165 research outputs found
RCD: Rapid Close to Deadline Scheduling for Datacenter Networks
Datacenter-based Cloud Computing services provide a flexible, scalable and
yet economical infrastructure to host online services such as multimedia
streaming, email and bulk storage. Many such services perform geo-replication
to provide necessary quality of service and reliability to users resulting in
frequent large inter- datacenter transfers. In order to meet tenant service
level agreements (SLAs), these transfers have to be completed prior to a
deadline. In addition, WAN resources are quite scarce and costly, meaning they
should be fully utilized. Several recently proposed schemes, such as B4,
TEMPUS, and SWAN have focused on improving the utilization of inter-datacenter
transfers through centralized scheduling, however, they fail to provide a
mechanism to guarantee that admitted requests meet their deadlines. Also, in a
recent study, authors propose Amoeba, a system that allows tenants to define
deadlines and guarantees that the specified deadlines are met, however, to
admit new traffic, the proposed system has to modify the allocation of already
admitted transfers. In this paper, we propose Rapid Close to Deadline
Scheduling (RCD), a close to deadline traffic allocation technique that is fast
and efficient. Through simulations, we show that RCD is up to 15 times faster
than Amoeba, provides high link utilization along with deadline guarantees, and
is able to make quick decisions on whether a new request can be fully satisfied
before its deadline.Comment: World Automation Congress (WAC), IEEE, 201
Enabling Work-conserving Bandwidth Guarantees for Multi-tenant Datacenters via Dynamic Tenant-Queue Binding
Today's cloud networks are shared among many tenants. Bandwidth guarantees
and work conservation are two key properties to ensure predictable performance
for tenant applications and high network utilization for providers. Despite
significant efforts, very little prior work can really achieve both properties
simultaneously even some of them claimed so.
In this paper, we present QShare, an in-network based solution to achieve
bandwidth guarantees and work conservation simultaneously. QShare leverages
weighted fair queuing on commodity switches to slice network bandwidth for
tenants, and solves the challenge of queue scarcity through balanced tenant
placement and dynamic tenant-queue binding. QShare is readily implementable
with existing switching chips. We have implemented a QShare prototype and
evaluated it via both testbed experiments and simulations. Our results show
that QShare ensures bandwidth guarantees while driving network utilization to
over 91% even under unpredictable traffic demands.Comment: The initial work is published in IEEE INFOCOM 201
NUMFabric: Fast and Flexible Bandwidth Allocation in Datacenters
We present xFabric, a novel datacenter transport design that provides flexible and fast bandwidth allocation control. xFabric is flexible: it enables operators to specify how bandwidth is allocated amongst contending flows to optimize for different service-level objectives such as minimizing flow completion times, weighted allocations, different notions of fairness, etc. xFabric is also very fast, it converges to the specified allocation one-to-two order of magnitudes faster than prior schemes. Underlying xFabric, is a novel distributed algorithm that uses in-network packet scheduling to rapidly solve general network utility maximization problems for bandwidth allocation. We evaluate xFabric using realistic datacenter topologies and highly dynamic workloads and show that it is able to provide flexibility and fast convergence in such stressful environments.Google Faculty Research Awar
QuickCast: Fast and Efficient Inter-Datacenter Transfers using Forwarding Tree Cohorts
Large inter-datacenter transfers are crucial for cloud service efficiency and
are increasingly used by organizations that have dedicated wide area networks
between datacenters. A recent work uses multicast forwarding trees to reduce
the bandwidth needs and improve completion times of point-to-multipoint
transfers. Using a single forwarding tree per transfer, however, leads to poor
performance because the slowest receiver dictates the completion time for all
receivers. Using multiple forwarding trees per transfer alleviates this
concern--the average receiver could finish early; however, if done naively,
bandwidth usage would also increase and it is apriori unclear how best to
partition receivers, how to construct the multiple trees and how to determine
the rate and schedule of flows on these trees. This paper presents QuickCast, a
first solution to these problems. Using simulations on real-world network
topologies, we see that QuickCast can speed up the average receiver's
completion time by as much as while only using more
bandwidth; further, the completion time for all receivers also improves by as
much as faster at high loads.Comment: [Extended Version] Accepted for presentation in IEEE INFOCOM 2018,
Honolulu, H
Weighted Scheduling of Time-Sensitive Coflows
Datacenter networks commonly facilitate the transmission of data in
distributed computing frameworks through coflows, which are collections of
parallel flows associated with a common task. Most of the existing research has
concentrated on scheduling coflows to minimize the time required for their
completion, i.e., to optimize the average dispatch rate of coflows in the
network fabric. Nevertheless, modern applications often produce coflows that
are specifically intended for online services and mission-crucial computational
tasks, necessitating adherence to specific deadlines for their completion. In
this paper, we introduce \wdcoflow,~ a new algorithm to maximize the weighted
number of coflows that complete before their deadline. By combining a dynamic
programming algorithm along with parallel inequalities, our heuristic solution
performs at once coflow admission control and coflow prioritization, imposing a
-order on the set of coflows. With extensive simulation, we demonstrate
the effectiveness of our algorithm in improving up to more coflows
that meet their deadline in comparison the best SoA solution, namely
. Furthermore, when weights are used to differentiate
coflow classes, \wdcoflow~ is able to improve the admission per class up to
, while increasing the average weighted coflow admission rate.Comment: Submitted to IEEE Transactions on Cloud Computing. Parts of this work
have been presented at IFIP Networking 202
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