packet can be sent from a consumer to the moved producer using the tunnel. The piggybacked Data packet which is sent back to the consumer will trigger the consumer to send the following Interest packets through the optimized path to the producer. Moreover, a naming scheme is proposed so that the NDN caching function can be fully utilized. An analysis is carried out to evaluate the performance of the proposal. The results indicate that the proposed scheme reduces the network cost compared to related works and supports route optimization for enhanced producer mobility support in NDN.

Internet Engineering Task Force (IETF) has proposed an extension based on Mobile IPv6 (MIPv6), named as network mobility basic support protocol (NEMO-BSP), to support NEMO in IPv6 networks. However, NEMO-BSP inherits all the drawbacks of MIPv6, such as inefficient routing path, high handover latency, and packet encapsulation overhead. To address these drawbacks of NEMO-BSP, this paper proposes an NEMO supporting scheme based on a novel Locator/ID Separation (LIDS) architecture, namely LIDS-NEMO. In LIDS-NEMO, Multiple Virtual Mapping (MVM) scheme is proposed to differentiate the intra-NEMO and inter-NEMO mobility. Besides, packets are transmitted through the most optimized route in LIDS-NEMO. The simulation results show that LIDS-NEMO reduces the signaling cost significantly when compared with NEMO-BSP and it will be a promising scheme to provide NEMO support in the LIDS context.

We introduce a novel cache replacement policy to improve the entire network performance of video delivery over content-centric networking (CCN). In the case of the CCN structure, we argue that: 1) for video multiplexing scenario, general cache strategies that ignore the intrinsic linear time characteristic of video requests are unable to make better use of the cache resources, and 2) it is inadequate to simply extend the existing research conclusions of file-oriented popularity to chunk-by-chunk popularity, which are widely used in CCN. Unlike previous works in this field, the proposed policy in this study, named two-level popularity-oriented time-to-hold cache replacement policy (TLP-TTH), is designed on the basis of the following principles. Firstly, the proposed cache replacement strategy is customized for video delivery by carefully considering the essential auto-correlated request feature of video chunks within a video file. Furthermore, the popularity in video delivery is subdivided into two levels, namely chunk-level access probability and file-level popularity, in order to efficiently utilize cache resources. We evaluated the proposed policy in both a hierarchical topology and a real network based hybrid topology, and took viewers departure into consideration as well. The results validate that for video delivery over CCN, TLP-TTH policy improves the network performance from several aspects. In particular, we observed that the proposed policy not only increases the cache hit ratio at the edge of the network but the cache utilization at the intermediate routers is also improved markedly. Further, with respect to the video popularity variation scenario, the cache hit ratio of TLP-TTH policy responds sensitively to maintain efficient cache utilization.

Parallelization paradigms for effective execution in a Directed Acyclic Graph (DAG) application have been widely studied in the area of task scheduling. Schedule length can be varied depending on task assignment policies, scheduling policies, and heterogeneity in terms of each processor and each communication bandwidth in a heterogeneous system. One disadvantage of existing task scheduling algorithms is that the schedule length cannot be reduced for a data intensive application. In this paper, we propose a clustering-based task scheduling algorithm called Clustering for Minimizing the Worst Schedule Length (CMWSL) to minimize the schedule length in a large number of heterogeneous processors. First, the proposed method derives the lower bound of the total execution time for each processor by taking both the system and application characteristics into account. As a result, the number of processors used for actual execution is regulated to minimize the Worst Schedule Length (WSL). Then, the actual task assignment and task clustering are performed to minimize the schedule length until the total execution time in a task cluster exceeds the lower bound. Experimental results indicate that CMWSL outperforms both existing list-based and clustering-based task scheduling algorithms in terms of the schedule length and efficiency, especially in data-intensive applications.

Information Centric Networking (ICN) is a new networking paradigm in which the network provides users with named content, instead of communication channels between hosts. However, many issues, such as naming, routing, resource control, and security, still need to be resolved before it can be realized practically. Further, the energy efficiency of ICNs has not been sufficiently considered. In this study, we evaluate the energy consumption of an ICN network by first measuring the power consumption of actual PCs/smartphones and constructing an energy consumption model. Then, by using the model and the simulator ndnSIM, we calculate the energy consumed by the network. We also evaluate the energy consumed for video delivery, with a view toward power-saving video delivery.

The recent development of network technologies that offer centralized control of explicit routes opens the door to the online optimization of explicit routing. For this kind of Traffic Engineering optimization, raising the calculation speeds by using multi-core processors with effective parallel algorithms is a key goal. This paper proposes an effective parallel algorithm for General purpose Programming on Graphic Processing Unit (GPGPU); its massively parallel style promises strong acceleration of calculation speed. The proposed algorithm parallelizes not only the search method of the Genetic Algorithm, but also its fitness functions, which calculate the network congestion ratio, so as to fully utilize the power of modern GPGPUs. Concurrently, each execution is designed for thread-block execution on the GPU with consideration of thread occupancy, local resources, and SIMT execution to maximize GPU performance. Evaluations show that the proposed algorithm offers, on average, a nine fold speedup compared to the conventional CPU approach.

A Traditional Air Traffic Management (ATM) system comprises of a wide variety of information services that characterized by many custom communication protocols. Each service is a self-contained information system by itself. Each of these services is custom designed, developed, managed, and maintained individually and locally at a significant cost [1]. System-Wide Information Management (SWIM) is a concept for modern ATM systems that present a complete change in paradigm of how ATM related information is handled and shared while services interoperate. The aim of SWIM is to provide information to users with understandable information. However, such a large amount of information that flow through a network may affect overall system performance. Quality of Service (QoS) describes network performance parameters reflect the quality of the connection and is experienced on end-to-end traffic flow.
In this paper, we propose a model base on SWIM with implementation of Quality of Service configuration and application of network virtualization technique to separate network traffic into individual services, which intent to improve the quality of end-to-end network traffic. This network model is developed and deployed using Juniper MX series router.

Content Centric Networking (CCN), which is regarded as an innovational architecture of future network, is increasingly gaining attention in recent years. Based on the observation of current Internet usage, CCN presents a content-centric model which focuses on data distribution and retrieval. To enhance the efficiency of content distribution, In-network caching is adopted, which enables routers to temporarily store the passing by data. Thus the scheme of cache management will affect the network performance significantly. In this paper, a cache replacement policy named Popularity Proportional Cache Size Allocation (PPCSA) policy has been proposed, which designed for video content delivery on CCN. Unlike other previous works in this area, which have focused on the popularity of individual content chunks, in this study, we not only consider the title-level popularity of a video which is composed of a set of video segments, but also take the chunk-level probability of video segments within a video title into consideration. We evaluated our policy through simulations, by using a simple 4-level tree topology and a real network based hybrid topology. The results validate that on both scenarios, the proposed approach can increase the average cache hit ratio and shorten the average content delivery distance between requesters and object contents. In addition, we analyzed the energy efficiency of PPCSA policy, and we found that compared to other three cache replacement policies, Least Recently Used (LRU), Least Frequently Used (LFU) and First in First out (FIFO), PPCSA reduced the transmission energy consumption, and improved the energy efficiency in idle time.

Video traffic shares a large portion of the current Internet bandwidth. One of the objectives of designing Named Data Networking (NDN) is to reduce the burden of the large content delivery. Since mobile devices are the prime means of content access for many users, NDN is also required to support mobility. In this paper, we propose an architecture and mechanism called On-Path Resolver Architecture (OPRA) for NDN to support both consumer and producer mobility while maintaining scalability. OPRA exploits the semantics of hierarchical-name-based routing and places route resolvers on multiple points on a path to a content. The series of resolvers on a path provides means to contain naming scope and makes the system scalable while supporting mobility.

A layout engine combines markup language (HTML, etc.) with formatting information (CSS, etc.), and displays the combined contents on the screen. It is commonly used for many existing web development applications and web browsers. Especially, web development applications use layout engine to confirm the impact on the layout of web editing results to achieve what you see is what you get (WYSIWYG). At present, different layout engines are adopted by various web development applications and web browsers. As there is not a uniform algorithm adopted by layout engine, the results displayed by web development applications are not necessarily consistent with the display on the web browsers. In addition, the results displayed by web development applications depend on layout engines. Hence, it is difficult to edit the common web contents since it cannot objectively reflect the result on web browsers. In this paper, we propose a progressive WYSIWYG solution, by completely separating web layout design from web data editing to generate cross-browser web data. Our proposed approach does not use layout engine, and thus can avoid the disadvantages mentioned above.© 2014 Inderscience Enterprises Ltd.

In the last decades, many kinds of task models such as grid and cloud computing have been developed. In such distributed systems, each task is processed by respective processor in multicored computers e.g., household PCs which we can easily harness in recent years. If there is one policy to automatically decide the "best" combination and the number of processors (and computers), we effectively utilize those computational resources, thereby large number of jobs can be executed in parallel. In this paper, we propose a method for mapping of execution units for such environments. The method adopts a remapping technology after processor-execution unit mapping[11] is finished. Experimental comparisons by a simulation show the advantages of the proposed method.

Network coding, with the redundant information it generates, has been shown to achieve optimal multicast throughput. Such redundancy plays a key role in maximizing throughput over multiple content delivery paths between network nodes. In this paper, we study the use of network coding to speed up content distribution in peer-to-peer networks where only a constraint number of selected peers can encode, i.e. become network coders. Our goal is to optimize the redundancy network coding generates, i.e. we want to answer the question how much a given network coder should encode. This serves as a step towards understanding how network coding improves content distribution in order to optimize its deployment. Given the network topology, we analytically figure the optimal redundancy ratio at each network coder to achieve shortest distribution time. Simulation results confirm the effectiveness of the proposed coding redundancy in shortening content distribution time while noticeably reducing the amount of encoding.

MapReduce framework allows users to quickly develop big-data applications and process big-data effectively. However, unexpected malfunction may be found in cloud environment because a distributed system consists of several hardware, and this malfunction often causes delay of overall processing. MapReduce framework provides Speculative Execution (SE). SE reduces delay in a homogeneous environment by assigning delayed tasks to additional nodes. As cloud computing prevails, cloud computing environment is moving from homogeneous to heterogeneous. Original SE is not perfect and sometimes produces inefficient result in a heterogeneous environment. This paper proposes Dynamic Scheduling for Speculative Execution (DSSE) which enhances performance in a heterogeneous environment by improving existing SE. DSSE prevents wasted SE since it calculates processing capability of each node more objectively and precisely. DSSE has reduced entire processing time approximately 10% compared to original SE. Success rate of SE was 100%.

We study the use of network coding to speed up content distribution in peer-to-peer (P2P) networks. Our goal is to get the underlying reason for network coding's improved performance in P2P content distribution and to optimize resource consumption of network coding. We observe analytically and experimentally that in pure P2P networks, a considerable amount of data is sent multiple times from one peer to another when there are multiple paths connecting those two particular peers. Network coding, on the other hand, when applied at upstream peers, eliminates information duplication on paths to downstream peers, which results in more efficient content distribution. Based on that insight, we propose a network coder placement algorithm which achieves comparable distribution time as network coding, yet substantially reduces the number of encoders compared to a pure network coding solution in which all peers have to encode. Our placement method puts encoders at critical network positions to eliminate information duplication the most, thus, effectively shortens distribution time with just a portion of encoders.

This paper proposes an IP fast rerouting method which can be implemented in OpenFlow framework. While the current IP is robust, its reactive and global rerouting processes require the long recovery time against failure. On the other hand, IP fast rerouting provides a milliseconds-order recovery time by proactive and local restoration mechanism. Implementation of IP fast rerouting is not common in real systems, however; it requires the coordination of additional forwarding functions to a commercial hardware. We propose an IP fast rerouting mechanism using OpenFlow that separates control function from hardware implementation. Our mechanism does not require any extension of current forwarding hardware. On the contrary, increase of backup routes becomes main overhead of our proposal. We also embed the compression mechanism to our IP fast rerouting mechanism. We show the effectiveness of our IP fast rerouting in terms of the fast restoration and the backup routes compression effect through computer simulations.

In the last decades, many kinds of task execution models such as grid and cloud computing have been developed. In such distributed systems, each task is processed by respective processor in multi cored computers e. g., household PCs which we can easily harness in recent years. If there is one policy to automatically decide the "best" combination and the number of processors (and computers), we effectively utilize those computational resources, thereby large number of jobs can be executed in parallel. In this paper, we propose a method for mapping of execution units for such environments. T he method adopts a re-mapping technology after processor-execution unit mapping[6] is finished. Experimental comparisons by a simulation show the advantages of the proposed method.

IP fast rerouting has widely been studied for realizing millisecond-order recovery on pure IP networks. This paper proposes IP fast rerouting using backup topologies against concurrent double failures. The main issue in recovering from multiple failures is avoiding forwarding loops. To avoid forwarding loops, we propose a deterministic forwarding algorithm, which estimates the concurrently occurring failures from the packet header information. We also propose an efficient backup topology design algorithm which is both loop-free and which reduces the number of backup topologies. Our key idea is preparing the adequate diversity of backup routes for arbitrary source and destination pairs by combination of backup topologies. For efficient computation of diverse routes, we propose a similarity comparison-based algorithm between the original topology and the backup topologies. Our algorithm can achieve nearly optimal loop-free restoration from double failures on realistic topologies without explicit failure notification.

Multimedia applications such as music or video streaming, video teleconferencing and IP telephony are flourishing in packet-switched networks. Applications that generate such real-time data can have very diverse quality-of-service (QoS) requirements. In order to guarantee diverse QoS requirements, the combined use of a packet scheduling algorithm based on Generalized Processor Sharing (GPS) and leaky bucket traffic regulator is the most successful QoS mechanism. GPS can provide a minimum guaranteed service rate for each session and tight delay bounds for leaky bucket constrained sessions. However, the delay bounds for leaky bucket constrained sessions under GPS are unnecessarily large because each session is served according to its associated constant weight until the session buffer is empty. In order to solve this problem, a scheduling policy called Output Rate-Controlled Generalized Processor Sharing (ORC-GPS) was proposed in [17]. ORC-GPS is a rate-based scheduling like GPS, and controls the service rate in order to lower the delay bounds for leaky bucket constrained sessions. In this paper, we propose a call admission control (CAC) algorithm for ORC-GPS, for leaky-bucket constrained sessions with deterministic delay requirements. This CAC algorithm for ORC-GPS determines the optimal values of parameters of ORC-GPS from the deterministic delay requirements of the sessions. In numerical experiments, we compare the CAC algorithm for ORC-GPS with one for GPS in terms of schedulable region and computational complexity.

Network coding has been applied successfully in peer-to-peer (P2P) systems to shorten the distribution time. Pieces of data, i.e. blocks, are combined, i.e. encoded, by the sending peers before forwarding to other peers. Even though requiring all peers to encode might achieve shortest distribution time, it is not necessarily optimal in terms of computational resource consumption. Short finish time, in many cases, can be achieved with just a subset of carefully chosen peers. P2P systems, in addition, tend to be heterogeneous in which some peers, such as hand-held devices, would not have the required capacity to encode. We therefore envision a P2P system where some peers encode to improve distribution time and other peers, due to limited computational capacity or due to some system-wide optimization, do not encode. Such a system gives rise to a block-selection problem which has never happened in both pure non-coding and full network coding-enabled P2P systems. We identify the problem and fix the current block-selection algorithm to address it. Simulation evaluation confirms the effectiveness of our proposed algorithm without which the system performance degrades considerably. © 2012 IEEE.

Network coding has been applied successfully in peer-to-peer systems to shorten the distribution time, particularly in extreme conditions where peers in the network are clustered and separated by physical links with limited bandwidth. In this paper, we focus more closely on the use of source coding, i.e. encoding is done only at the source, as an alternative to network coding to facilitate content distribution under such limited bandwidth configuration. We observe analytically and experimentally that, in this specific case, with appropriately chosen expansion factors, source coding can have comparable performance to network coding in terms of distribution time, yet consuming much less computational resources than the latter approach does.

The 21st century witnessed the wide spread of smart phones such as iPhone. The daily growing importance of smart phones' also implies the increasing amount of user sensitive data stored in a cell phone, which positions mobile user authentication in an even more important role. Existing mobile user authentication methods either require special hardware or are not user transparent. In this paper, we present a mobile user authentication scheme using a data mining method that identifies a user based on cell phones' application history and GPS information. These data can be collected on almost every smart phone without user awareness and are prone to reflect a user's habit and biometric feature. We organize these data in directional graphs and introduce a metric based on which to classify the data. Experiments and results on real data are explained to show our scheme's effectiveness. © 2010 i-Society.

In a distributed system, which consists of an unknown number of processors, it is important to derive an appropriate number of processors by which the good schedule length is obtained by a task scheduling. Many task clustering heuristics have been proposed to determine the number of processors and to minimize the schedule length for scheduling a directed acyclic graph (DAG) application. However, those heuristics are not aware of the actual number of existing processors. As a result, the number of processors determined by an existing task clustering may exceed that of actually existing processors. Therefore, conventional approaches adopt merging of each cluster for reducing the number of clusters at the expense of decreasing degree of task parallelism. In this paper, we present a static cluster size determination method, which derives the lower bound of the cluster size with considering the DAG structure and the task size to data size ratio to suppress the schedule length with the small number of processors. Our experimental evaluations by simulations show that the lower bound of each cluster size determined by the proposed method has a good impact on both the schedule length and the processor utilization. © 2010 Springer Science+Business Media LLC.

Future home appliances will have many useful built-in functions. A communication network that allows a user to access these built-in functions and to control the appliances is highly desirable. An ad hoc network can be easily deployed to connect these home appliances. For such a home network, ease of use must be given higher consideration. This paper discusses description based ad-hoc network routing where the nodes are given descriptions or keywords such as living room TV, kitchen oven etc. as identifiers. We propose three description based routing mechanisms: Single Routing Cache, Directional Routing Cache, and Proportional-Directional Routing Cache. Through simulation, we show that in all three methods the message delivery is 100% success in our configuration, but the amount of misdirected messages experienced by each method is different. The Proportional-Directional Routing Cache shows the least message misdirection with most preferable resource usage.

Recently multimedia applications such as music or video streaming, video teleconferencing and IP telephony have been flourishing in packet-switched networks. Applications that generate such real-time data can have very diverse quality-of-service (QoS) requirements.
In order to guarantee diverse QoS requirements, the combined use of a packet scheduling algorithm based on Generalized Processor Sharing (GPS) and leaky bucket traffic regulator is the most successful QoS mechanism. GPS can provide a minimum guaranteed service rate for each session and tight delay bounds for leaky bucket constrained sessions. However, the delay bounds for leaky bucket constrained sessions under GPS are unnecessarily large constant weight until the session buffer is empty. In order to solve the problem, a scheduling policy called Output Rate-Controlled Generalized Processor Sharing (ORC-GPS) has been proposed in [10]. ORC-GPS is a rate-based scheduling like GPS, and controls the service rate in order to lower the delay bounds for leaky bucket constrained sessions.
In this paper, we extend the delay bounds calculation for single-node networks introduced in [10] to multi-node networks and compare ORC-GPS with GPS in terms of end-to-end delay bounds through numerical experiments.

Wireless mobile ad hoc networks can be used as a means to ease and support life in an ordinary house. As future home appliances will have many useful built-in functions, a communication network that allows a user to access these built-in functions and to control the appliances is highly desirable. This paper proposes a routing algorithm for mobility in a description-based clustered ad-hoc network where the nodes are given descriptions or keywords such as "living room TV", "kitchen oven", etc. as identifiers. In this paper, we discuss and compare the routing and the number of control messages required to achieve a higher message delivery success rate for frequent and infrequent communication of nodes with mobility. When communication is infrequent a search is performed for the destination regardless of mobility. The proposed algorithm handles mobility efficiently while ensuring a high accuracy on message delivery with the least amount of control messages.

Divisible Load Theory (DLT) is an established framework to study Divisible Load Scheduling (DLS). Traditional DLT ignores the result collection phase, and specifies no solution to the general case where both the network speed and computing capacity of the nodes are heterogeneous. In this paper, the DLS with Result Collection on HETerogeneous Systems (DLSRCHETS) problem is formulated as a linear program and analyzed. The papers to date that have dealt with result collection, proposed Simplistic LIFO (Last In, First Out) and FIFO (First In, First Out) type of schedules as solutions. The main contributions of this paper are: (a) A proof of the Allocation Precedence Condition, which is inconsequential in LIFO or FIFO, but is important in a general schedule. (b) A proof of the Idle Time Theorem, which states that irrespective of whether load is allocated to all available processors, in the optimal solution to the DLSRCHETS problem, at the most one processor that is allocated load has idle time, and that the idle time exists only when the result collection begins immediately after the completion of load distribution.

Divisible Load Theory (DLT) is an established mathematical framework to study Divisible Load Scheduling (DLS). However traditional DLT does not comprehensively deal with the scheduling of results back to source (i.e., result collection) on heterogeneous systems. In this paper, the DLSRCHETS (DLS with Result Collection on HETerogeneous Systems) problem is addressed. The few papers to date that have dealt with DLSRCHETS, proposed simplistic LIFO (Last In, First Out) and FIFO (First In, First Out) type of schedules as solutions to DLSRCHETS. In this paper, a new heuristic algorithm, ITERLP, is proposed as a solution to the DLSRCHETS problem. With the help of simulations, it is proved that the performance of ITERLP is significantly better than existing algorithms.

Divisible Load Theory (DLT) is an established mathematical framework to study Divisible Load Scheduling (DLS). However, traditional DLT does not comprehensively deal with the scheduling of results back to source (i.e., result collection) on heterogeneous systems. In this paper, the DLSRCHETS (DLS with Result Collection on HETerogeneous Systems) problem is addressed. The few papers to date that have dealt with DLSRCHETS, proposed simplistic LIFO (Last In, First Out) and FIFO (First In, First Out) type of schedules as solutions to DLSRCHETS. In this paper, a new heuristic algorithm, ITERLP, is proposed as a solution to the DLSRCHETS problem. With the help of simulations, it is proved that the performance of ITERLP is significantly better than existing algorithms. ©2008 IEEE.

Existing anomaly intrusion detection that monitors system calls has two problems: vast false positives and lack of risk information on detection. In order to solve the two problems, we propose an intrusion detection method called "Callchains." Callchains reduces the false positives of existing anomaly intrusion detection by restricting monitoring to the activities with process capabilities prescribed by POSIX 1003. le. Additionally, Callchains provides an administrator information of used POSIX capabilities in sytem call execution as an indicator of risk. This paper shows Callchains' design, its implementation, and experimental results comparing Callchains with existing approaches.

Recently packet scheduling algorithms such as Packetized GPS (PGPS), Worst-case Fair Weighted Fair Queueing (WF(2)Q) and Self-Clocked Fair Queueing (SCFQ) have been proposed in order to guarantee deterministic or statistical delay bounds. These algorithms are based on Generalized Processor Sharing (GPS) which is an ideal scheduling algorithm based on fluid flow model in which the traffic is infinitely divisible. GPS provides a minimum guaranteed service rate for each session and tight delay bounds for leaky bucket constrained sessions. However, the delay bounds are unnecessarily large because each session is served according to its associated constant weight until the session buffer is empty. In this paper, we present a scheduling policy called Output Rate-Controlled Generalized Processor Sharing (ORC-GPS). ORC-GPS is a rate-based scheduling like GPS and controls the service rate to lower the delay bounds for leaky bucket constrained sessions. In numerical experiments, we compare ORC-GPS with GPS in terms of delay bounds.

Recently many packet scheduling algorithms based on Generalized Processor Sharing (GPS) and Earliest Deadline First (EDF) have been proposed in order to guarantee deterministiic or statistical delay bounds. GPS provides a minimum guaranteed service rate for each session and tight end-to-end delay bounds for leaky bucket constrained sessions. However, the delay bounds are unnecessarily large because each session is served according to its associated constant weight until the session buffer is empty. EDF is the optimal scheduling algorithm in terms of schedulable region in a single-node network. However, using EDF to provide end-to-end delay bounds is problematic because the traffic will be distorted after traffic aggregation in a multi-node network.
In this paper, we present a scheduling policy called Output Rate-Controlled Generalized Processor Sharing (ORC-GPS) in order to guarantee deterministic delay bounds. ORC-GPS is a rate-based scheduling like GPS and controls the service rate to lower the delay bounds for leaky bucket constrained sessions. We compare ORC-GPS with GPS and EDF in terms of delay bounds.

Recently many packet scheduling algorithms based on Generalized Processor Sharing (GPS) and Earliest Deadline First (EDF) have been proposed in order to guarantee deterministic or statistical delay bounds. GPS provides a minimum guaranteed service rate for each session and tight end-to-end delay bounds for leaky bucket constrained sessions. However, the delay bounds are unnecessarily large because each session is served according to its associated constant weight until the session buffer is empty. EDF is the optimal scheduling algorithm in terms of schedulable region in a single-node network. However, using EDF to provide end-to-end delay bounds is problematic because the traffic will be distorted after traffic aggregation in a multi-node network. In this paper, we present a scheduling policy called Output Rate-Controlled Generalized Processor Sharing (ORC-GPS) in order to guarantee deterministic delay bounds. ORC-GPS is a rate-based scheduling like GPS and controls the service rate to lower the delay bounds for leaky bucket constrained sessions. We compare ORC-GPS with GPS and EDF in terms of delay bounds.

Due to recent advances in multimedia technology, a real-time capability is needed to a certain extent even in information systems, in addition to control systems. In general, information systems are classified as soft real-time systems, which keep operating even if a deadline is missed. In soft real-time systems, it is more important to provide statistical guarantees (probabilistic analysis) than absolute guarantees (deadline guarantees). In current real-time systems, real-time scheduling such as EDF (Earliest Deadline First) is used to meet timing constraints. EDF scheduling simply assigns higher priorities in the order of earlier deadlines. It can be applied to aperiodic tasks without prior knowledge of their starting times. In this paper, it is assumed that each attribute of the task follows a probabilistic distribution. The system performance when a nonpreemptive EDF scheduling is applied is then mathematically analyzed. © 2005 Wiley Periodicals, Inc.

QoS control for multicast over the Internet is necessary to provide high quality multicast video streaming. With existing DiffServ multicast schemes, multicast traffic in a DiffServ domain increases as the multicast-group membership grows, and consequently packets guaranteed for delivery may be discarded. Since it is hard to guarantee QoS for all flows over the entire network, we propose DERACS a delivery range control scheme which guarantees QoS in a limited delivery range formed by actively discarding packets. In this paper, we show the delivery range of DERACS through simulation and analysis.

Optimistic Concurrency control demonstrates a few improvements over pessimistic concurrency controls like two-phase locking protocol or time-stamp based protocol. But the price of coarse detection of conflicts may sometimes be high and consequently discounts the advantage of optimistic concurrency control protocol. In this paper we have reduced the space of coarse detection of conflicts of optimistic concurrency control protocol.

QoS control for multicast over the Internet is necessary to provide high quality multicast video streaming. With existing DiffServ multicast schemes, multicast traffic in a DiffServ domain increases as the multicast-group membership grows, and consequently packets guaranteed for delivery may be discarded. Since it is hard to guarantee QoS for all flows over the entire network, we propose DERACS a delivery range control scheme which guarantees QoS in a limited delivery range formed by actively discarding packets. In this paper, we show the delivery range of DERACS through simulation and analysis.

Speech services are widely deployed on new communication infrastructures. However, compared with traditional infrastructures, the ever varying conditions are native of these new infrastructures. Modeling the time-varying speech quality becomes an urgent issue. This paper proposes a computable model for time-varying speech quality evaluation, called M-Model. Considering the time-varying issue, EWMA is used in the model. M-Model is validated by a subjective experiment. The model indicates that time-varying quality with extremely high frequency will not affect perceived quality at all. In addition, we observed that the recency effect influences not only the perceived overall quality but also the perceived instantaneous quality.

Speech services are widely deployed on new communication infrastructures. However, compared with traditional infrastructures, the ever varying conditions are native of these new infrastructures. Modeling the time-varying speech quality becomes an urgent issue. This paper proposes a computable model for time-varying speech quality evaluation, called M-Model. Considering the time-varying issue, EWMA is used in the model. M-Model is validated by a subjective experiment. The model indicates that time-varying quality with extremely high frequency will not affect perceived quality at all. In addition, we observed that the recency effect influences not only the perceived overall quality but also the perceived instantaneous quality.

P2P content distribution networks are collecting interest to efficiently deliver rich contents such as audio and video. Stream interruption caused by relay peers dropping out of the P2P network is a problem in P2P live streaming systems. In this paper we propose a P2P live streaming system with low stream interruption. In the proposed system, peers are connected to the peers with low drop-out probability and a wide and short P2P network is constructed. Also, multiple paths to the source is considered. We show the improvement in the interruption time by our algorithm through simulations.

The demand to access multimedia data such as live video is increasing due to the spread of broadband access network. However, the Internet only provides so called best-effort service. The network condition of the Internet shows fluctuation. Delivering video without any control causes degradation of the final video quality. To avoid such degradation, we must control video traffic to adapt to the fluctuating available bandwidth. In this paper, we propose a technique to find available bandwidth for end-to-end stream connections over the Internet. We claim that the correlation coefficient of network delay of an end-to-end stream is closely related to the bottleneck-link load for the end-to-end stream connection. We show and prove the relationship through analysis and simulations.

The demand to access multimedia data such as live video is increasing due to the spread of broadband access network. However, the Internet only provides so called best-effort service. The network condition of the Internet shows fluctuation. Delivering video without any control causes degradation of the final video quality. To avoid such degradation, we must control video traffic to adapt to the fluctuating available bandwidth. In this paper, we propose a technique to find available bandwidth for end-to-end stream connections over the Internet. We claim that the correlation coefficient of network delay of an end-to-end stream is closely related to the bottleneck-link load for the end-to-end stream connection. We show and prove the relationship through analysis and simulations.

The demand to access multimedia data such as live video is increasing due to the spread of broadband access network. However, the Internet only provides so called best-effort service. The network condition of the Internet shows fluctuation. Delivering video without any control causes degradation of the final video quality. In this paper we propose a stream-rate control algorithm RC3 that uses correlation of network delay to evaluate available bandwidth. We show performance of RC3 derived from simulations.

The demand to access multimedia data such as live video is increasing due to the spread of broadband access network. However, the Internet only provides so called best-effort service. The network condition of the Internet shows fluctuation. Delivering video without any control causes degradation of the final video quality. In this paper we propose a stream-rate control algorithm RC3 that uses correlation of network delay to evaluate available bandwidth. We show performance of RC3 derived from simulations.

Recently the Internet is used for real-time traffic such as voice and video that requires guarantee on band-width and delay. However, the guarantee is difficult to attain in.the current Internet since packets are forwarded in so called best-effort fashion. We a.re studying class-based service differentiation in order to guarantee bandwidth and delay. Implementation of the service differentiation is achieved by a packet scheduling algorithm that discriminates each service class. In this paper, we propose a scheduling algorithm with priority-based congestion control to be used in a core network for service differentiation. Our algorithm is an extension of the Elastic Weighted Round Robin(EWRR). We show the effectiveness of our algorithm through simulation.

The forwarding speed of IP routers must grow to accommodate the skyrocketing amount of traffic on the Internet. MPLS, which relies on the high processing power of lower layers, is a solution and it is under developing. On the other hand, a WDM network has been expected as a high-speed network, but it is also called a stupid network because of lacking its traffic granularity In order to bridge between these two layers, an IP over WDM network by a concept of MPLS has been proposed. This network has a potential to effectively use large transmission capacity provided by WDM technology. In this paper, we design IP over WDM networks that reconfigure IP routing and lightpaths each day or month. We formulate a problem that maximizes the network throughput based on integer linear programming. Through numerical examples, we show that the increase of the network throughput in IP over WDM networks is larger than that of IP networks. We also show the area where this method is applicable to the reconfigurable network.