Robert C. Green

On Cloud-based Oversubscription

Rising trends in the number of customers turning to the cloud for their computing needs has made effective resource allocation imperative for cloud service providers. In order to maximize profits and reduce waste, providers have started to explore the role of oversubscribing cloud resources. However, the benefits of cloud-based oversubscription are not without inherent risks. This paper attempts to unveil the incentives, risks, and techniques behind oversubscription in a cloud infrastructure. Additionally, an overview of the current research that has been completed on this highly relevant topic is reviewed, and suggestions are made regarding potential avenues for future work.

Cloudlet Scheduling with Particle Swarm Optimization

Cloud computing is a particularly interesting and truly complex concept of providing services over networks on demand. Many tools have previously been created to simulate the work of the clouds, such as CloudSim. The main use of these tools is evaluation and testing of architectures and services before deployment on network centers and hosts in order to avoid any potential failures or inconveniences. The benefits of using the pay-per-use clouds may be affected by underutilization of the already reserved resources, so the maximization of system utilization while simultaneously minimizing makespan is of great interest to cloud providers wishing to reduce costs. To minimize makespan and increase resource utilization, a hybrid of particle swarm optimization and simulated annealing is implemented inside of CloudSim to advance the work of the already implemented simple broker. The new method maximizes the resource utilization and minimizes the makespan.

Evaluation and design of highly reliable and highly utilized cloud computing systems

Cloud computing paradigm has ushered in the need to provide resources to users in a scalable, flexible, and transparent fashion much like any other utility. This has led to a need for developing evaluation techniques that can provide quantitative measures of reliability of a cloud computing system (CCS) for efficient planning and expansion. This paper presents a new, scalable algorithm based on non-sequential Monte Carlo Simulation (MCS) to evaluate large scale cloud computing system (CCS) reliability, and it develops appropriate performance measures. Also, a new iterative algorithm is proposed and developed that leverages the MCS method for the design of highly reliable and highly utilized CCSs. The combination of these two algorithms allows CCSs to be evaluated by providers and users alike, providing a new method for estimating the parameters of service level agreements (SLAs) and designing CCSs to match those contractual requirements posed in SLAs. Results demonstrate that the proposed methods are effective and applicable to systems at a large scale. Multiple insights are also provided into the nature of CCS reliability and CCS design.

Simulating the Effects of Cloud-Based Oversubscription on Datacenter Revenues and Performance in Single and Multi-class Service Levels

Rising trends in the number of customers turning to the cloud for their computing needs has made effective resource allocation imperative for cloud service providers. In order to maximize profits and reduce waste, providers have started to explore the role of oversubscribing cloud resources. However, the benefits of oversubscription in the cloud are not without inherent risks. This paper attempts to unveil the different incentives, risks, and techniques behind oversubscription in a cloud infrastructure. CloudSim is used to compare the generated revenue and performance of oversubscribed and non-oversubscribed datacenters. The idea of multi-class service levels used in other overbooked industries is implemented in simulations modeling a priority class of VMs that pay a higher price for better performance. Results show that oversubscription has the potential to increase datacenter revenue, but the benefit comes with the risk of degraded QoS.

Reliability and Utilization Evaluation of a Cloud Computing System Allowing Partial Failures

Maintaining high reliability and device utilization in a cloud computing system (CCS) is crucial to any cloud service provider who will face high penalties and lose revenues if they fail to be good at both. This study proposes that allowing device partial failure in a CCS for graceful service degrading would help to obtain higher system reliability and device utilization without purchasing extra resource for the system. A model is created to represent such a multi-state system composed of multi-state devices. The system model is evaluated with Non-sequential Monte Carlo Simulation (MCS) on its reliability and device utilization. The preliminary results positively suggest that introducing and adding device multi-state increases the CCS reliability against device failures during simulation. Also, for the less reliable devices, like HDD, the results recommended a higher multi-state to compensate for their vulnerability and negative effect on system performance. A utilization index along all device dimensions is proposed in this research for a wise decision about maintaining a well-balanced and high utilized system at a lower cost.

ReliaCloud‐NS: A scalable web‐based simulation platform for evaluating the reliability of cloud computing systems

This paper discusses the implementation, architecture, and use of a graphical web‐based application called ReliaCloud‐NS that allows users to (1) evaluate the reliability of a cloud computing system (CCS) and (2) design a CCS to a specified reliability level for both public and private clouds. The software was designed with a RESTful application programming interface for performing nonsequential Monte Carlo simulations to perform reliability evaluations of a CCS. Simulation results are stored and presented to the user in the form of interactive charts and graphs from within a web browser. The software contains multiple types of CCS components, simulations, and virtual machine allocation schemes. ReliaCloud‐NS also contains a novel feature that evaluates CCS reliability across a range of varying virtual machine allocations and establishes and graphs a CCS reliability curve. This paper discusses the software architecture, the interactive web‐based interface, and the different types of simulations available in ReliaCloud‐NS and presents an overview of the results generated from a simulation.

PSO trained Neural Networks for predicting forest fire size: A comparison of implementation and performance.

Forest fires are a dangerous and devastating phenomenon. Being able to accurately predict the burned area of a forest fire could potentially limit human casualties as well as better prepare for the ensuing economical and ecological damage. A data set from the Montesinho Natural Park in Portugal provides a difficult regression task regarding the prediction of forest fire burn area due to the limited amount of data entries and the right skew nature of the data set. This paper shows how the use of a novel input structure and representation of the data, along with using Particle Swarm Optimization (PSO) instead of Backpropagation to determine weights of an Artificial Neural Network (ANN), improves error rates significantly.

Strategic and suave processing for performing similarity joins using MapReduce

An efficient MapReduce Algorithm for performing Similarity Joins between multisets is proposed. Filtering techniques for similarity joins minimize the number of pairs of entities joined and hence improve the efficiency of the algorithm. Multisets represent real-world data better by considering the frequency of its elements. Prior serial algorithms incorporate filtering techniques only for sets, but not multisets, while prior MapReduce algorithms do not incorporate any filtering technique or inefficiently and unscalably incorporate prefix filtering. This work extends the filtering techniques, namely the prefix, size and positional to multisets, and also achieves the challenging task of efficiently incorporating them in the shared-nothing MapReduce model, thereby minimizing the pairs generated and joined, resulting in I/O, network and computational efficiency. A technique to enhance the scalability of the algorithm is also presented as a contingency need. Algorithms are developed using Hadoop and tested using real-world Twitter data. Experimental results demonstrate unprecedented performance gain.

Mobile Edge Offloading Using Markov Decision Processes

Considering where to process data and perform computation is becoming a more difficult problem as Mobile Edge Computing (MEC) and Mobile Cloud Computing (MCC) continue to evolve. In order to balance constraints and objectives regarding items like computation time and energy consumption, computation and data should be automatically shifted between mobile devices, the edge, and the cloud. To address this issue, this study proposes a Markov Decision Process (MDP) based methodology to intelligently make such choices while optimizing multiple objectives. Results demonstrate an 17.47% or greater increase in performance.

Classifying commit messages: A case study in resampling techniques

In practice, there are a variety of real-world datasets that have an imbalanced nature where one of two classes dominates the data. These datasets are generally difficult to classify using machine learning algorithms as the skewed nature of the data has a significant impact on the training process. In order to combat this difficulty, many methods of under sampling and over sampling have been proposed in order to generate comparable data sets that are more easily classifiable. This study applies multiple resampling techniques to a set of commit messages that have been extracted from multiple Github and Sourceforge projects in order to answer the question, “Do developers discuss design?” This dataset is highly imbalanced with less than 15% of all commit messages being classified as having to do with design. Results demonstrate that the combined use of resampling as coupled with various classification algorithms yields improvements in classification over the state-of-the-art by more than 10% in terms of accuracy.