Client Access Patterns Research Articles

An Efficient Fuzzy Clustering Algorithm for Mining User Session Clusters on Web Log Data

Data mining is extremely vital to get important information from the web. Additionally, web usage mining (WUM) is essential for companies. WUM permits organizations to create rich information related to the eventual fate of their commercial capacity. The utilization of data that is assembled by Web Usage Mining gives the organizations the capacity to deliver results more compelling to their organizations and expanding of sales. Client access patterns can be mined from web access log information using Web Usage Mining (WUM) techniques. Because there are so many end-user sessions and URL resources, the size of web user session data is enormous. Human communications and non-deterministic browsing patterns increment equivocalness and dubiousness of client session information. The fuzzy set-based approach can solve most of the challenges listed above. This paper proposes an efficient Fuzzy Clustering algorithm for mining client session clusters from web access log information to find the groups of client profiles. In addition, the methodologies to preprocess the net log data as well as data cleanup client identification and session identification are going to be mentioned. This incorporates the strategy to do include choice (or dimensionality decrease) and meeting weight task assignments

Online Inter-Datacenter Service Migrations

Service migration between datacenters can reduce the network overhead within a cloud infrastructure; thereby, also improving the quality of service for the clients. Most of the algorithms in the literature assume that the client access pattern remains stable for a sufficiently long period so as to amortize such migrations. However, if such an assumption does not hold, these algorithms can take arbitrarily poor migration decisions that can substantially degrade system performance. In this paper, we approach the issue of performing service migrations for an unknown and dynamically changing client access pattern. We propose an online algorithm that minimizes the inter-datacenter network, taking into account the network load of migrating a service between two datacenters, as well as the fact that the client request pattern may change “quickly”, before such a migration is amortized. We provide a rigorous mathematical proof showing that the algorithm is 3.8-competitive for a cloud network structured as a tree of multiple datacenters. We briefly discuss how the algorithm can be modified to work on general graph networks with an $O{\mathrm{(log\vert V\vert)}}$ O ( log | V | ) probabilistic approximation of the optimal algorithm. Finally, we present an experimental evaluation of the algorithm based on extensive simulations.

Bisexual People’s Utilization of Sexual Health Services at an LGBTQ Community Center in Chicago

Bisexual people are at increased risk for sexually transmitted infections (STIs), including HIV. In STI data collection, bisexual people are often miscategorized as or conflated with heterosexual or gay/lesbian people. Such poor data capture practices invisibilize bisexual people and prevent the tailoring of HIV and STI services to cater to bisexual populations. The Center on Halsted (COH) is an LGBTQ community center in Chicago. COH’s HIV & STD Services Department provides HIV testing to people who present for HIV screening services age 12 and older. COH also administers the State of Illinois AIDS, HIV, & STD Hotline. This manuscript reports HIV-related service access patterns of COH clients by sexual identity, with an emphasis on reporting patterns among bisexual and other non-monosexual (e.g., “bi+”) populations in comparison with other sexual minority populations. Among COH’s HIV testing clients who were sexual minorities in fiscal years 2018 and 2019, 15.42% in 2018 and 16.71% in 2019 identified as bisexual, respectively. Among sexual minority hotline callers, approximately one quarter (25.1% in 2018 and 28.8% in 2019) identified as bisexual. Given that bisexual individuals comprise over half of sexual minority populations, these findings indicate that bi + individuals may be under-represented in accessing HIV and STI services at COH. These results provide previously unknown insight into the sexual identities of populations accessing HIV- and STD-related services within a LGBTQ community setting. Funders of HIV and STI surveillance services should change reporting requirements to make visible the HIV services access patterns and outcomes of bisexual populations.

A Tale of Two Trees: One Writes, and Other Reads

Abstract The Bitcoin network has offered a new way of securely performing financial transactions over the insecure network. Nevertheless, this ability comes with the cost of storing a large (distributed) ledger, which has become unsuitable for personal devices of any kind. Although the simplified payment verification (SPV) clients can address this storage issue, a Bitcoin SPV client has to rely on other Bitcoin nodes to obtain its transaction history and the current approaches offer no privacy guarantees to the SPV clients. This work presents T 3, a trusted hardware-secured Bitcoin full client that supports efficient oblivious search/update for Bitcoin SPV clients without sacrificing the privacy of the clients. In this design, we leverage the trusted execution and attestation capabilities of a trusted execution environment (TEE) and the ability to hide access patterns of oblivious random access machine (ORAM) to protect SPV clients’ requests from potentially malicious nodes. The key novelty of T 3 lies in the optimizations introduced to conventional ORAM, tailored for expected SPV client usages. In particular, by making a natural assumption about the access patterns of SPV clients, we are able to propose a two-tree ORAM construction that overcomes the concurrency limitation associated with traditional ORAMs. We have implemented and tested our system using the current Bitcoin Unspent Transaction Output (UTXO) Set. Our experiment shows that T 3 is feasible to be deployed in practice while providing strong privacy and security guarantees to Bitcoin SPV clients.

Spatial Air Index Based on Largest Empty Rectangles for Non-Flat Wireless Broadcast in Pervasive Computing

In pervasive computing, location-based services (LBSs) are valuable for mobile clients based on their current locations. LBSs use spatial window queries to enable useful applications for mobile clients. Based on skewed access patterns of mobile clients, non-flat wireless broadcast has been shown to efficiently disseminate spatial objects to mobile clients. In this paper, we consider a scenario in which spatial objects are broadcast to mobile clients over a wireless channel in a non-flat broadcast manner to process window queries. For such a scenario, we propose an efficient spatial air index method to handle window query access in non-flat wireless broadcast environments. The concept of largest empty rectangles is used to avoid unnecessary examination of the broadcast content, thus reducing the processing time for window queries. Simulation results show that the proposed spatial air index method outperforms the existing methods under various settings.

An extensible and configurable trace-driven web server cluster simulation platform

Scheduling algorithm and cache replacement algorithm are two important aspects in a web server cluster, which affect, even determine, the performance of the cluster system. However, their performance depends on client access pattern and some key system parameters to a large extent. In this paper, we design and implement a trace–driven web server cluster simulation platform based on JavaSim, called WSCSim, to evaluate the two kinds of algorithms. The algorithms are directly written in Java, so complicated algorithms can be simulated. In the platform, users are allowed to add new trace parsers to support different trace formats, and can utilise the timer and hook component to collect related information and then calculate the performance indexes they are interested in. Especially, the platform adopts loosely–coupled component architecture to facilitate extension, and all the extensible components are plug and play (PnP). All the components and their parameters can be configured in a configuration file of XML format. Two simulation application scenarios show the feasibility and validity of WSCSim.

Social video caching

We investigate online browsing of interrelated content, represented as a catalog of items of interest featuring graph dependencies. The content is served to clients via a system of decentralized proxy caches connected to cloud servers. A client selects the next item to browse from the list of recommended items, displayed on the currently browsed item׳s catalog page. A cache has a limited size to have every item selected by its browsing clients available for local access. Thus, the system pays a penalty, whenever a client selects an item that cannot be served directly from the proxy. Conversely, the system gains a reward, if a client selects an immediately available item. We aim to select the items to cache that maximize the profit earned by the system, for the given cache capacity. We design two linear-time optimization techniques for finding the desired items to cache. We enhance the operation of the system via two additional strategies. The first one dynamically tracks the items׳ selection probabilities for a client, as a function of its prior catalog access pattern and those of its community peers. The second one constructs dynamic overlays, on behalf of the clients, that are used to share the selected items directly among them. This augments the system׳s serving capacity and enhances the clients׳ browsing experience. We study the performance of the optimization techniques via numerical experiments. They exhibit efficiency gains over reference methods, by exploiting the content dependencies and correlated community-driven access patterns of the clients. We also report proxy bandwidth savings achieved by our overlay strategy over state-of-the-art methods, on content access patterns of clients with Facebook or Twitter ties.

Write-Once Read-Many Oblivious RAM

We introduce WORM-ORAM, a first mechanism that combines Oblivious RAM (ORAM) access privacy and data confidentiality with Write-Once Read-Many (WORM) regulatory data retention guarantees. Clients can outsource their database to a server with full confidentiality and data access privacy, and, for data retention, the server ensures client access WORM semantics. In general simple confidentiality and WORM assurances are easily achievable, e.g., via an encrypted outsourced data repository with server-enforced read-only access to existing records (albeit encrypted). However, this becomes hard when also access privacy is to be ensured-when client access patterns are necessarily hidden and the server cannot enforce access control directly. WORM-ORAM overcomes this by deploying a set of zero-knowledge proofs to convince the server that all stages of the protocol are WORM-compliant.

Online Capacity Identification of Multitier Websites Using Hardware Performance Counters

Understanding server capacity is crucial to system capacity planning, configuration, and QoS-aware resource management. Conventional stress testing approaches measure server capacity offline in terms of application-level performance metrics like response time and throughput. They are limited in measurement accuracy and timeliness. In a multitier website, resource bottleneck often shifts between tiers as client access pattern changes. This makes the problem of online capacity measurement even more challenge. This paper presents an online measurement approach based on low-level hardware performance metrics such as instructions execution rate and cache access behavior. Such metrics together define a system internal running state. The measurement approach uses machine learning techniques to infer application-level performance at each tier from a set of selected hardware performance counters. A coordinated predictor is induced over individual tier-wide models to make global system performance prediction and identify the bottleneck when the system becomes overloaded. Experiments were conducted on a two-tier Tomcat/MySQL-configured website using TPC-W benchmarks. Experimental results demonstrated that this approach was able to achieve an overload prediction accuracy of higher than 90 percent for a priori known input traffic mix and over 85 percent accuracy even for traffic causing frequent bottleneck shifting. It costs less than 0.5 percent runtime overhead for data collection and no more than 50 ms for each online decision making.

Coordinated session-based admission control with statistical learning for multi-tier internet applications

Popular Internet applications deploy a multi-tier architecture, with each tier provisioning a certain functionality to its preceding tier. In this paper, we address a challenging issue, session-based admission control for peak load management for multi-tier Internet applications. The session-based admission control approach (SBAC) designed for a single Web server is not effective for a multi-tier architecture. This is due to the fact that the bottleneck in a multi-tier website dynamically shifts among tiers as client access patterns change. Admission control based on only the bottleneck tier is not efficient as different sessions impose different resource consumptions at the different tiers. First, we propose a multi-tier measurement based admission control (MBAC), which pro-actively accepts different session mixes based on the utilization state of all tiers. More importantly, we design a coordinated session-based admission control approach (CoSAC) based on a machine learning technique. It uses a Bayesian network to correlate the states of all tiers. The probability with which a session is admitted is determined by the probabilistic inference of the network after applying the evidence in terms of utilization and processing time at each tier to the network. We compare CoSAC with MBAC and a Blackbox approach tailored from SBAC, using the industry standard TPC-W benchmark in a typical three-tier e-commerce website. Experimental results demonstrate the superior performance of CoSAC with respect to the effective session throughput.

Query privacy in wireless sensor networks

Existing mechanisms for querying wireless sensor networks leak client interests to the servers performing the queries. The leaks are not only in terms of specific regions of interest but also of client access patterns. In this article we introduce the problem of preserving the privacy of clients querying a wireless sensor network owned by untrusted organizations. We first propose an efficient protocol, SPYC, that ensures full client privacy in settings where the servers providing access to the network are honest-but-curious and whose collaboration does not extend beyond well-defined administrative purposes. Furthermore, we study the same query privacy problem in a setting where servers exhibit malicious behavior or where powerful external attackers have access to sensor network traffic information. In this setting we propose two metrics for quantifying the privacy achieved by a client's query sequence. We then extend SPYC with a suite of practical algorithms, then analyze the privacy and efficiency levels they provide. Our TOSSIM simulations show that the proposed extensions are communication efficient while significantly improving client privacy levels.

A concurrency control scheme for mobile transactions in broadcast disk environments

Broadcast disk technique has been often used to disseminate frequently requested data efficiently to a large volume of mobile clients over wireless channels. In broadcast disk environments, a server often broadcasts different data items with differing frequencies to reflect the skewed data access patterns of mobile clients. Previously proposed concurrency control methods for mobile transactions in wireless broadcast environments are focused on the mobile transactions with uniform data access patterns. These protocols perform poorly in broadcast disk environments where the data access patterns of mobile transactions are skewed. In broadcast disk environments, the time length of a broadcast cycle usually becomes large to reflect the skewed data access patterns. This will often cause read-only transactions to access old data items rather than the latest data items. Furthermore, updating mobile transactions will be frequently aborted and restarted in the final validation stage due to the update conflict of the same data items with high access frequencies. This problem will increase the average response time of the update mobile transactions and waste the uplink communication bandwidth. In this paper, we extend the existing FBOCC concurrency control method to efficiently handle mobile transactions with skewed data access patterns in broadcast disk environments. Our method allows read-only transactions to access the more updated data, and reduces the average response time of updating transactions through early aborts and restarts. Our method also reduces the amount of uplink communication bandwidth for the final validation of the update transactions. We present an in-depth experimental analysis of our method by comparing with existing concurrency control protocols. Our performance analysis shows that it significantly decreases the average response time and the amount of uplink bandwidths over existing methods.

Lease-based consistency schemes in the web environment

There are many methods to maintain consistency in the distributed computing environment. Ideally, efficient schemes for maintaining consistency should take into account the following factors: lease duration of replicated data, data access pattern and system parameters. One method used to supply strong consistency in the web environment is the lease method. During the proxy’s lease time from a web server, the web server can notify the modification to the proxy by invalidation or update. In this paper, we analyze lease protocol performance by the varying update/invalidation scheme, lease duration and read rates. By using these analyses, we can choose the adaptive lease time and proper protocol (invalidation or update scheme of the modification for each proxy in the web environment). As the number of proxies for web caching increases exponentially, a more efficient method for maintaining consistency needs to be designed. We also present three-tier hierarchies on which each group and node independently and adaptively choose the proper lease time and protocol for each proxy cache. These classifications of the scheme make proxy caching adaptive to client access pattern and system parameters.

An efficient nonuniform index in the wireless broadcast environments

Data broadcast is an efficient dissemination method to deliver information to mobile clients through the wireless channel. It allows a huge number of the mobile clients simultaneously access data in the wireless environments. In real-life applications, more popular data may be frequently accessed by clients than less popular ones. Under such scenarios, Acharya et al.’s Broadcast Disks algorithm ( BD) allocates more popular data appeared more times in a broadcast period than less popular ones, i.e., the nonuniform broadcast, and provides a good performance on reducing client waiting time. However, mobile devices should constantly tune in to the wireless broadcast channel to examine data, consuming a lot of energy. Using index technologies on the broadcast file can reduce a lot of energy consumption of the mobile devices without significantly increasing client waiting time. In this paper, we propose an efficient nonuniform index called the skewed index, SI, over BD. The proposed algorithm builds an index tree according to skewed access patterns of clients, and allocates index nodes for the popular data more times than those for the less popular ones in a broadcast cycle. From our experimental study, we have shown that our proposed algorithm outperforms the flexible index and the flexible distributed index.

Supporting Interactive Video-on-Demand With Adaptive Multicast Streaming

Recent advances in multicast video streaming algorithms have opened up new ways to provision video-on-demand services to potentially millions of users. However, the spectacular efficiency of multicast streaming algorithms can only be realized by restricting or even prohibiting interactive playback control. Experiments reveal that the performance of current state-of-the-art multicast streaming algorithms will degrade significantly even at very low levels of interactivity (e.g., one control per five users). This study tackles this challenge by investigating the fundamental limitations of multicast streaming algorithms in supporting interactive playback control and presents a general solution-static full stream scheduling (SFSS)-which can be applied to many of the existing multicast streaming algorithms to substantially improve their performance when interactive playback control is to be supported. Moreover, to solve the problem of optimizing the algorithm for the often unknown client access patterns (e.g., arrival rates and interactivity rates), we present a novel just-in-time simulation (JTS) scheme to dynamically and automatically tune operating parameters of the SFSS algorithm while the system is online. This JTS scheme not only eliminates the need for a priori knowledge of the often unknown system parameters, but also can adapt to changes in the client access pattern over time. Extensive simulation results show that the proposed adaptive algorithm can reduce the admission and interactive control latencies by as much as 90%

A skewed distributed indexing for skewed access patterns on the wireless broadcast

Data broadcast is an efficient way to disseminate information to a large number of mobile clients in the wireless environment. Adding an index data organization to the broadcast file can save client power consumption with little increase in client waiting time. The existing index technologies only consider equal access probabilities of data items. However, in real-life applications, some data items may be more popular than others; that is, access patterns of clients are skewed. In this paper, we propose a skewed distributed indexing, SDI, which considers the access probabilities of data items and the replication of index nodes. The proposed algorithm traverses an index tree to determine whether an index node should be replicated by considering the access probability of its child node. In our experimental results, we have shown that our proposed algorithm outperforms the variant-fanout index tree and the distributed indexing.

Modeling and generating realistic streaming media server workloads

Currently, Internet hosting centers and content distribution networks leverage statistical multiplexing to meet the performance requirements of a number of competing hosted network services. Developing efficient resource allocation mechanisms for such services requires an understanding of both the short-term and long-term behavior of client access patterns to these competing services. At the same time, streaming media services are becoming increasingly popular, presenting new challenges for designers of shared hosting services. These new challenges result from fundamentally new characteristics of streaming media relative to traditional web objects, principally different client access patterns and significantly larger computational and bandwidth overhead associated with a streaming request. To understand the characteristics of these new workloads we use two long-term traces of streaming media services to develop MediSyn, a publicly available streaming media workload generator . In summary, this paper makes the following contributions: (i) we propose a framework for modeling long-term behavior of network services by capturing the process of file introduction, non-stationary popularity of media accesses, file duration, encoding bit rate, and session duration. (ii) We propose a variety of practical models based on the study of the two workloads. (iii) We develop an open-source synthetic streaming service workload generator to demonstrate the capability of our framework to capture the models.

Adaptive multiversion data broadcast organizations

Broadcasting provides an efficient means for disseminating information in both wired and wireless setting. In this paper, we propose a suite of broadcast organization schemes for multiversion data broadcast, i.e., data broadcast in which more than one value is broadcast per data item. Besides increasing the concurrency of client transactions, multiversion broadcast provides clients with the possibility of accessing multiple server states. For example, such a functionality is essential to support applications that require access to data sequences and have limited local memory to store the previous versions, such as in the case of sensor networks. We identify two basic multiversion organizations, namely vertical and horizontal broadcasts and propose an efficient compression scheme applicable to both. We also consider a multiversion client data cache and introduce appropriate cache replacement techniques. Finally, we propose an adaptive scheme that dynamically selects the appropriate broadcast organization based on the client access pattern. We provide performance evaluation results for both flat and broadcast disk organizations and for a variety of client query patterns.

CLUSTERING OF CLIENT-SITES IN THREE-TIER DATABASE ARCHITECTURES

Conventional two-tier databases have shown performance limitations in the presence of many concurrent clients. We propose logical grouping of clients (or clustering) as the means to improve the performance of collaborative networked databases. In this paper, we discuss a three-tier client-server database architecture (3t-CSD) featuring the above partitioning. The proposed clustering is based on the similarity of clients' access patterns. Each cluster is supervised by a designated manager that coordinates data sharing among its members. A number of clients is optimally partitioned if sites in each individual cluster have the maximum common data access probability possible. We initially show that the optimal client clustering problem is NP-complete and then we develop two approximate solutions based on abstraction and filtering of statistics for client access patterns. Our main goal is to compare the performance of the conventional and three-tier client-server database architecture with respect to the transaction turnaround times and object response times. After developing system prototypes that implement both two-tier and 3t-CSDs, we experimentally show that as long as good client-clustering is possible, the 3t-CSD architecture yields sizable gains over its conventional counterpart. We also compare and evaluate the effectiveness of the two proposed techniques used to create client clusters. Finally, we examine the role of several preprocessing schemes used to reduce the volume of the input data supplied to the clustering techniques.

Changes in Web client access patterns: Characteristics and caching implications

Understanding the nature of the workloads and system demands created by users of the World Wide Web is crucial to properly designing and provisioning Web services. Previous measurements of Web client workloads have been shown to exhibit a number of characteristic featuress however, it is not clear how those features may be changing with time. In this study we compare two measurements of Web client workloads separated in time by three years, both captured from the same computing facility at Boston University. The older dataset, obtained in 1995, is well known in the research literature and has been the basis for a wide variety of studies. The newer dataset was captured in 1998 and is comparable in size to the older dataset. The new dataset has the drawback that the collection of users measured may no longer be representative of general Web userss however, using it has the advantage that many comparisons can be drawn more clearly than would be possible using a new, different source of measurement. Our results fall into two categories. First we compare the statistical and distributional properties of Web requests across the two datasets. This serves to reinforce and deepen our understanding of the characteristic statistical properties of Web client requests. We find that the kinds of distributions that best describe document sizes have not changed between 1995 and 1998, although specific values of the distributional parameters are different. Second, we explore the question of how the observed differences in the properties of Web client requests, particularly the popularity and temporal locality properties, affect the potential for Web file caching in the network. We find that for the computing facility represented by our traces between 1995 and 1998, (1) the benefits of using size-based caching policies have diminisheds and (2) the potential for caching requested files in the network has declined.