A Framework for Robust Traffic Engineering Using Evolutionary Computation

2015

JCOMSS

Self Cite

Nowadays, many P2P applications proliferate in the Internet. The attractiveness of many of these systems relies on the collaborative approach used to exchange large resources without the dependence and associated constraints of centralized approaches where a single server is responsible to handle all the requests from the clients. As consequence, some P2P systems are also interesting and cost-effective approaches to be adopted by content providers and other Internet players. However, there areseveral coexistence problems between P2P applications and Internet Service Providers (ISPs) due to the unforeseeable behavior of P2P traffic aggregates in ISP infrastructures.In this context, this work proposes a collaborative P2P/ISPsystem able to underpin the development of novel Traffic Engineering (TE) mechanisms contributing for a better coexistence between P2P applications and ISPs. Using the devised system, two TE methods are described being able to estimate and control the impact of P2P traffic aggregates on the ISP network links. One of the TE methods allows that ISP administrators are able to foresee the expected impact that a given P2P swarm will have in the underlying network infrastructure. The other TE method enables the definition of ISP friendly P2P topologies, where specific network links are protected from P2P traffic. As result, the proposed system and associated mechanisms will contributefor improved ISP resource management tasks and to foster the deployment of innovative ISP-friendly systems.

Section: A P2p Link Impact Valuesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Towards Effective Control of P2P Traffic Aggregates in Network Infrastructures

2015

JCOMSS

Self Cite

“…Many of such routing optimization approaches usually translate to NP-hard optimization problems that seek to find a set of routing weights that are able to optimize the congestion levels of the network, considering specific aggregated traffic demands. For this specific purpose, the use of computational intelligence methods to solve TE related problems has presented encouraging results and Evolutionary Algorithms (EAs) have been successfully used to solve congestion based formulations, or other variants involving multi-constrained optimization approaches ( [14], [10], [11], [12], [13]). In addition, other meta-heuristics were also used in such TE weight setting optimization problems, namely techniques such as Local Search and Simulated Annealing [15], [16].…”

Section: Introductionmentioning

confidence: 99%

A Framework for Improving Routing Configurations using Multi-Objective Optimization Mechanisms

Pereira

Cortez

et al. 2017

JCOMSS

Self Cite

IP networks are nowadays well established technologiesbeing used to support a myriad of applications and services,thus assuming a crucial role in todays telecommunication systems. Nevertheless, such infrastructures usually require network administrators to perform a wide set of complex planning and management tasks trying to attain adequate network configurations. Many of such management tasks can be mathematically formulated as NP-hard optimization problems, sometimes involving several objective functions. In this context, this work explores and demonstrates the potential of using computational intelligence methods as optimization engines to tackle complex network optimization problems. In particular, Multi-objective Evolutionary Algorithms (MOEAs) are used to attain near-optimal link state routing configurations robust to distinct operational conditions. As result, network administrators will be provided with a set of alternative routing configurations representing distinct tradeoffs between the considered optimization goals. The robustness of the proposed methods is illustrated by presenting several multiobjective optimization examples able to improve the performance and resilience levels of a network infrastructure. In this perspective, this work presents a contribution for this research area by proposing specific MOEAs based optimization methods able to improve network routing configurations. Furthermore, the devised methods are also integrated in a freely available Traffic Engineering optimization framework able to be used by network administrators interested in improving the routing configurations of their network infrastructures.

“…As depicted in Figure 1 the ability to manage P2P traffic aggregates in a given infrastructure has also some relevance for other management/optimization tasks (e.g. traffic matrices estimation [3], routing optimization [4], QoS provisioning, etc.). The proposed framework assumes the existence of a P2P traffic management module (which may assume an automated behavior or be directly controlled by an administrator) able to interact with a configurable P2P tracker(s) (e.g.…”

Section: Introductionmentioning

confidence: 99%

An Automated Framework for the Management of P2P Traffic in ISP Infrastructures

Intelligent Distributed Computing IX

2015

Self Cite

Peer-to-Peer (P2P) is nowadays a widely used paradigm underpinning the deployment of several Internet services and applications. However, the management of P2P traffic aggregates is not an easy task for Internet Service Providers (ISPs). In this perspective, and considering an expectable proliferation in the use of such applications, future networks require the development of smart mechanisms fostering an easier coexistence between P2P applications and ISP infrastructures. This paper aims to contribute for such research efforts presenting a framework incorporating useful mechanisms to be activated by network administrators, being also able to operate as an automated management tool dealing with P2P traffic aggregates.