University of Babylon Performance in Setting and Infrastructure Indicator through UIGreenMetric 2017-2020. (A comparative study)

The category of setting and infrastructure indicator (SI) is chosen in this comparative study referring to the fact file scores obtained by the University of Babylon during the last four participations into the UI Green Metric Rankings. In general, the scores indicated an escalation in the university performance of the whole six sub-indicators of the setting and infrastructure criterion (SI) from 2017-2020. These results confirmed that the university; by its leadership, staff and students, focused on upgrading and developing its infrastructure for setting a green and safe educational environment for most employees and students. That is to achieve a green campus contributing in the global efforts for achieving the UN agenda of SDGs 2015-2030. Furthermore, all practices of university performance coped with its vision, mission, and objective goals of university strategic plan from 2018-2022, which contained the commitments to set up with most of SDGs, especially related to sustainable cities or campuses. The results also showed an ascending and remarkable progress from 2017 to 2020 with an unexpected regression in 2019, which required the development of an improvement plan to address weaknesses, maintain and enhance strengths, to get good results for university in the 2020 edition of UI GreenMetric Ranking. Keyword: Green university, practices, Sustainable Development Goals (SDGs), SI, setting and infrastructure indicator, University of Babylon

Designing Cuckoo Based Pending Interest Table for CCN Networks

Content Centric Networking (CCN) is a modern architecture that got wide attention in the current researches as a substitutional for the current IP-based architecture. Many studies have been investigated on this novel architecture but only little of them focused on Pending Interest Table (PIT) which is very important component in every CCN router. PIT has fundamental role in packet processing in both upstream process (Interest packets) and downstream process (Data packets). PIT must be fast enough in order to not become an obstruction in the packet processing and also it must be big enough to save a lot of incoming information. In this paper, we suggest a new PIT design and implementation named CF-PIT for CCN router. Our PIT design depends on modifying and utilizing an approximate data structure called Cuckoo filter (CF). Cuckoo filter has ideal characteristics like: high insertion/query/deletion performance, acceptable storage demands and false positive probability which make it with our modification convenient for PIT implementation. The experimental results showed that our CF-PIT design has high performance in different side of views which make it very suitable to be implemented on CCN routers.</p

Many-fields Packet Classification Using R-Tree and Field Concatenation Technique

Software-defined Networking is an approach that decouples the software-based control plane from the hardware-based data plane proposed for enterprise networks; OpenFlow is the most famous flexible protocol that can manage network traffic between the control and the data plane. Software-Defined Networking (SDN) requires up to 18 fields of the packets header to be checked against a big many-fields ruleset to categorize packets into flows, the process of categorizing packets into flows is called packet classification. Network switches process all packets belonging to the same flow in a similar manner by applying the same actions defined in the corresponding rule. Packet classification facilitates supporting new services such as filtering, blocking unsafe sites traffic, routing packets based on the packet's header information, and giving priority to specific flows. High-performance algorithms for many-field packet classification had been gained much interest in the research communities. This paper presents a new method to implement the many-fields packet classification of SDN flow table using Rectangle Tree (R-Tree). In this method, source and destination IP addresses from each flow table entry have been converted to a two-dimensional point. The remainders of the rule's fields have been concatenated into a single field by taking the most important bits with rules' ID in order to be inserted into the R-tree, for each rule an effective small binary flag is used to indicate the field's size, type, and ranges. Subsequently, searching is performed on the rectangle tree to find the matched rules according to the highest priority. In the simulation using the class-bench databases, the results show that this method achieves very good performance, classification speed and reduces the number of memory accesses significantly.Comment: 13 pages, 7 figure