14,948 research outputs found
Universality of Load Balancing Schemes on Diffusion Scale
We consider a system of parallel queues with identical exponential
service rates and a single dispatcher where tasks arrive as a Poisson process.
When a task arrives, the dispatcher always assigns it to an idle server, if
there is any, and to a server with the shortest queue among randomly
selected servers otherwise . This load balancing scheme
subsumes the so-called Join-the-Idle Queue (JIQ) policy and the
celebrated Join-the-Shortest Queue (JSQ) policy as two crucial
special cases. We develop a stochastic coupling construction to obtain the
diffusion limit of the queue process in the Halfin-Whitt heavy-traffic regime,
and establish that it does not depend on the value of , implying that
assigning tasks to idle servers is sufficient for diffusion level optimality
Load Balancing in the Non-Degenerate Slowdown Regime
We analyse Join-the-Shortest-Queue in a contemporary scaling regime known as
the Non-Degenerate Slowdown regime. Join-the-Shortest-Queue (JSQ) is a
classical load balancing policy for queueing systems with multiple parallel
servers. Parallel server queueing systems are regularly analysed and
dimensioned by diffusion approximations achieved in the Halfin-Whitt scaling
regime. However, when jobs must be dispatched to a server upon arrival, we
advocate the Non-Degenerate Slowdown regime (NDS) to compare different
load-balancing rules.
In this paper we identify novel diffusion approximation and timescale
separation that provides insights into the performance of JSQ. We calculate the
price of irrevocably dispatching jobs to servers and prove this to within 15%
(in the NDS regime) of the rules that may manoeuvre jobs between servers. We
also compare ours results for the JSQ policy with the NDS approximations of
many modern load balancing policies such as Idle-Queue-First and
Power-of--choices policies which act as low information proxies for the JSQ
policy. Our analysis leads us to construct new rules that have identical
performance to JSQ but require less communication overhead than
power-of-2-choices.Comment: Revised journal submission versio
A survey of machine learning techniques applied to self organizing cellular networks
In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future
Adaptive Dispatching of Tasks in the Cloud
The increasingly wide application of Cloud Computing enables the
consolidation of tens of thousands of applications in shared infrastructures.
Thus, meeting the quality of service requirements of so many diverse
applications in such shared resource environments has become a real challenge,
especially since the characteristics and workload of applications differ widely
and may change over time. This paper presents an experimental system that can
exploit a variety of online quality of service aware adaptive task allocation
schemes, and three such schemes are designed and compared. These are a
measurement driven algorithm that uses reinforcement learning, secondly a
"sensible" allocation algorithm that assigns jobs to sub-systems that are
observed to provide a lower response time, and then an algorithm that splits
the job arrival stream into sub-streams at rates computed from the hosts'
processing capabilities. All of these schemes are compared via measurements
among themselves and with a simple round-robin scheduler, on two experimental
test-beds with homogeneous and heterogeneous hosts having different processing
capacities.Comment: 10 pages, 9 figure
The impact of policy elements on the financing costs of RE investment: The case of wind power in Germany
Renewable energy support mechanisms affect the attractiveness of projects by influencing uncertainties in revenues or expenditures and ultimately result in a change in the financing costs. The influence of feed-in tariffs on financing costs was investigated. 26 wind onshore investors were surveyed in a conjoint analysis and the results were used in a cash flow model to quantify the impact. The introduction of premium models under a fixed remuneration tariff scheme seems to increase the financing costs considerably. --
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