Search

Empowered by today’s rich media generating devices and convenient Internet access, crowdsourced live streaming (CSLS) service has developed rapidly and become one of the most popular Internet services. Large crowdsourced live streaming providers (CSLSPs) are migrating their services to geo-distributed cloud platforms (GDCPs) for lower costs and higher availability. A CSLSP may rent compute and network resources from cloud providers for video transcoding, video delivering, user-requests handling, and other related tasks. However, due to dynamic requests by viewers and widely spread locations of broadcasters and viewers, it is still challenging for a CSLSP to serve demands of users with reasonable resources from the cloud-based geo-distributed data centers. To overcome this challenge cost-effectively, we propose an online algorithm to save operational costs for CSLSPs by jointly and dynamically choosing right data centers for broadcasters and viewers. Mathematical analysis is presented and proves that our proposed online algorithm can ensure operational costs to be within an upper bound above the optimal solution, while guaranteeing the QoE for viewers. We conduct extensive trace-driven illustrative studies and show that the proposed method can achieve suboptimal results and outperforms other alternative methods.

Software defect prediction constructs a model from the previous version of a software project to predict defects in the current version, which can help software testers to focus on software modules with more defects in the current version. Most existing methods construct defect prediction models through minimizing the defect prediction error measures. Some researchers proposed model construction approaches that directly optimized the ranking performance in order to achieve an accurate order. In some situations, the model complexity is also considered. Therefore, defect prediction can be seen as a multi-objective optimization problem and should be solved by multi-objective approaches. And hence, in this paper, we employ an existing multi-objective evolutionary algorithm and propose a new multi-objective learning method based on it, to construct defect prediction models by simultaneously optimizing more than one goal. Experimental results over 30 sets of cross-version data show the effectiveness of the proposed multi-objective approaches.

Dynamic requests of viewers from sparse and dispersed locations for crowdsourced-live-streaming (CSLS) service make current cloud service providers (CSPs) inadequate to provide sufficient quality of experience (QoE). To solve this issue, we propose a multi-CDN-assisted-CSLS (MCACLS) architecture, a novel cloud architecture complemented by multiple content delivery networks (Multi-CDNs). MCACLS architecture can enhance a CSP’s capacity of video distribution service and improve the quality of CSLS service for end-users while reducing the overall operational cost. MCACLS adaptively adjusts resources between a CSP and its leased CDN service in a fine granularity to deal with the volatility of user requests. However, scheduling resources cost-effectively in response to user requests from different regions is a critical issue that must be addressed. We formulate the above problem into a constrained stochastic optimization problem and propose an algorithm based on the Nash bargaining solution. Our proposed algorithm makes tradeoff between QoE of users and the overall operational cost for CSPs. Illustrative studies validate the advantages of MCACLS and show that it is more cost-effective, reducing the overall operational cost by up to 15% compared with other alternatives while achieving sufficient QoE for viewers.

Methylmalonic acidemia (MMA) is an autosomal recessive metabolic disorder. Traditional diagnosis needs physicians' personal level of professional medical knowledge and clinical experience. In this paper, we employ machine learning methods to diagnose MMA based on patients' laboratory blood tests and laboratory urine tests, in order to make a timely diagnosis and reduce dependence on physicians' personal level of professional medical knowledge and clinical experience. By comparing different machine learning algorithms for diagnosing MMA, we obtain the following conclusions: (a) machine learning methods can perform well for diagnosing MMA (all established predictive models obtain high accuracies and AUC values which are greater than 0.85 over all data sets, and some of these results are even more than 0.98); (b) random forest algorithm performs best among the compared algorithms; and (c) diagnosis based on the data combining both urine tests and blood tests is better than diagnosis based on single test alone in general. The conclusions show that applying machine learning algorithms to the diagnosis of MMA can achieve good performance. Thus, it is credible to build machine learning models to give an initial diagnosis without professional medical knowledge.

The offloading policy has attracted much attention in the study of Device-to-Device (D2D) Communication underlaying cellular network integrated with Mobile Edge Computing (MEC) service. D2D communication can improve energy efficiency and prolong the battery life of user equipment (UEs) by offloading computation tasks from a UE to a nearby UE via a D2D link. How to coordinate UEs to offload tasks in D2D underlay communication integrated with MEC Service for reducing interference and improving network energy efficiency is a very important subject. In this paper, we study a D2D underlay communication integrated with MEC service system that provides three offloading policies that are: (i) executing tasks in local device, (ii) offloading tasks to servers in local region, (iii) offloading tasks to a nearby UE. Furthermore, we formulate the computation offloading problem as a potential game and propose a D2D communication integrated with MEC service offloading computation algorithm for solving the computation offloading problem in a short period. The simulation results show that our proposed algorithm can reduce overall system energy consumption under time constraints.

Mobile edge computing (MEC) is a promising paradigm to integrate computing and communication resources in mobile networks. MEC can improve mobile service quality and enhance Quality of Experience (QoE) by offloading computation tasks to MEC servers. However, a MEC server only can provide limited computational resources for users. In this paper, we consider a mobile edge computing system that provides three offloading policies that are: (i) executing tasks in local device, (ii) offloading tasks to servers in a local region, (iii)offloading tasks to servers in a nearby region. In the policy (iii), mobile user equipment can utilize computational resources of MEC servers in nearby regions to solve the problem of insufficient computational resources in local region servers. We formulate the computation offloading problem as a potential game and propose a Distributed Offloading strategy based on Jacobi algorithm (DOJ) for solving the computation offloading problem in a short period. The simulation results show that our proposed algorithm can reduce overall system costs and guarantee the QoE of users.

Software defect prediction can help to allocate testing resources efficiently through ranking software modules according to their defects. Existing software defect prediction models that are optimized to predict explicitly the number of defects in a software module might fail to give an accurate order because it is very difficult to predict the exact number of defects in a software module due to noisy data. This paper introduces a learning-to-rank approach to construct software defect prediction models by directly optimizing the ranking performance. In this paper, we build on our previous work, and further study whether the idea of directly optimizing the model performance measure can benefit software defect prediction model construction. The work includes two aspects: one is a novel application of the learning-to-rank approach to real-world data sets for software defect prediction, and the other is a comprehensive evaluation and comparison of the learning-to-rank method against other algorithms that have been used for predicting the order of software modules according to the predicted number of defects. Our empirical studies demonstrate the effectiveness of directly optimizing the model performance measure for the learning-to-rank approach to construct defect prediction models for the ranking task.

This paper applies the learning-to-rank approach to software defect prediction. Ranking software modules in order of defect-proneness is important to ensure that testing resources are allocated efficiently. However, prediction models that are optimized for predicting explicitly the number of defects often fail to correctly predict rankings based on those defect numbers. We show in this paper that the model construction methods, which include the ranking performance measure in the objective function, perform better in predicting defect-proneness rankings of multiple modules. We present the experimental results, in which our method is compared against three other methods from the literature, using five publicly available data sets.

Recently, building sparse SVMs becomes an active research topic due to its potential applications in large scale data mining tasks. One of the most popular approaches to building sparse SVMs is to select a small subset of training samples and employ them as the support vectors. In this paper, we explain that selecting the support vectors is equivalent to selecting a number of columns from the kernel matrix, and is equivalent to selecting a subset of features in the feature selection domain. Hence, we propose to use an effective feature selection algorithm, namely the Minimum Redundancy -- Maximum Relevance (MRMR) algorithm to solve the support vector selection problem. MRMR algorithm was then compared to two existing methods, namely back-fitting (BF) and pre-fitting (PF) algorithms. Preliminary results showed that MRMR generally outperformed BF algorithm while it was inferior to PF algorithm, in terms of generalization performance. However, the MRMR approach was extremely efficient and significantly faster than the two compared algorithms.