A game-theoretic and hybrid genetic meta-heuristics model for security-assured scheduling of independent jobs in computational grids

Scheduling independent tasks in Computational Grids commonly arises in many Grid-enabled large scale applications. Much of current research in this domain is focused on the improvement of the efficiency of the Grid schedulers, both at global and local levels, which is the basis for Grid systems to leverage large computing capacities. However, unlike traditional scheduling, in Grid systems security requirements are very important to scheduling tasks/applications to Grid resources. The objective is thus to achieve efficient and secure allocation of tasks to machines. In this paper we propose a new model for secure scheduling at the Grid sites by combining game-theoretic and genetic-based meta-heuristic approaches. The game-theoretic model takes into account the realistic feature that Grid users usually perform independently of each other. The scheduling problem is then formalized as a noncooperative non-zero sum game with Nash equilibria as the solutions. The game cost function is minimized, at global and user levels, by using four genetic-based hybrid meta-heuristics. We have evaluated the proposed model through a static benchmark of instances, for which we have measured two basic metrics, namely the makespan and flowtime. The obtained results suggest that it is more resilient for the Grid users (and local schedulers) to tolerate some job delays defined as additional scheduling cost due to security requirements instead of taking a risk of allocating at unreliable resources.Peer ReviewedPostprint (published version

Game-theoretic, market and meta-heuristics approaches for modelling scheduling and resource allocation in grid systems

Task scheduling and resource allocation are the crucial issues in any large scale distributed system, such as Computational Grids (CGs). However, traditional computational models and resolution methods cannot effectively tackle the complex nature of Grid, where the resources and users belong to many administrative domains with their own access policies, users' privileges, etc. Recently, researchers are investigating the use of game theoretic approaches for modelling task and resource allocation problems in CGs. In this paper, we present a compact survey of the most relevant research proposals in the literature to use game-based models for the resource allocation problems and their resolution using metaheuristic methods. We emphasize the need of the translation of the traditional economical models into the game scenarios and the use of metaheuristic schedulers for solving such games in order to address the new complex scheduling and allocation criterions. We study the case of asymmetric Stackelberg game used for modelling the Grid users' behavior, where the security and reliability criterions are aggregated and defined as the users' costs functions. The obtained results show the efficiency of the hybridization of heuristic-based approaches with game models, which enables to include additional requirements and features into the computational models and tackle more effectively the resolution of the applied schedulers.Peer ReviewedPostprint (published version

Modern approaches to modeling user requirements on resource and task allocation in hierarchical computational grids

Peer ReviewedPostprint (published version

A web interface for meta-heuristics based grid schedulers

The use of meta-heuristics for designing efficient Grid schedulers is currently a common approach. One issue related to Grid based schedulers is their evaluation under different Grid configurations, such as dynamics of tasks and machines, task arrival, scheduling policies, etc. In this paper we present a web application that interfaces the final user with several meta-heuristics based Grid schedulers. The application interface facilities for each user the remote evaluation of the different heuristics, the configuration of the schedulers as well as the configuration of the Grid simulator under which the schedulers are run. The simulation results and traces are graphically represented and stored at the server and can retrieved in different formats such as spreadsheet form or pdf files. Historical executions are as well kept enabling a full study of use cases for different types of Grid schedulers. Thus, through this application the user can extract useful knowledge about the behavior of different schedulers by simulating realistic conditions of Grid system without needing to install and configure any specific software.Peer ReviewedPostprint (published version

Modelling of user requirements and behaviors in computational grids

In traditional distributed computing systems a few user types are found having ratherPeer ReviewedPostprint (published version

Data and Task Scheduling in Distributed Computing Environments, Journal of Telecommunications and Information Technology, 2014, nr 4

ecome a major research and engineering issue. Data Grids (DGs), Data Clouds (DCs) and Data Centers are designed for supporting the processing and analysis of massive data, which can be generated by distributed users, devices and computing centers. Data scheduling must be considered jointly with the application scheduling process. It generates a wide family of global optimization problems with the new scheduling criteria including data transmission time, data access and processing times, reliability of the data servers, security in the data processing and data access processes. In this paper, a new version of the Expected Time to Compute Matrix (ETC Matrix) model is defined for independent batch scheduling in physical network in DG and DC environments. In this model, the completion times of the computing nodes are estimated based on the standard ETC Matrix and data transmission times. The proposed model has been empirically evaluated on the static grid scheduling benchmark by using the simple genetic-based schedulers. A simple comparison of the achieved results for two basic scheduling metrics, namely makespan and average flowtime, with the results generated in the case of ignoring the data scheduling phase show the significant impact of the data processing model on the schedule execution times

Journal of Telecommunications and Information Technology, 2012, nr 1

kwartalni

Data-Intensive Computing in Smart Microgrids

Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area

Dynamic Virtual Machine Placement in Cloud Computing

Cloud computing enables users to have access to resources on demand. This leads to an increased number of physical machines and data centers in order to fulfill the needs of users which are continuously on the increase. The increase in the number of active physical machines is directly proportional to the increase in the energy consumption. Thus, minimization of energy consumption has become one of the major challenges of cloud computing in recent years. There are many ways to power savings in data centers, but the most effective one is the optimal placement of virtual machines on physical machines. In this thesis, the problem of dynamic placement of virtual machines is solved in order to optimize the energy consumption. A cloud computing model is built along with the energy consumption model considering the states of physical machines and the energy consumption during live virtual machine migrations and the changes in the states of physical machines. The intelligent algorithms having a centralized approach, like genetic algorithm and simulated annealing algorithm have been used to solve the dynamic virtual machine placement problem in earlier research works but many unreachable solutions may result. Thus, a decentralized approach based on game theoretic method is used here in order to reach optimal solutions and also a list of executable live virtual machine migrations is provided to reach the optimal placement. In real world scenario, physical machines may or may not cooperate with each other to arrive at an optimal solution. Therefore, in this thesis both cooperative as well as non-cooperative game theoretic approaches have been used to find optimal solution to the dynamic virtual machine placement problem. It is seen that Nash equilibrium is achieved in polynomial time. The experimental results are compared with the results of best fit approach. Results show that energy consumption is minimized by modifying the placement of virtual machines dynamically

Optimal Distribution Reconfiguration and Demand Management within Practical Operational Constraints

This dissertation focuses on specific aspects of the technical design and operation of a `smart\u27 distribution system incorporating new technology in the design process. The main purpose of this dissertation is to propose new algorithms in order to achieve a more reliable and economic distribution system. First, a general approach based on Mixed Integer Programming (MIP) is proposed to formulate the reconfiguration problem for a radial/weakly meshed distribution network or restoration following a fault. Two objectives considered in this study are to minimize the active power loss, and to minimize the number of switching operations with respect to operational constraints, such as power balance, line ow limits, voltage limit, and radiality of the network. The latter is the most challenging issue in solving the problem by MIP. A novel approach based on Depth-First Search (DFS) algorithm is implemented to avoid cycles and loops in the system. Due to insufficient measurements and high penetration of controllable loads and renewable resources, reconfiguration with deterministic optimization may not lead to an optimal/feasible result. Therefore, two different methods are proposed to solve the reconfiguration problem in presence of load uncertainty. Second, a new pricing algorithm for residential load participation in demand response program is proposed. The objective is to reduce the cost to the utility company while mitigating the impact on customer satisfaction. This is an iterative approach in which residents and energy supplier exchange information on consumption and price. The prices as well as appliance schedule for the residential customers will be achieved at the point of convergence. As an important contribution of this work, distribution network constraints such as voltage limits, equipment capacity limits, and phase balance constraints are considered in the pricing algorithm. Similar to the locational marginal price (LMP) at the transmission level, different prices for distribution nodes will be obtained. Primary consideration in the proposed approach, and frequently ignored in the literature, is to avoid overly sophisticated decision-making at the customer level. Most customers will have limited capacity or need for elaborate scheduling where actual energy cost savings will be modest