265 research outputs found
Asymptotic bounds for M^X/G/1 processor sharing queues
This paper analyzed the asymptotic bounds of an M/G/1 processor sharing queue with bulk arrivals
Moment-Generating Algorithm for Response Time in Processor Sharing Queueing Systems
Response times are arguably the most representative and important metric for measuring the performance of modern computer systems. Further, service level agreements (SLAs), ranging from data centres to smartphone users, demand quick and, equally important, predictable response times. Hence, it is necessary to calculate moments, at least, and ideally response time distributions, which is not straightforward. A new moment-generating algorithm for calculating response times analytically is obtained, based on M/M/1 processor sharing (PS) queueing models. This algorithm is compared against existing work on response times in M/M/1-PS queues and extended to M/M/1 discriminatory PS queues. Two real-world case studies are evaluated
Performance modelling with adaptive hidden Markov models and discriminatory processor sharing queues
In modern computer systems, workload varies at different times and locations. It is important to model the performance of such systems via workload models that are both representative and efficient. For example, model-generated workloads represent realistic system behaviour, especially during peak times, when it is crucial to predict and address performance bottlenecks. In this thesis, we model performance, namely throughput and delay, using adaptive models and discrete queues. Hidden Markov models (HMMs) parsimoniously capture the correlation and burstiness of workloads with spatiotemporal characteristics. By adapting the batch training of standard HMMs to incremental learning, online HMMs act as benchmarks on workloads obtained from live systems (i.e. storage systems and financial markets) and reduce time complexity of the Baum-Welch algorithm. Similarly, by extending HMM capabilities to train on multiple traces simultaneously it follows that workloads of different types are modelled in parallel by a multi-input HMM. Typically, the HMM-generated traces verify the throughput and burstiness of the real data. Applications of adaptive HMMs include predicting user behaviour in social networks and performance-energy measurements in smartphone applications. Equally important is measuring system delay through response times. For example, workloads such as Internet traffic arriving at routers are affected by queueing delays. To meet quality of service needs, queueing delays must be minimised and, hence, it is important to model and predict such queueing delays in an efficient and cost-effective manner. Therefore, we propose a class of discrete, processor-sharing queues for approximating queueing delay as response time distributions, which represent service level agreements at specific spatiotemporal levels. We adapt discrete queues to model job arrivals with distributions given by a Markov-modulated Poisson process (MMPP) and served under discriminatory processor-sharing scheduling. Further, we propose a dynamic strategy of service allocation to minimise delays in UDP traffic flows whilst maximising a utility function.Open Acces
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Queues don't matter when you can JUMP them!
QJUMP is a simple and immediately deployable approach
to controlling network interference in datacenter
networks. Network interference occurs when congestion
from throughput-intensive applications causes queueing
that delays traffic from latency-sensitive applications.
To mitigate network interference, QJUMP applies Internet
QoS-inspired techniques to datacenter applications.
Each application is assigned to a latency sensitivity level
(or class). Packets from higher levels are rate-limited
in the end host, but once allowed into the network can
“jump-the-queue” over packets from lower levels. In settings
with known node counts and link speeds, QJUMP
can support service levels ranging from strictly bounded
latency (but with low rate) through to line-rate throughput
(but with high latency variance).
We have implemented QJUMP as a Linux Traffic Control
module. We show that QJUMP achieves bounded
latency and reduces in-network interference by up to
300Ă—, outperforming Ethernet Flow Control (802.3x),
ECN (WRED) and DCTCP. We also show that QJUMP
improves average flow completion times, performing
close to or better than DCTCP and pFabric.This work was supported
by a Google Fellowship, EPSRC INTERNET Project
EP/H040536/1, Defense Advanced Research Projects
Agency (DARPA) and Air Force Research Laboratory
(AFRL), under contract FA8750-11-C-0249.This is the final published version. It first appeared at https://www.usenix.org/conference/nsdi15/technical-sessions/presentation/grosvenor
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