Flows of packets are dynamically mapped to resource queues. Flows of packets are received at a network device to be routed from the network device in a network. Each flow comprises packets to be sent from a source to a connection. Data is stored for a queue allocation table that maintains a plurality of buckets to which received packets for a flow are assigned and indicating which of a plurality of resource queues are allocated for respective buckets. For each packet in a flow, a hash function is computed from values in a header of the packet and the packet is assigned to one of the plurality of buckets based on the computed hash function. One of a plurality of resource queues is allocated for each bucket to which packets are assigned based on the computed hash function.

Dynamic load balancing without packet reordering

Software Defined Networking (SDN) is an emerging networking paradigm that greatly simplifies network management tasks. In addition, it opens the door for network innovation through a programmable flexible interface controlling the behavior of the entire network. In the opposite side, for decades traditional IP networks were very hard to manage, error prone and hard to introduce new functionalities. In this paper, we introduce the concepts & applications of SDN with a focus on the open research challenges in this new technology.

Software Defined Networking concepts and challenges

Joint flow routing-scheduling for energy efficient software defined data center networks

A survey of energy efficiency in SDN: Software-based methods and optimization models

https://pure.uva.nl/ws/files/1535192/134924_INDL_Journal_2013.pdf

A semantic-web approach for modeling computing infrastructures

Including the energy profile of the computing infrastructure in the decision process for scheduling computing tasks and allocating resources is essential to improve the system energy efficiency. However, the lack of an effective model of the infrastructure energy information makes it difficult for the resource managers to provide up-to-date energy metrics of underlying environment. We modeled the relationship between energy components and infrastructure components and defined energy description elements and energy metrics using semantic web technologies, and developed a description model called Energy Description Language (EDL). Based on the EDL, we prototyped an basic knowledge system called Energy Knowledge Base (EKB) that provides contextbased information retrieval support for applications, in order to make energy aware decisions on scheduling and resource allocation. We validated the logic and effectiveness of the energy ontology by testing the performance of the EKB.

/pdf/energy-aware-semantic-modeling-in-large-scale-32q8hugzzo.pdf

Energy-aware semantic modeling in large scale infrastructures

Energy efficiency is becoming an important requirement in more and more computing systems for optimizing resource allocation and task scheduling. By switching active copper Ethernet links to a low power model the IEEE 802.3az protocol can reduce the network energy consumption when no traffic exists. However, the effect of 802.3az heavily depends on network traffic patterns, which makes its utilization challenging in scheduling computing tasks. In this research, we examined the 802.3az technology with the goal of deploying it in distributed computing systems such as clusters. We devised an energy budget calculator that includes the energy model of 802.3az compliant Ethernet devices and supports the resource management service. We show a few practical examples of how applications can better plan their execution by integrating this knowledge in their decision strategies. We also present a solution for enhancing the calculator by using a semantic energy information system.

A semantic enhanced Power Budget Calculator for distributed computing using IEEE 802.3az

Most monitoring and discovery systems deployed in computer infrastructures do not take dynamic energy characteristics into consideration, and do not provide the mechanisms to exchange information across systems and administrative domains. Our energy-aware semantic information system, called Energy Knowledge Base (EKB), aggregates metadata and data from a large number of distributed information sources. The EKB supports therefore management, scheduling and management of computing and data transport tasks across domains. In computing infrastructures, the available information can differ quite a lot, changes frequently, has differing sources, but there is no well-defined data model. Our system leverages the Energy Description Language (EDL) to model dynamic energy-related attributes of resources from different layers, the EDL metadata provides data interoperability across providers. In this article we present the EKB architecture, show the usability.

Xiangke Liao

Papers

Joint flow routing-scheduling for energy efficient software defined data center networks

Energy-aware semantic modeling in large scale infrastructures

A semantic enhanced Power Budget Calculator for distributed computing using IEEE 802.3az

EKB: Semantic Information System for Energy-Aware Monitoring in Distributed Infrastructures

Evaluation of non-linear power estimation models in a computing cluster