322 research outputs found
Optimal Hyper-Scalable Load Balancing with a Strict Queue Limit
Load balancing plays a critical role in efficiently dispatching jobs in
parallel-server systems such as cloud networks and data centers. A fundamental
challenge in the design of load balancing algorithms is to achieve an optimal
trade-off between delay performance and implementation overhead (e.g.
communication or memory usage). This trade-off has primarily been studied so
far from the angle of the amount of overhead required to achieve asymptotically
optimal performance, particularly vanishing delay in large-scale systems. In
contrast, in the present paper, we focus on an arbitrarily sparse communication
budget, possibly well below the minimum requirement for vanishing delay,
referred to as the hyper-scalable operating region. Furthermore, jobs may only
be admitted when a specific limit on the queue position of the job can be
guaranteed.
The centerpiece of our analysis is a universal upper bound for the achievable
throughput of any dispatcher-driven algorithm for a given communication budget
and queue limit. We also propose a specific hyper-scalable scheme which can
operate at any given message rate and enforce any given queue limit, while
allowing the server states to be captured via a closed product-form network, in
which servers act as customers traversing various nodes. The product-form
distribution is leveraged to prove that the bound is tight and that the
proposed hyper-scalable scheme is throughput-optimal in a many-server regime
given the communication and queue limit constraints. Extensive simulation
experiments are conducted to illustrate the results
On the Benefit of Information Centric Networks for Traffic Engineering
Current Internet performs traffic engineering (TE) by estimating traffic
matrices on a regular schedule, and allocating flows based upon weights
computed from these matrices. This means the allocation is based upon a guess
of the traffic in the network based on its history. Information-Centric
Networks on the other hand provide a finer-grained description of the traffic:
a content between a client and a server is uniquely identified by its name, and
the network can therefore learn the size of different content items, and
perform traffic engineering and resource allocation accordingly. We claim that
Information-Centric Networks can therefore provide a better handle to perform
traffic engineering, resulting in significant performance gain.
We present a mechanism to perform such resource allocation. We see that our
traffic engineering method only requires knowledge of the flow size (which, in
ICN, can be learned from previous data transfers) and outperforms a min-MLU
allocation in terms of response time. We also see that our method identifies
the traffic allocation patterns similar to that of min-MLU without having
access to the traffic matrix ahead of time. We show a very significant gain in
response time where min MLU is almost 50% slower than our ICN-based TE method
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Experimentation on Dynamic Congestion Control in Software Defined Networking (SDN) and Network Function Virtualisation (NFV)
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonIn this thesis, a novel framework for dynamic congestion control has been
proposed. The study is about the congestion control in broadband communication
networks. Congestion results when demand temporarily exceeds capacity and leads to
severe degradation of Quality of Service (QoS) and possibly loss of traffic. Since traffic
is stochastic in nature, high demand may arise anywhere in a network and possibly
causing congestion. There are different ways to mitigate the effects of congestion, by
rerouting, by aggregation to take advantage of statistical multiplexing, and by discarding
too demanding traffic, which is known as admission control. This thesis will try to
accommodate as much traffic as possible, and study the effect of routing and aggregation
on a rather general mix of traffic types. Software Defined Networking (SDN) and Network Function Virtualization (NFV) are concepts that allow for dynamic configuration of network resources by
decoupling control from payload data and allocation of network functions to the most suitable physical node. This allows implementation of a centralised control that takes the
state of the entire network into account and configures nodes dynamically to avoid
congestion. Assumes that node controls can be expressed in commands supported by
OpenFlow v1.3. Due to state dependencies in space and time, the network dynamics are
very complex, and resort to a simulation approach. The load in the network depends on
many factors, such as traffic characteristics and the traffic matrix, topology and node
capacities. To be able to study the impact of control functions, some parts of the
environment is fixed, such as the topology and the node capacities, and statistically
average the traffic distribution in the network by randomly generated traffic matrices. The
traffic consists of approximately equal intensity of smooth, bursty and long memory
traffic. By designing an algorithm that route traffic and configure queue resources so that
delay is minimised, this thesis chooses the delay to be the optimisation parameter because
it is additive and real-time applications are delay sensitive. The optimisation being studied
both with respect to total end-to-end delay and maximum end-to-end delay. The delay is used as link weights and paths are determined by Dijkstra’s algorithm. Furthermore, nodes are configured to serve the traffic optimally which in turn depends on the routing. The proposed algorithm is a fixed-point system of equations that iteratively evaluates routing – aggregation – delay until an equilibrium point is found.
Three strategies are compared: static node configuration where each queue is
allocated 1/3 of the node resources and no aggregation, aggregation of real-time (taken
as smooth and bursty) traffic onto the same queue, and dynamic aggregation based on the
entropy of the traffic streams and their aggregates. The results of the simulation study
show good results, with gains of 10-40% in the QoS parameters. By simulation, the
positive effects of the proposed routing and aggregation strategy and the usefulness of the
algorithm. The proposed algorithm constitutes the central control logic, and the resulting
control actions are realisable through the SDN/NFV architecture
EUROPEAN CONFERENCE ON QUEUEING THEORY 2016
International audienceThis booklet contains the proceedings of the second European Conference in Queueing Theory (ECQT) that was held from the 18th to the 20th of July 2016 at the engineering school ENSEEIHT, Toulouse, France. ECQT is a biannual event where scientists and technicians in queueing theory and related areas get together to promote research, encourage interaction and exchange ideas. The spirit of the conference is to be a queueing event organized from within Europe, but open to participants from all over the world. The technical program of the 2016 edition consisted of 112 presentations organized in 29 sessions covering all trends in queueing theory, including the development of the theory, methodology advances, computational aspects and applications. Another exciting feature of ECQT2016 was the institution of the Takács Award for outstanding PhD thesis on "Queueing Theory and its Applications"
Sleep Mode Analysis via Workload Decomposition
The goal of this paper is to establish a general approach for analyzing
queueing models with repeated inhomogeneous vacations. The server goes on for a
vacation if the inactivity prolongs more than the vacation trigger duration.
Once the system enters in vacation mode, it may continue for several
consecutive vacations. At the end of a vacation, the server goes on another
vacation, possibly with a different probability distribution; if during the
previous vacation there have been no arrivals. However the system enters in
vacation mode only if the inactivity is persisted beyond defined trigger
duration. In order to get an insight on the influence of parameters on the
performance, we choose to study a simple M/G/1 queue (Poisson arrivals and
general independent service times) which has the advantage of being tractable
analytically. The theoretical model is applied to the problem of power saving
for mobile devices in which the sleep durations of a device correspond to the
vacations of the server. Various system performance metrics such as the frame
response time and the economy of energy are derived. A constrained optimization
problem is formulated to maximize the economy of energy achieved in power save
mode, with constraints as QoS conditions to be met. An illustration of the
proposed methods is shown with a WiMAX system scenario to obtain design
parameters for better performance. Our analysis allows us not only to optimize
the system parameters for a given traffic intensity but also to propose
parameters that provide the best performance under worst case conditions
Bridging the gap between dataplanes and commodity operating systems
The conventional wisdom is that aggressive networking requirements, such as high packet rates for small messages and microsecond-scale tail latency, are best addressed outside the kernel, in a user-level networking stack. In particular, dataplanes borrow design elements from network middleboxes to run tasks to completion in tight loops. In its basic form, the dataplane design leverages sweeping simplifications such as the elimination of any resource management and any task scheduling to improve throughput and lower latency. As a result, dataplanes perform best when the request rate is predictable (since there is no resource management) and the service time of each task has a low execution time and a low dispersion. On the other hand, they exhibit poor energy proportionality and workload consolidation, and suffer from head-of-line blocking.
This thesis proposes the introduction of resource management to dataplanes. Current dataplanes decrease latency by constantly polling for incoming network packets. This approach trades energy usage for latency. We argue that it is possible to introduce a control plane, which manages the resources in the most optimal way in terms of power usage without affecting the performance of the dataplane.
Additionally, this thesis proposes the introduction of scheduling to dataplanes. Current designs operate in a strict FIFO and run-to-completion manner. This method is effective only when the incoming request requires a minimal amount of processing in the order of a few microseconds. When the processing time of requests is (a) longer or (b) follows a distribution with higher dispersion, the transient load imbalances and head-of-line blocking deteriorate the performance of the dataplane. We claim that it is possible to introduce a scheduler to dataplanes, which routes requests to the appropriate core and effectively reduce the tail latency of the system while at the same time support a wider range of workloads
Some aspects of traffic control and performance evaluation of ATM networks
The emerging high-speed Asynchronous Transfer Mode (ATM) networks are expected to integrate through statistical multiplexing large numbers of traffic sources having a broad range of statistical characteristics and different Quality of Service (QOS) requirements. To achieve high utilisation of network resources while maintaining the QOS, efficient traffic management strategies have to be developed. This thesis considers the problem of traffic control for ATM networks. The thesis studies the application of neural networks to various ATM traffic control issues such as feedback congestion control, traffic characterization, bandwidth estimation, and Call Admission Control (CAC). A novel adaptive congestion control approach based on a neural network that uses reinforcement learning is developed. It is shown that the neural controller is very effective in providing general QOS control. A Finite Impulse Response (FIR) neural network is proposed to adaptively predict the traffic arrival process by learning the relationship between the past and future traffic variations. On the basis of this prediction, a feedback flow control scheme at input access nodes of the network is presented. Simulation results demonstrate significant performance improvement over conventional control mechanisms. In addition, an accurate yet computationally efficient approach to effective bandwidth estimation for multiplexed connections is investigated. In this method, a feed forward neural network is employed to model the nonlinear relationship between the effective bandwidth and the traffic situations and a QOS measure. Applications of this approach to admission control, bandwidth allocation and dynamic routing are also discussed. A detailed investigation has indicated that CAC schemes based on effective bandwidth approximation can be very conservative and prevent optimal use of network resources. A modified effective bandwidth CAC approach is therefore proposed to overcome the drawback of conventional methods. Considering statistical multiplexing between traffic sources, we directly calculate the effective bandwidth of the aggregate traffic which is modelled by a two-state Markov modulated Poisson process via matching four important statistics. We use the theory of large deviations to provide a unified description of effective bandwidths for various traffic sources and the associated ATM multiplexer queueing performance approximations, illustrating their strengths and limitations. In addition, a more accurate estimation method for ATM QOS parameters based on the Bahadur-Rao theorem is proposed, which is a refinement of the original effective bandwidth approximation and can lead to higher link utilisation
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