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• Formulate a non-cooperative game with both UEs and MECs as players. • Prove the existence of and convergence to a Nash equilibrium. • Develop a set of algorithms to find the Nash equilibrium. • Demonstrate numerical examples of non-cooperative games with and without MECs' participation. In a newly emerged fog computing environment, various user equipments (UE) enhance their computing power and extend their battery lifetime by computation offloading to mobile edge cloud (MEC) servers. Such an environment is distributed and competitive in nature. In this paper, we take a game theoretical approach to computation offloading optimization in a fog computing environment. Such an approach captures and characterizes the nature of a competitive environment. The main contributions of the paper can be summarized as follows. First, we formulate a non-cooperative game with both UEs and MECs as players. Each UE attempts to minimize the execution time of its tasks with an energy constraint. Each MEC attempts to minimize the product of its power consumption for computation and execution time for allocated tasks. Second, we develop a heuristic algorithm for a UE to determine its "heuristically" best response to the current situation, an algorithm for an MEC to determine its best response to the current situation, and an iterative algorithm to find the Nash equilibrium. Third, we prove that our iterative algorithm converges to a Nash equilibrium. We demonstrate numerical examples of our non-cooperative games with and without MECs' participation. We observe that our iterative algorithm always quickly converges to a Nash equilibrium. The uniqueness of our non-cooperative games is that the strategy set of a player can be discrete and the payoff function of a player can be obtained by a heuristic algorithm for combinatorial optimization. To the best of the author's knowledge, there has been no such investigation of non-cooperative games based on combinatorial optimization for computation offloading optimization in a fog computing environment. [ABSTRACT FROM AUTHOR]

The paper presents a new method of optimization by the Layered Algorithm (LA). The proposed algorithm reduces the initial area to the sub-area containing the optimum. The proposed technique is based on the classification of the optimized function values (data sampled by sensors). The classification method uses a two-dimensional three-state Cellular Automata (CA). The CA classifies all area points ascribed to the CA cells based on their values. Specification of the categorization layers to the data gives a possibility to identify the different levels areas. Consequently, after analysis, a sub-area containing the optimum can be designated. In this paper, the proposed algorithm is applied to find the location of the airborne contaminant source by analyzing the concentration of released substances reported by mobile sensors distributed over the domain of interest. The Gaussian dispersion model simulation of the contaminant dispersion in the urbanized area is applied to generate the data used to verify the efficiency of the proposed Layered Algorithm. The LA successfully estimates the sub-area of the considered domain where the contamination source is located, taking to account data from sensors solely. • Optimization method reducing the initial area to the sub-area containing the optimum. • Verification of the Layered Algorithm (LA) with Selected Benchmark Functions. • Analysis of the LA efficiency to localize the airborne contaminant source. • LA results assessment by the measures: classification and accuracy error, relevance. [ABSTRACT FROM AUTHOR]

In edge computing, application components can be placed over a range of computational devices from cloud data centers to nodes at the network edge. Application placement can have significant impact on important metrics like latency and resource utilization. Thus, application placement is an important optimization problem. In edge computing, the characteristics of both the infrastructure and the application may change over time, which may require the dynamic re-optimization of the application placement. Most algorithms suggested so far for the dynamic re-optimization of edge application placement are centralized, i.e., they rely on one entity collecting information from the whole infrastructure and making decisions centrally. However, centralized approaches suffer from limited scalability and are vulnerable to failures. In this paper, we present a decentralized approach for the dynamic re-optimization of edge application placement. We adopt an algorithm of Malek et al. for distributed systems and modify it to make it applicable to edge computing. In this approach, each node makes decisions autonomously, using auctions for coordination. Our empirical results demonstrate that the proposed algorithm is very effective in optimizing edge application placement. In an edge system with 637 edge nodes and 563 end devices, our algorithm achieves 54% higher reduction of application latency than a previous decentralized algorithm. • Optimized placement of application components on cloud and edge devices. • Decentralized optimization algorithm using local knowledge and local modifications. • Auctions and distributed locking for synchronization. • Minimization of application latency. [ABSTRACT FROM AUTHOR]

• The placement of data and the scheduling of request are studied in edge caching. • Network unreliability and limited storage and bandwidth resources are considered. • Two algorithms with different computation complexity and cache efficiency are developed. • The experiments are conducted to evaluate the performance of proposed caching strategies. Edge caching is a prevailing media delivery technology where data is hosted at the edge nodes with computing and storage capability in close proximity to users, in order to expand the backhaul network capacity and enhance users' quality of experience (QoE). The existing work in this area often neglects the fact that large-scale distributed cache networks are not particularly reliable and many edge nodes are prone to failure. In this paper we investigate and develop a novel, cooperative caching mechanism for content placement and request routing. We aim to minimize the content access delay and achieve the optimization in polynomial time, taking into account failures in an unreliable network environment with limited edge storage and bandwidth. We introduce two optimization algorithms: 1) a primal-dual algorithm that is based on the Lagrangian dual decomposition and subgradient method, and 2) a greedy-based approximation algorithm with a proven approximation ratio. Numerical results show that the proposed algorithms outperform other comparative approaches in synthetic and real network environments, and the approximation algorithm is particularly suitable for networking scenarios with sparse node connectivity and resources in short supply. [ABSTRACT FROM AUTHOR]

Software systems rely on events for logging, coordination, handling unusual situations, and more. Monitoring event streams from systems that provide services can ensure the service complies with policies, regulations, and other business rules. Notably, monitoring to detect violations of rules as early as possible is much desired as, for example, the service may reclaim more resources from erring enactments. The primary goal of this paper is to develop techniques for detecting violations of time constraints as early as possible. We formalize a model for events and a language to specify constraints on event timing and data. We develop algorithms to detect violations of individual rules, then use a chase process to detect violations of acyclic sets of rules. We also present optimization techniques to reduce monitoring overhead. Finally, we implement and evaluate our algorithms through experiments to demonstrate our approach is feasible and beneficial. [ABSTRACT FROM AUTHOR]

Multimedia event detection is the task of detecting a specific event of interest in an user-generated video on websites. The most fundamental challenge facing this task lies in the enormously varying quality of the video as well as the high-level semantic abstraction of event inherently. In this paper, we decompose the video into several segments and intuitively model the task of complex event detection as a multiple instance learning problem by representing each video as a "bag" of segments in which each segment is referred to as an instance. Instead of treating the instances equally, we associate each instance with a reliability variable to indicate its importance and then select reliable instances for training. To measure the reliability of the varying instances precisely, we propose a visual-semantic guided loss by exploiting low-level feature from visual information together with instance-event similarity based high-level semantic feature. Motivated by curriculum learning, we introduce a negative elastic-net regularization term to start training the classifier with instances of high reliability and gradually taking the instances with relatively low reliability into consideration. An alternative optimization algorithm is developed to solve the proposed challenging non-convex non-smooth problem. Experimental results on standard datasets, i.e., TRECVID MEDTest 2013 and TRECVID MEDTest 2014, demonstrate the effectiveness and superiority of the proposed method to the baseline algorithms. • A visual-semantic guided loss is proposed to measure reliability of instance for event detection. • Training begins with high-reliability instances and gradually added instances of low reliability. • Promising experimental results show the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Matrix regression uses matrix data as input and directly selects the features from matrix data by employing several couples of left and right regression matrices. However, the existing matrix regression methods do not consider the relationship between different classes of data and cannot get discriminant left/right regression matrix, which results in poor classifications. In this paper, a margin-based discriminant embedding sparse matrix regression (MDESMR) model for image supervised feature selection is proposed. For each matrix data, a margin is first defined as the difference between two types of distances determined by the left/right regression matrix. Maximizing the average margin for all training matrix data can get the nonlinear discriminant embedding. Thus, a nonlinear embedding and its linear approximation can be obtained simultaneously. An alternative iterative optimization algorithm for solving the proposed model is also designed and the corresponding closed-form solutions in each iteration are found. Some experiments on several datasets demonstrate the superiority of our method. • Our model preserves the spatial information of elements in the original image data. • The margin of each matrix data is defined by using left/right regression matrices. • Maximizing the average margin can obtain the non-linear discriminant embedding. • Discriminative features are selected by the row sparsity of transformation matrix. • An iterative optimization algorithm is designed to solve the proposed model. • The effectiveness and superiority of our method are verified on several datasets. [ABSTRACT FROM AUTHOR]