Hu-Keun Kwak

Autonomous learning for efficient resource utilization of dynamic VM migration

Dynamic migration of virtual machines on a cluster of physical machines is designed to maximize resource utilization by balancing loads across the cluster. When the utilization of a physical machine is beyond a fixed threshold, the machine is deemed overloaded. A virtual machine is then selected within the overloaded physical machine for migration to a lightly loaded physical machine. Key to such threshold-based VM migration is to determine when to move which VM to what physical machine, since wrong or inadequate decisions can cause unnecessary migrations that would adversely affect the overall performance. We present in this paper a learning framework that autonomously finds and adjusts thresholds at runtime for different computing requirements. Central to our approach is the previous history of migrations and their effects before and after each migration in terms of standard deviation of utilization. We set up an experimental environment that consists of extensive real world benchmarking problems and a cluster of 16 physical machines each of which has on average eight virtual machines. We demonstrate through experimental results that our approach autonomously finds thresholds close to the optimal ones for different computing scenarios and that such varying thresholds yield an optimal number of VM migrations for maximizing resource utilization.

A Dynamic Server Power Mode Control for Saving Energy in a Server Cluster Environment

All the servers in a traditional server cluster environment are kept On. If the request load reaches to the maximum, we exploit its maximum possible performance, otherwise, we exploit only some portion of maximum possible performance so that the efficiency of server power consumption becomes low. We can improve the efficiency of power consumption by controlling power mode of servers according to load situation, that is, by making On only minimum number of servers needed to handle current load while making Off the remaining servers. In the existing power mode control method, they used a static policy to decide server power mode at a fixed time interval so that it cannot adapt well to the dynamically changing load situation. In order to improve the existing method, we propose a dynamic server power control algorithm. In the proposed method, we keep the history of server power consumption and, based on it, predict whether power consumption increases in the near future. Based on this prediction, we dynamically change the time interval to decide server power mode. We performed experiments with a cluster of 30 PCs. Experimental results show that our proposed method keeps the same performance while reducing 29% of power consumption compared to the existing method. In addition, our proposed method allows to increase the average CPU utilization by 66%.

A Method for Optimal Bandwidth Utilization in IEEE 802.11 WLAN Networks

This paper proposes a load sharing scheme to maximize network bandwidth utilization in IEEE 802.11 WLAN networks using the SSED (Service Set Identifier) hiding. For this purpose, the proposed scheme keeps checking the available bandwidths of a group of wireless routers; selects the most bandwidth-optimal one; makes it visible to clients. Such that, whenever a client connects to a wireless router, only the selected one is visible to it while others are hiding. We implemented the proposed scheme with modifying the firmware of ASUS WL- 500 G wireless router and performed experiments. Experimental results show 35.6% performance increase in the bandwidth utilization compared to the conventional scheme.

An Improved Estimation Model of Server Power Consumption for Saving Energy in a Server Cluster Environment

In the server cluster environment, one of the ways saving energy is to control server`s power according to traffic conditions. This is to determine the ON/OFF state of servers according to energy usage of data center and each server. To do this, we need a way to estimate each server`s energy. In this paper, we use a software-based power consumption estimation model because it is more efficient than the hardware model using power meter in terms of energy and cost. The traditional software-based power consumption estimation model has a drawback in that it doesn`t know well the computing status of servers because it uses only the idle status field of CPU. Therefore it doesn`t estimate consumption power effectively. In this paper, we present a CPU field based power consumption estimation model to estimate more accurate than the two traditional models (CPU/Disk/Memory utilization based power consumption estimation model and CPU idle utilization based power consumption estimation model) by using the various status fields of CPU to get the CPU status of servers and the overall status of system. We performed experiments using 2 PCs and compared the power consumption estimated by the power consumption model (software) with that measured by the power meter (hardware). The experimental results show that the traditional model has about 8-15% average error rate but our proposed model has about 2% average error rate.

An Energy Efficient Cluster Management Method based on Autonomous Learning in a Server Cluster Environment

Energy aware server clusters aim to reduce power consumption at maximum while keeping QoS(Quality of Service) compared to energy non-aware server clusters. They adjust the power mode of each server in a fixed or variable time interval to let only the minimum number of servers needed to handle current user requests ON. Previous studies on energy aware server cluster put efforts to reduce power consumption further or to keep QoS, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management based on autonomous learning for energy aware server clusters. Using parameters optimized through autonomous learning, our method adjusts server power mode to achieve maximum performance with respect to power consumption. Our method repeats the following procedure for adjusting the power modes of servers. Firstly, according to the current load and traffic pattern, it classifies current workload pattern type in a predetermined way. Secondly, it searches learning table to check whether learning has been performed for the classified workload pattern type in the past. If yes, it uses the already-stored parameters. Otherwise, it performs learning for the classified workload pattern type to find the best parameters in terms of energy efficiency and stores the optimized parameters. Thirdly, it adjusts server power mode with the parameters. We implemented the proposed method and performed experiments with a cluster of 16 servers using three different kinds of load patterns. Experimental results show that the proposed method is better than the existing methods in terms of energy efficiency: the numbers of good response per unit power consumed in the proposed method are 99.8%, 107.5% and 141.8% of those in the existing static method, 102.0%, 107.0% and 106.8% of those in the existing prediction method for banking load pattern, real load pattern, and virtual load pattern, respectively.

Dynamic Shutdown of Server Power Mode Control for Saving Energy in a Server Cluster Environment

In order to ensure high performance, all the servers in an existing server cluster are always On regardless of number of real-time requests. They ensure QoS, but waste server power if some of them are idle. To save energy consumed by servers, the server power mode control was developed by shutdowning a server when a server is not needed. There are two types of server power mode control depending on when a server is actually turned off if the server is selected to be off: static or dynamic. In a static mode, the server power is actually turned off after a fixed time delay from the time of the server selection. In a dynamic mode, server power is actually turned off if all the services served in the server are done. This corresponds to a turn off after a variable time delay. The static mdoe has disadvantages. It takes much time to find an optimal shutdown time manually through repeated experiments. In this paper, we propose a dynamic shutdown method to overcome the disadvantages of static shutdown. The proposed method allows to guarantee user QoS with good power-saving because it automatically approaches an optimal shutdown time. We performed experiments using 30 PCs cluster. Experimental results show that the proposed dynamic shutdown method is almost same as the best static shutdown in terms of power saving, but better than the best static shutdown in terms of QoS.

An enhanced reactive Chord for mobile networks

Chord is a peer-to-peer look up algorithm based on a distributed hash table protocol in wired IP networks, exploiting the advantage of scalability for large-scale of distributed applications. However, deploying Chord into mobile networks should be inherently accompanied with supplementary network traffics to maintain the hash key mapping rules because of a high rate of joining and leaving nodes. This paper proposes an enhanced reactive Chord for mobile networks, which can reduce network traffics and achieve fast lookup services. For this purpose, a conventional Chord is modified to act reactively, and then the table activity checking feature is devised into it, called enhanced reactive Chord. Simulation results show that the proposed Chord can decrease network traffics by an average of 41.3% maintaining same setup latency, compared with conventional Chord in mobile networks.

A Performance Improvement of Linux TCP/IP Stack based on Flow-Level Parallelism in a Multi-Core System

With increasing multicore system, much effort has been put on the performance improvement of its application. Because multicore system has multiple processing devices in one system, its processing power increases compared to the single core system. However in many cases the advantages of multicore can not be exploited fully because the existing software and hardware were designed to be suitable for single core. When the existing software runs on multicore, its performance improvement is limited by the bottleneck of sharing resources and the inefficient use of cache memory on multicore. Therefore, according as the number of core increases, it doesn`t show performance improvement and shows performance drop in the worst case. In this paper we propose a method of performance improvement of multicore system by applying Flow-Level Parallelism to the existing TCP/IP network application and operating system. The proposed method sets up the execution environment so that each core unit operates independently as much as possible in network application, TCP/IP stack on operating system, device driver, and network interface. Moreover it distributes network traffics to each core unit through L2 switch. The proposed method allows to minimize the sharing of application data, data structure, socket, device driver, and network interface between each core. Also it allows to minimize the competition among cores to take resources and increase the hit ratio of cache. We implemented the proposed methods with 8 core system and performed experiment. Experimental results show that network access speed and bandwidth increase linearly according to the number of core.

DISH: dynamic information-based scalable hashing on a cluster of web cache servers

Caching web pages is an important part of web infrastructure. The effects of caching services are even more pronounced for wireless infrastructures due to their limited bandwidth. Medium to large-scale infrastructures deploy a cluster of servers to solve the scalability problem and hot spot problem inherent in caching. In this report, we present Dynamic Information-based Scalable Hashing (DISH) that evenly hashes client requests to a cluster of cache servers. Three types of runtime information are used to determine when and how to cache pages, including cache utilization, CPU usage, and number of connections. Pages cached are stored and retrieved mutually exclusively to/from all the servers. We have implemented our approach and performed various experiments using publicly available traces. Experimental results on a cluster of 16 cache servers demonstrate that the proposed hashing method gives 45% to 114% performance improvement over other widely used methods, while addressing the hot spot problem.

Autonomous learning of load and traffic patterns to improve cluster utilization

Adaptive clustering aims at improving cluster utilization for varying load and traffic patterns. Locality-based least-connection with replication (LBLCR) scheduling that comes with Linux is designed to help improve cluster utilization through adaptive clustering. A key issue with LBLCR, however, is that cluster performance depends much on a single threshold value that is used to determine adaptation. Once set, the threshold remains fixed regardless of the load and traffic patterns. If a cluster of PCs is to adapt to different traffic patterns for high utilization, a good threshold has to be selected and used dynamically. We present in this report an adaptive clustering framework that autonomously learns and adapts to different load and traffic patterns at runtime with no administrator intervention. Cluster is configured once and for all. As the patterns change the cluster automatically expands/contracts to meet the changing demands. At the same time, the patterns are proactively learned that when similar patterns emerge in the future, the cluster knows what to do to improve utilization. We have implemented this autonomous learning method and compared with LBLCR using published Web traces. Experimental results indicate that our autonomous learning method shows high performance scalability and adaptability for different patterns. On the other hand LBLCR-based clustering suffers from performance scalability and adaptability for different traffic patterns since it is not designed to obtain good threshold values and use them at runtime.