Kwangman Ko

Towards native code offloading based MCC frameworks for multimedia applications

A number of resource-intensive applications, such as augmented reality, natural language processing, object recognition, and multimedia-based software are pushing the computational and energy boundaries of smartphones. Cloud-based services augment the resource-scare capabilities of smartphones while offloading compute-intensive methods to resource-rich cloud servers. The amalgam of cloud and mobile computing technologies has ushered the rise of Mobile Cloud Computing (MCC) paradigm which envisions operating smartphones and modern mobile devices beyond their intrinsic capabilities. System virtualization, application virtualization, and dynamic binary translation (DBT) techniques are required to address the heterogeneity of smartphone and cloud architectures. However, most of the current research work has only focused on the offloading of virtualized applications while giving limited consideration to native code offloading. Moreover, researchers have not attended to the requirements of multimedia based applications in MCC offloading frameworks. In this study, we present a survey and taxonomy of state-of-the-art MCC frameworks, DBT techniques for native offloading, and cross-platform execution techniques for multimedia based applications. We survey the MCC frameworks from the perspective of offload enabling techniques. We focus on native code offloading frameworks and analyze the DBT and emulation techniques of smartphones (ARM) on a cloud server (x86) architectures. Furthermore, we debate the open research issues and challenges to native offloading of multimedia based smartphone applications. We deliberate on DBT techniques for cross-platform heterogeneous smartphone and cloud architectures. DBT and emulation techniques are an essential part of native code based MCC offloading frameworks.We discuss techniques of cross-platform SIMD instruction translation and porting for multimedia based MCC applications.We provide a detailed taxonomy and parametric comparison of all the state-of-the-art studies discussed in the aforementioned directions.We identify research issues in current MCC offloading techniques, DBT optimization for process code migration based MCC offloading, and DBT optimizations for translation of SIMD instructions

Software-Defined Network Forensics: Motivation, Potential Locations, Requirements, and Challenges

The separation of the control plane from the data plane of a switch enables abstraction of a network through a logically centralized controller. The controller functions as the “brain” of a software-defined network. However, centralized control draws attackers to exploit different network devices by hijacking the controller. Security was initially not a key characteristic of SDN architecture, which left it vulnerable to various attackers. The investigation of such attacks in the newly emerging SDN architecture is a challenging task. Therefore, a comprehensive forensic mechanism is required to investigate different forms of attacks by determining their root cause. This article discusses an important area in SDN security, SDN forensics, which until now has received minimal focus. We compare traditional network forensics with SDN forensics to highlight the key differences between them. A brief motivation for SDN forensics is presented to emphasize its significance. Moreover, the potential locations with possible evidence against attackers are identified in SDN. Key requirements are highlighted for SDN forensics with respect to baseline investigation procedures. Finally, we identify challenges in SDN forensics by highlighting potential research areas for researchers, investigators, and academicians.

SIMDOM: A framework for SIMD instruction translation and offloading in heterogeneous mobile architectures

Fog and mobile edge computing is a paradigm that augments resource-scarce mobile devices with resource-rich network servers to enable ubiquitous computing. Smartphone applications rely on code offloading techniques to leverage high-performance computing opportunities available on edge and cloud servers for compute-intensive applications. Mobile (ARM) and edge/cloud (x86) architectures are heterogeneous and necessitate dynamic binary translation for compiled code migration that increases the application execution time. The application execution time and energy consumption should be lesser on the edge/cloud server as compared with the local mobile execution for optimal offload. Multimedia-based applications contain a large set of single instruction multiple data (SIMD) instructions that are compute and resource intensive. However, dynamic binary translation techniques of SIMD instructions lose the parallelism and optimization because of inefficient vector-to-scalar translation. We present a framework for SIMD instruction translation and offloading for mobile devices (SIMDOM) in cloud and edge environments. The SIMDOM framework reduces the execution overhead of migrated vectorized multimedia application by using vector-to-vector instruction mappings. The framework maps and translates ARM SIMD intrinsic instructions to x86 SIMD intrinsic instructions such that an application programmed for the mobile platform can be executed on the cloud server without any modification. The offload decision is based on inputs from the device energy, network, and application profilers. Experiments show that SIMDOM framework provides 84.78%, 3.41%, and 79.93% energy, time, and performance efficiency, respectively, compared with local offload-disabled execution. Compared with compiled code offloading, the SIMDOM framework provides 55.99%, 57.50%, and 96.23% energy, time, and performance efficiency, respectively.

Optimization techniques to enable execution offloading for 3D video games

Nowadays, mobile devices are becoming the most popular computing device as their computing capabilities increase rapidly. However, it is still challenging to execute highly sophisticated applications such as 3D video games on mobile devices due to its constrained key computational resources. Execution offloading approaches have been proposed to resolve this problem by strengthening mobile devices with powerful cloud. Unfortunately, the existing offloading approaches are not suitable for 3D video games because of the unique execution characteristics of them. In this paper, we propose a streaming-based execution offloading framework to enable execution offloading for 3D video games. The experiments show that our framework successfully guarantees 20 frames per second for our benchmark.

Energy-Reduction Offloading Technique for Streaming Media Servers

Recent growth in popularity of mobile video services raises a demand for one of the most popular and convenient methods of delivering multimedia data, video streaming. However, heterogeneity of currently existing mobile devices involves an issue of separate video transcoding for each type of mobile devices such as smartphones, tablet PCs, and smart TVs. As a result additional burden comes to media servers, which pretranscode multimedia data for number of clients. Regarding even higher increase of video data in the Internet in the future, the problem of media servers overload is impending. To struggle against the problem an offloading method is introduced in this paper. By the use of SorTube offloading framework video transcoding process is shifted from the centralized media server to the local offloading server. Thus, clients can receive personally customized video stream; meanwhile the overload of centralized servers is reduced.

Towards an Applicability of Current Network Forensics for Cloud Networks: A SWOT Analysis

In recent years, the migration of the computational workload to computational clouds has attracted intruders to target and exploit cloud networks internally and externally. The investigation of such hazardous network attacks in the cloud network requires comprehensive network forensics methods (NFM) to identify the source of the attack. However, cloud computing lacks NFM to identify the network attacks that affect various cloud resources by disseminating through cloud networks. In this paper, the study is motivated by the need to find the applicability of current (C-NFMs) for cloud networks of the cloud computing. The applicability is evaluated based on strengths, weaknesses, opportunities, and threats (SWOT) to outlook the cloud network. To the best of our knowledge, no research to date has been conducted to assist network forensics investigators and cloud service providers in finding an optimal method for investigation of network vulnerabilities found in cloud networks. To this end and in this paper, the state-of-the-art C-NFMs are classified and analyzed based on the cloud network perspective using SWOT analysis. It implies that C-NFMs have a suitable impact on cloud network, which further requires for reformation to ensure its applicability in cloud networks.

Implementation of 3D Motion Display Viewer Based on Kalman Filter Using the 9-Axis Sensor

The technical challenge of 3D image display using the wireless sensor were applied to variable areas and scopes. This techniques need to very accurate location and speed informations and recognitions to moving objects. The tracking challenges of the moving objects location that combined 3D location and speed sensor were applied to sports and healthcare areas. This areas seek to the accuracy and collections and tightly related to image display and analysis techniques. In this paper, We developed the 3D motion display that can be monitor moving objects on the 3D virtual space. For this works, we constructed the database that collected informations through 9-axis sensor and demonstrated the result of 3D motion display on the Window based environments.

A String Analysis based System for Classifying Android Apps Accessing Harmful Sites

This paper proposes a string analysis based system for classifying Android Apps that may access so called harmful sites, and shows an experiment result for real Android apps on the market. The system first transforms Android App binary codes into Java byte codes, it performs string analysis to compute a set of strings at all program points, and it classifies the Android App as bad ones if the computed set contains URLs that are classified because the sites provide inappropriate contents. In the proposed approach, the system performs such a classification in the stage of distribution before installing and executing the Apps. Furthermore, the system is suitable for the automatic management of Android Apps in the market. The proposed system can be combined with the existing methods using DNS servers or monitoring modules to identify harmful Android apps better in different stages.

Developing a custom DSP for vision based human computer interaction applications

As the computing power of modern devices become greater, computer vision is increasingly adopted as the means of human-computer interaction. The industry is struggling hard to bring computer vision into the mobile domain, but there are some difficulties mainly due to the computationally expansive nature of vision applications. On the other hand, there are various kinds of vision applications, which means using fixed hardware block will meet the performance criteria, but it will be possible to run only one application. To meet our needs, we can use another programmable component that is specialized for parallel processing and has domain specific instructions. In this paper, we introduce our work on the development of a low-power VLIW processor with vision-application specific parallel computation logic and a set of application-specific instructions, to improve the processor’s performance while reducing the power consumption. Our VLIW processor accelerates by adding special instructions on the application’s specific part in which processes heavy computation. We introduce our work on developing a custom DSP for vision system that includes designing the processor with Synopsys Processor Designer tool, and porting LLVM compiler. As the target, we ported a hand recognition application to our system, which we have constructed on Xilinx Zynq evaluation board. Even though our DSP was run at much lower frequency, it could run compute intensive part of the application four times faster than ARM processor, greatly speeding up the whole application.

Intersection-based Distance and Traffic-Aware Routing (IDTAR) protocol for smart vehicular communication

The Vehicular Ad-hoc Networks (VANET) raises as an emerging technology for smart transport as observed in the recent decade. However, there are some hurdles affect the applications of VANET, the advancement of VANET needed to cope up with the requirement of smart transport in smart cities. The routing is the important factor for having effective communication between smart vehicles, which need to be addressed smartly. Many factors affect the communication between the vehicles such as topology fragmentation which results in the phenomenon of Local-Maximum-Problem that lead to packet delivery failure. The traditional routing schemes failed to address the problem of the trade-off between Packet-Delivery-Ratio and the high cost, in term of End-to-End-Delay, of packet recovery from the failures that occurs frequently because of Local-Maximum-Problem. Therefore, this article works out to analyses the performance of existing position-based routing protocols for Inter-vehicle ad-hoc network and introduced Intersection-based Distance and Traffic-Aware Routing (IDTAR) protocol. IDTAR aims to provide optimal performance in environments of smart transport in smart cities. The experiments evaluate IDTAR against famous position-based routing protocols such as GyTAR, A-STAR-SR and GSR considering several densities of vehicles. Simulation result shows that IDTAR protocol has a fewer End-to-End-Delay and high packet delivery ratio in smart cities circumstances. This concludes that IDTAR can be adaptive for smart transport in smart cities communication with some consideration in terms of its security, compatibility and reliability.