Relevant bibliographies by topics / Heavy-tailed workloads

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Academic literature on the topic 'Heavy-tailed workloads'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Heavy-tailed workloads"

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Psounis, Konstantinos, Pablo Molinero-Fernández, Balaji Prabhakar, and Fragkiskos Papadopoulos. "Systems with multiple servers under heavy-tailed workloads." Performance Evaluation 62, no. 1-4 (October 2005): 456–74. http://dx.doi.org/10.1016/j.peva.2005.07.030.

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Tai, Jianzhe, Zhen Li, Jiahui Chen, and Ningfang Mi. "Load balancing for cluster systems under heavy-tailed and temporal dependent workloads." Simulation Modelling Practice and Theory 44 (May 2014): 63–77. http://dx.doi.org/10.1016/j.simpat.2014.03.006.

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Foss, Sergey, and Masakiyo Miyazawa. "Two-node fluid network with a heavy-tailed random input: the strong stability case." Journal of Applied Probability 51, A (December 2014): 249–65. http://dx.doi.org/10.1017/s0021900200021318.

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We consider a two-node fluid network with batch arrivals of random size having a heavy-tailed distribution. We are interested in the tail asymptotics for the stationary distribution of a two-dimensional workload process. Tail asymptotics have been well studied for two-dimensional reflecting processes where jumps have either a bounded or an unbounded light-tailed distribution. However, the presence of heavy tails totally changes these asymptotics. Here we focus on the case of strong stability where both nodes release fluid at sufficiently high speeds to minimise their mutual influence. We show that, as in the one-dimensional case, big jumps provide the main cause for workloads to become large, but now they can have multidimensional features. We first find the weak tail asymptotics of an arbitrary directional marginal of the stationary distribution at Poisson arrival epochs. In this analysis, decomposition formulae for the stationary distribution play a key role. Then we employ sample-path arguments to find the exact tail asymptotics of a directional marginal at renewal arrival epochs assuming one-dimensional batch arrivals.
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Foss, Sergey, and Masakiyo Miyazawa. "Two-node fluid network with a heavy-tailed random input: the strong stability case." Journal of Applied Probability 51, A (December 2014): 249–65. http://dx.doi.org/10.1239/jap/1417528479.

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We consider a two-node fluid network with batch arrivals of random size having a heavy-tailed distribution. We are interested in the tail asymptotics for the stationary distribution of a two-dimensional workload process. Tail asymptotics have been well studied for two-dimensional reflecting processes where jumps have either a bounded or an unbounded light-tailed distribution. However, the presence of heavy tails totally changes these asymptotics. Here we focus on the case of strong stability where both nodes release fluid at sufficiently high speeds to minimise their mutual influence. We show that, as in the one-dimensional case, big jumps provide the main cause for workloads to become large, but now they can have multidimensional features. We first find the weak tail asymptotics of an arbitrary directional marginal of the stationary distribution at Poisson arrival epochs. In this analysis, decomposition formulae for the stationary distribution play a key role. Then we employ sample-path arguments to find the exact tail asymptotics of a directional marginal at renewal arrival epochs assuming one-dimensional batch arrivals.
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Janevski, Nikola, and Katerina Goseva-Popstojanova. "Session Reliability of Web Systems under Heavy-Tailed Workloads: An Approach Based on Design and Analysis of Experiments." IEEE Transactions on Software Engineering 39, no. 8 (August 2013): 1157–78. http://dx.doi.org/10.1109/tse.2013.3.

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Tang, Cheng-Jen, and Miau-Ru Dai. "Modeling and Analysis of Queueing-Based Vary-On/Vary-Off Schemes for Server Clusters." Mathematical Problems in Engineering 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/594264.

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A cloud system usually consists of a lot of server clusters handling various applications. To satisfy the increasing demands, especially for the front-end web applications, the computing capacity of a cloud system is often allocated for the peak demand. Such installation causes resource underutilization during the off-peak hours. Vary-On/Vary-Off (VOVO) schemes concentrate workloads on some servers instead of distributing them across all servers in a cluster to reduce idle energy waste. Recent VOVO schemes adopt queueing theory to model the arrival process and the service process for determining the number of powered-on servers. For the arrival process, Poisson process can be safely assumed in web services due to the large number of independent sources. On the other hand, the heavy-tailed distribution of service times is observed in real web systems. However, there are no exact solutions to determine the performance forM/heavy-tailed/mqueues. Therefore, this paper presents two queueing-based sizing approximations for Poisson and non-Poisson governed service processes. The simulation results of the proposed approximations are analyzed and evaluated by comparing with the simulated system running at full capacity. This relative measurement indicates that the Pareto distributed service process may be adequately modeled by memoryless queues when VOVO schemes are adopted.
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Borst, Sem, Michel Mandjes, and Miranda van Uitert. "Generalized processor sharing queues with heterogeneous traffic classes." Advances in Applied Probability 35, no. 03 (September 2003): 806–45. http://dx.doi.org/10.1017/s0001867800012556.

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We consider a queue fed by a mixture of light-tailed and heavy-tailed traffic. The two traffic flows are served in accordance with the generalized processor sharing (GPS) discipline. GPS-based scheduling algorithms, such as weighted fair queueing (WFQ), have emerged as an important mechanism for achieving service differentiation in integrated networks. We derive the asymptotic workload behaviour of the light-tailed traffic flow under the assumption that its GPS weight is larger than its traffic intensity. The GPS mechanism ensures that the workload is bounded above by that in an isolated system with the light-tailed flow served in isolation at a constant rate equal to its GPS weight. We show that the workload distribution is, in fact, asymptotically equivalent to that in the isolated system, multiplied by a certain prefactor, which accounts for the interaction with the heavy-tailed flow. Specifically, the prefactor represents the probability that the heavy-tailed flow is backlogged long enough for the light-tailed flow to reach overflow. The results provide crucial qualitative insight in the typical overflow scenario.
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Borst, Sem, Michel Mandjes, and Miranda van Uitert. "Generalized processor sharing queues with heterogeneous traffic classes." Advances in Applied Probability 35, no. 3 (September 2003): 806–45. http://dx.doi.org/10.1239/aap/1059486830.

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We consider a queue fed by a mixture of light-tailed and heavy-tailed traffic. The two traffic flows are served in accordance with the generalized processor sharing (GPS) discipline. GPS-based scheduling algorithms, such as weighted fair queueing (WFQ), have emerged as an important mechanism for achieving service differentiation in integrated networks. We derive the asymptotic workload behaviour of the light-tailed traffic flow under the assumption that its GPS weight is larger than its traffic intensity. The GPS mechanism ensures that the workload is bounded above by that in an isolated system with the light-tailed flow served in isolation at a constant rate equal to its GPS weight. We show that the workload distribution is, in fact, asymptotically equivalent to that in the isolated system, multiplied by a certain prefactor, which accounts for the interaction with the heavy-tailed flow. Specifically, the prefactor represents the probability that the heavy-tailed flow is backlogged long enough for the light-tailed flow to reach overflow. The results provide crucial qualitative insight in the typical overflow scenario.
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Borst, Sem, and Bert Zwart. "A reduced-peak equivalence for queues with a mixture of light-tailed and heavy-tailed input flows." Advances in Applied Probability 35, no. 03 (September 2003): 793–805. http://dx.doi.org/10.1017/s0001867800012544.

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We determine the exact large-buffer asymptotics for a mixture of light-tailed and heavy-tailed input flows. Earlier studies have found a ‘reduced-load equivalence’ in situations where the peak rate of the heavy-tailed flows plus the mean rate of the light-tailed flows is larger than the service rate. In that case, the workload is asymptotically equivalent to that in a reduced system, which consists of a certain ‘dominant’ subset of the heavy-tailed flows, with the service rate reduced by the mean rate of all other flows. In the present paper, we focus on the opposite case where the peak rate of the heavy-tailed flows plus the mean rate of the light-tailed flows is smaller than the service rate. Under mild assumptions, we prove that the workload distribution is asymptotically equivalent to that in a somewhat ‘dual’ reduced system, multiplied by a certain prefactor. The reduced system now consists of only the light-tailed flows, with the service rate reduced by the peak rate of the heavy-tailed flows. The prefactor represents the probability that the heavy-tailed flows have sent at their peak rate for more than a certain amount of time, which may be interpreted as the ‘time to overflow’ for the light-tailed flows in the reduced system. The results provide crucial insight into the typical overflow scenario.
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Borst, Sem, and Bert Zwart. "A reduced-peak equivalence for queues with a mixture of light-tailed and heavy-tailed input flows." Advances in Applied Probability 35, no. 3 (September 2003): 793–805. http://dx.doi.org/10.1239/aap/1059486829.

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We determine the exact large-buffer asymptotics for a mixture of light-tailed and heavy-tailed input flows. Earlier studies have found a ‘reduced-load equivalence’ in situations where the peak rate of the heavy-tailed flows plus the mean rate of the light-tailed flows is larger than the service rate. In that case, the workload is asymptotically equivalent to that in a reduced system, which consists of a certain ‘dominant’ subset of the heavy-tailed flows, with the service rate reduced by the mean rate of all other flows. In the present paper, we focus on the opposite case where the peak rate of the heavy-tailed flows plus the mean rate of the light-tailed flows is smaller than the service rate. Under mild assumptions, we prove that the workload distribution is asymptotically equivalent to that in a somewhat ‘dual’ reduced system, multiplied by a certain prefactor. The reduced system now consists of only the light-tailed flows, with the service rate reduced by the peak rate of the heavy-tailed flows. The prefactor represents the probability that the heavy-tailed flows have sent at their peak rate for more than a certain amount of time, which may be interpreted as the ‘time to overflow’ for the light-tailed flows in the reduced system. The results provide crucial insight into the typical overflow scenario.
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Dissertations / Theses on the topic "Heavy-tailed workloads"

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Broberg, James Andrew, and james@broberg com au. "Effective task assignment strategies for distributed systems under highly variable workloads." RMIT University. Computer Science and Information Technology, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080130.150130.

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Heavy-tailed workload distributions are commonly experienced in many areas of distributed computing. Such workloads are highly variable, where a small number of very large tasks make up a large proportion of the workload, making the load very hard to distribute effectively. Traditional task assignment policies are ineffective under these conditions as they were formulated based on the assumption of an exponentially distributed workload. Size-based task assignment policies have been proposed to handle heavy-tailed workloads, but their applications are limited by their static nature and assumption of prior knowledge of a task's service requirement. This thesis analyses existing approaches to load distribution under heavy-tailed workloads, and presents a new generalised task assignment policy that significantly improves performance for many distributed applications, by intelligently addressing the negative effects on performance that highly variable workloads cause. Many problems associated with the modelling and optimisations of systems under highly variable workloads were then addressed by a novel technique that approximated these workloads with simpler mathematical representations, without losing any of their pertinent original properties. Finally, we obtain advance queuing metrics (such as the variance of key measurements like waiting time and slowdown that are difficult to obtain analytically) through rigorous simulation.
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Nair, Jayakrishnan U. "Scheduling for Heavy-Tailed and Light-Tailed Workloads in Queueing Systems." Thesis, 2012. https://thesis.library.caltech.edu/7121/1/thesis.pdf.

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In much of classical queueing theory, workloads are assumed to be light-tailed, with job sizes being described using exponential or phase type distributions. However, over the past two decades, studies have shown that several real-world workloads exhibit heavy-tailed characteristics. As a result, there has been a strong interest in studying queues with heavy-tailed workloads. So at this stage, there is a large body of literature on queues with light-tailed workloads, and a large body of literature on queues with heavy-tailed workloads. However, heavy-tailed workloads and light-tailed workloads differ considerably in their behavior, and these two types of workloads are rarely studied jointly.

In this thesis, we design scheduling policies for queueing systems, considering both heavy-tailed as well as light-tailed workloads. The motivation for this line of work is twofold. First, since real world workloads can be heavy-tailed or light-tailed, it is desirable to design schedulers that are robust in their performance to distributional assumptions on the workload. Second, there might be scenarios where a heavy-tailed and a light-tailed workload interact in a queueing system. In such cases, it is desirable to design schedulers that guarantee fairness in resource allocation for both workload types.

In this thesis, we study three models involving the design of scheduling disciplines for both heavy-tailed as well as light-tailed workloads. In Chapters 3 and 4, we design schedulers that guarantee robust performance across heavy-tailed and light-tailed workloads. In Chapter 5, we consider a setting in which a heavy-tailed and a light-tailed workload complete for service. In this setting, we design scheduling policies that guarantee good response time tail performance for both workloads, while also maintaining throughput optimality.

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Conference papers on the topic "Heavy-tailed workloads"

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Crovella, Mark E., and Lester Lipsky. "Long-lasting transient conditions in simulations with heavy-tailed workloads." In the 29th conference. New York, New York, USA: ACM Press, 1997. http://dx.doi.org/10.1145/268437.268733.

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Li, Zhen, Jianzhe Tai, Jiahui Chen, and Ningfang Mi. "ADuS: Adaptive resource allocation in cluster systems under heavy-tailed and bursty workloads." In ICC 2012 - 2012 IEEE International Conference on Communications. IEEE, 2012. http://dx.doi.org/10.1109/icc.2012.6364020.

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Jayasinghe, M., Z. Tari, P. Zeephongsekul, and J. Broberg. "On the performance of multi-level time sharing policy under heavy-tailed workloads." In 2009 IEEE Symposium on Computers and Communications (ISCC). IEEE, 2009. http://dx.doi.org/10.1109/iscc.2009.5202272.

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4

Li, J. H., S. Luo, W. Tang, R. Levy, and K. Park. "Heavy-Tailed Workload Aware Ad Hoc Routing." In 2008 IEEE International Conference on Communications. IEEE, 2008. http://dx.doi.org/10.1109/icc.2008.463.

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