3,187 research outputs found
Datacenter Traffic Control: Understanding Techniques and Trade-offs
Datacenters provide cost-effective and flexible access to scalable compute
and storage resources necessary for today's cloud computing needs. A typical
datacenter is made up of thousands of servers connected with a large network
and usually managed by one operator. To provide quality access to the variety
of applications and services hosted on datacenters and maximize performance, it
deems necessary to use datacenter networks effectively and efficiently.
Datacenter traffic is often a mix of several classes with different priorities
and requirements. This includes user-generated interactive traffic, traffic
with deadlines, and long-running traffic. To this end, custom transport
protocols and traffic management techniques have been developed to improve
datacenter network performance.
In this tutorial paper, we review the general architecture of datacenter
networks, various topologies proposed for them, their traffic properties,
general traffic control challenges in datacenters and general traffic control
objectives. The purpose of this paper is to bring out the important
characteristics of traffic control in datacenters and not to survey all
existing solutions (as it is virtually impossible due to massive body of
existing research). We hope to provide readers with a wide range of options and
factors while considering a variety of traffic control mechanisms. We discuss
various characteristics of datacenter traffic control including management
schemes, transmission control, traffic shaping, prioritization, load balancing,
multipathing, and traffic scheduling. Next, we point to several open challenges
as well as new and interesting networking paradigms. At the end of this paper,
we briefly review inter-datacenter networks that connect geographically
dispersed datacenters which have been receiving increasing attention recently
and pose interesting and novel research problems.Comment: Accepted for Publication in IEEE Communications Surveys and Tutorial
On Asymptotic Optimality of Dual Scheduling Algorithm In A Generalized Switch
Generalized switch is a model of a queueing system where parallel servers are interdependent and have time-varying service capabilities. This paper considers the dual scheduling algorithm that uses rate control and queue-length based scheduling to allocate resources for a generalized switch. We consider a saturated system in which each user has infinite amount of data to be served. We prove the asymptotic optimality of the dual scheduling algorithm for such a system, which says that the vector of average service rates of the scheduling algorithm maximizes some aggregate concave utility functions. As the fairness objectives can be achieved by appropriately choosing utility functions, the asymptotic optimality establishes the fairness properties of the dual scheduling algorithm.
The dual scheduling algorithm motivates a new architecture for scheduling, in which an additional queue is introduced to interface the user data queue and the time-varying server and to modulate the scheduling process, so as to achieve different performance objectives. Further research would include scheduling with Quality of Service guarantees with the dual scheduler, and its application and implementation in various versions of the generalized switch model
An Optimal Lower Bound for Buffer Management in Multi-Queue Switches
In the online packet buffering problem (also known as the unweighted FIFO
variant of buffer management), we focus on a single network packet switching
device with several input ports and one output port. This device forwards
unit-size, unit-value packets from input ports to the output port. Buffers
attached to input ports may accumulate incoming packets for later transmission;
if they cannot accommodate all incoming packets, their excess is lost. A packet
buffering algorithm has to choose from which buffers to transmit packets in
order to minimize the number of lost packets and thus maximize the throughput.
We present a tight lower bound of e/(e-1) ~ 1.582 on the competitive ratio of
the throughput maximization, which holds even for fractional or randomized
algorithms. This improves the previously best known lower bound of 1.4659 and
matches the performance of the algorithm Random Schedule. Our result
contradicts the claimed performance of the algorithm Random Permutation; we
point out a flaw in its original analysis
Buffer Overflow Management with Class Segregation
We consider a new model for buffer management of network switches with
Quality of Service (QoS) requirements. A stream of packets, each attributed
with a value representing its Class of Service (CoS), arrives over time at a
network switch and demands a further transmission. The switch is equipped with
multiple queues of limited capacities, where each queue stores packets of one
value only. The objective is to maximize the total value of the transmitted
packets (i.e., the weighted throughput).
We analyze a natural greedy algorithm, GREEDY, which sends in each time step
a packet with the greatest value. For general packet values , we show that GREEDY is -competitive, where . Furthermore, we show a lower bound of on the competitiveness of any deterministic online algorithm.
In the special case of two packet values (1 and ), GREEDY is shown
to be optimal with a competitive ratio of
Dynamic Server Allocation over Time Varying Channels with Switchover Delay
We consider a dynamic server allocation problem over parallel queues with
randomly varying connectivity and server switchover delay between the queues.
At each time slot the server decides either to stay with the current queue or
switch to another queue based on the current connectivity and the queue length
information. Switchover delay occurs in many telecommunications applications
and is a new modeling component of this problem that has not been previously
addressed. We show that the simultaneous presence of randomly varying
connectivity and switchover delay changes the system stability region and the
structure of optimal policies. In the first part of the paper, we consider a
system of two parallel queues, and develop a novel approach to explicitly
characterize the stability region of the system using state-action frequencies
which are stationary solutions to a Markov Decision Process (MDP) formulation.
We then develop a frame-based dynamic control (FBDC) policy, based on the
state-action frequencies, and show that it is throughput-optimal asymptotically
in the frame length. The FBDC policy is applicable to a broad class of network
control systems and provides a new framework for developing throughput-optimal
network control policies using state-action frequencies. Furthermore, we
develop simple Myopic policies that provably achieve more than 90% of the
stability region. In the second part of the paper, we extend our results to
systems with an arbitrary but finite number of queues.Comment: 38 Pages, 18 figures. arXiv admin note: substantial text overlap with
arXiv:1008.234
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