Yunpeng Chai

Efficient Data Migration to Conserve Energy in Streaming Media Storage Systems

Reducing energy consumption has been an important design issue for large-scale streaming media storage systems. Existing energy conservation techniques are inadequate to achieve high energy efficiency for streaming media computing environments due to high data migration overhead. To address this problem, we propose in this paper a new energy-efficient method called Explicit Energy Saving Disk Cooling or EESDC. EESDC significantly reduces data migration overhead because of two reasons. First, a set of disks referred to Explicit Energy Saving Disks (EESD) is explicitly fixed according to temporal system load. Second, all the migrated data in EESDC directly contribute on extending the idle time of EESD to conserve more energy efficiently. Therefore, the EESDC method is conducive to saving more energy by quickly achieving energy-efficient data layouts without unnecessary data migrations. We implement EESDC in a simulated disk system, which is validated against a prototype system powered by our EESDC. Our experimental results using both real-world traces and synthetic traces show that EESDC can save up to 28.13-29.33 percent energy consumption for typical streaming media traces. Energy efficiency of streaming media storage systems can be improved by 3.3-6.0 times when EESDC is coupled.

WEC: Improving Durability of SSD Cache Drives by Caching Write-Efficient Data

Serving as cache disks, flash-based solid-state drives (SSDs) can significantly boost the performance of read-intensive applications. However, frequent data updating, the necessary condition for classical replacement algorithms (e.g., LRU, MQ, LIRS, and ARC) to achieve a high hit rate, makes SSDs wear out quickly. To address this problem, we propose a new approach-write-efficient caching (WEC)-to greatly improve the write durability of SSD cache. WEC is conducive to reducing the total number of writes issued to SSDs while achieving high hit rates. WEC takes two steps to improve write durability and performance of SSD cache. First, WEC discovers write-efficient data, which tend to be active for a long time period and to be frequently accessed. Second, WEC keeps the write-efficient data in SSDs long enough to avoid excessive number of unnecessary updates. Our findings based on a wide range of popular real-world traces show that write-efficient data does exist in a wide range of popular read-intensive applications. Our experimental results indicate that compared with the classical algorithms, WEC judiciously improves the mean hits of each written block by approximately two orders of magnitude while exhibiting similar or even higher hit rates.

A stepwise optimization algorithm of clustered streaming media servers

The optimization of Clustered Streaming Media Servers (CSMS), which aims at using as few hardware resources and as cost-effective as possible, while providing satisfactory performance and QoS, has a great impact on the practicability and efficiency of CSMS. Based on the analysis and formulization of critical performance factors of CSMS and the relationship among the performance, QoS, and the costs in CSMS, a stepwise optimization algorithm is developed to solve the optimization problem efficiently. The algorithm is based on an approach that models the optimization problem into a directed acyclic graph and then addresses the complex optimization problem step by step. The algorithm applies a divide and conquer model that not only reduces the complexity of the optimization problem, but also accelerates the optimization process. Progressive information is collected in the process and used in solving the problem. Furthermore, a simulation system of CSMS is necessary for the optimization algorithm to generate the accurate information produced in the entire streaming service process. Thus, we designed and implemented such a simulation system based on the theoretical performance model of CSMS and the parameters measured in practical CSMS testbed. Finally, a case study of the optimization problem is given to demonstrate the process of the algorithm, and an appropriate plan for designing practical CSMS system is illustrated.

A Delayed Container Organization Approach to Improve Restore Speed for Deduplication Systems

Data deduplication has become necessary to improve the space-efficiency of large-scale distributed storage systems, as the global data have accumulated at an exponential rate and they have significant redundancy. However, the negative impact on restore performance is a main challenge for deduplication systems. One of the key reasons is that when restoring data, the low average useful data ratio (UDR) of containers wastes a considerable part of disk bandwidth to read useless data. This is mainly attributed to the uncontrollable compositions of containers. To solve this problem, we propose a new approach called Delayed Container Organization (DCO) to delay the construction of containers after accumulating some redundant data chunks in fast Non-Volatile Memory (NVM) devices to organize high-UDR containers. For example, data chunks in the intersection of some data segments can be organized together in one container to achieve both high deduplication ratio and high UDRs when restoring these related data segments. DCO is implemented in a prototype deduplication system. The experimental results indicate that compared with Capping, DCO promotes the average UDR of containers by 38.30 percent, improves the restore performance by a factor of 2.2, and achieves better space-efficiency and higher cost performance.

Three-state disk model for high quality and energy efficient streaming media servers

Energy conservation and emission reduction is an increasingly prominent and global issue in green computing. Among the various components of a streaming media server, the storage system is the biggest power consumer. In this paper, a Three-State Disk Model (3SDM) is proposed to conserve energy for streaming media servers without losing quality. According to the load threshold, the disks are dynamically divided into three states: overload, normal and standby. With the requests arriving and departing, the disk state transition among these three states. The purpose of 3SDM is to skew the load among the disks to achieve high quality and energy efficiency for streaming media applications. The load of disks in overload state will move to disks in normal state to improve the quality of service (QoS) level. The load of disks in normal state will be packed together to switch some disks into standby state to save energy. The key problem here is to identify the blocks that need migrating among disks. A sliding window replacement (SWR) algorithm is developed for this purpose, which calculates the block weight based on the request frequency falling within the window of a block. Employing a validated simulator, this paper evaluates the SWR algorithm for conventional disks based on the proposed 3SDM model. The results show that this scheme is able to yield energy efficient streaming media servers.

Priori information and sliding window based prediction algorithm for energy-efficient storage systems in cloud

Abstract One of the major challenges in cloud computing and data centers is the energy conservation and emission reduction. Accurate prediction algorithms are essential for building energy efficient storage systems in cloud computing. In this paper, we first propose a Three-State Disk Model (3SDM), which can describe the service quality and energy consumption states of a storage system accurately. Based on this model, we develop a method for achieving energy conservation without losing quality by skewing the workload among the disks to transmit the disk states of a storage system. The efficiency of this method is highly dependent on the accuracy of the information predicting the blocks to be accessed and the blocks not be accessed in the near future. We develop a priori information and sliding window based prediction (PISWP) algorithm by taking advantage of the priori information about human behavior and selecting suitable size of sliding window. The PISWP method targets at streaming media applications, but we also check its efficiency on other two applications, news in webpage and new tool released. Disksim, an established storage system simulator, is applied in our experiments to verify the effect of our method for various users’ traces. The results show that this prediction method can bring a high degree energy saving for storage systems in cloud computing environment.

A New Scheduling Algorithm for Distributed Streaming Media System Based on Multicast

To meet the growing service scale, distributed streaming media system has been widely used for streaming media applications. The schedule algorithm of distributed streaming media system on how to reduce the network bandwidth consumption and balance the load among service nodes has become a challenging problem. We develop the period patching algorithm with unfixed period to achieve the best efficiency on reducing bandwidth consumption. Furthermore, the minimum bandwidth schedule algorithm is proposed. Its major objective is reducing network bandwidth consumption and at the same time balancing the load balancing between nodes. The experiment results show that this proposed algorithm outperforms the classical scheduling algorithms both in reducing total bandwidth consumption and service response time, which is used to reflect the quality of service and load balance effect.

Load Sharing Based on PSO Algorithm for Isolated Distributed Stream Servers

Isolated Distributed Stream Servers (IDSS) is the main form of video-on-demand (VOD) service architecture in industrial community nowadays. Contrast to the previous work on load sharing in distributed VOD system which all follows the idea of having high-speed inner network among service nodes, in this paper, we firstly focus on load sharing algorithm under IDSS architecture and introduce global stream distribution optimization, future user arrival rate estimation and more disk storage redundancy rate to make the effect of load sharing more satisfactory. Moreover the influence of video redundancy rate and some other parameters in our algorithm is detected through simulation. Preliminary experiment results suggest that our algorithm outperforms existing load sharing algorithms under IDSS architecture and sometimes even works better than existing ones with inner network.

Endurable SSD-Based Read Cache for Improving the Performance of Selective Restore from Deduplication Systems

Deduplication has been commonly used in both enterprise storage systems and cloud storage. To overcome the performance challenge for the selective restore operations of deduplication systems, solid-state-drive-based (i.e., SSD-based) read cache can be deployed for speeding up by caching popular restore contents dynamically. Unfortunately, frequent data updates induced by classical cache schemes (e.g., LRU and LFU) significantly shorten SSDs’ lifetime while slowing down I/O processes in SSDs. To address this problem, we propose a new solution — LOP-Cache — to greatly improve the write durability of SSDs as well as I/O performance by enlarging the proportion of long-term popular (LOP) data among data written into SSD-based cache. LOP-Cache keeps LOP data in the SSD cache for a long time period to decrease the number of cache replacements. Furthermore, it prevents unpopular or unnecessary data in deduplication containers from being written into the SSD cache. We implemented LOP-Cache in a prototype deduplication system to evaluate its performance. Our experimental results indicate that LOP-Cache shortens the latency of selective restore by an average of 37.3% at the cost of a small SSD-based cache with only 5.56% capacity of the deduplicated data. Importantly, LOP-Cache improves SSDs’ lifetime by a factor of 9.77. The evidence shows that LOP-Cache offers a cost-efficient SSD-based read cache solution to boost performance of selective restore for deduplication systems.

Online Random Seeking in FLV Video Based on File Cutting

To support online watching any part of the videos smoothly on the streaming media cluster architecture, a file cutting based random seeking technology in FLV video is proposed in this paper. Three important aspects in design and implementation of this technology, the policy of player access to the back-end server, the FLV video file cutting algorithm, and the file deleting strategy, are described in detail. The method has been employed in the Outstanding Courses Integration System of China and the simulation results help us to find the performance bottleneck to be broken in the future work.