Prediction of the outcome of a Twenty-20 Cricket Match : A Machine Learning Approach

Twenty20 cricket, sometimes written Twenty-20, and often abbreviated to T20, is a short form of cricket. In a Twenty20 game the two teams of 11 players have a single innings each, which is restricted to a maximum of 20 overs. This version of cricket is especially unpredictable and is one of the reasons it has gained popularity over recent times. However, in this paper we try four different machine learning approaches for predicting the results of T20 Cricket Matches. Specifically we take in to account: previous performance statistics of the players involved in the competing teams, ratings of players obtained from reputed cricket statistics websites, clustering the players' with similar performance statistics and propose a novel method using an ELO based approach to rate players. We compare the performances of each of these feature engineering approaches by using different ML algorithms, including logistic regression, support vector machines, bayes network, decision tree, random forest.Comment: Machine Learning Applications, Sports, Cricket Outcome Predictio

Titan: Fair Packet Scheduling for Commodity Multiqueue NICs

The performance of an OS’s networking stack can be measured by its achieved throughput, CPU utilization, latency, and per-flow fairness. To be able to drive increasing line-rates at 10Gbps and beyond, modern OS networking stacks rely on a number of important hardware and software optimizations, including but not limited to using multiple transmit and receive queues and segmentation offloading. Unfortunately, it not clear how best to leverage these optimizations to extract performance. The first contribution of this paper is a detailed empirical study of the impact of different OS and NIC configurations on this four-dimensional trade-off space. We find that enabling certain specific features is crucial for latency, CPU utilization, and throughput. However, substantial flow-level unfairness still remains. The second contribution of this paper is Titan, an extension to the Linux networking stack that systematically addresses unfairness arising in different operating conditions, while minimally impacting CPU utilization, latency, and throughput

Adaptive SLA-based elasticity management algorithms for a virtualized IP multimedia subsystem

© 2014 IEEE. The IP Multimedia System (IMS) is an important reference service delivery platform for next generation networks and is considered as a de-facto standard for IP-based multimedia communication services. In its current design, the IMS faces important challenges in terms of scalability and elasticity, and lacks the ability to adaptively manage the network resources and dynamically dimension the network nodes based on load and demand. Network function virtualization and cloud computing are two important concepts that can be leveraged to address those challenges in IMS environments. In this work, we propose two adaptive SLA-based elasticity management algorithms for virtualized IMS environments. Our proposed algorithms use two SLA attributes (the call setup delay and user priority) to dynamically control the CPU resources allocated/de-allocated to virtualized IMS nodes. The aims of our proposed algorithms are: 1) to ensure efficient usage and sharing of CPU resources by various IMS components; 2) to reduce the overall power consumption in virtualized IMS platforms; and 3) to enhance the user experience when using IMS networks. We have tested the proposed algorithms by setting up a virtualized IMS environment using OpenIMS Core and Xen as the hypervisor. The results obtained show that our proposed algorithms meet the SLA constraints, even when subjected to dynamic load, thereby enhancing the overall QoS. We have also compared the proposed algorithms with Xen Server\u27s existing CPU resource scaling governors and the results indicate that our algorithms work better when compared to the existing governors

A REGIONALIZED COLLABORATIVE COMMUNITY BASED CLOUD COMPUTING AWARENESS EVANGELISM INITIATIVE

Knowledge acquisition often is considered as a prerequisite for enhancing human wisdom and to ensure proper amalgamation into the civilized society. This often is achieved through society approved legal frameworks leading to formal education. It is practically impossible for formal education to encompass new knowledge evolutions which usually have dynamic trending. This may be because of advances that the societal ecosystem creates for its survival. It is often proved and empirically observed that knowledge acquisition through an informal knowledge delivery framework results in filling up the fragments that exist in an entity due to knowledge evolution. Such frameworks are also called as "knowledge communities" who are driven by passion to dissipate the acquired knowledge back to the society. The very fact of the success of the open source communities across the world legitimates the above said claim. It is always a challenge to disseminate knowledge for large spectrum of learner communities due to the initial gaps that may exist about a specific concept. This may again be more challenging if it is a technology based topic and/or concept such as cloud computing. The interesting aspect of informal teaching learning process gives an opportunity to propose and experiment novel pedagogical approaches to address these challenges. These approaches can also enable us to generate statistics about the awareness of the entities in the society about a trending topic. This work carried out addresses a cloud evangelism framework to effectively inculcate the trending cloud computing ecosystem into the society. This work also proposes an efficient pedagogical approach to create cloud computing ecosystem awareness among the desirable community at large.</jats:p