7,906 research outputs found
A Framework for QoS-aware Execution of Workflows over the Cloud
The Cloud Computing paradigm is providing system architects with a new
powerful tool for building scalable applications. Clouds allow allocation of
resources on a "pay-as-you-go" model, so that additional resources can be
requested during peak loads and released after that. However, this flexibility
asks for appropriate dynamic reconfiguration strategies. In this paper we
describe SAVER (qoS-Aware workflows oVER the Cloud), a QoS-aware algorithm for
executing workflows involving Web Services hosted in a Cloud environment. SAVER
allows execution of arbitrary workflows subject to response time constraints.
SAVER uses a passive monitor to identify workload fluctuations based on the
observed system response time. The information collected by the monitor is used
by a planner component to identify the minimum number of instances of each Web
Service which should be allocated in order to satisfy the response time
constraint. SAVER uses a simple Queueing Network (QN) model to identify the
optimal resource allocation. Specifically, the QN model is used to identify
bottlenecks, and predict the system performance as Cloud resources are
allocated or released. The parameters used to evaluate the model are those
collected by the monitor, which means that SAVER does not require any
particular knowledge of the Web Services and workflows being executed. Our
approach has been validated through numerical simulations, whose results are
reported in this paper
A Priority-based Fair Queuing (PFQ) Model for Wireless Healthcare System
Healthcare is a very active research area, primarily due to the increase in the elderly population that leads to increasing number of emergency situations that require urgent actions. In recent years some of wireless networked medical devices were equipped with different sensors to measure and report on vital signs of patient remotely. The most important sensors are Heart Beat Rate (ECG), Pressure and Glucose sensors. However, the strict requirements and real-time nature of medical applications dictate the extreme importance and need for appropriate Quality of Service (QoS), fast and accurate delivery of a patientâs measurements in reliable e-Health ecosystem.
As the elderly age and older adult population is increasing (65 years and above) due to the advancement in medicine and medical care in the last two decades; high QoS and reliable e-health ecosystem has become a major challenge in Healthcare especially for patients who require continuous monitoring and attention. Nevertheless, predictions have indicated that elderly population will be approximately 2 billion in developing countries by 2050 where availability of medical staff shall be unable to cope with this growth and emergency cases that need immediate intervention. On the other side, limitations in communication networks capacity, congestions and the humongous increase of devices, applications and IOT using the available communication networks add extra layer of challenges on E-health ecosystem such as time constraints, quality of measurements and signals reaching healthcare centres.
Hence this research has tackled the delay and jitter parameters in E-health M2M wireless communication and succeeded in reducing them in comparison to current available models. The novelty of this research has succeeded in developing a new Priority Queuing model ââPriority Based-Fair Queuingââ (PFQ) where a new priority level and concept of ââPatientâs Health Recordââ (PHR) has been developed and
integrated with the Priority Parameters (PP) values of each sensor to add a second level of priority. The results and data analysis performed on the PFQ model under different scenarios simulating real M2M E-health environment have revealed that the PFQ has outperformed the results obtained from simulating the widely used current models such as First in First Out (FIFO) and Weight Fair Queuing (WFQ).
PFQ model has improved transmission of ECG sensor data by decreasing delay and jitter in emergency cases by 83.32% and 75.88% respectively in comparison to FIFO and 46.65% and 60.13% with respect to WFQ model. Similarly, in pressure sensor the improvements were 82.41% and 71.5% and 68.43% and 73.36% in comparison to FIFO and WFQ respectively. Data transmission were also improved in the Glucose sensor by 80.85% and 64.7% and 92.1% and 83.17% in comparison to FIFO and WFQ respectively. However, non-emergency cases data transmission using PFQ model was negatively impacted and scored higher rates than FIFO and WFQ since PFQ tends to give higher priority to emergency cases.
Thus, a derivative from the PFQ model has been developed to create a new version namely âPriority Based-Fair Queuing-Tolerated Delayâ (PFQ-TD) to balance the data transmission between emergency and non-emergency cases where tolerated delay in emergency cases has been considered. PFQ-TD has succeeded in balancing fairly this issue and reducing the total average delay and jitter of emergency and non-emergency cases in all sensors and keep them within the acceptable allowable standards. PFQ-TD has improved the overall average delay and jitter in emergency and non-emergency cases among all sensors by 41% and 84% respectively in comparison to PFQ model
Low latency via redundancy
Low latency is critical for interactive networked applications. But while we
know how to scale systems to increase capacity, reducing latency --- especially
the tail of the latency distribution --- can be much more difficult. In this
paper, we argue that the use of redundancy is an effective way to convert extra
capacity into reduced latency. By initiating redundant operations across
diverse resources and using the first result which completes, redundancy
improves a system's latency even under exceptional conditions. We study the
tradeoff with added system utilization, characterizing the situations in which
replicating all tasks reduces mean latency. We then demonstrate empirically
that replicating all operations can result in significant mean and tail latency
reduction in real-world systems including DNS queries, database servers, and
packet forwarding within networks
Bayesian inference for queueing networks and modeling of internet services
Modern Internet services, such as those at Google, Yahoo!, and Amazon, handle
billions of requests per day on clusters of thousands of computers. Because
these services operate under strict performance requirements, a statistical
understanding of their performance is of great practical interest. Such
services are modeled by networks of queues, where each queue models one of the
computers in the system. A key challenge is that the data are incomplete,
because recording detailed information about every request to a heavily used
system can require unacceptable overhead. In this paper we develop a Bayesian
perspective on queueing models in which the arrival and departure times that
are not observed are treated as latent variables. Underlying this viewpoint is
the observation that a queueing model defines a deterministic transformation
between the data and a set of independent variables called the service times.
With this viewpoint in hand, we sample from the posterior distribution over
missing data and model parameters using Markov chain Monte Carlo. We evaluate
our framework on data from a benchmark Web application. We also present a
simple technique for selection among nested queueing models. We are unaware of
any previous work that considers inference in networks of queues in the
presence of missing data.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS392 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Traffic-Driven Spectrum Allocation in Heterogeneous Networks
Next generation cellular networks will be heterogeneous with dense deployment
of small cells in order to deliver high data rate per unit area. Traffic
variations are more pronounced in a small cell, which in turn lead to more
dynamic interference to other cells. It is crucial to adapt radio resource
management to traffic conditions in such a heterogeneous network (HetNet). This
paper studies the optimization of spectrum allocation in HetNets on a
relatively slow timescale based on average traffic and channel conditions
(typically over seconds or minutes). Specifically, in a cluster with base
transceiver stations (BTSs), the optimal partition of the spectrum into
segments is determined, corresponding to all possible spectrum reuse patterns
in the downlink. Each BTS's traffic is modeled using a queue with Poisson
arrivals, the service rate of which is a linear function of the combined
bandwidth of all assigned spectrum segments. With the system average packet
sojourn time as the objective, a convex optimization problem is first
formulated, where it is shown that the optimal allocation divides the spectrum
into at most segments. A second, refined model is then proposed to address
queue interactions due to interference, where the corresponding optimal
allocation problem admits an efficient suboptimal solution. Both allocation
schemes attain the entire throughput region of a given network. Simulation
results show the two schemes perform similarly in the heavy-traffic regime, in
which case they significantly outperform both the orthogonal allocation and the
full-frequency-reuse allocation. The refined allocation shows the best
performance under all traffic conditions.Comment: 13 pages, 11 figures, accepted for publication by JSAC-HC
Information Design for Congested Social Services: Optimal Need-Based Persuasion
We study the effectiveness of information design in reducing congestion in
social services catering to users with varied levels of need. In the absence of
price discrimination and centralized admission, the provider relies on sharing
information about wait times to improve welfare. We consider a stylized model
with heterogeneous users who differ in their private outside options: low-need
users have an acceptable outside option to the social service, whereas
high-need users have no viable outside option. Upon arrival, a user decides to
wait for the service by joining an unobservable first-come-first-serve queue,
or leave and seek her outside option. To reduce congestion and improve social
outcomes, the service provider seeks to persuade more low-need users to avail
their outside option, and thus better serve high-need users. We characterize
the Pareto-optimal signaling mechanisms and compare their welfare outcomes
against several benchmarks. We show that if either type is the overwhelming
majority of the population, information design does not provide improvement
over sharing full information or no information. On the other hand, when the
population is a mixture of the two types, information design not only Pareto
dominates full-information and no-information mechanisms, in some regimes it
also achieves the same welfare as the "first-best", i.e., the Pareto-optimal
centralized admission policy with knowledge of users' types.Comment: Accepted for publication in the 21st ACM Conference on Economics and
Computation (EC'20). 40 pages, 6 figure
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