Scalable optimization in grid, cloud, and intelligent network computing - foreword

Global optimization in large-scale distributed systems requires massive amounts of computations for complex objective functions. Conventional global optimization based on stochastic algorithms cannot guarantee an actual global optimum with a finite searching iteration. Therefore, scalability is a desirable feature for the optimization techniques in highly distributed dynamic environments, where the storage and computing capabilities can be spread over a wide geographical area. They must dynamically adapt to organizational relationships and real-world uncertainties. Intelligent Networks, such as grids, peer-to-peer, ad hoc networks, constellations, and clouds enable the flexible routing and charging, advanced user interactions and the aggregation and sharing of geographically distributed resources. Collectively owned and managed by distinct organizational bodies, such complex large-scale distributed systems typically encompass computational resources from different institutions, enterprises, and individuals and are governed by heterogeneous administrative policies and regulations. System management techniques must therefore be able to group, predict, and classify different sets of rules, configuration directives, and environmental conditions to impose dissimilar usage policies on various users and resources. They must effectively deal with various optimization criteria, users’ requirements, massive data processing, and, finally, uncertainties in system information that may be incomplete, imprecise, and fragmentary. Next information technology architectures, such as green cloud-to-cloud systems and green mobile clouds, provide elastic and in fact unlimited resources, including storage, as various services to cloud users with possible minimal energy utilization. However, both cloud users and cloud service providers are almost certain to be from different trust domains. Therefore, a secure user-enforced data access control mechanism must be provided before cloud users have the liberty to outsource sensitive data to the cloud for storage and further processing. With the advent of intelligent networks, where efficient interdomain operation and high scalability of the whole system are the most important features, it is arguably required to investigate novel methods and techniques to enable secure access to data and resources, flexible communication, efficient scheduling, self-adaptation, decentralization, and self-organization. This special issue herewith presents six research papers with novel concepts in the analysis, implementation, and evaluation of the next generation of intelligent scalable techniques for data-intensive processing and global optimization problems in large-scale distributed systems. The first three papers discuss novel scalable solutions of data-intensive global optimization problems in well-known large-scale network environments. The presented techniques and their implementations are based on formal mathematical and logical models with the new optimization criteria (energy conservation), semantic rules and ontology, and modern synchronization modules of parallel computational processes. Li et al. in [1] introduced a methodology for improvement of the performance of the dynamic core of Global/Regional Assimilation and Prediction System (GRAPES) – the Numerical Weather Prediction system used by Chinese Meteorology Administration. The system performance is formally modeled as a sequence of large, sparse linear systems formulated by the discretization of global 3D Helmholtz equation. The authors developed a solver that enables an effective synchronization of the numerical processes at the global units of the system. The results of simple empirical analysis show good scalability of the proposed methodology achieved by using up to 6144 active cores in GRAPES. In [2], the authors present a framework for the energy-aware system management in backbone networks. The energy optimization problem is formulated as a