Performance Guarantees In Networks Research Articles

Kraken: Online and Elastic Resource Reservations for Cloud Datacenters

In cloud environments, the absence of strict network performance guarantees leads to unpredictable job execution times. To address this issue, recently, there have been several proposals on how to provide guaranteed network performance. These proposals, however, rely on computing resource reservation schedules a priori . Unfortunately, this is not practical in today’s cloud environments, where application demands are inherently unpredictable, e.g., due to differences in the input data sets or phenomena, such as failures and stragglers. To overcome these limitations, we designed K raken , a system that allows to dynamically update minimum guarantees for both network bandwidth and compute resources at runtime . Unlike previous work, Kraken does not require prior knowledge about the resource needs of the applications but allows to modify reservations at runtime. Kraken achieves this through an online resource reservation scheme , which comes with provable optimality guarantees. In this paper, we motivate the need for dynamic resource reservation schemes, present how this is provided by Kraken, and evaluate Kraken via extensive simulations and a preliminary Hadoop prototype.

What we talk about when we talk about cloud network performance

Infrastructure-as-a-Service ("Cloud") data-centers intrinsically depend on high-performance networks to connect servers within the data-center and to the rest of the world. Cloud providers typically offer different service levels, and associated prices, for different sizes of virtual machine, memory, and disk storage. However, while all cloud providers provide network connectivity to tenant VMs, they seldom make any promises about network performance, and so cloud tenants suffer from highly-variable, unpredictable network performance. Many cloud customers do want to be able to rely on network performance guarantees, and many cloud providers would like to offer (and charge for) these guarantees. But nobody really agrees on how to define these guarantees, and it turns out to be challenging to define "network performance" in a way that is useful to both customers and providers. We attempt to bring some clarity to this question.

Stochastic performance analysis of non-feedforward networks

Many Internet applications are both delay and loss sensitive, and need network performance guarantees that include bandwidth, delay/delay jitter, and packet loss rate. It is very important to quantify and exploit the capabilities of guaranteed service provisioning of communication networks. In this paper, we study the queueing behaviors of non-feedforward networks (a non-feedforward network is a network in which at least one set of acyclic traffic routes forms a cycle; a feedforward network is a network in which any set of acyclic traffic routes does not form a cycle) with FIFO scheduling discipline and Regulated, Markov On-Off, and Fractional Brownian traffic sources. We develop a new methodology to analyze the probabilistic bounds on the delays experienced by traffic. By leveraging the large deviations and fixed-point techniques, we turn probability problems into deterministic optimization problems and translate a probabilistic delay bound into a fixed point of a non-linear real function. Our contribution in this paper is the derivation of a probabilistic bound on the delays experienced by traffic in non-feedforward networks, based on an assumption, i.e., the tail probability of the difference between the beginning time of a busy interval of a server and the earliest arriving time at the corresponding network ingress of the traffic arrivals that arrive at this server during this busy interval can be bounded by the maximum of the violation probabilities of the accumulative upper stream delay bound suffered by this server`s traffic arrivals. Consequently, our new results not only consummate the theory of stochastic analysis of network performance, but also facilitate the design of protocols and algorithms for non-feedforward networks to provide performance guarantees to various applications with diverse performance requirements.

Theories and models for Internet quality of service

We survey advances in theories and models for Internet quality of service (QoS). We start with the theory of network calculus, which lays the foundation for support of deterministic performance guarantees in networks, and illustrate its applications to integrated services, differentiated services, and streaming media playback delays. We also present mechanisms and architecture for scalable support of guaranteed services in the Internet, based on the concept of a stateless core. Methods for scalable control operations are also discussed. We then turn our attention to statistical performance guarantees and describe several new probabilistic results that can be used for a statistical dimensioning of differentiated services. Lastly, we review proposals and results in supporting performance guarantees in a best effort context. These include models for elastic throughput guarantees based on TCP performance modeling, techniques for some QoS differentiation without access control, and methods that allow an application to control the performance it receives, in the absence of network support.