The strict latency constraints of emerging vehicular applications make it unfeasible to forward sensing data from vehicles to the cloud for processing. To shorten network latency, vehicular fog computing (VFC) moves computation to the edge of the Internet, with the extension to support the mobility of distributed computing entities [a.k.a fog nodes (FNs)]. In other words, VFC proposes to complement stationary FNs co-located with cellular base stations with mobile ones carried by moving vehicles (e.g., buses). Previous works on VFC mainly focus on optimizing the assignments of computing tasks among available FNs. However, capacity planning, which decides where and how much computing resources to deploy, remains an open and challenging issue. The complexity of this problem results from the spatiotemporal dynamics of vehicular traffic, varying computing resource demand generated by vehicular applications, and the mobility of FNs. To solve the above challenges, we propose a data-driven capacity planning framework that optimizes the deployment of stationary and mobile FNs to minimize the installation and operational costs under the quality-of-service constraints, taking into account the spatiotemporal variation in both demand and supply. Using real-world traffic data and application profiles, we analyze the cost efficiency potential of VFC in the long term. We also evaluate the impacts of traffic patterns on the capacity plans and the potential cost savings. We find that high traffic density and significant hourly variation would lead to dense deployment of mobile FNs and create more savings in operational costs in the long term.

Remote rendering systems comprise powerful servers that render graphics on behalf of low-end client devices and stream the graphics as compressed video, enabling high end gaming and Virtual Reality on those devices. One key challenge with them is the amount of bandwidth required for streaming high quality video. Humans have spatially non-uniform visual acuity: We have sharp central vision but our ability to discern details rapidly decreases with angular distance from the point of gaze. This phenomenon called foveation can be taken advantage of to reduce the need for bandwidth. In this paper, we study three different methods to produce a foveated video stream of real-time rendered graphics in a remote rendered system: 1) foveated shading as part of the rendering pipeline, 2) foveation as post processing step after rendering and before video encoding, 3) foveated video encoding. We report results from a number of experiments with these methods. They suggest that foveated rendering alone does not help save bandwidth. Instead, the two other methods decrease the resulting video bitrate significantly but they also have different quality per bit and latency profiles, which makes them desirable solutions in slightly different situations.

The availability of high bandwidth with low-latency communication in 5G mobile networks enables remote rendered real-time virtual reality (VR) applications. Remote rendering of VR graphics in a cloud removes the need for local personal computer for graphics rendering and augments weak graphics processing unit capacity of stand-alone VR headsets. However, to prevent the added network latency of remote rendering from ruining user experience, rendering a locally navigable viewport that is larger than the field of view of the HMD is necessary. The size of the viewport required depends on latency: Longer latency requires rendering a larger viewport and streaming more content. In this article, we aim to utilize multi-access edge computing to assist the backend cloud in such remote rendered interactive VR. Given the dependency between latency and amount and quality of the content streamed, our objective is to jointly optimize the tradeoff between average video quality and delivery latency. Formulating the problem as mixed integer nonlinear programming, we leverage the interpolation between client’s field of view frame size and overall latency to convert the problem to integer nonlinear programming model and then design efficient online algorithms to solve it. The results of our simulations supplemented by real-world user data reveal that enabling a desired balance between video quality and latency, our algorithm particularly achieves the improvements of on average about 22% and 12% in term of video delivery latency and 8% in term of video quality compared to respectively order-of-arrival, threshold-based, and random-location strategies.

Humans have sharp central vision but low peripheral visual acuity. Prior work has taken advantage of this phenomenon in two ways: foveated rendering (FR) reduces the computational workload of rendering by producing lower visual quality for peripheral regions and foveated video encoding (FVE) reduces the bitrate of streamed video through heavier compression of peripheral regions. Remote rendering systems require both rendering and video encoding and the two techniques can be combined to reduce both computing and bandwidth consumption. We report early results from such a combination with remote VR rendering. The results highlight that FR causes large bitrate overhead when combined with normal video encoding but combining it with FVE can mitigate it.

Cloud gaming enables playing high-end games, originally designed for PC or game console setups, on low-end devices such as netbooks and smartphones, by offloading graphics rendering to GPU-powered cloud servers. However, transmitting the high-resolution video requires a large amount of network bandwidth, even though it is a compressed video stream. Foveated video encoding (FVE) reduces the bandwidth requirement by taking advantage of the non-uniform acuity of human visual system and by knowing where the user is looking. Based on a consumer-grade real-time eye tracker and an open source cloud gaming platform, we provide a cloud gaming FVE prototype that is game-agnostic and requires no modifications to the underlying game engine. In this article, we describe the prototype and its evaluation through measurements with representative games from different genres to understand the effect of parametrization of the FVE scheme on bandwidth requirements and to understand its feasibility from the latency perspective. We also present results from a user study on first-person shooter games. The results suggest that it is possible to find a “sweet spot” for the encoding parameters so the users hardly notice the presence of foveated encoding but at the same time the scheme yields most of the achievable bandwidth savings.

Virtual machine consolidation aims at reducing the number of active physical servers in a data center so as to decrease the total power consumption. In this context, most of the existing solutions rely on aggressive virtual machine migration, thus resulting in unnecessary overhead and energy wastage. Besides, virtual machine consolidation should take into account multiple resource types at the same time, since CPU is not the only critical resource in cloud data centers. In fact, also memory and network bandwidth can become a bottleneck, possibly causing violations in the service level agreement. This article presents a virtual machine consolidation algorithm with multiple usage prediction (VMCUP-M) to improve the energy efficiency of cloud data centers. In this context, multiple usage refers to both resource types and the horizon employed to predict future utilization. Our algorithm is executed during the virtual machine consolidation process to estimate the long-term utilization of multiple resource types based on the local history of the considered servers. The joint use of current and predicted resource utilization allows for a reliable characterization of overloaded and underloaded servers, thereby reducing both the load and the power consumption after consolidation. We evaluate our solution through simulations on both synthetic and real-world workloads. The obtained results show that consolidation with multiple usage prediction reduces the number of migrations and the power consumption of the servers while complying with the service level agreement.

Multi-access edge computing (MEC) enables placing video content at the edge of a mobile network with the aim of reducing data traffic in the backhaul network. Direct device-to-device (D2D) communication can further alleviate load from the backhaul network. Both MEC and D2D have already been examined by prior work, but their combination applied to adaptive video streaming have not yet been explored in detail. In this paper, we analyze how enabling D2D jointly with edge computing affects the quality of experience (QoE) of video streaming clients and contributes to reducing the backhaul traffic. To this end, we formulate the problem of jointly maximizing the QoE of the clients and minimizing the backhaul traffic and edge processing as an integer non-linear programming (INLP) optimization model and propose a low-complexity algorithm using self-parameterization technique to solve the problem. The main takeaway from simulation results is that enabling D2D with edge computing reduces the backhaul traffic by approximately 18% and edge processing by 30% on average while maintaining roughly the same average video bitrate per client compared to edge computing without D2D. Our results provide a guideline for system designers to judge the effectiveness of enabling D2D into MEC in the next generation of 5G mobile networks.

| openaire: EC/H2020/815191/EU//PriMO-5G

Smartphone-based indoor navigation services are desperately needed in indoor environments. However, the adoption of them has been relatively slow, due to the lack of fine-grained and up-to-date indoor maps, or the potentially high deployment and maintenance cost of infrastructure-based indoor localization solutions. This work proposes ViNav, a scalable and cost-efficient system that implements indoor mapping, localization, and navigation based on visual and inertial sensor data collected from smartphones. ViNav applies structure-from-motion (SfM) techniques to reconstruct 3D models of indoor environments from crowdsourced images, locates points of interest (POI) in 3D models, and compiles navigation meshes for path finding. ViNav implements image-based localization that identifies users' positions and facing directions, and leverages this feature to calibrate dead-reckoning-based user trajectories and sensor fingerprints collected along the trajectories. The calibrated information is utilized for building more informative and accurate indoor maps, and lowering the response delay of localization requests. According to our experimental results in a university building and a supermarket, the system works properly and our indoor localization achieves competitive performance compared with traditional approaches: in a supermarket, ViNav locates users within 2 seconds, with a distance error less than 1 meter and a facing direction error less than 6 degrees.

Nearly all bitrate adaptive video content delivered today is streamed using protocols that run a purely client based adaptation logic. The resulting lack of coordination may lead to suboptimal user experience and resource utilization. As a response, approaches that include the network and servers in the adaptation process are emerging. In this article, we present an optimized solution for network assisted adaptation specifically targeted to mobile streaming in multi-access edge computing (MEC) environments. Due to NP-Hardness of the problem, we have designed a heuristic-based algorithm with minimum need for parameter tuning and having relatively low complexity. We then study the performance of this solution against two popular client-based solutions, namely Buffer-Based Adaptation (BBA) and Rate-Based Adaptation (RBA), as well as to another network assisted solution. Our objective is two fold: First, we want to demonstrate the efficiency of our solution and second to quantify the benefits of network-assisted adaptation over the client-based approaches in mobile edge computing scenarios. The results from our simulations reveal that the network assisted adaptation clearly outperforms the purely client-based DASH heuristics in some of the metrics, not all of them, particularly, in situations when the achievable throughput is moderately high or the link quality of the mobile clients does not differ from each other substantially.

High quality immersive Virtual Reality experience currently requires a PC setup with cable connected head mounted display, which is expensive and restricts user mobility. This paper presents CloudVR which is a system for cloud accelerated interactive mobile VR. It is designed to provide short rotation and interaction latencies through panoramic rendering and dynamic object placement. CloudVR also includes rendering optimizations to reduce server-side computational load and bandwidth requirements between the server and client. Performance measurements with a CloudVR prototype suggest that the optimizations make it possible to double the server's framerate and halve the amount of bandwidth required and that small objects can be quickly moved at run time to client device for rendering to provide shorter interaction latency. A small-scale user study indicates that CloudVR users do not notice small network latencies (20ms) and even much longer ones (100-200ms) become non-trivial to detect when they do not affect the interaction with objects. Finally, we present a design of CloudVR extension to multi-user scenarios.

We study performance characteristics of convolutional neural networks (CNN) for mobile computer vision systems. CNNs have proven to be a powerful and efficient approach to implement such systems. However, the system performance depends largely on the utilization of hardware accelerators, which are able to speed up the execution of the underlying mathematical operations tremendously through massive parallelism. Our contribution is performance characterization of multiple CNN-based models for object recognition and detection with several different hardware platforms and software frameworks, using both local (on-device) and remote (network-side server) computation. The measurements are conducted using real workloads and real processing platforms. On the platform side, we concentrate especially on TensorFlow and TensorRT. Our measurements include embedded processors found on mobile devices and high-performance processors that can be used on the network side of mobile systems. We show that there exists significant latency-throughput trade-offs but the behavior is very complex. We demonstrate and discuss several factors that affect the performance and yield this complex behavior.

Emerging vehicular applications, such as real-time situational awareness and cooperative lane change, demand for sufficient computing resources at the edge to conduct time-critical and data-intensive tasks. This paper proposes Fog Following Me (Folo), a novel solution for latency and quality balanced task allocation in vehicular fog computing. Folo is designed to support the mobility of vehicles, including ones generating tasks and the others serving as fog nodes. We formulate the process of task allocation across stationary and mobile fog nodes into a joint optimization problem, with constraints on service latency, quality loss, and fog capacity. As it is a NP-hard problem, we linearize it and solve it using Mixed Integer Linear Programming. To evaluate the effectiveness of Folo, we simulate the mobility of fog nodes at different times of day based on real-world taxi traces, and implement two representative tasks, including video streaming and real-time object recognition. Compared with naive and random fog node selection, the latency and quality balanced task allocation provided by Folo achieves higher performance. More specifically, Folo shortens the average service latency by up to 41\% while reducing the quality loss by up to 60\%.

An increasing number of online services are hosted on public clouds. However, since a centralized cloud architecture imposes high network latency, researchers suggested moving latency sensitive applications, such as virtual and augmented reality ones, to the edge of the network. Nevertheless, little has been done for edge layer capacity estimation resulting in a great need towards practical tools and techniques for initial capacity planning. In this work we provide a novel capacity planning solution for hierarchical edge cloud that considers QoS requirements in terms of response delay, and diverse demands for CPU, GPU and network resources. Our solution improves edge utilization by combining complementary resource demands while satisfying QoS requirements. We prove effectiveness of our solution through a case study where we plan edge capacity for deploying an AR navigation and information system.

Cloud gaming is a relatively new paradigm in which the game is rendered in the cloud and is streamed to an end-user device through a thin client. Latency is a key challenge for cloud gaming. In order to optimize the end-to-end latency, it is first necessary to understand how the end-to-end latency builds up from the mobile device to the cloud gaming server. In this paper we dissect the delays occurring in the mobile device and measure access delays in various networks and network conditions. We also perform a Europe-wide latency measurement study to find the optimal server locations and see how the number of server locations affects the network delay. The results are compared to limits found for perceivable delays in recent human-computer interaction studies. We show that the limits can be achieved only with the latest mobile devices with specific control methods. In addition, we study the expected latency reduction by near future technological development and show that its potential impact is bigger on the end-to-end latency than that of replication of the service and server placement optimization.

Information-Centric Networking (ICN) introduces a paradigm shift from a host centric communication model for Future Internet architectures. It supports the retrieval of a particular content regardless of the physical location of the content. Emergency network in a disaster scenario or disruptive network presents a significant challenge to the ICN deployment. In this paper, we present a Content distribution and retrieval framework in disaster netwOrks for public pRotection (CIDOR) which exploits the design principle of the native CCN architecture in the native Delay Tolerant Networking (DTN) architecture. We prove the feasibility and investigate the performance of our proposed solution using extensive simulation with different classes of the DTN routing strategies in different mobility scenarios. The simulation result shows that CIDOR can reduce the content retrieval time up to 50% while the response ratio is close to 100%.

MPEG has recently proposed Server and Network Assisted Dynamic Adaptive Streaming over HTTP (SANDDASH) for video streaming over the Internet. In contrast to the purely client-based video streaming in which each client makes its own decision to adjust its bitrate, SAND-DASH enables a group of simultaneous clients to select their bitrates in a coordinated fashion in order to improve resource utilization and quality of experience. In this paper, we study the performance of such an adaptation strategy compared to the traditional approach with large number of clients having mobile Internet access. We propose a multi-servers multi-coordinators (MSsMCs) framework to model groups of remote clients accessing video content replicated to spatially distributed edge servers. We then formulate an optimization problem to maximize jointly the QoE of individual clients, proportional fairness in allocating the limited resources of base stations as well as balancing the utilized resources among multiple serves. We then present an efficient heuristic-based solution to the problem and perform simulations in order to explore parameter space of the scheme as well as to compare the performance to purely client-based DASH.

Recent advances from wearables have significantly changed the way how humans communicate with the surrounding environment. To some extent, they have extended and augmented the capability of humans. For example, with a Google Glass, people can take pictures simply by winking eyes twice, which releases human hands from the cumbersome image-taking process. Thus, it enables new application scenarios that were not possible before. In this paper, we investigate utilizing vision-based techniques to provide a wearable positioning system. Specifically, we propose a Human-centric Positioning System (HoPS) that utilizes traffic signposts together with context information for real-time positioning. Towards that direction, we make three primary contributions: (1) we make several important observations that guide our design of HoPS system; for example, we find out that approximately 40 percent of traffic signposts monopolize a cell tower, and there are at most six signposts within the coverage of a single cell tower; (2) we investigate the impact factors of object detection success rate, and find its correlation with image quality, and resolution; and (3) we design and implement HoPS and an advanced version of HoPS based on additional context information from Wi-Fi network, which we name HoPS-WiFi. Experimental results demonstrate the effectiveness of HoPS, especially HoPS-WiFi, which can estimate the relevant location correctly within 1.3 seconds.

Analysing the power usage of an Internet of Things (IoT) device is essential in contexts such as smart building, testbed and forensics. For example, in the smart building case, schemes have been proposed to detect and analyse the activity of electric appliances based on the total power usage. In this work, we intend to take this concept a step further and devise a method to analyse the activity of a single IoT device (mote) based on its power usage. To that end, we develop a neural network (NN) based classifier PowerShark that is capable of classifying mote activity based solely on the power usage of the mote, both in online and offline mode. PowerShark can detect radio activity, hardware, operating system (OS), and the MAC layer used. To verify the accuracy of PowerShark, we use power traces from OpenMote and Zolertia Z1 motes, with ContikiOS configured either with ContikiMAC or X-MAC. Experimental results demonstrate the accuracy of our proposed classifier. Specifically, PowerShark can achieve over 95% accuracy in both training and testing phases.

Using crowdsourced visual and inertial sensor data for indoor mapping has attracted much attention in recent years. Nevertheless, the opportunities and challenges of indoor tracking using crowdsourced maps have not been fully explored. In this work, we aim at tackling the challenges due to incomplete obstacle information in crowdsourced indoor maps, especially at the initialization stage of crowdsourcing. We propose a novel solution for particle-filtering-based indoor tracking, using the crowdsourced maps derived from image-based 3D point clouds. Our solution enhances particle filtering with density-based collision detection and history-based particle regeneration. Evaluation with real user traces demonstrates that our solution outperforms the state-of-the-art. In particular, it reduces the average distance error of indoor tracking by 47% when using crowdsourced 3D point clouds.

This work investigates the performance for D2D enabled wireless caching in distributed storage system by taking the typical erasure correcting code namely maximum distance separable (MDS) into consideration. Targeting on the minimization of the transmission cost for content sharing, the spectrum resource allocation is optimized based on their statistical channel state information (CSI). To evaluate the quality of D2D links, two cases are considered with different assumptions for whether those physical links are stable or not regarding to mobility impacted user behaviors. Numerical results demonstrate the effectiveness of the proposed scheme.

Virtual machine consolidation aims at reducing the number of active physical servers in a data center, with the goal to reduce the total power consumption. In this context, most of the existing solutions rely on aggressive virtual machine migration, thus resulting in unnecessary overhead and energy wastage. This article presents a virtual machine consolidation algorithm with usage prediction (VMCUP) for improving the energy efficiency of cloud data centers. Our algorithm is executed during the virtual machine consolidation process to estimate the short-term future CPU utilization based on the local history of the considered servers. The joint use of current and predicted CPU utilization metrics allows a reliable characterization of overloaded and under loaded servers, thereby reducing both the load and the power consumption after consolidation. We evaluate our proposed solution through simulations on real workloads from the Planet Lab and the Google Cluster Data datasets. In comparison with the state of the art, the obtained results show that consolidation with usage prediction reduces the total migrations and the power consumption of the servers while complying with the service level agreement.

Energy consumption of data center has become an important challenge due to high electric cost and carbon dioxide emissions. Previous work has mainly focused on saving energy cost of servers, though the energy consumption of data center networks (DCNs), consisting of networking equipments like switches, also takes a significant part of the overall energy consumption. In this paper, we propose ProCons, an energy saving mechanism that dynamically consolidates traffic flows onto a small set of networking equipments in order to shut down idle ones for energy saving. Different from previous works that assume the traffic demands to be stable, ProCons takes into account the variance of traffic demand over time, and predicts future demand based on historical statistics. The traffic flows are then scheduled based on the predicted future demands and the capacity of each link. We evaluate ProCons with real life traces collected from data centers using a flow-level simulator. Our experimental results show that using ProCons, 40% of energy savings for DCNs can be gained while maintaining the good performance of flow transmission.

Information-Centric Networking (ICN) is a prominent topic in current networking research. ICN design significantly considers the increased demand of scalable and efficient content distribution for Future Internet. However, intermittently connected mobile environments or disruptive networks present a significant challenge to ICN deployment. In this context, delay tolerant networking (DTN) architecture is an initiative that effectively deals with network disruptions. Among all ICN proposals, Content Centric Networking (CCN) is gaining more and more interest for its architectural design, but still has the limitation in highly disruptive environment. In this paper, we design a protocol stack referred as CCNDTN which integrates DTN architecture in the native CCN to deal with network disruption. We also present the implementation details of the proposed CCNDTN. We extend CCN routing strategies by integrating Bundle protocol of DTN architecture. The integration of CCN and DTN enriches the connectivity options of CCN architecture in fragmented networks. Furthermore, CCNDTN can be beneficial through the simultaneous use of all available connectivities and opportunistic networking of DTN for the dissemination of larger data items. This paper also highlights the potential use cases of CCNDTN architecture and crucial questions about integrating CCN and DTN.

Utilizing network traffic scheduling to improve the energy efficiency of smartphones has been studied extensively in the past few years. These studies usually take certain approaches and make some assumptions concerning traffic predictability, regardless of whether these assumptions hold or whether the approaches have been studied before. In this paper, we conduct an analysis of existing work to find common approaches and assumptions among the proposed solutions. We find out the following: 1. A large part of the solutions target a specific (single) application or category of applications, and do not schedule the whole traffic transmitted on the smartphone. 2. A common assumption is that network traffic for smart phones is predictable. The focus of our work is to test these assumptions against real-world data and analyze whether the approaches presented in the literature are feasible. By leveraging two data sets from NetSense, we make several major contributions: 1. We demonstrate clearly, based on a large dataset, that background apps are the largest energy consumers for smart phones. 2. although some traffic traces exhibit long-term trends, in general traffic from a single app or a user is not predictable in the short-term. 3. achieving energy savings is difficult by scheduling traffic only from a specific app, since multi-app scenarios are so prevalent on today's smartphones. We also pinpoint future directions for traffic scheduling schemes.

The adoption of indoor navigation for smartphones has been relatively slow in the past years, although it would be direly needed in complex indoor areas. The primary barriers for its adoption include the lack of fine-grained and up-to-date indoor maps and the potential deployment and maintenance cost. In this paper we investigate the feasibility of utilizing crowdsourced data for building a smartphone-based indoor navigation system, focusing on the technical challenges caused by the varying quality of crowdsourced data. We developed iMoon, an indoor navigation system based on sensor-enriched 3D models of indoor environment, and evaluated its performance via a field study in a public building covering around 1,100 square meters.

The indoor location market is growing rapidly. However, fine-grained and up-to-date indoor maps are rarely available, and the existing indoor localization and navigation services mostly require extra infrastructures to provide accurate locations. In this demonstration we show iMoon, an indoor navigation system that provides indoor mapping, localization and navigation services using photos and sensor data collected from widely available mobile devices. The demonstration leverages a cohesive suite of computer vision, mobile sensing, and wireless networking techniques.

Large wireless sensor networks (WSNs) with thousands of motes are expected to be a significant part of the future connected world. As battery life of such motes is still an issue, it is important to provide scalable methods to estimate energy usage without introducing significant overhead. We foresee that in the future significant amount of data will be collected and inferred on the behavior of motes. Such collection and inference of data can be considered as a virtual representation of the physical mote, with a potentially longer life-cycle. The virtual mote is a valuable resource for application development, diagnostics and behavior prediction. In this paper, we focus on the energy consumption aspect of such motes. To that end, we develop a packet-based real-time energy model, which works by analyzing network traffic traces collected at the backend to estimate energy consumption of the mote. Such approach scales well to the number of motes and does not require modification to the mote or its network stack. Experimental results from extensive measurements conducted on two platforms, i.e., OpenMote and Zolertia Z1, demonstrate promising potential. The energy model can achieve an estimation accuracy over 90% for platform power, and over 85% accuracy for radio power consumption, on various scenarios.

Web service composition is a challenging research issue. This paper presents an automatic Web service composition method that deals with both input/output compatibility and behavioral constraint compatibility of fuzzy semantic services. First, user input and output requirements are modeled as a set of facts and a goal statement in the Horn clauses, respectively. A service composition problem is transformed into a Horn clause logic reasoning problem. Next, a Fuzzy Predicate Petri Net (FPPN) is applied to model the Horn clause set, and T-invariant technique is used to determine the existence of composite services fulfilling the user input/output requirements. Then, two algorithms are presented to obtain the composite service satisfying behavioral constraints, as well as to construct an FPPN model that shows the calling order of the selected services.

With the tremendous growth in wireless network deployment and increasing use of mobile devices, e.g., smartphones and tablets, improving energy efficiency in such devices, especially with communication driven workloads, is critical to providing a satisfactory user experience. Studies show that signal strength plays an important role on energy consumption of cellular data communications. While energy consumption can be minimized by accurately predicting signal strengths and reacting to it in real-time, the dynamic nature of wireless environments makes signal strengths highly unpredictable. In this paper, after analyzing in detail the signal strength variation and its impact on energy consumption, we propose to use crowdsourcing approach to optimize mobile devices' energy efficiency by utilizing signal strength traces reported/shared by other users/devices in cellular networks. Via a comprehensive measurement study, we observe that signal strength traces collected from different devices are pseudo-identical, and they even exhibit similar threshold-based behaviors in the relationship between signal strength and device power consumption. Based on our observations, we propose a predictive scheduling algorithm that: (i) selects the right set of signal strength traces based on its location, (ii) applies a filter to smooth out signal strengths and hide abrupt changes, (iii) digitizes the signal strength to “good” and “bad” areas, and (iv) schedules transmissions based on power-throughput characteristics to optimize the transmission energy efficiency. To demonstrate the efficacy of the proposed algorithms, we prototype the crowdsourcing-based predicative scheduling algorithm on Android-based smartphones. Our experiment results from real-life driving tests demonstrate that, by leveraging others' signal traces, mobile devices can save energy up to 35 percent compared to the conventional opportunistic scheduling, i.e., schedule transmissions only based on instantaneous channel conditions.

Virtual machine (VM) placement is the process of selecting the most suitable server in large cloud data centers to deploy newly-created VMs. Several approaches have been proposed to find a solution to this problem. However, most of the existing solutions only consider a limited number of resource types, thus resulting in unbalanced load or in the unnecessary activation of physical servers. In this article, we propose an algorithm, called Max-BRU, that maximizes the resource utilization and balances the usage of resources across multiple dimensions. Our algorithm is based on multiple resource-constraint metrics that help to find the most suitable server for deploying VMs in large cloud data centers. The proposed Max-BRU algorithm is evaluated by simulations based on synthetic datasets. Experimental results show two major improvements over the existing approaches for VM placement. First, Max-BRU increases the resource utilization by minimizing the amount of physical servers used. Second, Max-BRU effectively balances the utilization of multiple types of resources.

Resources used in a cloud data center could be spread across a large number of servers that are not fully utilized. This situation results in significant operational costs which are directly related to the power consumption of active servers. Virtual machine migration enables reducing the number of active servers by consolidating the load on a limited amount of nodes. Several schemes have actually been proposed to consolidate virtual machines on the minimum number of physical servers in order to reduce power consumption. However, most of the existing solutions only consider a limited trade off among multiple types of resources, thus resulting in unnecessarily activated physical servers. This article proposes a multi-resource selection (MRS) scheme for consolidating virtual machines in cloud data centers. With MRS, each physical server is first characterized in terms of multiple types of resources and then classified through its overall resource utilization. Based on the MRS scheme, a balanced multiple-resource utilization algorithm is also used to spread the load across different types of resources while consolidating virtual machines. The proposed solution is evaluated through simulations on both synthetic and real-world workloads. Experimental results show that the proposed approach outperforms several existing schemes in terms of the number of active physical servers and the utilization of multiple resources.

Sharing of devices across separate networks is currently challenging. Users may want to give their friends remote access to their home entertainment devices, but a secure and easy-to-use solution has not yet emerged. While service discovery frameworks provide a uniform interface for different networked devices to interact, most of them, especially the widely adopted Universal Plug and Play, fail to connect devices in separate networks. This paper proposes a proxy-based architecture H-box that bridges different UPnP-based networks. We integrate a number of standardized protocols, namely XMPP, HIP and Teredo in a novel way to achieve our goal. The prototype runs on Raspberry Pi, bridging commodity UPnP devices located in remote networks. The performance evaluation shows that the approach is feasible even for media streaming between homes. Further, we envision the H-box architecture to be adopted for car entertainment systems and extended to support device sharing and collaboration between separate networks.

Wireless data transmission consumes a significant part of the overall energy consumption of smartphones, due to the popularity of Internet applications. In this paper, we investigate the energy consumption characteristics of data transmission over Wi-Fi, focusing on the effect of Internet flow characteristics and network environment. We present deterministic models that describe the energy consumption of Wi-Fi data transmission with traffic burstiness, network performance metrics like throughput and retransmission rate, and parameters of the power saving mechanisms in use. Our models are practical because their inputs are easily available on mobile platforms without modifying low-level software or hardware components. We demonstrate the practice of model-based energy profiling on Maemo, Symbian, and Android phones, and evaluate the accuracy with physical power measurement of applications including file transfer, web browsing, video streaming, and instant messaging. Our experimental results show that our models are of adequate accuracy for energy profiling and are easy to apply.

Multipath TCP (MPTCP) is a major extension of TCP that aims to offer higher aggregate bandwidth and robustness by pooling multiple paths within one transport connection. One of the current weaknesses of the protocol is the Delayed ACK scheme inherited from TCP. The Delayed ACK is an option of TCP that allows the receiver to delay sending an ACK for every other packet within a window given by the Delayed ACK timer. At the sender, the RTO should be no less than the Minimum RTO to avoid spurious timeouts. This strategy can lead to significant performance degradation in the presence of timeouts, especially in high speed networks, where RTT is usually one- or two-order of magnitude smaller than the Minimum RTO. When a subflow occurs a timeout, the receiver has to buffer data from all the subflows until the missing packet is received. The data may overrun the receive buffer to cause flow control at the sender, which seriously impacts the overall performance. In order to avoid MPTCP performance degradation, we propose a new Delayed ACK aiming to remove the Minimum RTO constraint at the sender while to reserve the Delayed ACK function at the receiver. Our solution requires only minor modification to the legacy Delayed ACK scheme and it introduces negligible computational overhead and no extra traffic overhead. We use a NS-3 network simulator to evaluate the performance of our new Delayed ACK in three typical network environments. The results indicate that MPTCP using new Delayed ACK scheme requires much smaller aggregate buffer than MPTCP using the legacy Delayed ACK scheme, especially in high speed networks where the buffer requirement reduces one- or two-order of magnitude.

Information-centric network (ICN), which is one of the prominent Internet re-design architectures, relies on in-network caching for its fundamental operation. However, previous works argue that the performance of in-network caching is highly degraded with the current cache-along-default-path design, which makes popular objects to be cached redundantly in many places. Thus, it would be beneficial to have a distributed and uncoordinated design. Although cooperative caches could be an answer to this, previous research showed that they are generally unfeasible due to excessive signaling burden, protocol complexity, and a need for fault tolerance. In this work we illustrate the ICN caching problem, and propose a novel architecture to overcome the problem of uncooperative caches. Our design possesses the cooperation property intrinsically. We utilize controlled off-path caching to achieve almost 9-fold increase in cache efficiency, and around 20% increase in server load reduction when compared to the classic on-path caching used in ICN proposals.

Providing high capacity and cost effective backhaul is a looming challenge for mobile industry. In a packet-switched wireless communication system such as in the Third Generation Partnership Project (3GPP) Long term Evolution (LTE), data packets are forwarded between base stations (BSs) during handover over the bachhaul. The Packet Data Convergence Protocol (PDCP) of the source base station (BS) is responsible for forwarding the data packets to the target BS. The paper analyses, evaluates and improves the packet forwarding handover mechanism by reducing the amount of forwarded data between BSs. The performance criteria considered for evaluating these techniques include the PDCP buffer size at the source BS, the uplink Radio Link Control status load and the user object bit rate. We found frequent User Equipment (UE) polling by the packet network during handover helps in considerably reducing the unacknowledged PDCP data packets, and thereby reduces the amount of packet forwarding data, as the source BS has up-to-date information of the UE reception state.

When a TCP connection experiences a timeout, the sender must wait at least RTOmin (Minimum Retransmission Timeout) before doing the retransmission, during which the channel may be completely idle, undermining the throughput and channel efficiency. In this paper, we investigate the origin of RTOmin and find that it is needed to mitigate against spurious timeouts when the Delayed ACK (DA) scheme for TCP is implemented.

No abstract available.

Public cloud platforms might start with homogeneous hardware; nevertheless, because of inevitable hardware upgrades, or adding more capacity, the initial homogeneous platform will gradually evolve into heterogeneous as time passes by. The consequent performance heterogeneity is of concern to cloud users. In this paper, we evaluate performance variations from hardware heterogeneity and scheduling mechanisms of public clouds. Amazon Elastic Compute Cloud (Amazon EC2) and Rackspace Cloud are used as the representatives because of their relatively long record and wide usage among small and medium enterprises (SMEs). A comprehensive set of microbenchmarks and application-level macrobenchmarks have been used to investigate performance variation. Several major contributions have been made. First, we find out that heterogeneous hardware is a commonality among the relatively long-lasting cloud platforms, although the level of heterogeneity varies. Second, we observe that heterogeneous hardware is the primary culprit of performance variation of cloud platforms. Third, we discover that varied CPU acquisition percentages and different virtual machine scheduling mechanisms exacerbate the performance variation problem, especially for network related operations. Finally, based on the observations, we propose cost-saving approaches and analyze Nash equilibrium from cloud user perspective. By using a simple "trial-and-better" approach, i.e., keep good-performing instances and discard bad-performing instances, cloud users can achieve up to 30 percent cost saving.

Unbalanced traffic demands of different data center applications are an important issue in designing data center networks (DCN). In this paper, we present our exploratory investigation on a hybrid DCN solution of utilizing wireless transmissions in DCNs. Our work aims to solve the congestion problem caused by a few hot nodes to improve the global performance. We model the wireless transmissions in DCN by considering both the wireless interference and the adaptive transmission rate. Besides, both throughput and job completion time are considered to measure the impact of wireless transmissions on the global performance. Based on the model, we formulate the problem of channel allocation as an optimization problem. We also design an approximation algorithm with an approximation bound of 1/2 and a genetic algorithm to address the scheduling problem. A series of simulations are performed to evaluate and demonstrate the effectiveness of our wireless DCN scheme.

General-purpose computing domain has experienced strategy transfer from scale-up to scale-out in the past decade. In this paper, we take a step further to analyze ARM-processor based cluster against Intel X86 workstation, from both energy-efficiency and cost-efficiency perspectives. Three applications are selected and evaluated to represent diversified applications, including Web server throughput, in-memory database, and video transcoding. Through detailed measurements, we make the observations that the energy-efficiency ratio of the ARM cluster against the Intel workstation varies from 2.6-9.5 in in-memory database, to approximately 1.3 in Web server application, and 1.21 in video transcoding. We also find out that for the Intel processor that adopts dynamic voltage and frequency scaling (DVFS) techniques, the power consumption is not linear with the CPU utilization level. The maximum energy saving achievable from DVFS is 20%. Finally, by utilizing a monthly cost model of data centers, we conclude that ARM cluster based data centers are feasible, and are advantageous in computationally lightweight applications, e.g. in-memory database and network-bounded Web applications. The cost advantage of ARM cluster diminishes progressively for computation-intensive applications, i.e. dynamic Web server application and video transcoding, because the number of ARM processors needed to provide comparable performance increases.

Energy consumption of wireless data transmission heavily depends on the shape of the outgoing traffic of the mobile device. In this paper, we propose a traffic scheduler that shapes the packets into consistent bursts based on per-packet performance constraints in order to reduce the overall transmission cost. Our scheduler takes into account the scenarios where multiple network applications run concurrently on the mobile device. We evaluate the traffic scheduler with real-life traffic traces from delay-sensitive applications, e.g. Internet radio and YouTube, and delay-tolerant applications e.g. Web browsing. The results show that depending on scenarios 23% to 72% energy savings can be achieved without noticeable performance degradation. Furthermore, our traffic scheduler is of low time complexity O(n), which makes it suitable to be deployed on proxies or directly on mobile devices.

Energy consumption of wireless data transmission, a significant part of the overall energy consumption on a mobile device, is context-dependent—it depends on both internal and external contexts, such as application workload and wireless signal strength. In this paper, we propose an event-driven framework that can be used for efficient power management on mobile devices. The framework adapts the behavior of a device component or an application to the changes in contexts, defined as events, according to developer-specified event-condition-action (ECA) rules that describe the power management mechanism. In contrast to previous work, our framework supports complex event processing. By correlating events, complex event processing helps to discover complex events that are relevant to power consumption. Using our framework developers can implement and configure power management applications by editing event specifications and ECA rules through XML-based interfaces. We evaluate this framework with two applications in which the data transmission is adapted to traffic patterns and wireless link quality. These applications can save roughly 12 percent more energy compared to normal operation.

Distributed Denial of Service (DDoS) attack continues to be one of the main vulnerabilities of today's Internet. Client's puzzle mechanism is a well-known solution against such threat, however with badly tuned puzzle sizes it may harm the clients in the peaceful time, as well as produce additional difficulties during an attack. Here, we introduce a novel algorithm - DISPUTE - auto-tunable distributed puzzle mechanism with variable puzzle sizes. Main feature of it is that the server does not need to adjust any puzzle sizes, instead the clients during the “fight for” server resources find some form of equilibrium situation on the server side. We describe the algorithm and show the DISPUTE's performance using a simulation tool. The results suggest that regular (laptop) users, as well as light (sensor) users can successfully access a server even during a heavy DDoS attack.

Energy consumption caused by wireless transmission poses a big challenge to the battery lifetime of mobile devices. While the potential of using lossless compression for saving energy has been long acknowledged, no general solution has been proposed for applying lossless compression to energy adaptation for mobile services. We propose a proxy-based energy adaptation framework, in which the data to be transmitted is losslessly compressed on a proxy server according to context-aware policies. The context includes factors relevant to computational and communication cost, as well as the user's preferences. We showcase a context-aware policy which aims at minimizing clientside energy consumption caused by transmission and decompression. Using our framework, we implement an energy-aware mobile e-mail service, and present power measurement results that show significant energy savings.

This paper presents SLA (Segment Level Authentication), a transport segment level solution designed to prevent both of the intra-domain and inter-domain source spoofing. SLA is based on public key cryptography authentication. It enables intermediate network nodes the ability to validate the packet authenticity by verifying authentication information carried in packets. Although public key cryptography is computationally intensive and induces the traffic overhead, SLA leverages FPGA (Field Programmable Gate Array) based ECC (Elliptic Curve Cryptography) hardware cryptography accelerator to decrease the computation and traffic overhead. SLA provides incremental deployment and offers incentives for both of hosts and ASes. We find that the SLA is feasible for Gigabit links and can effectively mitigate source spoofing in both of intra-domain and inter-domain networks.

Maximizing network throughput is one of the main objectives in wireless multi-hop networks. However, small link bandwidth and severe interference become two great obstacles in network throughput improvement. Single route always encounters great congestion, especially for multiple flows with the same source and destination. Multi-path routing can solve the bandwidth shortage issue to some extent, but conventional routing algorithms of this type have some limitations during route selection and using, which makes bandwidth improvement and interference reduction unfeasible at the same time. In this paper we propose a new metric called effective bandwidth to describe the real bandwidth that a flow can get through a specific route. Based on this metric, we present a probabilistic multi-path routing protocol for multiple flows, which can improve the network throughput by selecting route with bigger effective bandwidth using higher probability. The simulation results show that probabilistic multi-path routing has high superiority over single-path routing in improving network throughput.

HSDPA networks are intensively being deployed to provide mobile broadband connectivity to mobile devices. In addition to HSDPA, FLASH-OFDM is another wireless broadband option operating in a licensed spectrum. However, if operated at very low frequencies like the network in Finland, it is particularly interesting for emerging markets, especially for rural areas which are lacking telecommunications infrastructure. In contrast to the HSDPA standards, the FLASH-OFDM systems are proprietary. The market dynamics are very different between standardized and proprietary systems, sometimes causing extra challenges for the deployment of proprietary systems.This paper provides quantitative measurements of the actual performance of two state-of-the-art live networks in Finland. The evaluation includes metrics such as throughput, delay, and VoIP quality considering network performance both on static and mobile scenarios. A commercial deployment evaluation is included in the analysis complementing the measurement data in order to provide a comprehensive view of the challenges.

Metropolitan wireless mesh networks are being deployed to provide broadband connectivity to city-wide hotspots. However, its commercial feasibility and business model have not yet proven successful. In particular, deploying large areas with good signal coverage is very difficult. The main problem is a result of the number of required nodes to cover an area with proper signal strength that can support services such as VoIP. Unfortunately, as the number of nodes increases, so does the cost of deployment. In an environment for which the economical model has not been proved, increasing the cost of deployment is not feasible. However, by the use of Mobile IP, it is possible to interwork with other networks. In this way, users can roam to other networks with broader coverage (e.g. 3G) in places where wireless mesh coverage is no longer available or suffers from coverage gaps. This paper, underlines the actual performance of the Mobile IP solution via actual measurements with available commercial handsets. The focus is on the feasibility of using VoIP services over wireless mesh networks and interwork with 3G networks. The results show that even though the performance is not yet optimal, it is ready for a service rollout under proper conditions. In particular, a good service offering can encourage users to make use of this technology despite the performance limitations.

This paper presents distributed and authenticated service discovery and session management schemes. The SLP and SIP protocols have been enhancedfor use in ad-hoc networks. We have coupled the protocols with the AODV ad-hoc routing protocol to make the signaling as efficient as possible. The design has been implemented and tried out in a real ad-hoc network testbed. Our results show that the system is feasible in practice, and very efficient both in terms of bandwidth consumption and computational load.

Network information theory states that novel methods, such as, cooperative communication and relaying can increase the overall network transport capacity. These methods are not utilized in current networking technologies. The 3G RAN will not scale up to meet the requirements set for telecommunication in the next decade. We need a novel fourth generation (4G) RAN design. This paper analyses challenges in wireless packet scheduling algorithms for 4G systems and proposes energy efficient scheduling and queue management solutions for the 4G wireless environment. Focus in our work is kept on a relay networking scenarios, which comprise of operator managed RAN including relays together with user controlled mobile routers

Multicast address allocation in the Internet has been the focus of much research and standardization. However, the multicast address allocation problem has been overlooked in ad hoc networking research. In this paper, we survey approaches to solving the multicast address allocation problem and analyze the security characteristics of the proposals. We show that using random assignment in IPv6 based ad hoc networks for multicast address allocation is more secure and bandwidth efficient than the previous proposals. We use passive listening to obtain network prefixes for autoconfiguring a global multicast address. The ideas presented in this paper are useful for practical deployment of ad hoc networks

The requirements for telecommunication at 2012 are estimated to be on such demanding level that the 3G technologies will not be sufficient. It is unlikely that the 3G Radio Access Network (RAN) would scale up, i.e., Fourth Generation (4G) RAN has to be redesigned. The requirements for 4G are demanding and the level of uncertainty is high. The developed ad hoc networking technologies could provide flexible solutions for the 4G RAN and extend the operator radio coverage. Combining these differing communication approaches provides insight for the 4G RAN design. This paper analyses the Mobile Ad hoc Networks (MANET) requirements against the 4G requirements in terms of energy conservation. The results describe, how these two approaches could co-exist in a mutually beneficial way. Our finding is that there is mismatch between the requirements. We propose solutions for interoperable requirements for MANET and 4G routing in terms of energy conservation.

Reliable detection of objects using the Hough transform is often possible even if just a small random poll of edge points is used for voting. This can lead to significant computational savings. To reduce the risk of errors, it is customary to preset the poll size to a value that is much larger than necessary in average conditions. An adaptive setting of the poll size in the probabilistic Hough transform is suggested. It is experimentally demonstrated that by monitoring changes in the ranks of peaks in the parameter space, sensible decisions on voting termination can be made. Adaptive stopping leads to polls that are on average smaller than the fixed poll that leads to the same error rate. In many applications the number of objects to be detected is unknown. Finding the number of appearances of an object in a noisy image is difficult, especially with partial data. The authors present an adaptive stopping rule that terminates voting as soon as any number of objects seem to be reliably detected, even though the existence of others may not be ruled out yet.>

Considers a restricted axial shape description, i.e. a line segment ribbon (LSR), which is constructed by pairing linear segments. Due to its specialization, new useful attributes can be computed for a LSR. A richer and advantageous description becomes possible compared to more general ribbons. The LSR based methods are beneficial in time limited applications, e.g. object recognition for robotics.>