14511182
Does internet media traffic really follow Zipf-like distribution?
It is commonly agreed that Web traffic follows the Zipf-like distribution, which is an analytical foundation for improving Web access performance by client-server based proxy caching systems on the Internet. However, some recent studies have observed non-Zipf-like distributions of Internet media traffic in different content delivery systems. Due to the variety of media delivery systems and the diversity of media content, existing studies on media traffic are largely workload specific, and the observed access patterns are often different from or even conflict with each other. For Web media systems, study [3] reports that the access pattern of streaming media is Zipf-like in a university campus network, while study [2] finds that it is not Zipf-like in an enterprise media server. For VoD media systems, study [1] finds that it is not Zipf-like in a multicast-based Media-on-Demand server of a campus network, while study [9] reports it is Zipf-like in a large VoD streaming system of an ISP. For P2P media systems, study [4] reports that the access pattern of media workload in KaZaa system collected in a campus network is not Zipf-like, while study [5] reports that it is Zipf-like in another campus network. For live streaming media systems, study [8] reports it is Zipf-like while study [6] reports it is not Zipf-like. A number of models have been proposed to explain the observed media access patterns, such as the generalized Zipf-like model [7], “fetch-at-most-once” model [4], and two-mode Zipf model [6]. However, each of these models can only explain a very limited scope of measurement results. A general model of Internet media access patterns is highly desirable for traffic engineering on the Internet and is critical to design, benchmark, and evaluate Internet media delivery systems. In this study, we have analyzed a wide variety of media workloads on the Internet. The workloads were collected from both the client side and the server side in Web, VoD, P2P, and live streaming environments between 1998 and 2006, where the media content is delivered via Web/P2P downloading or unicast/multicast streaming. The duration of these workloads ranges from a few days to more than two years and the user population ranges from several thousands to more than one hundred thousand. The number of client requests
8572893
Access-Mode Predictions for Low-Power Cache Design
An access-mode prediction technique based on cache hit and miss speculation for cache design-achieves minimal energy consumption. Using this method, cache accesses can be adaptively switched between the way-prediction and the phased accessing modes.
14269691
Multiprocessor scalability predictions through detailed program execution analysis
Scalability measures the ability of a parallel system to improve performance as the size of an application problem and the number of processors involved increase. There are some limits to existing scalability studies. First, the problem size in a computation is not well-defined. Second, the methods used to differentiate algorithmic and architectural scalabilities are not effective enough. Thh-dly, most approaches to scalability study are either highly time-consuming or restricted to simple problem/architecture structures. A major effort of this work is to address these limits. We have extended the latency metric [11] for more complex scaling of problems, and to possibly isolate the scalability of an algorithm from a parallel system. The scalability y prediction is based on a semi-empirical approach [10] that significantly reduces the time and cost of measurements and simulation. Our prediction results were validated on the KSR1 and on the CM-5.
10102756
Semi-Empirical Multiprocessor Performance Predictions
This paper presents a multiprocessor performance prediction methodology supported by experimental measurements, which predicts the execution time of large application programs on large parallel architectures based on a small set of sample data. We propose a graph model to describe application program behavior. In order to precisely abstract an architecture model for the prediction, important and implicit architecture parameters are obtained by experiments. We focus on performance predictions of application programs in shared-memory and data-parallel architectures. Real world applications are implemented using the shared-memory model on the KSR-1 and using the data-parallel model on the CM-5 for performance measurements and prediction validation. We show that experimental measurements provide strong support for performance predictions on multiprocessors with implicit communications and complex memory systems, such as shared-memory and data-parallel systems, while analytical techniques partially applied in the prediction significantly reduce computer simulation and measurement time.
1459531
SmartSaver: Turning Flash Drive into a Disk Energy Saver for Mobile Computers
In a mobile computer the hard disk consumes a considerable amount of energy. Existing dynamic power management policies usually take conservative approaches to save disk energy, and disk energy consumption remains a serious issue. Meanwhile, the flash drive is becoming a must-have portable storage device for almost every laptop user on travel. In this paper, we propose to make another highly desired use of the flash drive - saving disk energy. This is achieved by using the flash drive as a standby buffer for caching and prefetching disk data. Our design significantly extends disk idle times with careful and deliberate consideration of the particular characteristics of the flash drive. Trace-driven simulations show that up to 41% of disk energy can be saved with a relatively small amount of data written to the flash drive
1520083
Token-ordered LRU: an effective page replacement policy and its implementation in Linux systems
Most computer systems use a global page replacement policy based on the LRU principle to approximately select a Least Recently Used page for a replacement in the entire user memory space. During execution interactions, a memory page can be marked as LRU even when its program is conducting page faults. We define the LRU pages under such a condition as false LRU pages because these LRU pages are not produced by program memory reference delays, which is inconsistent with the LRU principle. False LRU pages can significantly increase page faults, even cause system thrashing. This poses a more serious risk in a large parallel systems with distributed memories because of the existence of coordination among processes running on individual node. In the case, the process thrashing in a single node or a small number of nodes could severely affect other nodes running coordinating processes, even crash the whole system. In this paper, we focus on how to improve the page replacement algorithm running on one node. After a careful study on characterizing the memory usage and the thrashing behaviors in the multi-programming system using LRU replacement. we propose an LRU replacement alternative, called token-ordered LRU, to eliminate or reduce the unnecessary page faults by effectively ordering and scheduling memory space allocations. Compared with traditional thrashing protection mechanisms such as load control, our policy allows more processes to keep running to support synchronous distributed process computing. We have implemented the token-ordered LRU algorithm in a Linux kernel to show its effectiveness.
2953976
A performance study of monitoring and information services for distributed systems
Monitoring and information services form a key component of a distributed system, or Grid. A quantitative study of such services can aid in understanding the performance limitations, advise in the deployment of the monitoring system, and help evaluate future development work. To this end, we study the performance of three monitoring and information services for distributed systems: the Globus Toolkit/spl reg/ Monitoring and Discovery Service (MDS2), the European Data Grid Relational Grid Monitoring Architecture (R-GMA) and Hawkeye, part of the Condor project. We perform experiments to test their scalability with respect to number of users, number of resources and amount of data collected. Our study shows that each approach has different behaviors, often due to their different design goals. In the four sets of experiments we conducted to evaluate the performance of the service components under different circumstances, we found a strong advantage to caching or pre-fetching the data, as well as the need to have primary components at well-connected sites because of the high load seen by all systems.
17933107
Safety analysis of usage control authorization models
The usage control (UCON) model was introduced as a unified approach to capture a number of extensions for traditional access control models. While the policy specification flexibility and expressive power of this model have been studied in previous work, as a related and fundamental problem, the safety analysis of UCON has not been explored. This paper presents two fundamental safety results for UCONA, a sub-model of UCON only considering authorizations. In UCONA, an access control decision is based on the subject and/or the object attributes, which can be changed as the side-effects of using the access right, resulting in possible changes to future access control decisions. Hence the safety question in UCONA is all the more pressing since every access can potentially enable additional permissions due to the mutability of attributes in UCON. In this paper, first we show that the safety problem is in general undecidable. Then, we show that a restricted form of UCONA with finite attribute value domains and acyclic attribute creation relation has a decidable safety property. The decidable model maintains good expressive power as shown by specifying an RBAC system with a specific user-role assignment scheme and a DRM application with consumable rights.
14687114
Price Cycles in Online Advertising Auctions
Paid placement in search engines has become one of the most successful and rapidly growing sectors of the online advertising industry. The innovative use of auctions to sell keyword-related advertisement positions is perhaps the most important factor driving the success of this market. There has been no systematic analysis, however, of the advertisers’ strategies to bid for ranks in a dynamic environment, where each bidder’s bid can be updated and observed by the competitors in real time. We capture this dynamic setting using a Markov process and identify the Markov perfect equilibrium. We find that in such a dynamic environment, bidders’ bidding strategies follow a cyclical pattern (Edgeworth cycle) similar to that conjectured by Edgeworth (1925) in a totally different context. A new data set that contains a detailed bidding history of all advertisers for sample keywords in a leading search engine makes it possible for us to study the real-world behavior of bidders. We propose an empirical framework based on maximum likelihood estimation of latent Markov state switching to confirm the theory. We also discuss the theoretical and practical significance of finding such cycles in an online market place.
9003534
A Language Modeling Text Mining Approach to the Annotation of Protein Community
This paper discusses an ontology based language modeling text mining approach to the annotation of protein community. Communities appear to play an important role in the functional properties of complex networks. Being able to annotate the identified the community structure in a biological network can help us to understand better the structure and dynamics of biological systems. Traditional method such as gene ontology (GO) provides information about the functionality of gene products, but they are not enough to annotate community as for only limited number of proteins in the database, limited protein properties available for annotation and the inability to annotate a group of gene products as a whole. Thus, we present an ontology based mixture language model approach to annotate protein community. Compared to traditional method, we have the following three advantages. First, biomedical literature mining brings much richer information than existed gene databases. Second, the mixture language model can help "purify" the document by eliminating some background noise. Third, using domain ontology, we extract biological concept and concept pairs from abstracts. Biological concept is more meaningful than word or multi-word phrases. Moreover, using concept pairs can deliver much more information and serve as evidence of annotation results. We test our approach on four communities SAGA-SRB, CCR-NOT, RFC and ARP2/3, detected from dataset of interactions for Saccharomyces cerevisae from the general repository for interaction datasets (GRID). Annotation results provide a very coherent indication of functionality of each community
28741459
Learning Layouts of Biological Datasets Semi-automatically
A key challenge associated with the existing approaches for data integration and workflow creation for bioinformatics is the effort required to integrate a new data source. As new data sources emerge, and data formats and contents of existing data sources evolve, wrapper programs need to be written or modified. This can be extremely time consuming, tedious, and error-prone. 
 
This paper describes our semi-automatic approach for learning the layout of a flat-file bioinformatics dataset. Our approach involves three key steps. The first step is to use a number of heuristics to infer the delimiters used in the program. Specifically, we have developed a metric that uses information on the frequency and starting position of sequences. Based on this metric, we are able to find a superset of delimiters, and then we can seek user input to eliminate the incorrect ones. Our second step involves generating a layout descriptor based on the relative order in which the delimiters occur. Our final step is to generate a parser based on the layout descriptor. Our heuristics for finding the delimiters has been evaluated using three popular flat-file biological datasets.