Answering Multiple-Item Queries in Data Broadcast Systems

In this article, we investigate the performance behavior of the hierarchical storage architecture. To maximize the performance of the hierarchical storage system, the capacity and bandwidth of individual storage hierarchical needs to be well balanced. Another important factor which governs the performance of hierarchical storage system is the user access pattern. We use two orthogonal performance metrics, namely expected service time and system congestion and blocking probability to evaluate the server performance. We establish an analytical formulation for the given performance metrics and investigate how these performance metrics are affected by the parameter of the individual storage hierarchy. This analytical formulation enables us to investigate the effects of different configurations of the storage hierarchy and different data access patterns; it also provides a framework to determine the optimal configuration of storage hierarchies. The results of a simulation-based performance evaluation are also presented.

Recently, many broadcast satellites have been launched to provide data broadcasting services for public users. Although the provided services can cover many kinds of data and a wide range of user interest, it is considered that, in general, a user is interested in only some specific genres of data. Consequently, storing all received data is considered to be inefficient and only wasting a large amount of memory. This motivated us to introduce an information filtering system into a broadcast data receiving system. The use of filtering system increases the efficiency of memory usage and reduces time for searching interesting data. In this paper, we propose a filtering method that uses a tree structure to represent user preferences. The use of this filtering method enable the receiving system to select only data that match user's interest and classify the stored data in the way that suits the user's access pattern. We also describe the design and implementation of our broadcast data. Receiving system that makes use of the proposed filtering method. Further, we evaluate the performance of our method by showing some simulation results.

Various air indexing and data scheduling schemes for wireless broadcast of spatial data have been developed for energy efficient query processing. The existing schemes are not effective when the clients' data access patterns are skewed to some items. It is because the schemes are based on flat broadcast that does not take the popularity of the data items into consideration. In this paper, thus, we propose a data scheduling scheme letting the popular items appear more frequently on the channel, and grid-based distributed index for non-flat broadcast (GDIN) for window query processing. The proposed GDIN allows quick and energy efficient processing of window query, matching the clients' linear channel access pattern and letting the clients access only the queried data items. The simulation results show that the proposed GDIN significantly outperforms the existing schemes in terms of access time, tuning time, and energy efficiency.

SummarySecuring user identity from data breach in a web server is one of the major concerns for the users of the web applications. Similarly, protecting user access pattern from unauthorized access should be taken seriously, because the potential threats such attacks may pose, are huge. However, these security measures should not be adopted at the expense of user experience and convenience. Nevertheless, any extra overhead in the form of security measures introduced in a distributed system results in significant performance declination. The target of a secured framework for a distributed system like web application should be a reasonable trade‐off between security and user experience. Thus, in this work, we present a framework that ensures security for the user identity along with keeping the online activities of the users anonymous while ensuring scalability of the system. Our framework is designed in a scalable form that can work with other distributed architectures that provide security to user data and identities. To ensure all these measures, our proposal includes the implementation of Forward Secrecy using Diffie‐Hellman Key exchange protocol where the server cannot remember a user's history after a session ends. In addition, we present our own mechanism to hide logical data sharing strategies to protect users against selective DoS attacks. Moreover, we implemented a modified version of bloom filter to safeguard user access pattern in a compromised server. Our proposed implementation of bloom filter also ensures that the scalability of distributed system is preserved even with little infrequent overhead in the server because of security measures proposed in this work. Finally, we implemented different modules of our framework using both Web Socket and Long Polling transport protocols and recorded the time required to perform various tasks. Web socket protocol took less time to perform each task than the long polling protocol, which is convincing enough to suggest that web socket performs better than long polling in the given scenarios. Copyright © 2016 John Wiley & Sons, Ltd.

SummaryMultitiered storage systems, which are made up of heterogeneous devices, are widely used in distributed environments to accelerate the I/O performance of upper big data applications. It raises new challenges in efficient data migration through smart caching mechanisms among heterogeneous storage levels, such as MEM‐SSD‐HDD. To optimize the cache policy scheduling mechanism on the distributed tiered storage architecture, we proposed a general framework with five layers, including a tiered storage system layer, a cache migration policy layer, a cache policy adaptive scheduling layer, a data access pattern layer, and a big data application layer. The framework prototype has been designed and implemented on the widely used distributed hybrid storage system named Alluxio. To meet the demands of the big data application layer, on the one hand, we designed a couple of cache eviction policies and promotion policies covering various access patterns on the cache migration policy layer (several proposed eviction policies have been adopted by the Alluxio open‐source community). On the other hand, two adaptive cache policy scheduling algorithms for selecting appropriate policies in various scenarios are designed and implemented on the cache policy adaptive scheduling layer. The scheduling algorithms are designed based on the hit ratio statistics and data access pattern model prediction, respectively. Experimental results show that the proposed cache policies are very effective for various big data applications, such as Spark SQL. The proposed cache policy scheduling algorithms with various eviction policies can improve around 20% hit ratio than that with a single eviction policy.

Three-weight cyclic codes and their weight distributions

Cyclic codes from cyclotomic sequences of order four

According to data broadcast, we can satisfy multiple requests for the same data item in a broadcast tick. However, there is no significant breakthrough in performance improvement until recently that some studies proposed to use network coding in data broadcast. After broadcasting an encoded packet which encodes a number of data items, multiple clients can retrieve different requested data items in a broadcast tick. This not only utilizes bandwidth more efficiently, but also improves system performance. In this work, we propose a generalized encoding framework to incorporate network coding into data scheduling algorithms for on-demand broadcast. In the framework, data scheduling can be formulated as a weighted maximum clique problem in a graph where the weight of the clique is defined according to the performance objectives of the applications. Under the proposed framework, existing data scheduling algorithms for on-demand broadcast can be migrated into their corresponding coding versions while preserving their original criteria in scheduling data items. Our simulation results using a number of representative scheduling algorithms show that significant performance improvement can be achieved with coding.

Although processing speed, storage capacity and network bandwidth are steadily increasing, network latency remains a bottleneck for scientists accessing large remote data sets. This problem is most acute with n-dimensional data. Grid researchers have only recently begun to develop tools for efficient remote access to n-dimensional data sets. Within the context of the Granite Scientific Database system, we show that latency penalties can be dramatically reduced using explicit knowledge of a user's access pattern represented as an iterator. The iterator not only performs an n-dimensional iteration for the user, but also communicates the access pattern to Granite so that a prefetching cache can be constructed that is tuned to the user's access pattern. We experimentally evaluate a scenario for incorporating Granite's prefetching mechanism into the grid, demonstrating extraordinary performance gains. In light of these results, we describe planned additions to existing grid services to allow selection of datasets according to the user access pattern

High-performance computing (HPC) has enabled advancements in computation speed and resource cost by utilizing all available server resources and using parallelization for speedup. This computation scheme encourages simulation model development, massive data collection, and AI computation models, all which store and compute on massive amounts of data. Data compression has enhanced the performance of storing and transferring this HPC application data to enable acceleration, but the benefits of data compression can also be transferred to the active allocated memory used by the application. In-line compression is a compression method that keeps the application memory compressed in allocated memory, decompressing memory as its data is needed by the algorithm. The actively decompressed data size in allocated memory can be limited by grouping and compressing the data into blocks informed by the application's computational kernel's data access patterns and a selected compressor. This research explores factors such as block size and compressor choice on the runtime and memory usage of the Matrix Multiplication kernel. Matrix multiplication (MM) is a fundamental HPC algorithm that exemplifies the memory access patterns and use cases present in other HPC kernels. MM kernels provide a baseline for evaluating in-line compression due to MM's row-based data access patterns and the usage of several matrices to compute the resulting matrix. Trade-offs between memory size and the compression runtime necessitate tuned parameters for each in-line compression-enabled kernel. The results of this research demonstrate essential parameter trends, trade-offs, and the importance of locality in the kernel's data access patterns.

In order to effectively utilize the broadcast bandwidth, it is necessary to have efficient on-line scheduling algorithms that can balance individual and overall performance and can scale in terms of database sizes and client populations. Moreover, the scheduling algorithm should be applicable to a heterogeneous environment where data items have different sizes. We address these issues in this paper. As stretch is widely adopted as a performance metric for variable-size data requests, we propose a broadcast scheduling algorithm to optimize the system performance in terms of stretch. We show by analysis that the proposed algorithm can indeed achieve the optimal performance in terms of stretch. Moreover, the proposed scheduling algorithm has very low decision overhead, which makes it a practical solution for on-demand broadcast scheduling. Simulation results demonstrate that our algorithm significantly outperforms existing scheduling algorithms under various scenarios.

Auction is an effective mechanism to distribute spectrum resources. Although many privacy-preserving auction schemes for spectrum allocation have been proposed, none of them is able to perform practical spectrum auctions while ensuring enough security for bidders' private information, such as geo-locations, bid values, and data access patterns. To address this problem, we propose SLISA, a lightweight auction framework which enables an efficient spectrum allocation without revealing anything but the auction outcome, i.e., the winning bidders and their clearing prices. We present contributions on two fronts. First, as a foundation of our design, we adopt a Shuffle-then-Compute strategy to build a series of secure sub-protocols based on lightweight cryptographic primitives (e.g., additive secret sharing and basic garbled circuits). Second, we improve an advanced spectrum auction mechanism to make it data-oblivious, such that data access patterns can be hidden. Meanwhile, the modified protocols adapt to our elaborate building blocks without affecting its validity and security. We formally prove the security of all protocols under a semi-honest adversary model, and demonstrate performance improvements compared with state-of-the-art works through extensive experiments.

As the speed gap between CPU and external memory widens, memory latency has become the dominant performance bottleneck in modern applications. Closely connected are caches which play an important role in reducing the average memory latency. The way data is accessed strongly influences cache performance. Numerous multimedia algorithms operating on data such as images and videos, perform processing over rectangular regions of pixels. If this and other data access patterns are properly exploited, significant performance improvements can be achieved. This paper proposes a prefetch-aware memory system that exploits 2D, stride and sequential data access patterns in multimedia applications. It aims at reducing the average memory access latency, lowering the number of memory accesses and utilizing the bandwidth efficiently. Our results reveal significant average memory access time (AMAT) reduction of 21.2% when utilizing effectively the proposed approach compared to the baseline in the evaluated workloads.

Much work has been done on characterizing the workload of a database system. Previous studies focused on providing different types of statistical summaries, and modeling the run-time behavior on the physical resource level. In this paper, we focus on characterizing the database system’s workload from the view of database users. We use user access patterns to describe how a client application or a group of users accesses the data of a database system. The user access patterns include a set of user access events that represent the format of the queries and a set of user access graphs that represent the query execution orders. User access patterns can help database administrators tune the system, help database users optimize queries, and help to predict and cache future queries. In this paper, we present several approaches to using user access patterns to improve system performance, and report some experimental results.

Understanding the inherent system characteristics is crucial to the design and optimization of cloud storage system, and few studies have systematically investigated its data characteristics and access patterns. This paper presents an analysis of file system snapshot and five-month access trace of a campus cloud storage system that has been deployed on Tsinghua campus for three years. The system provides online storage and data sharing services for more than 19,000 students and 500 student groups. We report several data characteristics including file size and file type, as well as some access patterns, including read/write ratio, read-write dependency and daily traffic. We find that there are many differences between cloud storage system and traditional file systems: our cloud storage system has larger file sizes, lower read/write ratio, and smaller set of active files than those of a typical traditional file system. With a trace-driven simulation, we find that the cache efficiency can be improved by 5 times using the guidance from our observations.