Byung Chul Tak

Reducing the Delay and Power Consumption of Web Browsing on Smartphones in 3G Networks

Smart phone is becoming a key element in providing greater user access to the mobile Internet. Many complex applications, which are used to be only on PCs, have been developed and run on smart phones. These applications extend the functionalities of smart phones and make them more convenient for users to be connected. However, they also greatly increase the power consumption of smart phones and many users are frustrated with the long delay of web browsing when using smart phones. In this paper, we have discovered that the key reason of the long delay and high power consumption in web browsing is not due to the bandwidth limitation most of time in 3G networks. The local computation limitation at the smart phone is the real bottleneck for opening most web pages. To address this issue, we propose an architecture, called Virtual-Machine based Proxy (VMP), to shift the computing from smart phones to the VMP. To illustrate the feasibility of deploying the proposed VMP system in 3G networks, we have built a prototype using Xen virtual machines and Android Phones with T-Mobile UMTS network. Experimental results show that compared to normal smart phone browser, our VMP approach reduces the delay by more than 80% and reduces the power consumption during web browsing by more than 45%.

CAP3: A Cloud Auto-Provisioning Framework for Parallel Processing Using On-Demand and Spot Instances

Cloud computing has drawn increasing attention from the scientific computing community due to its ease of use, elasticity, and relatively low cost. Because a high-performance computing (HPC) application is usually resource demanding, without careful planning, it can incur a high monetary expense even in Cloud. We design a tool called CAP3 (Cloud Auto-Provisioning framework for Parallel Processing) to help a user minimize the expense of running an HPC application in Cloud, while meeting the user-specified job deadline. Given an HPC application, CAP3 automatically profiles the application, builds a model to predict its performance, and infers a proper cluster size that can finish the job within its deadline while minimizing the total cost. To further reduce the cost, CAP3 intelligently chooses the Clouds reliable on-demand instances or low-cost spot instances, depending on whether the remaining time is tight in meeting the applications deadline. Experiments on Amazon EC2 show that the execution strategy given by CAP3 is cost-effective, by choosing a proper cluster size and a proper instance type (on-demand or spot).

Cloudy with a Chance of Cost Savings

Cloud-based hosting is claimed to possess many advantages over traditional in-house (on-premise) hosting such as better scalability, ease of management, and cost savings. It is not difficult to understand how cloud-based hosting can be used to address some of the existing limitations and extend the capabilities of many types of applications. However, one of the most important questions is whether cloud-based hosting will be economically feasible for my application if migrated into the cloud. It is not straightforward to answer this question because it is not clear how my application will benefit from the claimed advantages, and, in turn, be able to convert them into tangible cost savings. Within cloud-based hosting offerings, there is a wide range of hosting options one can choose from, each impacting the cost in a different way. Answering these questions requires an in-depth understanding of the cost implications of all the possible choices specific to my circumstances. In this study, we identify a diverse set of key factors affecting the costs of deployment choices. Using benchmarks representing two different applications (TPC-W and TPC-E) we investigate the evolution of costs for different deployment choices. We consider important application characteristics such as workload intensity, growth rate, traffic size, storage, and software license to understand their impact on the overall costs. We also discuss the impact of workload variance and cloud elasticity, and certain cost factors that are subjective in nature.

A dynamic energy management scheme for multi-tier data centers

Multi-tier data centers have become a norm for hosting modern Internet applications because they provide a flexible, modular, scalable and high performance environment. However, these benefits come at a price of the economic dent incurred in powering and cooling these large hosting centers. Thus, energy efficiency has become a critical consideration in designing Internet data centers. In this paper, we propose a multifaceted approach, Hybrid, consisting of dynamic provisioning, frequency scaling and dynamic power management (DPM) schemes to reduce the energy consumption of multi-tier data centers, while meeting the Service Level Agreements (SLAs). We formulate a mathematical model of the energy and performance/SLA optimization problem followed by a queueing theory based approach to develop two heuristics for solving the optimization problem. The first heuristic dynamically provisions the optimal number of servers required in each tier. The second heuristic proactively decides the CPU speed and the duration of sleep states of a server to achieve further energy savings. We evaluate our heuristics using a simulator that was validated with real measurements on a prototype three-tier data center consisting of 25 servers with two multi-tier application benchmarks. Our experimental results indicate that the proposed scheme, Hybrid, can reduce the energy consumption by 50% relative to static provisioning without CPU frequency scaling and DPM. We demonstrate that Hybrid satisfies the SLAs for dynamically varying workloads. In addition, the proposed multifaceted approach is more energy efficient than the other methods such as dynamic provisioning with exploiting deep sleep states.

LOGAN: Problem Diagnosis in the Cloud Using Log-Based Reference Models

Problem diagnosis is one crucial aspect in the cloud operation that is becoming increasingly challenging. On the one hand, the volume of logs generated in todays cloud is overwhelmingly large. On the other hand, cloud architecture becomes more distributed and complex, which makes it more difficult to troubleshoot failures. In order to address these challenges, we have developed a tool, called LOGAN, that enables operators to quickly identify the log entries that potentially lead to the root cause of a problem. It constructs behavioral reference models from logs that represent the normal patterns. When problem occurs, our tool enables operators to inspect the divergence of current logs from the reference model and highlight logs likely to contain the hints to the root cause. To support these capabilities we have designed and developed several mechanisms. First, we developed log correlation algorithms using various IDs embedded in logs to help identify and isolate log entries that belong to the failed request. Second, we provide efficient log comparison to help understand the differences between different executions. Finally we designed mechanisms to highlight critical log entries that are likely to contain information pertaining to the root cause of the problem. We have implemented the proposed approach in a popular cloud management system, OpenStack, and through case studies, we demonstrate this tool can help operators perform problem diagnosis quickly and effectively.

AppCloak: Rapid Migration of Legacy Applications into Cloud

Although cloud has been adopted by many organizations as their main infrastructure for IT delivery, there are still a large number of legacy applications running in non-cloud hosting environments. Thus, it is crucial to have migration techniques for such legacy applications so that they can benefit from many advantages of cloud such as elasticity, low upfront investment, and fast time-to-market. However, migrating large number of legacy applications into cloud in a timely manner is a daunting task. Common techniques such as redeveloping (i.e., modernizing) them or reinstalling from the scratch entails high costs. To mitigate these problems, we have developed a rapid migration technique, called AppCloak, that allows users to literally copy an already-installed application to cloud and run it without any modifications. The technique is based on intercepting a selected set of system calls and replacing the parameters and return values to hide any differences of environments to the application. We demonstrate that our technique works in Amazon EC2 and quantify the performance overhead.

Mobile Web Browsing Using the Cloud

This chapter introduces the technical challenges arising from supporting web browsing on smartphones. For ease of understanding and further discussions, we briefly describe existing techniques on reducing the access delay and power consumption of smartphones. Then, we propose a new architecture, called Virtual Machine-based Proxy (VMP), which aims to shift the computationally intensive web browsing tasks from smartphones to reduce the delays and power consumption. Finally, we highlight the advantages of the VMP architecture and provide detailed organization of the book. Due to the arrival in the market of a new generation of improved smartphones, with large screens, improved user interfaces, and advanced features such as GPS, smartphones have become a key element in providing greater user access to the mobile Internet. For several years, the demand for smartphones has outpaced the remainder of the mobile phone market. Approximately half of the U.S. mobile consumers own smartphones and could account for nearly 70 % of all U.S. mobile devices in 2013 [1]. Different from traditional mobile phones, smartphones incorporate mobile operating systems such as Google’s Android, Apple’s iOS, or others, leading to more capability than traditional mobile phones. Many complex applications which used to be limited to Personal Computers (PCs), have been developed and operated on smartphones. These applications extend the functionalities of smartphones, making them more convenient for users to connect to the Internet; however, the trade-off for greater capability is a greatly increased workload leading to increased power consumption. Smartphones, powered by battery, create a major concern for the battery’s life in the mobile communication industry. Unfortunately, the rate at which battery performance improves has been much slower than the development of the devices’ capabilities [2]. Aside from major breakthroughs, significant improvement in the foreseeable future is doubtful. Rather than improving the amount of energy a power source contains, this research explores an alternative: carefully designing B. Zhao et al., Mobile Web Browsing Using the Cloud, 1 SpringerBriefs in Computer Science, DOI: 10.1007/978-1-4614-8103-4_1,

Auto-tuning Performance of MPI Parallel Programs Using Resource Management in Container-Based Virtual Cloud

Load imbalance problem is one of the major obstacles to achieving optimal performance of High Performance Computing applications. The approach of trying to distribute the problem pieces to each node with the hope of balancing execution time has limits since the performance depends not only on data size but also on many other dynamic factors. This paper describes an approach that uses adaptive resource management enabled by the container-based virtualization to solve the load imbalance problem of MPI programs running in the cloud. Our techniques dynamically adjust CPU resource allocation to MPI processes running as container instances according to the current program execution state and system resource status. The resource allocation among MPI processes is adjusted in two ways: the intra-host level, which dynamically adjusts resources within a host, and the inter-host level, which migrates containers together with MPI processes from one host to another host. We have implemented and evaluated our approach on Amazon EC2 platform using real-world scientific benchmarks and applications, which demonstrates that the performance can be improved up to 31% (with an average of 15%) when compared with the baseline.

Virtual Machine Based Proxy

This chapter presents detailed design of the Virtual Machine-based Proxy (VMP). We first give an overview of the VMP architecture, and then present efficient communication mechanisms based on compression and adaptation techniques. On the proxy side, we present resource management techniques to optimize the performance of the VMs and cloud techniques to address scalability issues. Security and privacy issues are addressed, and a technique based on trusted computing module is presented. Finally, we discuss deployment issues and issues on supporting interactive applications.