Sequence Of Requests Research Articles

The Study of Dynamic Caching via State Transition Field—the Case of Time-Varying Popularity

In the second part of this two-part paper, we extend the study of dynamic caching via state transition field (STF) to the case of time-varying content popularity. The objective of this part is to investigate the impact of time-varying content popularity on the STF and how such impact accumulates to affect the performance of a replacement scheme. Unlike the case in the first part, the STF is no longer static over time, and we introduce instantaneous STF to model it. Moreover, we demonstrate that many metrics, such as instantaneous state caching probability and average cache hit probability over an arbitrary sequence of requests, can be found using the instantaneous STF. As a steady state may not exist under time-varying content popularity, we characterize the performance of replacement schemes based on how the instantaneous STF of a replacement scheme after a content request impacts on its cache hit probability at the next request. From this characterization, insights regarding the relations between the pattern of change in the content popularity, the knowledge of content popularity exploited by the replacement schemes, and the effectiveness of these schemes under time-varying popularity are revealed. In the simulations, different patterns of time-varying popularity, including the shot noise model, are experimented. The effectiveness of example replacement schemes under time-varying popularity is demonstrated, and the numerical results support the observations from the analytic results.

Online optimization with look-ahead for freeway emergency vehicle dispatching considering availability

Traditional emergency management studies have made resource allocation decisions to serve the current emergency without knowing which future emergency will be occurring. Different ordered combinations of emergencies result in different performance outcomes. Even though future events can be anticipated, previous studies follow an assumption that events over a time interval are independent. This study follows an assumption that events are interdependent, because speed reduction and rubbernecking due to an initial incident provoke secondary incidents on freeways and the resource availability depends on service times of each request. The misconception that secondary incidents are not common has resulted in overlooking a look-ahead concept. This study is the pioneer in relaxing the structural assumptions of independency during the assignment of servers and approaching the challenge from an operational perspective, online optimization. The main objective is to minimize the time needed to respond to a sequence of requests. We introduce online dispatching strategies with visualization applied in different network sizes, number of requests, and service times to provide insights on model behavior and solution quality. The experimental evidence indicates that the algorithm works well in practice. We envision a new era in which an optimal resource allocation adapts to external events effectively and anticipates the future learning from the past to produce effective solutions.

Stochastic Dominance and the Bijective Ratio of Online Algorithms

Stochastic dominance is a technique for evaluating the performance of online algorithms that provides an intuitive, yet powerful stochastic order between the compared algorithms. When there is a uniform distribution over the request sequences, this technique reduces to bijective analysis. These methods have been applied in problems such as paging, list update, bin colouring, routing in array mesh networks, and in connection with Bloom filters, and have often provided a clear separation between algorithms whose performance varies significantly in practice. Despite their appealing properties, the above techniques are quite stringent, in that a relation between online algorithms may be either too difficult to establish analytically, or worse, may not even exist. In this paper, we propose remedies to these shortcomings. Our objective is to make all online algorithms amenable to the techniques of stochastic dominance and bijective analysis. First, we establish sufficient conditions that allow us to prove the bijective optimality of a certain class of algorithms for a wide range of problems; we demonstrate this approach in the context of well-studied online problems such as weighted paging, reordering buffer management, and 2-server on the circle. Second, to account for situations in which two algorithms are incomparable or there is no clear optimum, we introduce the bijective ratio as a natural extension of (exact) bijective analysis. Our definition readily generalizes to stochastic dominance. This makes it possible to compare two arbitrary online algorithms for an arbitrary online problem. In addition, the bijective ratio is a generalization of the Max/Max ratio (due to Ben-David and Borodin), and allows for the incorporation of other useful techniques such as amortized analysis. We demonstrate the applicability of the bijective ratio to one of the fundamental online problems, namely the continuous k-server problem on metrics such as the line, the circle, and the star. Among other results, we show that the greedy algorithm attains bijective ratios of O(k) across these metrics.

"Saako mustan tussin?" Oppilaiden pyynnöt venäjänkielisessä CLIL-alkuopetuksessa

Tässä artikkelissa tarkastellaan oppilaiden pyyntöjä venäjänkielisessä CLIL-alkuopetuksessa. Tutkimusaineisto koostuu kahden vuoden aikana videonauhoitetuista aidoista vuorovaikutustilanteista. Pyyntöjä tarkastellaan keskustelunanalyyttisesti vieruspareina, joissa etujäsen muodostuu yleensä oppilaan tuottamasta pyynnöstä ja jälkijäsen pyynnön hyväksymisestä tai hylkäämisestä. Sosiokulttuurisen ja tilanteisen oppimisen teorioiden pohjalta oletuksena on, että oppilaat sosiaalistuvat oman yhteisönsä toimintaan kahden alkuopetusvuoden aikana toimimalla yhdessä ja osallistumalla vuorovaikutuksessa opettajan ja vertaisoppijoiden kanssa. Jo ensimmäisen vuoden keväällä oppilaiden aloittamat pyynnöt muuttuvat lähes täysin venäjänkielisiksi johtuen todennäköisesti yhteisesti solmitun kielisopimuksen sisäistämisestä. Kielisopimuksella tarkoitetaan sitä, että oppilaiden edellytettiin ilmaisevan pyyntönsä kohdekielellä, venäjäksi (second language, L2). Kahden vuoden aikana oppilaat kehittyvät myös pyyntöjen rakenteellisessa muotoilemisessa käyttäen apunaan opettajan tarjoamia malleja ja rakennekehyksiä. Tutkimus osoittaa, että vuorovaikutustilanteissa voidaan omaksua sisällön ohessa myös kohdekielen rakenteita, sanastoa ja kielenkäyttöä sekä kielisopimuksen avulla ohjata oppilaiden toimijuutta kielen oppimisen näkökulmasta oikeaan suuntaan.

Incorporating the Raft consensus protocol in containers managed by Kubernetes: an evaluation

ABSTRACT Replication is a technique widely used to improve the reliability of applications. State machine replication is a special approach, where a set of computers are kept synchronised in the same state despite of failures that could occur in the system. The Raft algorithm can be used to implement a total order delivery protocol, delivering requests at the same order at all replicas, which is fundamental since in this approach all replicas must execute the same sequence of requests to present the same evolution in their states. Raft is easy to understand and implement, when compared to the Paxos algorithm. On the other hand, virtualisation can be seen as a technique that helps the development of reliable applications since it maintains each virtual machine (VM) isolated from the others. Virtualisation in data centres is changing from the traditional VMs to containers. In this context, this paper proposes KRaft, an incorporation of Raft in Kubernetes, a system that manages containers. After an evaluation of performance and resource consumption of KRaft, we found that it presents performance close to Raft executing on physical machines. Moreover, KRaft demands more network transmission while Raft executed in physical machines needs more processing power and memory.

Task-Completing Assessments in Service Encounters

ABSTRACTThis study examines positive low- and high-grade assessments in service encounters between customers and salespersons conducted in Swedish and recorded in Sweden and Finland. The assessments occur in a regular sequential pattern as third-turn moves that complete request-delivery sequences, longer coherent requesting sections, or request sequences in a pre-closing context. The positive valence of the assessments coheres with the satisfactory outcome of task completion, but their function is primarily pragmatic, used for segmenting the flow of task-oriented institutional interaction. The assessments stand as lexical TCUs, and their delivery is characterized by downgraded prosody and the speaker’s embodied shift away from the other. The analysis reveals distributional differences in the interactional practice: Customers produce task-completing assessments more often than the salespersons, and high-grade assessments are more frequent in the data from Sweden than from Finland. The data are in Sweden Swedish and Finland Swedish with English translations.

Preference structure in request sequences: What about role-play?

Preference structure in request sequences: What about role-play?

On Cost-Driven Collaborative Data Caching: A New Model Approach

In this paper we consider a new caching model that enables data sharing for network services in a cost-effective way. The proposed caching algorithms are characterized by using monetary cost and access information to control the cache replacements, instead of exploiting capacity-oriented strategies as in traditional approaches. In particular, given a stream of requests to a shared data item with respect to a homogeneous cost model, we first propose a fast off-line algorithm using dynamic programming techniques, which can generate an optimal schedule within $O(mn)$ time-space complexity by using cache, migration as well as replication to serve a $n$ -length request sequence in a $m$ -node network, substantially improving the previous results. Furthermore, we also study the online form of this problem, and present an 3-competitive online algorithm by leveraging an idea of anticipatory caching. The algorithm can serve an online request in constant time and is space efficient in $O(m)$ as well, rendering it more practical in reality. We evaluate our algorithms, together with some variants, by conducting extensive simulation studies. Our results show that the optimal cost of the off-line algorithm is changed in a parabolic form as the ratio of caching cost to transfer cost is increased, and the online algorithm is less than 2 times worse in most cases than its optimal off-line counterpart.

Telescoping responses to requests: Unpacking progressivity

In this paper, we identify and describe a new practice for responding to unfinished requests, which we call telescoping responses, due to their being designed for telescoping the request sequence forward in the face of troubles with progressivity and in producing the request. Considering cases from an American shoe repair shop, we demonstrate that telescoping responses serve to telescope request sequences exactly because they are neither syntactically, prosodically or pragmatically fitted to the unfinished requests that they respond to.

Near Optimal Online Algorithms and Fast Approximation Algorithms for Resource Allocation Problems

We present prior robust algorithms for a large class of resource allocation problems where requests arrive one-by-one (online), drawn independently from an unknown distribution at every step. We design a single algorithm that, for every possible underlying distribution, obtains a 1−ϵ fraction of the profit obtained by an algorithm that knows the entire request sequence ahead of time. The factor ϵ approaches 0 when no single request consumes/contributes a significant fraction of the global consumption/contribution by all requests together. We show that the tradeoff we obtain here that determines how fast ϵ approaches 0, is near optimal: We give a nearly matching lower bound showing that the tradeoff cannot be improved much beyond what we obtain. Going beyond the model of a static underlying distribution, we introduce the adversarial stochastic input model, where an adversary, possibly in an adaptive manner, controls the distributions from which the requests are drawn at each step. Placing no restriction on the adversary, we design an algorithm that obtains a 1−ϵ fraction of the optimal profit obtainable w.r.t. the worst distribution in the adversarial sequence. Further, if the algorithm is given one number per distribution, namely the optimal profit possible for each of the adversary’s distribution, then we design an algorithm that achieves a 1−ϵ fraction of the weighted average of the optimal profit of each distribution the adversary picks. In the offline setting we give a fast algorithm to solve very large linear programs (LPs) with both packing and covering constraints. We give algorithms to approximately solve (within a factor of 1+ϵ) the mixed packing-covering problem with O (γ m log ( n /δ)/ϵ 2 ) oracle calls where the constraint matrix of this LP has dimension n × m , the success probability of the algorithm is 1−δ, and γ quantifies how significant a single request is when compared to the sum total of all requests. We discuss implications of our results to several special cases including online combinatorial auctions, network routing, and the adwords problem.

Online Algorithm for Opportunistic Handling of Received Packets in Vehicular Networks

In vehicular ad-hoc networks, due to high mobility, vehicles usually communicate for short periods of time with several neighboring vehicles and are required to process data fast; sometimes in the order of few milliseconds. This urgency of data processing is further heightened in safety-critical scenarios that involve many vehicles. Such scenarios require data to be prioritized and processed with minimum delay. While packet scheduling has been extensively studied, these studies focus on channel scheduling, our work focuses on processing received packets by a vehicle in dense scenarios. In this paper, we formulate the prioritized data processing problem as an integer linear program given a prior knowledge of the request sequence and prove that it is NP-complete. Due to the difficulty of predicting the traffic patterns and obtaining the request sequence in advance, we propose an online algorithm that does not require the prior knowledge of the request sequence and achieves an O(1) competitive ratio. The proposed online algorithm strives to accept higher severity packets for processing in order to maximize the cumulative severity given vehicular communications/computation capacity constraints. Using real traffic traces, we evaluate the performance of the online algorithm against three online algorithms, in which two of them use an exponentially weighted moving average-based threshold while the other one accepts requests as capacity permits. Our evaluation shows that our algorithm achieves up to 492% more cumulative severity compared to the three other baseline algorithms.

A Meta-Analysis and Quality of High Probability Request Sequence Studies for Improving Youth Complianceand Persistence

A Meta-Analysis and Quality of High Probability Request Sequence Studies for Improving Youth Complianceand Persistence

Systematic Review of High Probability Requests in K-12 Settings: Examining the Evidence Base

We conducted this systematic review to classify the evidence-base status for high-probability request sequence (HPRS) as a strategy to improve students’ behavioral outcomes in general and special education settings across the K-12 continuum. Specifically, the purpose of this review was to determine whether HPRS could be classified as an evidence-based practice according to Council for Exceptional Children’s Standards for Evidence-Based Practices in Special Education employing a modified, weighted coding scheme specifying methodologically sound studies as meeting 80% or more of components across quality indicators (QIs). Two of the 22 included studies met all QIs, and 16 studies met or exceeded our 80% weighted criterion. Based on this body of evidence, we classified HPRS in K-12 school settings as potentially evidence based. We offer a discussion of educational implications, limitations, and future directions.

An O (log k )-Competitive Algorithm for Generalized Caching

In the generalized caching problem, we have a set of pages and a cache of size k . Each page p has a size w p ≥ 1 and fetching cost c p for loading the page into the cache. At any point in time, the sum of the sizes of the pages stored in the cache cannot exceed k . The input consists of a sequence of page requests. If a page is not present in the cache at the time it is requested, it has to be loaded into the cache, incurring a cost of c p . We give a randomized O (log k )-competitive online algorithm for the generalized caching problem, improving the previous bound of O (log 2 k ) by Bansal, Buchbinder, and Naor (STOC’08). This improved bound is tight and of the same order as the known bounds for the classic paging problem with uniform weights and sizes. We use the same LP-based techniques as Bansal et al. but provide improved and slightly simplified methods for rounding fractional solutions online.

Upper bounds for multi-level multi-server paging

The distributed multi-level multi-server paging problem (DMLMSP) defined in this paper extends and generalizes the classical distributed paging problem [3] to a distributed concurrent multi-level setting, in which multiple servers share caches at multiple levels. The DMLMSP can be used for modeling algorithms for efficient distributed storage systems, in which multiple servers use caches for accelerating access to a centralized storage, maintaining cache coherency across multiple nodes while minimizing access latency and optimizing cache-hit ratio. The DMLMSP model fits basic principles of Non-Uniform Cache Architectures (NUCAs) and can be used for analyzing multiple-level centralized storage. We present an optimal offline algorithm for the DMLMSP model, with minimum number of page faults and minimum makespan, whose time complexity is polynomial in the length of the servers' page request sequences. The new algorithm generalizes and simplifies the state of the art algorithms [3,1].

High-Probability Request Sequence: An Effective, Efficient Low-Intensity Strategy to Support Student Success

High-probability request sequence (HPRS) is a low-intensity strategy designed to increase student compliance by creating behavioral momentum. Momentum is established by providing three to five requests that a noncompliant student is most likely to do followed quickly by a less preferred request. Herein, we describe a step-by-step process for using HPRS in schools. Throughout, we offer lessons from practitioners who have used HPRS with students, including those at risk for emotional and behavioral disorders.

Competitive clustering of stochastic communication patterns on a ring

This paper studies a fundamental dynamic clustering problem. The input is an online sequence of pairwise communication requests between n nodes (e.g., tasks or virtual machines). Our goal is to minimize the communication cost by partitioning the communicating nodes into ell clusters (e.g., physical servers) of size k (e.g., number of virtual machine slots). We assume that if the communicating nodes are located in the same cluster, the communication request costs 0; if the nodes are located in different clusters, the request is served remotely using inter-cluster communication, at cost 1. Additionally, we can migrate: a node from one cluster to another at cost alpha ge 1. We initiate the study of a stochastic problem variant where the communication pattern follows a fixed distribution, set by an adversary. Thus, the online algorithm needs to find a good tradeoff between benefitting from quickly moving to a seemingly good configuration (of low inter-cluster communication costs), and the risk of prematurely ending up in a configuration which later turns out to be bad, entailing high migration costs. Our main technical contribution is a deterministic online algorithm which is O(log {n})-competitive with high probability (w.h.p.), for a specific but fundamental class of problems: namely on ring graphs. We also provide first insights in slightly more general models, where the adversary is not restricted to a fixed distribution or the ring.

Effective Low-Intensity Strategies to Enhance School Success: What Every Educator Needs to Know—A Closing Commentary

Researchers have identified low-intensity classroom management strategies that, when implemented consistently, lead to significant improvements in student behavior. The strategies detailed in this special issue—behavior-specific praise, high-probability request sequence, precorrection, active supervision, instructional choice, and instructional feedback—can easily be embedded within academic instruction and result in decreased student disruptive behavior and increased academic engagement.

Evaluating tasks within a high‐probability request sequence in children with autism spectrum disorder

AbstractNoncompliance is common in children with autism spectrum disorder (ASD). Antecedent interventions offer effective alternative to consequence procedures to decrease noncompliance. Although high‐probability request sequences have been shown to be effective, previous research has not evaluated types of tasks within a high‐probability request sequence. We compared the effects of relevant and irrelevant high‐probability tasks on compliance to low‐probability (low‐p) requests in children with ASD. After high levels of compliance to low‐p tasks were achieved across relevant and irrelevant conditions, fixed and variable presentations of high‐probability requests were compared. Results showed that relevant high‐probability requests increased the percentage of compliance more than irrelevant high‐probability requests across participants as compared with baseline. For two of three participants, variable presentations of the high‐probability requests resulted in higher percentages of compliance than fixed presentations. Results suggest that a variable presentation of relevant tasks should be considered within the high‐probability request sequence.

Moral licensing and moral cleansing applied to company-NGO collaborations in an online context

In this paper, we focus on a specific aspect of monetary value creation in CSR activities, namely, company–NGO collaborations in the solicitation of donations. During a customer's journey through an online retail environment, companies can control their presentation of options to donate to an NGO partner before or after the purchase of their products. We investigate how the sequence of purchase and donation requests influences the amount donated to an NGO and the potential revenues for a company. We apply two cognitive biases—moral licensing and moral cleansing—as theoretical bases. Prior research on moral licensing and moral cleansing frequently neglects ecological validity. Our research attempts to address this issue by applying experimental settings which increase the transferability of results into practical measures. The paper provides marketing managers with the opportunity to understand which sequence of donation and purchase options is the most suitable for their purpose.