1,358 research outputs found
RepFlow: Minimizing Flow Completion Times with Replicated Flows in Data Centers
Short TCP flows that are critical for many interactive applications in data
centers are plagued by large flows and head-of-line blocking in switches.
Hash-based load balancing schemes such as ECMP aggravate the matter and result
in long-tailed flow completion times (FCT). Previous work on reducing FCT
usually requires custom switch hardware and/or protocol changes. We propose
RepFlow, a simple yet practically effective approach that replicates each short
flow to reduce the completion times, without any change to switches or host
kernels. With ECMP the original and replicated flows traverse distinct paths
with different congestion levels, thereby reducing the probability of having
long queueing delay. We develop a simple analytical model to demonstrate the
potential improvement of RepFlow. Extensive NS-3 simulations and Mininet
implementation show that RepFlow provides 50%--70% speedup in both mean and
99-th percentile FCT for all loads, and offers near-optimal FCT when used with
DCTCP.Comment: To appear in IEEE INFOCOM 201
Convergence Analysis of Mixed Timescale Cross-Layer Stochastic Optimization
This paper considers a cross-layer optimization problem driven by
multi-timescale stochastic exogenous processes in wireless communication
networks. Due to the hierarchical information structure in a wireless network,
a mixed timescale stochastic iterative algorithm is proposed to track the
time-varying optimal solution of the cross-layer optimization problem, where
the variables are partitioned into short-term controls updated in a faster
timescale, and long-term controls updated in a slower timescale. We focus on
establishing a convergence analysis framework for such multi-timescale
algorithms, which is difficult due to the timescale separation of the algorithm
and the time-varying nature of the exogenous processes. To cope with this
challenge, we model the algorithm dynamics using stochastic differential
equations (SDEs) and show that the study of the algorithm convergence is
equivalent to the study of the stochastic stability of a virtual stochastic
dynamic system (VSDS). Leveraging the techniques of Lyapunov stability, we
derive a sufficient condition for the algorithm stability and a tracking error
bound in terms of the parameters of the multi-timescale exogenous processes.
Based on these results, an adaptive compensation algorithm is proposed to
enhance the tracking performance. Finally, we illustrate the framework by an
application example in wireless heterogeneous network
Service Quality Assessment for Cloud-based Distributed Data Services
The issue of less-than-100% reliability and trust-worthiness of third-party controlled cloud components (e.g., IaaS and SaaS components from different vendors) may lead to laxity in the QoS guarantees offered by a service-support system S to various applications. An example of S is a replicated data service to handle customer queries with fault-tolerance and performance goals. QoS laxity (i.e., SLA violations) may be inadvertent: say, due to the inability of system designers to model the impact of sub-system behaviors onto a deliverable QoS. Sometimes, QoS laxity may even be intentional: say, to reap revenue-oriented benefits by cheating on resource allocations and/or excessive statistical-sharing of system resources (e.g., VM cycles, number of servers). Our goal is to assess how well the internal mechanisms of S are geared to offer a required level of service to the applications. We use computational models of S to determine the optimal feasible resource schedules and verify how close is the actual system behavior to a model-computed \u27gold-standard\u27. Our QoS assessment methods allow comparing different service vendors (possibly with different business policies) in terms of canonical properties: such as elasticity, linearity, isolation, and fairness (analogical to a comparative rating of restaurants). Case studies of cloud-based distributed applications are described to illustrate our QoS assessment methods.
Specific systems studied in the thesis are: i) replicated data services where the servers may be hosted on multiple data-centers for fault-tolerance and performance reasons; and ii) content delivery networks to geographically distributed clients where the content data caches may reside on different data-centers. The methods studied in the thesis are useful in various contexts of QoS management and self-configurations in large-scale cloud-based distributed systems that are inherently complex due to size, diversity, and environment dynamicity
Asymptotic Approximations for TCP Compound
In this paper, we derive an approximation for throughput of TCP Compound
connections under random losses. Throughput expressions for TCP Compound under
a deterministic loss model exist in the literature. These are obtained assuming
the window sizes are continuous, i.e., a fluid behaviour is assumed. We
validate this model theoretically. We show that under the deterministic loss
model, the TCP window evolution for TCP Compound is periodic and is independent
of the initial window size. We then consider the case when packets are lost
randomly and independently of each other. We discuss Markov chain models to
analyze performance of TCP in this scenario. We use insights from the
deterministic loss model to get an appropriate scaling for the window size
process and show that these scaled processes, indexed by p, the packet error
rate, converge to a limit Markov chain process as p goes to 0. We show the
existence and uniqueness of the stationary distribution for this limit process.
Using the stationary distribution for the limit process, we obtain
approximations for throughput, under random losses, for TCP Compound when
packet error rates are small. We compare our results with ns2 simulations which
show a good match.Comment: Longer version for NCC 201
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