Evolutionary Multi-objective Scheduling for Anti-Spam Filtering Throughput Optimization

This paper presents an evolutionary multi-objective optimization problem formulation for the anti-spam filtering problem, addressing both the classification quality criteria (False Positive and False Negative error rates) and email messages classification time (minimization). This approach is compared to single objective problem formulations found in the literature, and its advantages for decision support and flexible/adaptive anti-spam filtering configuration is demonstrated. A study is performed using the Wirebrush4SPAM framework anti-spam filtering and the SpamAssassin email dataset. The NSGA-II evolutionary multi-objective optimization algorithm was applied for the purpose of validating and demonstrating the adoption of this novel approach to the anti-spam filtering optimization problem, formulated from the multi-objective optimization perspective. The results obtained from the experiments demonstrated that this optimization strategy allows the decision maker (anti-spam filtering system administrator) to select among a set of optimal and flexible filter configuration alternatives with respect to classification quality and classification efficiency

MapReduce based RDF assisted distributed SVM for high throughput spam filtering

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityElectronic mail has become cast and embedded in our everyday lives. Billions of legitimate emails are sent on a daily basis. The widely established underlying infrastructure, its widespread availability as well as its ease of use have all acted as catalysts to such pervasive proliferation. Unfortunately, the same can be alleged about unsolicited bulk email, or rather spam. Various methods, as well as enabling architectures are available to try to mitigate spam permeation. In this respect, this dissertation compliments existing survey work in this area by contributing an extensive literature review of traditional and emerging spam filtering approaches. Techniques, approaches and architectures employed for spam filtering are appraised, critically assessing respective strengths and weaknesses. Velocity, volume and variety are key characteristics of the spam challenge. MapReduce (M/R) has become increasingly popular as an Internet scale, data intensive processing platform. In the context of machine learning based spam filter training, support vector machine (SVM) based techniques have been proven effective. SVM training is however a computationally intensive process. In this dissertation, a M/R based distributed SVM algorithm for scalable spam filter training, designated MRSMO, is presented. By distributing and processing subsets of the training data across multiple participating computing nodes, the distributed SVM reduces spam filter training time significantly. To mitigate the accuracy degradation introduced by the adopted approach, a Resource Description Framework (RDF) based feedback loop is evaluated. Experimental results demonstrate that this improves the accuracy levels of the distributed SVM beyond the original sequential counterpart. Effectively exploiting large scale, ‘Cloud’ based, heterogeneous processing capabilities for M/R in what can be considered a non-deterministic environment requires the consideration of a number of perspectives. In this work, gSched, a Hadoop M/R based, heterogeneous aware task to node matching and allocation scheme is designed. Using MRSMO as a baseline, experimental evaluation indicates that gSched improves on the performance of the out-of-the box Hadoop counterpart in a typical Cloud based infrastructure. The focal contribution to knowledge is a scalable, heterogeneous infrastructure and machine learning based spam filtering scheme, able to capitalize on collaborative accuracy improvements through RDF based, end user feedback. MapReduce based RDF Assisted Distributed SVM for High Throughput Spam Filterin

Scheduling Independent Moldable Tasks on Multi-Cores with GPUs

The number of parallel systems using accelerators is growing up.The technology is now mature enough to allow sustainedpetaflop/s. However, reaching this performance scale requiresefficient scheduling algorithms to manage the heterogeneouscomputing resources.We present a new approach for scheduling independent tasks onmultiple CPUs and multiple GPUs. The tasks are assumed to beparallelizable on CPUs using the moldable model: the final numberof cores allotted to a task can be decided and set by thescheduler. More precisely, we design an algorithm aiming atminimizing the makespan---the maximum completion time of alltasks---for this scheduling problem. The proposed algorithmcombines a dual approximation scheme with a fast integer linearprogram (ILP). It determines both the partitioning of the tasks,ie whether a task should be mapped to CPUs or a GPU, and thenumber of CPUs allotted to a moldable task if mapped to the CPUs.A worst case analysis shows that the algorithm has anapproximation ratio of $\frac{3}{2} + \epsilon$. However, sincethe complexity of the ILP-based algorithm could benon-polynomial, we also present a proved polynomial-timealgorithm with an approximation ratio of $2+\epsilon$.We complement the theoretical analysis of our two novelalgorithms with an experimental study. In these experiments, wecompare our algorithms to a modified version of the classical\heft algorithm, adapted to handle moldable tasks. Theexperimental results show that our algorithm with the$\frac{3}{2} + \epsilon$ approximation ratio producessignificantly shorter schedules than the modified \heft for mostof the instances. In addition, the experiments provide evidencethat this ILP-based algorithm is also practically able to solvelarger problem instances in a reasonable amount of time

Multimedia delivery in the future internet

The term “Networked Media” implies that all kinds of media including text, image, 3D graphics, audio and video are produced, distributed, shared, managed and consumed on-line through various networks, like the Internet, Fiber, WiFi, WiMAX, GPRS, 3G and so on, in a convergent manner [1]. This white paper is the contribution of the Media Delivery Platform (MDP) cluster and aims to cover the Networked challenges of the Networked Media in the transition to the Future of the Internet. Internet has evolved and changed the way we work and live. End users of the Internet have been confronted with a bewildering range of media, services and applications and of technological innovations concerning media formats, wireless networks, terminal types and capabilities. And there is little evidence that the pace of this innovation is slowing. Today, over one billion of users access the Internet on regular basis, more than 100 million users have downloaded at least one (multi)media file and over 47 millions of them do so regularly, searching in more than 160 Exabytes1 of content. In the near future these numbers are expected to exponentially rise. It is expected that the Internet content will be increased by at least a factor of 6, rising to more than 990 Exabytes before 2012, fuelled mainly by the users themselves. Moreover, it is envisaged that in a near- to mid-term future, the Internet will provide the means to share and distribute (new) multimedia content and services with superior quality and striking flexibility, in a trusted and personalized way, improving citizens’ quality of life, working conditions, edutainment and safety. In this evolving environment, new transport protocols, new multimedia encoding schemes, cross-layer inthe network adaptation, machine-to-machine communication (including RFIDs), rich 3D content as well as community networks and the use of peer-to-peer (P2P) overlays are expected to generate new models of interaction and cooperation, and be able to support enhanced perceived quality-of-experience (PQoE) and innovative applications “on the move”, like virtual collaboration environments, personalised services/ media, virtual sport groups, on-line gaming, edutainment. In this context, the interaction with content combined with interactive/multimedia search capabilities across distributed repositories, opportunistic P2P networks and the dynamic adaptation to the characteristics of diverse mobile terminals are expected to contribute towards such a vision. Based on work that has taken place in a number of EC co-funded projects, in Framework Program 6 (FP6) and Framework Program 7 (FP7), a group of experts and technology visionaries have voluntarily contributed in this white paper aiming to describe the status, the state-of-the art, the challenges and the way ahead in the area of Content Aware media delivery platforms

A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

A growing trend for information technology is to not just react to changes, but anticipate them as much as possible. This paradigm made modern solutions, such as recommendation systems, a ubiquitous presence in today's digital transactions. Anticipatory networking extends the idea to communication technologies by studying patterns and periodicity in human behavior and network dynamics to optimize network performance. This survey collects and analyzes recent papers leveraging context information to forecast the evolution of network conditions and, in turn, to improve network performance. In particular, we identify the main prediction and optimization tools adopted in this body of work and link them with objectives and constraints of the typical applications and scenarios. Finally, we consider open challenges and research directions to make anticipatory networking part of next generation networks

NON-DUPLICATIVE APPROACH TO SHARING BETWEEN STREAMED QUERIES

The push for streaming database systems to handle massive amounts of data and multiple queries necessitates the development of efficient yet adaptive query sharing technology. This project designed an effective solution to this problem poised as NASSQ, an elegant hybrid between static and dynamic routing alternatives. Utilizing the adaptive architecture of dynamic routing systems, NASSQ supports adaptive sharing of operators among different queries while refraining from duplicating intermediate data tuples. However like static routing, NASSQ constructs optimized routes using statistics

Self managed virtual machine scheduling in Cloud systems

In Cloud systems, Virtual Machines (VMs) are scheduled to hosts according to their instant resource usage (e.g. to hosts with most available RAM) without considering their overall and long-term utilization. Also, in many cases, the scheduling and placement processes are computational expensive and affect performance of deployed VMs. In this work, we present a Cloud VM scheduling algorithm that takes into account already running VM resource usage over time by analyzing past VM utilization levels in order to schedule VMs by optimizing performance. We observe that Cloud management processes, like VM placement, affect already deployed systems (for example this could involve throughput drop in a database cluster), so we aim to minimize such performance degradation. Moreover, overloaded VMs tend to steal resources (e.g. CPU) from neighbouring VMs, so our work maximizes VMs real CPU utilization. Based on these, we provide an experimental analysis to compare our solution with traditional schedulers used in OpenStack by exploring the behaviour of different NoSQL (MongoDB, Apache Cassandra and Elasticsearch). The results show that our solution refines traditional instant-based physical machine selection as it learns the system behaviour as well as it adapts over time. The analysis is prosperous as for the selected setting we approximately minimize performance degradation by 19% and we maximize CPU real time by 2% when using real world workloads