Applications of Soft Computing in Mobile and Wireless Communications

Soft computing is a synergistic combination of artificial intelligence methodologies to model and solve real world problems that are either impossible or too difficult to model mathematically. Furthermore, the use of conventional modeling techniques demands rigor, precision and certainty, which carry computational cost. On the other hand, soft computing utilizes computation, reasoning and inference to reduce computational cost by exploiting tolerance for imprecision, uncertainty, partial truth and approximation. In addition to computational cost savings, soft computing is an excellent platform for autonomic computing, owing to its roots in artificial intelligence. Wireless communication networks are associated with much uncertainty and imprecision due to a number of stochastic processes such as escalating number of access points, constantly changing propagation channels, sudden variations in network load and random mobility of users. This reality has fuelled numerous applications of soft computing techniques in mobile and wireless communications. This paper reviews various applications of the core soft computing methodologies in mobile and wireless communications

Models and heuristics for robust resource allocation in parallel and distributed computing systems

Includes bibliographical references.This is an overview of the robust resource allocation research efforts that have been and continue to be conducted by the CSU Robustness in Computer Systems Group. Parallel and distributed computing systems, consisting of a (usually heterogeneous) set of machines and networks, frequently operate in environments where delivered performance degrades due to unpredictable circumstances. Such unpredictability can be the result of sudden machine failures, increases in system load, or errors caused by inaccurate initial estimation. The research into developing models and heuristics for parallel and distributed computing systems that create robust resource allocations is presented.This research was supported by NSF under grant No. CNS-0615170 and by the Colorado State University George T. Abell Endowment

Evaluating the Robustness of Resource Allocations Obtained through Performance Modeling with Stochastic Process Algebra

Recent developments in the field of parallel and distributed computing has led to a proliferation of solving large and computationally intensive mathematical, science, or engineering problems, that consist of several parallelizable parts and several non-parallelizable (sequential) parts. In a parallel and distributed computing environment, the performance goal is to optimize the execution of parallelizable parts of an application on concurrent processors. This requires efficient application scheduling and resource allocation for mapping applications to a set of suitable parallel processors such that the overall performance goal is achieved. However, such computational environments are often prone to unpredictable variations in application (problem and algorithm) and system characteristics. Therefore, a robustness study is required to guarantee a desired level of performance. Given an initial workload, a mapping of applications to resources is considered to be robust if that mapping optimizes execution performance and guarantees a desired level of performance in the presence of unpredictable perturbations at runtime. In this research, a stochastic process algebra, Performance Evaluation Process Algebra (PEPA), is used for obtaining resource allocations via a numerical analysis of performance modeling of the parallel execution of applications on parallel computing resources. The PEPA performance model is translated into an underlying mathematical Markov chain model for obtaining performance measures. Further, a robustness analysis of the allocation techniques is performed for finding a robustmapping from a set of initial mapping schemes. The numerical analysis of the performance models have confirmed similarity with the simulation results of earlier research available in existing literature. When compared to direct experiments and simulations, numerical models and the corresponding analyses are easier to reproduce, do not incur any setup or installation costs, do not impose any prerequisites for learning a simulation framework, and are not limited by the complexity of the underlying infrastructure or simulation libraries

Task allocation in group of nodes in the IoT: A consensus approach

The realization of the Internet of Things (IoT) paradigm relies on the implementation of systems of cooperative intelligent objects with key interoperability capabilities. In order for objects to dynamically cooperate to IoT applications' execution, they need to make their resources available in a flexible way. However, available resources such as electrical energy, memory, processing, and object capability to perform a given task, are often limited. Therefore, resource allocation that ensures the fulfilment of network requirements is a critical challenge. In this paper, we propose a distributed optimization protocol based on consensus algorithm, to solve the problem of resource allocation and management in IoT heterogeneous networks. The proposed protocol is robust against links or nodes failures, so it's adaptive in dynamic scenarios where the network topology changes in runtime. We consider an IoT scenario where nodes involved in the same IoT task need to adjust their task frequency and buffer occupancy. We demonstrate that, using the proposed protocol, the network converges to a solution where resources are homogeneously allocated among nodes. Performance evaluation of experiments in simulation mode and in real scenarios show that the algorithm converges with a percentage error of about±5% with respect to the optimal allocation obtainable with a centralized approach