Trust-based Service Composition and Binding for Tactical Networks with Multiple Objectives

Abstract —Tactical networks must select service providers to meet service requirements of an operation while facing resource constraints and high security vulnerability. In such an environment nodes provide services to support various operations and / may request services to support the operations as well. We formulate the problem of service composition and service binding as a multi-objective optimization (MOO) problem, minimizing the service cost, while maximizing the quality of service (QoS) and quality of information (QoI). The MOO problem is essentially a node-to-service assignment problem such that by dynamically formulating service composition, and selecting the right nodes to provide requested services, the network can support concurrent operations while achieving multiple system objectives. We develop a trust-based service composition and binding protocol. We demonstrate that the trust-based scheme outperforms the counterpart non-trustbased scheme. Furthermore, our trust-based scheme can effectively penalize malicious nodes performing self-promotion attacks, thus filtering out malicious nodes and can ultimately lead to high user satisfaction. Keywords—service composition, tactical networks, trust, multiobjective optimization. I

Quality-of-Service-Aware Service Selection in Mobile Environments

The last decade is characterized by the rise of mobile technologies (UMTS, LTE, WLAN, Bluetooth, SMS, etc.) and devices (notebooks, tablets, mobile phones, smart watches, etc.). In this rise, mobiles phones have played a crucial role because they paved the way for mobile pervasion among the public. In addition, this development has also led to a rapid growth of the mobile service/application market (Statista 2017b). As a consequence, users of mobile devices nowadays find themselves in a mobile environment, with (almost) unlimited access to information and services from anywhere through the Internet, and can connect to other people at any time (cf. Deng et al. 2016; Newman 2015). Additionally, modern mobile devices offer the opportunity to select the services or information that best fit to a user’s current context. In this regard, mobile information services support users in retrieving context and non-context information, such as about the current traffic situation, public transport options, and flight connections, as well as about real-world entities, such as sights, museums, and restaurants (cf. Deng et al. 2016; Heinrich and Lewerenz 2015; Ventola 2014). An example of the application of mobile information services is several users planning a joint city day trip. Here, the users could utilize information retrieved about real-world entities for their planning. Such a trip constitutes a process with multiple participating users and may encompass actions such as visiting a museum and having lunch. For each action, mobile information services (e.g., Yelp, TripAdvisor, Google Places) can help locate available alternatives that differ only in attributes such as price, average length of stay (i.e., duration), or recommendations published by previous visitors. In addition, context information (e.g., business hours, distance) can be used to more effectively support the users in their decisions. Moreover, because multiple users are participating in the same trip, some users want to or must conduct certain actions together. However, decision-makers (e.g., mobile users) attempting to determine the optimal solution for such processes – meaning the best alternative for each action and each participating user – are confronted with several challenges, as shown by means of the city trip example: First, each user most likely has his or her own preferences and requirements regarding attributes such as price and duration, which all must be considered. Furthermore, for each action of the day trip, a huge number of alternatives probably exist. Thus, users might face difficulties selecting the optimal alternatives because of an information overload problem (Zhang et al. 2009). Second, taking multiple users into account may require the coordination of their actions because of potential dependencies among different users’ tours, which, for example, is the case when users prefer to conduct certain actions together. This turns the almost sophisticated decision problem at hand into a problem of high complexity. The problem complexity is increased further when considering context information, because this causes dependencies among different actions of a user that must be taken into account. For instance, the distance to cover by a user to reach a certain restaurant depends on the location of the previously visited museum. In conclusion, it might be impossible for a user to determine an optimal city trip tour for all users, making decision support by an information system necessary. Because the available alternatives for each action of the process can be denoted as (information) service objects (cf. Dannewitz et al. 2008; Heinrich and Lewerenz 2015; Hinkelmann et al. 2013), the decision problem at hand is a Quality-of-Service (QoS)-aware service selection problem. This thesis proposes novel concepts and optimization approaches for QoS-aware service selection regarding processes with multiple users and context information, focusing on scenarios in mobile environments. In this respect, the developed multi user context-aware service selection approaches are able to deal with dependencies among different users’ service compositions, which result from the consideration of multiple users, as well as dependencies within a user’s service composition, which result from the consideration of context information. Consequently, these approaches provide suitable support for decision-makers, such as mobile users

Allocating MapReduce workflows with deadlines to heterogeneous servers in a cloud data center

[EN] Total profit is one of the most important factors to be considered from the perspective of resource providers. In this paper, an original MapReduce workflow scheduling with deadline and data locality is proposed to maximize total profit of resource providers. A new workflow conversion based on dynamic programming and ChainMap/ChainReduce is designed to decrease transmission times among MapReduce jobs of workflows. A new deadline division considering execution time, float time and job level is proposed to obtain better deadlines of MapReduce jobs in workflows. With the adapted replica strategy in MapReduce workflow, a new task scheduling is proposed to improve data locality which assigns tasks to servers with the earliest completion time in order to ensure resource providers obtain more profit. Experimental results show that the proposed heuristic results in larger total profit than other adopted algorithms.This work is supported by the National Key Research and Development Program of China (No. 2017YFB1400801), the National Natural Science Foundation of China (Nos. 61872077, 61832004) and Collaborative Innovation Center of Wireless Communications Technology. Rubén Ruiz is partly supported by the Spanish Ministry of Science, Innovation, and Universities, under the project ¿OPTEP-Port Terminal Operations Optimization¿ (No. RTI2018-094940-B-I00) financed with FEDER funds¿.Wang, J.; Li, X.; Ruiz García, R.; Xu, H.; Chu, D. (2020). Allocating MapReduce workflows with deadlines to heterogeneous servers in a cloud data center. Service Oriented Computing and Applications. 14(2):101-118. https://doi.org/10.1007/s11761-020-00290-1S101118142Zaharia M, Chowdhury M, Franklin M et al (2010) Spark: cluster computing with working sets. 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Optimizing Service Selection and Allocation in Situational Computing Applications

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6205728&contentType=Early+Access+Articles&searchWithin%3Dp_Authors%3A.QT.Ardagna%2C+D..QT.%26searchField%3DSearch_All This paper describes a novel model for the service selection problem of workflow-based applications in the context of self-managing situated computing. In such systems, the execution environment includes different types of devices, from remote servers to personal notebooks, smartphones, and wireless sensors, which build an infrastructure that can dynamically change both its physical and logical architecture at run-time. We assume that worflows are defined abstractly; i.e., they invoke abstract services whose concrete counterparts can be selected dynamically. We also assume that concrete service implementations may possibly migrate on the nodes of the infrastructure. The selection problem we address is framed as an optimization problem of the quality of service, which evaluates at run-time the optimal binding to concrete services as well as the trade-off between the remote execution of software fragments and their dynamic deployment on local nodes of the computational environment. The final deployment takes into account quality of service constraints, the capabilities of the physical devices involved, including their performance and energy consumption, and the characteristics of the networking links connecting them. <br/