nils-herrmann/clean_scirepeval huggingface dataset:
Predicting and bypassing end-to-end Internet service degradations

We study the patterns and predictability of Internet end-to-end service degradations, where a degradation is a significant deviation of the round-trip time (RTT) between a client and a server. We use simultaneous RTT measurements collected from several locations to a large representative set of Web sites and study the duration and extent of degradations. We combine these measurements with border gateway protocol cluster information to learn on the location of the cause. We evaluate a number of predictors based upon hidden Markov models and Markov models. Predictors typically exhibit a tradeoff between two types of errors, false positives (incorrect degradation prediction) and false negatives (a degradation is not predicted). The costs of these error types is application dependent, but we capture the entire spectrum using a precision versus recall tradeoff. Using this methodology, we learn what information is most valuable for prediction (recency versus quantity of past measurements). Surprisingly, we also conclude that predictors that utilize history in a very simple way perform as well as more sophisticated ones. One important application of prediction is gateway selection, which is applicable when a local-area network is connected through multiple gateways to one or several Internet service provider. Gateway selection can boost reliability and survivability by selecting for each connection the (hopefully) best gateway. We show that gateway selection using our predictors can reduce the degradations to half of that obtained by routing all the connections through the best gateway.
Adapting to a reliable network path

We consider the model of unreliable network links, where at each time unit a link might be either up or down. We consider two related problems. The first, establishing end to end communication between two given nodes, where the performance measure is the average number of times the chosen path was disconnected. The second, is to build a spanning tree rooted at a given source node, where the performance measure is the average number of nodes that are the disconnected from the source. For both problems we design competitive algorithms.
Buffer overflows of merging streams

Consider an Internet service provider (ISP), or a corporate intranet, that connects a large number of users with the Internet backbone using an "uplink." Within such a system, consider the traffic oriented towards the uplink, namely the streams whose start points are the local users and whose destination is outside the local domain. These streams are merged by a network that consists of merge nodes, typically arranged in a tree topology whose root is directly connected to the uplink. Without loss of generality, we may assume that the bandwidth of the link emanating from a merge node is less than the sum of bandwidths of incoming links (otherwise, we can assume that the incoming links are connected directly to the next node up). Hence, when all users inject data at maximum local speed, packets will eventually be discarded. A very effective way to mitigate some of the losses due to temporary overloads is to equip the merge nodes with buffers, that can absorb transient bursts by storing incoming packets while the outgoing link is busy. The merge nodes are controlled by local on-line buffer management algorithms whose job is to decide which packets to forward and which to drop so as to minimize the damage in case of an overflow.
Software ENgineering Improved Competitive Guarantees for QoS Buffering

We consider a network providing Differentiated Services (Diffserv), which allow Internet Service Providers (ISP’s) to offer different levels of Quality of Service (QoS) to different traffic streams. We study two types of buffering policies that are used in network switches supporting QoS. In the FIFO type, packets must be transmitted in the order they arrive. In the uniform boundeddelay type, there is a maximum delay time associated with the switch and each packet must be transmitted within this time, or otherwise it is dropped. In both models, the buffer space is limited, and packets are lost when the buffer overflows. Each packet has an intrinsic value, and the goal is to maximize the total value of transmitted packets. Our main contribution is an algorithm for the FIFO model with arbitrary packet values that for the first time achieves a competitive ratio better than 2, namely 2 epsilon for a constant epsilon > 0. We also describe an algorithm for the uniform bounded delay model which simulates our algorithm for the FIFO model, and show that it achieves the same competitive ratio.
AdaVegas: adaptive control for TCP Vegas

We introduce AdaVegas, an adaptive congestion control mechanism based on TCP Vegas. TCP Vegas has several parameters which control the way it increases the sending rate. While TCP Vegas holds these parameters constant, AdaVegas sets these values dynamically. In this way, AdaVegas is able to change its increment strategy dynamically and better adapt to the current environment. Using simulations, we both evaluate AdaVegas and compare it to TCP Vegas. Our simulations show that AdaVegas achieves significantly better performance than TCP Vegas, all this with a fairly low overhead.
Combining online algorithms for rejection and acceptance

Resource allocation and admission control are critical tasks in a communication network, that often must be performed online. Algorithms for these types of problems have been considered both under benefit models (e.g., with a goal of approximately maximizing the number of calls accepted) and under cost models (e.g., with a goal of approximately minimizing the number of calls rejected). Unfortunately, algorithms designed for these two measures can often be quite different, even polar opposites (e.g., [1, 8]). In this work we consider the problem of combining algorithms designed for each of these objectives in a way that simultaneously is good under both measures. More formally, we are given an algorithm A which is cA competitive w.r.t. the number of accepted calls and an algorithm R which is cR competitive w.r.t. the number of rejected calls. We derive a combined algorithm whose competitive ratio is O(cR cA) for rejection and O(cA2) for acceptance. We also show building on known techniques that given a collection of k algorithms, we can construct one master algorithm which performs similar to the best algorithm among the k for the acceptance problem and another master algorithm which performs similar to the best algorithm among the k for the rejection problem. Using our main result we can combine the two master algorithms to a single algorithm which guarantees both rejection and acceptance competitiveness.
Action Elimination and Stopping Conditions for Reinforcement Learning

We consider incorporating action elimination procedures in reinforcement learning algorithms. We suggest a framework that is based on learning an upper and a lower estimates of the value function or the Q-function and eliminating actions that are not optimal. We provide a model-based and a model-free variants of the elimination method. We further derive stopping conditions that guarantee that the learned policy is approximately optimal with high probability. Simulations demonstrate a considerable speedup and added robustness.
Buffer Overflows of Merging Streams

We consider a network merging streams of packets with different quality of service (QoS) levels, where packets are transported from input links to output links via multiple merge stages. Each merge node is equipped with a finite buffer, and since the bandwidth of a link outgoing from a merge node is in general smaller than the sum of incoming bandwidths, overflows may occur. QoS is modeled by assigning a positive value to each packet, and the goal of the system is to maximize the total value of packets transmitted on the output links. We assume that each buffer runs an independent local scheduling policy, and analyze FIFO policies that must deliver packets in the order they were received. We show that a simple local on-line algorithm called Greedy does essentially as well as the combination of locally optimal (off-line) schedules. We introduce a concept we call the weakness of a link, defined as the ratio between the longest time a packet spends in the system before transmitted over the link, and the longest time a packet spends in that link’s buffer. We prove that for any tree, the competitive factor of Greedy is at most the maximal link weakness.
Convergence Time to Nash Equilibria

We study the number of steps required to reach a pure Nash Equilibrium in a load balancing scenario where each job behaves selfishly and attempts to migrate to a machine which will minimize its cost. We consider a variety of load balancing models, including identical, restricted, related and unrelated machines. Our results have a crucial dependence on the weights assigned to jobs. We consider arbitrary weights, integer weights, K distinct weights and identical (unit) weights. We look both at an arbitrary schedule (where the only restriction is that a job migrates to a machine which lowers its cost) and specific efficient schedulers (such as allowing the largest weight job to move first).
Adaptive AIMD congestion control

The main objectives of a congestion control algorithm are high bandwidth utilization, fairness and responsiveness in changing environment. However, these objectives are contradicting in particular situations since the algorithm has to constantly probe available bandwidth, which may affect its stability. This paper proposes a novel congestion control algorithm that achieves high bandwidth utilization providing fairness among competing connections and, on the other hand, is sufficiently responsive to changes of available bandwidth. The main idea of the algorithm is to use adaptive setting for the additive increase/multiplicative decrease (AIMD) congestion control scheme, where parameters may change dynamically, with respect to the current network conditions.
Harmonic buffer management policy for shared memory switches

We introduce a new general scheme for shared memory nonpreemptive scheduling policies. Our scheme utilizes a system of inequalities and thresholds and accepts a packet if it does not violate any of the inequalities. We demonstrate that many of the existing policies can be described using our scheme, thus validating its generality. We propose a new scheduling policy, based on our general scheme, which we call the harmonic policy. Our simulations show that the harmonic policy both achieves high throughput and easily adapts to changing load conditions. We also perform a theoretical analysis of the harmonic policy and demonstrate that its throughput competitive ratio is almost optimal.
PAC Bounds for Multi-armed Bandit and Markov Decision Processes

The bandit problem is revisited and considered under the PAC model. Our main contribution in this part is to show that given n arms, it suffices to pull the arms O(n/?2 log 1/?) times to find an ?-optimal arm with probability of at least 1 - ?. This is in contrast to the naive bound of O(n/?2 log n/?). We derive another algorithm whose complexity depends on the specific setting of the rewards, rather than the worst case setting. We also provide a matching lower bound. We show how given an algorithm for the PAC model Multi-armed Bandit problem, one can derive a batch learningalg orithm for Markov Decision Processes. This is done essentially by simulatingV alue Iteration, and in each iteration invokingt he multi-armed bandit algorithm. Using our PAC algorithm for the multi-armed bandit problem we improve the dependence on the number of actions.
Simple Learning Algorithms for Decision Trees and Multivariate Polynomials

In this paper we develop a new approach for learning decision trees and multivariate polynomials via interpolation of multivariate polynomials. This new approach yields simple learning algorithms for multivariate polynomials and decision trees over finite fields under any constant bounded product distribution. The output hypothesis is a (single) multivariate polynomial that is an $\epsilon$-approximation of the target under any constant bounded product distribution. 
The new approach demonstrates the learnability of many classes under any constant bounded product distribution and using membership queries, such as j-disjoint disjunctive normal forms (DNFs) and multivariate polynomials with bounded degree over any field. 
The technique shows how to interpolate multivariate polynomials with bounded term size from membership queries only. This, in particular, gives a learning algorithm for an O(log n)-depth decision tree from membership queries only and a new learning algorithm of any multivariate polynomial over sufficiently large fields from membership queries only. We show that our results for learning from membership queries only are the best possible.
Predicting and bypassing end-to-end internet service degradations

We study the patterns and predictability of Internet End-to-End service degradations, where a degradation is a significant deviation of the round trip time between a client and a server. We use simultaneous RTT measurements collected from several locations to a large representative set of Web sites and study the duration and extent of degradations. We combine these measurements with BGP cluster information to learn on the location of the cause.We evaluate a number of predictors based upon Hidden Markov Models and Markov Models. Predictors typically exhibit a tradeoff between two types of errors, false positives (incorrect degradation prediction) and false negatives (a degradation is not predicted). The costs of these error-types is application dependent, but we capture the entire spectrum using a precision versus recall tradeoff. Using this methodology, we learn what information is most valuable for prediction (recency versus quantity of past measurements). Surprisingly, we also conclude that predictors that utilize history in a very simple way perform as well as more sophisticated ones.One important application of prediction is gateway selection, which is applicable when a LAN is connected through multiple gateways to one or several ISP's. Gateway selection can boost reliability and survivability by selecting for each connection the (hopefully) best gateway. We show that gateway selection using our predictors can reduce the degradations to half of that obtained by routing all the connections through the best gateway.
Jitter control in QoS networks

We study jitter control in networks with guaranteed quality of service (QoS) from the competitive analysis point of view: we propose on-line algorithms that control jitter and compare their performance to the best possible (by an off-line algorithm) for any given arrival sequence. For delay jitter, where the goal is to minimize the difference between delay times of different packets, we show that a simple on-line algorithm using a buffer of B slots guarantees the same delay jitter as the best off-line algorithm using buffer space B/2. We prove that the guarantees made by our on-line algorithm hold, even for simple distributed implementations, where the total buffer space is distributed along the path of the connection, provided that the input stream satisfies a certain simple property. For rate jitter, where the goal is to minimize the difference between inter-arrival times, we develop an on-line algorithm using a buffer of size 2B + h for any h ≥ 1, and compare its jitter to the jitter of an optimal off-line algorithm using buffer size B. We prove that our algorithm guarantees that the difference is bounded by a term proportional to B/h.
Buffer overflow management in QoS switches

We consider two types of buffering policies that are used in network switches supporting QoS (Quality of Service). In the FIFO type, packets must be released in the order they arrive; the difficulty in this case is the limited buffer space. In the bounded-delay type, each packet has a maximum delay time by which it must be released, or otherwise it is lost. We study the cases where the incoming streams overload the buffers, resulting in packet loss. In our model, each packet has an intrinsic value; the goal is to maximize the total value of packets transmitted
Our main contribution is a thorough investigation of the natural greedy algorithms in various models. For the FIFO model we prove tight bounds on the competitive ratio of the greedy algorithm that discards the packets with the lowest value. We also prove that the greedy algorithm that drops the earliest packets among all low-value packets is the best greedy algorithm. This algorithm can be as much as 1.5 times better than the standard tail-drop policy, that drops the latest packets.
In the bounded delay model we show that the competitive ratio of any online algorithm for a uniform bounded delay buffer is bounded away from 1, independent of the delay size. We analyze the greedy algorithm in the general case and in three special cases: delay bound 2; link bandwidth 1; and only two possible packet values.
Finally, we consider the off-line scenario. We give efficient optimal algorithms and study the relation between the bounded-delay and FIFO models in this case.
QoS-Competitive Video Buffering

Many multimedia applications require transmission of streaming video from a server to a client across an internetwork. In many cases loss may be unavoidable due to congestion or heterogeneous nature of the network. We explore how discard policies can be used in order to maximize the quality of service (QoS) perceived by the client. In our model the QoS of a video stream is measured in terms of a cost function, which takes into account the discarded frames. In this paper we consider online policies for selective frame discard and analyze their performance by means of competitive analysis. In competitive analysis the performance of a given online policy is compared with that of an optimal offline policy. In this work we present competitive policies for a wide range of cost functions, describing the QoS of a video stream.
Competitve buffer management for shared-memory switches

We consider buffer management policies for shared memory packet switches supporting Quality of Service (QoS). There are two interesting dimensions in which the setting may different. The first is the packet size, whether all the packets of the same fixed size or do packets have variable length. The second is the value of the packets, do all the packets have the same value or do different packets have different values.
The goal of the buffer management policy is to maximize the total value of packets transmitted. Our main result is to show that the well-known Longest Queue Drop (LQD) policy in 2-competitive and at least √2-competitive for the case of fixed size and value packets. We also show a 4/3 general lower bound on the competitiveness in this case. We extend the results to the case of variable size fixed value packets, and derive a slightly worse bound. For the case of variable value we derive randomized policy whose competitive ratio in logarithmic on the ratio of the maximal to minimal value.
Convergence of Optimistic and Incremental Q-Learning

We show the convergence of two deterministic variants of Q-learning. The first is the widely used optimistic Q-learning, which initializes the Q-values to large initial values and then follows a greedy policy with respect to the Q-values. We show that setting the initial value sufficiently large guarantees the converges to an e-optimal policy. The second is a new and novel algorithm incremental Q-learning, which gradually promotes the values of actions that are not taken. We show that incremental Q-learning converges, in the limit, to the optimal policy Our incremental Q-learning algorithm can be viewed as derandomization of the e-greedy Q-learning.
Learning Rates for Q-learning

In this paper we derive convergence rates for Q-learning. We show an interesting relationship between the convergence rate and the learning rate used in Q-learning. For a polynomial learning rate, one which is 1/tω at time t where ω∈(1/2,1), we show that the convergence rate is polynomial in 1/(1-γ), where γ is the discount factor. In contrast we show that for a linear learning rate, one which is 1/t at time t, the convergence rate has an exponential dependence on 1/(1-γ). In addition we show a simple example that proves this exponential behavior is inherent for linear learning rates.
Generalization Bounds for Decision Trees

The problem of over-fitting is central to both the theory and practice of machine learning. Intuitively, one over-fits by using too many parameters in the concept, e.g, fitting an nth order polynomial ton data points. One under-fits by using too few parameters, e.g., fitting a linear curve to clearly quadr atic data. The fundamental question is how many parameters, or what concept size, should one allow for a given amount of training data. A standard theoretical approach is to prove a bound on generalization error as a function of the training error and the concept size (or VC dimension). One can then select a concept minimizing this bound, i.e., optimizing a certain tradeoff, as expressed in the bound, between traini ng error and concept size. Bounds on generalization error that express a tradeoff between the training error and the size of the concept are often called structural risk minimization (SRM) formulas. A variety of SRM bounds have been proved in the literature [Vap82]. The following SRM bound was proved in [McA98] and, for completeness, is proved again in Section 2. It state s that with probability1 over the sampleS we have the following.
Optimal smoothing schedules for real-time streams

We consider the problem of smoothing real-time streams (such as video streams), where the goal is to reproduce a variable-bandwidth stream remotely, while minimizing bandwidth cost, space requirement, and playback delay. We focus on lossy schedules, where data may be dropped due to limited bandwidth or space. We present the following results. First, we determine the optimal tradeoff between buffer space, smoothing delay, and link bandwidth for lossy smoothing schedules. Specifically, this means that if two of these parameters are given, we can precisely calculate the value for the third which minimizes data loss while avoiding resource wastage. The tradeoff is accomplished by a simple generic algorithm, that allows one some freedom in choosing which data to discard. This algorithm is very easy to implement both at the server and at the client, and it enjoys the nice property that only the server decides which data to discard, and the client needs only to reconstruct the stream.In a second set of results we study the case where different parts of the data have different importance, modeled by assigning a real “weight” to each packet in the stream. For this setting we use competitive analysis, i.e., we compare the weight delivered by on-line algorithms to the weight of an optimal off-line schedule using the same resources. We prove that a natural greedy algorithm is 4-competitive. We also prove a lower bound of 1.23 on the competitive ratio of any deterministic on-line algorithm. Finally, we give a few experimental results which seem to indicate that smoothing is very effective in practice, and that the greedy algorithm performs very well in the weighted case.