Yeongpil Cho

Techniques to Minimize State Transfer Costs for Dynamic Execution Offloading in Mobile Cloud Computing

In order to meet the increasing demand for high performance in smartphones, recent studies suggested mobile cloud computing techniques that aim to connect the phones to adjacent powerful cloud servers to throw their computational burden to the servers. These techniques often employ execution offloading schemes that migrate a process between machines during its execution. In execution offloading, code regions to be executed on the server are decided statically or dynamically based on the complex analysis on execution time and process state transfer costs of every region. Expectedly, the transfer cost is a deciding factor for the success of execution offloading. According to our analysis, it is dominated by the total size of heap objects transferred over the network. But previous work did not try hard to minimize this size. Thus in this paper, we introduce novel techniques based on compiler code analysis that effectively reduce the transferred data size by transferring only the essential heap objects and the stack frames actually referenced in the server. The experiments exhibit that the reduced size positively influences not only the transfer time itself but also the overall effectiveness of execution offloading, and ultimately, improves the performance of our mobile cloud computing significantly in terms of execution time and energy consumption.

Fast dynamic execution offloading for efficient mobile cloud computing

In order to meet the increasing demand for high performance in smartphones, recent studies suggested mobile cloud computing techniques that aim to connect the phones to adjacent powerful cloud servers to throw their computational burden to the servers. These techniques often employ execution offloading schemes that migrate a process between machines during its execution. In execution offloading, code regions to be executed on the server are decided statically or dynamically based on the complex analysis on execution time and process state transfer time of every region. Expectedly, the transfer time is a deciding factor for the success of execution offloading. According to our analysis, it is dominated by the total size of heap objects transferred over the network. But previous work did not try hard to minimize this size. Thus in this paper, we introduce novel techniques based on compiler code analysis that effectively reduce the transferred data size by transferring only the essential heap objects. The experiments exhibit that the reduced size positively influences not only the transfer time itself but also the overall effectiveness of execution offloading, and ultimately, improves the performance of our mobile cloud computing significantly in terms of execution time and power consumption.

Precise execution offloading for applications with dynamic behavior in mobile cloud computing

In order to accommodate the high demand for performance in smartphones, mobile cloud computing techniques, which aim to enhance a smartphones performance through utilizing powerful cloud servers, were suggested. Among such techniques, execution offloading, which migrates a thread between a mobile device and a server, is often employed. In such execution offloading techniques, it is typical to dynamically decide what code part is to be offloaded through decision making algorithms. In order to achieve optimal offloading performance, however, the gain and cost of offloading must be predicted accurately for such algorithms. Previous works did not try hard to do this because it is usually expensive to make an accurate prediction. Thus in this paper, we introduce novel techniques to automatically generate accurate and efficient method-wise performance predictors for mobile applications and empirically show they enhance the performance of offloading.

CMcloud: Cloud Platform for Cost-Effective Offloading of Mobile Applications

Recent efforts towards mobile cloud propose to offload mobile applications to cloud servers for the improved performance and battery life of mobile devices. However, existing schemes completely ignore the costs of cloud resources by assuming that idle servers are always available for free of charge. These unrealistic assumptions make each server run only a small load to achieve the guaranteed high offload performance. Therefore, these schemes cannot be applied to real-world commercial clouds which aim to minimize the operation costs by maximizing the server throughput, and then charge users for their resource usage. In this paper, we propose CMcloud, a novel cost-effective mobile-to-cloud offloading platform, which works nicely under the real-world cloud environments. CMcloud minimizes both the server costs and the user service fee by offloading as many mobile applications to a single server as possible, while satisfying the target performance of all applications. To achieve such goals, CMcloud exploits novel architecture performance modeling and server migration techniques. Our implementation shows that CMcloud can improve the data enter throughput by 84% over a conventional static light-load scheme (or a 2.7x higher per-socket throughput.) Alternatively, CMcloud reduces the number of service failures by 83% over a static high-load scheme, while even improving the throughput by 31%.

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.

Hypernel: a hardware-assisted framework for kernel protection without nested paging

Large OS kernels always suffer from attacks due to their numerous inherent vulnerabilities. To protect the kernel, hypervisors have been employed by many security solutions. However, relying on a hypervisor has a detrimental impact on the system performance due mainly to nested paging. In this paper, we present Hypernel, a security framework combining hardware and software components to address this problem. Hypersec, the software component, provides an isolated execution environment for security solutions, and the hardware monitor component enables a word-granularity monitoring capability on the kernel memory. Our evaluation shows that Hypernel efficiently fulfills the role of a security framework, while imposing mere 3.1% of runtime overhead on the system.

Instruction-Level Data Isolation for the Kernel on ARM

As more sophisticated services are increasingly offered by the OS kernel on mobile devices, the security and sensitivity of kernel data that they depend on are becoming a critical issue. Data isolation has emerged as a key technique that can address the issue by providing strong protection for sensitive kernel data. However, existing data isolation mechanisms for mobile devices all incur non-negligible performance overhead. We deem that such computational burden would be a serious problem for mobile devices which already suffer from resource poverty. To alleviate this problem, we have developed a new mechanism that enforces data isolation very efficiently on ARM-based machines backed by unique hardware instructions. For evaluation, this instruction-level data isolation mechanism has been implemented in the Android/Linux kernel running on ARM. According to the experiment, it provides a lightweight data isolation capability for security services installed in the kernel.

A Survey and Design of a Scalable Mobile Edge Cloud Platform for the Smart IoT Devices and It’s Applications

Mobile Edge Computing offers real time RAN information (like network load, user’s location) to the application developers and content developers. These real time network information are used to provide context aware services to the mobile subscribers, thereby enriching user’s satisfaction and improving Quality-of-Experience. Mobile edge computing platform increases the edge responsibility and allows computation and services to be hosted at the edge, which reduces the network latency and bandwidth computation of the subscribers. In this research paper, we designed the scalar mobile edge cloud platform and it’s appropriate edge applications, such as 360° panorama image processing, which has a special characteristics and challenges to extend an edge servers on edge cloud by the subscribers demands. This research paper challenges capable to overcome the static al constrains of edge serve capacities and supports flexible computing facilities.