A large-scale volunteer computing system is a type of distributed system in which contributors volunteer their computing resources, such as personal computers or mobile devices, to contribute to a larger computing effort. Volunteer resources are connected over the Internet and together form a powerful computing system capable of providing a service without depending on a service provider. Volunteer network resource allocation is the process of assigning computing tasks or services to a network of volunteer resources. The allocation process includes identifying the needed resources, selecting appropriate volunteers, and assigning tasks or services based on their capabilities. Volunteer computing systems consist of a large number of heterogeneous resources - in terms of processing power, storage, and availability - belonging to different authorities - users or organizations - and exhibiting uncertain behavior in terms of connection, disconnection, capacity, and failure. All of this makes resource allocation a challenging task in terms of ensuring a minimum quality of service, requiring complex algorithms and optimization techniques to ensure that services are efficiently allocated while respecting the constraints of the available resource. This paper introduces the Network-Aware Resource Allocation mechanism, which leverages the location, connectivity, and network latency of volunteer nodes to minimize the time a service runs with degraded quality of service and aims to deal with the energy consumption resulting from data replication requirements. This resource allocation mechanism applies to both the initial deployment of the service in the network and to the reallocation of nodes in the event that one of the allocated nodes fails or becomes unavailable. Our method has been validated in a simulation environment of a realistic volunteer system. The analysis of the results shows how our mechanism meets the quality requirements of users while minimizing the synchronization and replication times of service data replicas, as well as the time that services run with degraded quality of service, reducing the times by more than 70% in the service deployment phase and by more than 60% in the service execution phase. It also helps to reduce overall energy consumption.

Abstract The uncapacitated facility location problem (UFLP) is a well-known combinatorial optimization problem that finds practical applications in several fields, such as logistics and telecommunication networks. While the existing literature primarily focuses on the deterministic version of the problem, real-life scenarios often involve uncertainties like fluctuating customer demands or service costs. This paper presents a novel algorithm for addressing the UFLP under uncertainty. Our approach combines a tabu search metaheuristic with path-relinking to obtain near-optimal solutions in short computational times for the determinisitic version of the problem. The algorithm is further enhanced by integrating it with simulation techniques to solve the UFLP with random service costs. A set of computational experiments is run to illustrate the effectiveness of the solving method.

Abstract Stochastic, as well as fuzzy uncertainty, can be found in most real‐world systems. Considering both types of uncertainties simultaneously makes optimization problems incredibly challenging. In this paper we propose a fuzzy simheuristic to solve the Time Capacitated Arc Routing Problem (TCARP) when the nature of the travel time can either be deterministic, stochastic or fuzzy. The main goal is to find a solution (vehicle routes) that minimizes the total time spent in servicing the required arcs. However, due to uncertainty, other characteristics of the solution are also considered. In particular, we illustrate how reliability concepts can enrich the probabilistic information given to decision‐makers. In order to solve the aforementioned optimization problem, we extend the concept of simheuristic framework so it can also include fuzzy elements. Hence, both stochastic and fuzzy uncertainty are simultaneously incorporated into the CARP. In order to test our approach, classical CARP instances have been adapted and extended so that customers' demands become either stochastic or fuzzy. The experimental results show the effectiveness of the proposed approach when compared with more traditional ones. In particular, our fuzzy simheuristic is capable of generating new best‐known solutions for the stochastic versions of some instances belonging to thetegl,tcarp,val, andruralbenchmarks.

Executing message-passing parallel applications on a large number of resources in an efficient way is not a trivial task. Due to the complex interaction between the parallel applications and the HPC system, many applications may suffer performance inefficiencies when they scale. To achieve an efficient use of these large-scale systems using thousands of cores, a point to consider before executing an application is to know its behavior in the system. In this work, we propose a novel methodology called P3S (Prediction of Parallel Program Scalability), which allows us to analyze and predict the scalability of message-passing applications on a given system. The methodology strives to use a bounded analysis time, and a reduced set of resources to predict the application behavior for large-scale. The experimental validation proves that the P3S is able to predict the application scalability with an average accuracy greater than 95 percent using a reduced set of resources.

In this paper we propose a methodology that allows us to predict the application scalability behavior in a specific system, providing information to select the most appropriate resources to run the application. We explain the general methodology, focusing on the presentation of a novel method to model the logical application trace for a large number of processes. This method is based on the projection of a set of executions of the application signature for a small number of processes. The generated traces are validated by comparing them with the real traces obtained with PAS2P tool. We present the experimental validation for the BT Nas Parallel Benchmark. The signatures for 16, 36, 64, 81 and 100 processes were executed and used to model and project the logical trace for 1024 processes. The results obtained show the accuracy of the method. The communication pattern was predicted without error, while the predicted error is less than 10% for the communication volume and less than 5% for the number of instructions.