With the rapid upsurge in large-scale computing, resource sharing in heterogeneous distributed systems where users’ goals are conflicting has become a paramount research issue. The resource sharing ...or resource allocation problem is attributed to allocating users’ tasks across multiple computing resources so that the utility of the resources is improved while ensuring the quality of services. In this paper, the resource allocation problem is considered as a bi-objective optimization problem, including minimizing response time and the utilization imbalance between resources. To optimize both the objectives simultaneously, the contributions of this paper are manifolds. First, the problem is modeled as an optimization problem by considering both the objectives in an integrated manner. Second, the resource allocation problem is formulated as a non-cooperative game. Finally, to derive the game’s solution in a distributed manner, a Best Response dynamics based Modified Grey Wolf Optimizer BR-MGWO is proposed. Further, to assess the efficacy of BR-MGWO, it is compared with two other approaches, i.e., GOS and NCOP on problem instances of various settings. The experimental results show that BR-MGWO not only provides less response time while provides better improvements in utilization imbalance, which is reduced by 50% and 71%, respectively, in comparison to the GOS and NCOP.
Localization in both indoor and outdoor environments is a long-studied problem. Using Smartphone for localization has also gained popularity recently. However, none of the existing solutions consider ...seamless localization and tracking of individuals in both indoor and outdoor stretches with significant accuracy. In this paper, we propose a human identification, monitoring, and location tracking system, called SmartITS, which continuously tracks MAC ids of user equipment (Smartphones, BLE tags, and Bluetooth devices) and can provide a Google map-based visualization of their trajectories. Our tracker is a portable mobile entity comprising of a Smartphone and an external Wi-Fi adapter which does not require any extra hardware infrastructure to deploy as well as does not need any modification in hardware design at all. Extensive testing with a prototype testbed system in densely populated areas shows that the SmartITS system can seamlessly track user trajectories in indoor and outdoor stretches with a high aggregate location accuracy which is up to 44.49% more accurate than the simple GPS based location tracking system. Our proof-of-concept prototype shows the usability of SmartITS architecture. We also perform several experiments for evaluating the Smartphone’s performance as a scanner and as a sensor tag.
Team formation in an environment where some relevant parameters are not known in advance is a challenging problem. Communicating automata and distributed algorithms have been used to describe ...protocols for team formation. A high‐level specification provides a mathematical description of a protocol or a program. TLA +$$ {}^{+} $$ is a formal specification language designed to provide high‐level specifications of concurrent and distributed systems. The associated model checker known as TLC is capable of model checking the TLA +$$ {}^{+} $$ specifications. Recently, formal specification of a team formation protocol is given using TLA +$$ {}^{+} $$ when there is a single initiator (an agent or a robot) that initiates the team formation. Using TLA +$$ {}^{+} $$, we examine the formal specification for the multiple initiator situation and demonstrate that a composition technique can yield a single monolithic specification for the multiple initiator situation from the single initiator situation specification. We have used models of varying sizes, and the TLC model checker has confirmed that the protocol's specifications meet certain desired characteristics in each case.
In the logistics and supply chain domain, coordinated efforts among agents play a pivotal role, particularly in the context of collaborative object transportation within a warehouse. This paper ...addresses the multifaceted challenge of multi-agent coordination in warehouse environments characterized by sparse reward structures, where the ability to communicate among agents may be limited or infeasible. Due to various constraints such as power limitations, weight capacity, or specialized abilities, the individual execution of this task by a single agent remains unattainable. Our study focuses on heterogeneous agents, where each agent possesses a distinct subset of skills and capabilities. Our research examines the emergence of cooperative behavior among groups of agents with the requisite skill sets, aiming to accomplish the task without explicit inter-agent communication or prior coordination. To encourage implicit agent coordination, we introduce a hierarchical approach integrating a global evaluation of abstract actions with curiosity-driven intrinsic learning. This approach is well-suited for real-world settings with scarce rewards. We evaluated its effectiveness in a warehouse domain, and the results show that our approach consistently achieves higher average returns, faster convergence, and improved exploration efficiency, highlighting its effectiveness in diverse scenarios.
Display omitted
•Overcoming challenges of sparse reward structures in collaborative scenarios.•Hierarchical reinforcement learning for effective coordination.•Global action evaluation enhances collaboration.•Implicit coordination through curiosity-driven learning.•Diverse agent skills addressed in collaboration.
PurposeThis paper aims to propose an approach for the analysis of user interest based on tweets, which can be used in the design of user recommendation systems. The extract topics are seen positively ...by the user.Design/methodology/approachThe proposed approach is based on the combination of sentiment extraction and classification analysis of tweet to extract the topic of interest. The proposed hybrid method is original. The topic extraction phase uses a method based on semantic distance in the WordNet taxonomy. Sentiment extraction uses NLPcore.FindingsThe algorithm has been extensively tested using real tweets generated by 1,000 users. The results are quite encouraging and outperform state-of-the-art results and confirm the suitability of the approach combining sentiment and categorization for the topic of interest extraction.Research limitations/implicationsThe hybrid method combining sentiment extraction and classification for user positive topics represents a novel contribution with many potential applications.Practical implicationsThe functionality of positive topic extraction is very useful as a component in the design of a recommender system based on user profiling from Twitter user behaviors.Social implicationsThe application of the proposed method in short-text social network can be massive and beyond the applications in tweets.Originality/valueThere are few works that have considered both sentiment analysis and classification to find out users’ interest. The algorithm has been extensively tested using real tweets generated by 1,000 users. The results are quite encouraging and outperform state-of-the-art results.
Many applications in mobile ad hoc networks (MANETs) require multiple nodes to act as leaders. Given the resource constraints of mobile nodes, it is desirable to elect resource-rich nodes with higher ...energy or computational capabilities as leaders. In this paper, we propose a novel distributed algorithm to elect top-K weighted leaders in MANETs where weight indicates available node resources. Frequent topology changes, limited energy supplies, and variable message delays in MANETs make the weight-based K leader election a non-trivial task. So far, there is no algorithm for weight-based K leader election in distributed or mobile environments. Moreover, existing single leader election algorithms for ad hoc networks are either unsuitable of extending to elect weight-based K leaders or they perform poorly under dynamic network conditions.
In our proposed algorithm, initially few coordinator nodes are selected locally which collect the weights of other nodes using the diffusing computation approach. The coordinator nodes then collaborate together, so that, finally the highest weight coordinator collects weights of all the nodes in the network. Besides simulation we have also implemented our algorithm on a testbed and conducted experiments. The results prove that our proposed algorithm is scalable, reliable, message-efficient, and can handle dynamic topological changes in an efficient manner.
PurposeThis paper aims to solve the web service selection problem using an efficient meta-heuristic algorithm. The problem of selecting a set of web services from a large-scale service environment ...(web service repository) while maintaining Quality-of-Service (QoS), is referred to as web service selection (WSS). With the explosive growth of internet services, managing and selecting the proper services (or say web service) has become a pertinent research issue.Design/methodology/approachIn this paper, to address WSS problem, the authors propose a new modified fruit fly optimization approach, called orthogonal array-based learning in fruit fly optimizer (OL-FOA). In OL-FOA, they adopt a chaotic map to initialize the population; they add the adaptive DE/best/2mutation operator to improve the exploration capability of the fruit fly approach; and finally, to improve the efficiency of the search process (by reducing the search space), the authors use the orthogonal learning mechanism.FindingsTo test the efficiency of the proposed approach, a test suite of 2500 web services is chosen from the public repository. To establish the competitiveness of the proposed approach, it compared against four other meta-heuristic approaches (including classical as well as state-of-the-art), namely, fruit fly optimization (FOA), differential evolution (DE), modified artificial bee colony algorithm (mABC) and global-best ABC (GABC). The empirical results show that the proposed approach outperforms its counterparts in terms of response time, latency, availability and reliability.Originality/valueIn this paper, the authors have developed a population-based novel approach (OL-FOA) for the QoS aware web services selection (WSS). To justify the results, the authors compared against four other meta-heuristic approaches (including classical as well as state-of-the-art), namely, fruit fly optimization (FOA), differential evolution (DE), modified artificial bee colony algorithm (mABC) and global-best ABC (GABC) over the four QoS parameter response time, latency, availability and reliability. The authors found that the approach outperforms overall competitive approaches. To satisfy all objective simultaneously, the authors would like to extend this approach in the frame of multi-objective WSS optimization problem. Further, this is declared that this paper is not submitted to any other journal or under review.
Resource allocation in a distributed computing system is the process of allocating the workload across multiple computing resources to optimize the required performance criteria. In this article, a ...resource allocation problem that arises in a distributed system consisting of multiple heterogeneous servers is addressed. The problem is modeled as a multi-objective problem with two conflicting objectives: (a) to minimize the users’ expected response time and (b) to reduce the utilization imbalance between servers. To satisfy these objectives simultaneously, first, both the objectives are considered in an integrated manner, and an optimization problem is formulated. Second, the optimization problem is cast into a game-theoretic setting and modeled as a non-cooperative game, called a non-cooperative resource allocation game. Finally, to solve the game, a differential evolution-based co-evolutionary framework (DECEF) is proposed. To evaluate the performance of DECEF, a rigorous simulation study is carried out. Furthermore, to assess the relative performance of DECEF, it is compared against two existing approaches, from various aspects, including system utilization, system heterogeneity, and system size. The experimental results show that DECEF provides better system-wide performance while optimizing both the objectives.
The article addresses the issue of developing of software tools for manual segmentation of tomography images supporting radiologist's personal content. Through an expert survey we have identified the ...requirements for such software tools from the doctors' point of view as end users, as well as to the nomenclature and functionality of tools that implement these requirements. In order to meet the identified requirements, we have developed a solution based on a client-server architecture with a cloud access point. The nomenclature of tools for marking tomographic images implemented in the solution, as well as the methodology for working with them, fully complies with the identified requirements. The following functions have been developed and implemented: calculation of the volume of the region of interest, as well as three options for the semiautomatic segmentation of the image based on threshold, extreme points and neuron networks. All functions have customizable parameters and (or) implementation options, which provides flexibility in solving specific markup problems. Experimental studies have shown that the constructed service meets all the requirements put forward by radiologists and corresponds to the global level in terms of accuracy and speed (performance) of segmentation.
In this paper, we consider a load balancing problem in distributed systems, that has two conflicting objectives: (i) minimizing the users’ expected response time and (ii) minimizing the total ...monetary cost incurred by each user. To satisfy both the objectives simultaneously, we consider the objectives in an integrated manner and formulate the problem as an optimization problem. We then cast it into a game-theoretic setting and model the load balancing problem as a non-cooperative game. To solve the game, we characterize the best response strategy for each player, and derive a decentralized algorithm called Cost-Aware Load Balancing Algorithm (CALBA). We conduct a rigorous experimental study to demonstrate the effectiveness of CALBA. Further, to establish the effectiveness of CALBA, we compare it with three other load balancing schemes, i.e., MinRT, MinCost, and GPMS, using various system configurations such as varying system size, varying system utilization, and system heterogeneity, across multiple performance indicators. The computational results show that textitCALBA outperforms the competitive schemes by reducing the response time and cost, and unlike others, CALBA produces an allocation of load which guarantees fairness (in terms of response time) between the users. In a nutshell, the results demonstrate the suitability of CALBA in realistic scenarios as it is an adaptable and feasible approach to get a cost-aware load balancing solution.
•A multi-objective load balancing problem is studied.•The load balancing problem is formulated as a non-cooperative game.•A decentralized algorithm called Cost-Aware Load Balancing Algorithm (CALBA) is proposed to solve the game.•The efficacy and the utility of the CALBA is experimentally demonstrated.
