Donghyun Kwon

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.

Mantis: Efficient Predictions of Execution Time, Energy Usage, Memory Usage and Network Usage on Smart Mobile Devices

We present Mantis, a framework for predicting the computational resource consumption (CRC) of Android applications on given inputs accurately, and efficiently. A key insight underlying Mantis is that program codes often contain features that correlate with performance and these features can be automatically computed efficiently. Mantis synergistically combines techniques from program analysis and machine learning. It constructs concise CRC models by choosing from many program execution features only a handful that are most correlated with the programs CRC metric yet can be evaluated efficiently from the programs input. We apply program slicing to reduce evaluation time of a feature and automatically generate executable code snippets for efficiently evaluating features. Our evaluation shows that Mantis predicts four CRC metrics of seven Android apps with estimation error in the range of 0-11.1 percent by executing predictor code spending at most 1.3 percent of their execution time on Galaxy Nexus.

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.

VM-CFI: Control-Flow Integrity for Virtual Machine Kernel Using Intel PT.

Nowadays cloud computing technology is used for a variety of services, such as the internet of things and artificial intelligence. However, as more data is being processed in the cloud, there is growing concern about security issues in the cloud computing environment. To solve this concern, many studies have been conducted to ensure the integrity of virtual machines in a cloud computing environment. However, in the case of the control-flow integrity for the virtual machine, existing studies are not only necessary to modify the kernel code, but also cannot protect it efficiently. In this paper, we propose VM-CFI which efficiently protects the control-flow integrity of VM kernel without modification of VM kernel in cloud computing environment. For this purpose, VM-CFI utilizes Processor Trace (PT), a hardware feature that is recently supported by Intel architecture. According to the experimental results, VM-CFI incurs on average 4.2% overhead.

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.