A prescriptive analytics approach for energy efficiency in datacentres.

Given the evolution of Cloud Computing in recent years, users and clients adopting Cloud Computing for both personal and business needs have increased at an unprecedented scale. This has naturally led to the increased deployments and implementations of Cloud datacentres across the globe. As a consequence of this increasing adoption of Cloud Computing, Cloud datacentres are witnessed to be massive energy consumers and environmental polluters. Whilst the energy implications of Cloud datacentres are being addressed from various research perspectives, predicting the future trend and behaviours of workloads at the datacentres thereby reducing the active server resources is one particular dimension of green computing gaining the interests of researchers and Cloud providers. However, this includes various practical and analytical challenges imposed by the increased dynamism of Cloud systems. The behavioural characteristics of Cloud workloads and users are still not perfectly clear which restrains the reliability of the prediction accuracy of existing research works in this context. To this end, this thesis presents a comprehensive descriptive analytics of Cloud workload and user behaviours, uncovering the cause and energy related implications of Cloud Computing. Furthermore, the characteristics of Cloud workloads and users including latency levels, job heterogeneity, user dynamicity, straggling task behaviours, energy implications of stragglers, job execution and termination patterns and the inherent periodicity among Cloud workload and user behaviours have been empirically presented. Driven by descriptive analytics, a novel user behaviour forecasting framework has been developed, aimed at a tri-fold forecast of user behaviours including the session duration of users, anticipated number of submissions and the arrival trend of the incoming workloads. Furthermore, a novel resource optimisation framework has been proposed to avail the most optimum level of resources for executing jobs with reduced server energy expenditures and job terminations. This optimisation framework encompasses a resource estimation module to predict the anticipated resource consumption level for the arrived jobs and a classification module to classify tasks based on their resource intensiveness. Both the proposed frameworks have been verified theoretically and tested experimentally based on Google Cloud trace logs. Experimental analysis demonstrates the effectiveness of the proposed framework in terms of the achieved reliability of the forecast results and in reducing the server energy expenditures spent towards executing jobs at the datacentres.N/

Human-Centric Cyber Social Computing Model for Hot-Event Detection and Propagation

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Microblogging networks have gained popularity in recent years as a platform enabling expressions of human emotions, through which users can conveniently produce contents on public events, breaking news, and/or products. Subsequently, microblogging networks generate massive amounts of data that carry opinions and mass sentiment on various topics. Herein, microblogging is regarded as a useful platform for detecting and propagating new hot events. It is also a useful channel for identifying high-quality posts, popular topics, key interests, and high-influence users. The existence of noisy data in the traditional social media data streams enforces to focus on human-centric computing. This paper proposes a human-centric social computing (HCSC) model for hot-event detection and propagation in microblogging networks. In the proposed HCSC model, all posts and users are preprocessed through hypertext induced topic search (HITS) for determining high-quality subsets of the users, topics, and posts. Then, a latent Dirichlet allocation (LDA)-based multiprototype user topic detection method is used for identifying users with high influence in the network. Furthermore, an influence maximization is used for final determination of influential users based on the user subsets. Finally, the users mined by influence maximization process are generated as the influential user sets for specific topics. Experimental results prove the superiority of our HCSC model against similar models of hot-event detection and information propagation

RVLBPNN: A Workload Forecasting Model for Smart Cloud Computing

Given the increasing deployments of Cloud datacentres and the excessive usage of server resources, their associated energy and environmental implications are also increasing at an alarming rate. Cloud service providers are under immense pressure to significantly reduce both such implications for promoting green computing. Maintaining the desired level of Quality of Service (QoS) without violating the Service Level Agreement (SLA), whilst attempting to reduce the usage of the datacentre resources is an obvious challenge for the Cloud service providers. Scaling the level of active server resources in accordance with the predicted incoming workloads is one possible way of reducing the undesirable energy consumption of the active resources without affecting the performance quality. To this end, this paper analyzes the dynamic characteristics of the Cloud workloads and defines a hierarchy for the latency sensitivity levels of the Cloud workloads. Further, a novel workload prediction model for energy efficient Cloud Computing is proposed, named RVLBPNN (Rand Variable Learning Rate Backpropagation Neural Network) based on BPNN (Backpropagation Neural Network) algorithm. Experiments evaluating the prediction accuracy of the proposed prediction model demonstrate that RVLBPNN achieves an improved prediction accuracy compared to the HMM and Naïve Bayes Classifier models by a considerable margin

Impact of large-scale organic conversion on food production and food security in two Indian states, Tamil Nadu and Madhya Pradesh

The millions of food insecure people in India are not solely due to inadequate food production, but also because some people are simply too poor to buy food. This study assessed how a large-scale conversion from conventional to organic production would impact on the economics of marginal and small farmers in Tamil Nadu and Madhya Pradesh, and on the total food production in these states. This study also considered a situation where fertilizer subsidies would be discontinued, with farmers having to carry the full cost of fertilizer. Results show that conversion to organic improved the economic situation of farmers although food production was reduced by 3–5% in the organic situation. Thus, the estimated economic values were higher in the organic system (5–40% in fertilizer subsidy scenario and 22–132% in no fertilizer subsidy scenario) than in the conventional system, whereas the total state-level food productions were lowered by 3–5% in the organic compared to the conventional system. Food production was higher when rainfed, and lower in the irrigated situation in the large-scale organic scenario. Although the study addresses short-term perspectives of large-scale conversion to organic farming, more research is needed to understand the long-term impact of organic conversion on food production, nutrient supply, food security and poverty reduction

Efficient resampling for fraud detection during anonymised credit card transactions with unbalanced datasets

The rapid growth of e-commerce and online shopping have resulted in an unprecedented increase in the amount of money that is annually lost to credit card fraudsters. In an attempt to address credit card fraud, researchers are leveraging the application of various machine learning techniques for efficiently detecting and preventing fraudulent credit card transactions. One of the prevalent common issues around the analytics of credit card transactions is the highly unbalanced nature of the datasets, which is frequently associated with the binary classification problems. This paper intends to review, analyse and implement a selection of notable machine learning algorithms such as Logistic Regression, Random Forest, K-Nearest Neighbours and Stochastic Gradient Descent, with the motivation of empirically evaluating their efficiencies in handling unbalanced datasets whilst detecting credit card fraud transactions. A publicly available dataset comprising 284807 transactions of European cardholders is analysed and trained with the studied machine learning techniques to detect fraudulent transactions. Furthermore, this paper also evaluates the incorporation of two notable resampling methods, namely Random Under-sampling and Synthetic Majority Oversampling Techniques (SMOTE) in the aforementioned algorithms, in order to analyse their efficiency in handling unbalanced datasets. The proposed resampling methods significantly increased the detection ability, the most successful technique of combination of Random Forest with Random Under-sampling achieved the recall score of 100% in contrast to the recall score 77% of model without resampling technique. The key contribution of this paper is the postulation of efficient machine learning algorithms together with suitable resampling methods, suitable for credit card fraud detection with unbalanced dataset.N/

ARSH-FATI a Novel Metaheuristic for Cluster Head Selection in Wireless Sensor Networks

Wireless sensor network (WSN) consists of a large number of sensor nodes distributed over a certain target area. The WSN plays a vital role in surveillance, advanced healthcare, and commercialized industrial automation. Enhancing energy-efficiency of the WSN is a prime concern because higher energy consumption restricts the lifetime (LT) of the network. Clustering is a powerful technique widely adopted to increase LT of the network and reduce the transmission energy consumption. In this article (LT) we develop a novel ARSH-FATI-based Cluster Head Selection (ARSH-FATI-CHS) algorithm integrated with a heuristic called novel ranked-based clustering (NRC) to reduce the communication energy consumption of the sensor nodes while efficiently enhancing LT of the network. Unlike other population-based algorithms ARSH-FATI-CHS dynamically switches between exploration and exploitation of the search process during run-time to achieve higher performance trade-off and significantly increase LT of the network. ARSH-FATI-CHS considers the residual energy, communication distance parameters, and workload during cluster heads (CHs) selection. We simulate our proposed ARSH-FATI-CHS and generate various results to determine the performance of the WSN in terms of LT. We compare our results with state-of-the-art particle swarm optimization (PSO) and prove that ARSH-FATI-CHS approach improves the LT of the network by ∼25%

Mobilouds: An Energy Efficient MCC Collaborative Framework With Extended Mobile Participation for Next Generation Networks

Given the emergence of mobile cloud computing (MCC), its associated energy implications are witnessed at larger scale. With offloading computationally intensive tasks to the cloud datacentres being the basic concept behind MCC, most of the mobile terminal resources participating in the MCC collaborative execution are wasted as they remain idle until the mobile terminals receive responses from the datacentres. This is an additional wastage of resources alongside the cloud resources are already being addressed as massive energy consumers. Though the energy consumed of the idle mobile resources is insignificant in comparison with the cloud counterpart, such consumptions have drastic impacts on the mobile devices causing unnecessary battery drains. To this end, this paper proposes Mobilouds which encompass a multi-tier processing architecture with various levels of process cluster capacities and a software application to manage energy efficient utilization of such process clusters. Our proposed Mobilouds framework encourages the mobile device participation in the MCC collaborative execution, thereby reduces the presence of idle mobile resources and utilizes such idle resources in the actual task execution. Our performance evaluation results demonstrate that the Mobilouds framework offers the most energy-time balancing process clusters for task execution by effectively utilizing the available resources, in comparison with an entire cloud offloading strategy using 5G/4G networks

A critical review of the routing protocols in opportunistic networks.

The goal of Opportunistic Networks (OppNets) is to enable message transmission in an infrastructure less environment where a reliable end-to-end connection between the hosts in not possible at all times. The role of OppNets is very crucial in today’s communication as it is still not possible to build a communication infrastructure in some geographical areas including mountains, oceans and other remote areas. Nodes participating in the message forwarding process in OppNets experience frequent disconnections. The employment of an appropriate routing protocol to achieve successful message delivery is one of the desirable requirements of OppNets. Routing challenges are very complex and evident in OppNets due to the dynamic nature and the topology of the intermittent networks. This adds more complexity in the choice of the suitable protocol to be employed in opportunistic scenarios, to enable message forwarding. With this in mind, the aim of this paper is to analyze a number of algorithms under each class of routing techniques that support message forwarding in OppNets and to compare those studied algorithms in terms of their performances, forwarding techniques, outcomes and success rates. An important outcome of this paper is the identifying of the optimum routing protocol under each class of routing

Energy-efficient Static Task Scheduling on VFI based NoC-HMPSoCs for Intelligent Edge Devices in Cyber-Physical Systems

The interlinked processing units in the modern Cyber-Physical Systems (CPS) creates a large network of connected computing embedded systems. Network-on-Chip (NoC) based multiprocessor system-on-chip (MPSoC) architecture is becoming a de-facto computing platform for real-time applications due to its higher performance and Quality-of-Service (QoS). The number of processors has increased significantly on the multiprocessor systems in CPS therefore, Voltage Frequency Island (VFI) recently adopted for effective energy management mechanism in the large scale multiprocessor chip designs. In this paper, we investigate energy and contention-aware static scheduling for tasks with precedence and deadline constraints on intelligent edge devices deploying heterogeneous VFI based NoC-MPSoCs with DVFS-enabled processors. Unlike the existing population-based optimization algorithms, we propose a novel population-based algorithm called ARSH-FATI that can dynamically switch between explorative and exploitative search modes at run-time. Our static scheduler ARHS-FATI collectively performs task mapping, scheduling, and voltage scaling. Consequently, its performance is superior to the existing state-of-the-art approach proposed for homogeneous VFI based NoC-MPSoCs. We also developed a communication contention-aware Earliest Edge Consistent Deadline First (EECDF) scheduling algorithm and gradient descent inspired voltage scaling algorithm called Energy Gradient Decent (EGD). We have introduced a notion of Energy Gradient (EG) that guides EGD in its search for islands voltage settings and minimize the total energy consumption. We conducted the experiments on 8 real benchmarks adopted from Embedded Systems Synthesis Benchmarks (E3S). Our static scheduling approach ARSH-FATI outperformed state-of-the-art technique and achieved an average energy-efficiency of ~ 24% and ~ 30% over CA-TMES-Search and CA-TMES-Quick respectively