Scalability and Performance Analysis of OpenMP Codes Using the Periscope Toolkit

In this paper, we present two new approaches while rendering necessary extensions to Periscope to perform scalability and performance analysis on OpenMP codes. Periscope is an online-based performance analysis toolkit which consists of a user defined number of analysis agents that automatically search for the performance properties while the application is running. In order to detect the scalability and performance bottlenecks of OpenMP codes using Periscope, a few newly defined performance properties and meta properties are formalized. We manifest our implementation by evaluating NAS OpenMP benchmarks. As shown in our results, our approach identifies the code regions which do not scale well and other performance problems, e.g. load imbalance in NAS parallel benchmarks

A Niched Pareto GA Approach for Scheduling Scientific Workflows in Wireless Grids

We present a Niched Pareto Genetic Algorithm (NPGA) approach to the scheduling of scientific workflows in a wireless grid environment that connects computational resources, wired grids and wireless device resources such as cameras, microphones, network interfaces and so on where the maximization of job completion ratio and minimization of lateness is crucial. Our approach supports handling uncertainty in the field of decision analysis, a rigorous technique for combining multiple objectives simultaneously. We made comparisons of our approach with respect to other scheduling policies; it performed significantly better than the majority of cases, and in worst cases, it was as good as the best of the others

Incremental Multilayer Resource Partitioning for Application Placement in Dynamic Fog

Fog computing platforms became essential for deploying low-latency applications at the network's edge. However, placing and managing time-critical applications over a Fog infrastructure with many heterogeneous and resource-constrained devices over a dynamic network is challenging. This paper proposes an incremental multilayer resource-aware partitioning (M-RAP) method that minimizes resource wastage and maximizes service placement and deadline satisfaction in a dynamic Fog with many application requests. M-RAP represents the heterogeneous Fog resources as a multilayer graph, partitions it based on the network structure and resource types, and constantly updates it upon dynamic changes in the underlying Fog infrastructure. Finally, it identifies the device partitions for placing the application services according to their resource requirements, which must overlap in the same low-latency network partition. We evaluated M-RAP through extensive simulation and two applications executed on a real testbed. The results show that M-RAP can place 1.6 times as many services, satisfy deadlines for 43% more applications, lower their response time by up to 58%, and reduce resource wastage by up to 54% compared to three state-of-the-art methods

Modelling energy consumption of network transfers and virtual machine migration

Reducing energy consumption has become a key issue for data centres, not only because of economical benefits but also for environmental and marketing reasons. Therefore, assessing their energy consumption requires precise models. In the past years, many models targeting different hardware components, such as CPU, storage and network interface cards (NIC) have been proposed. However, most of them neglect energy consumption related to VM migration. Since VM migration is a network-intensive process, to accurately model its energy consumption we also need energy models for network transfers, comprising their complete software stacks with different energy characteristics. In this work, we present a comparative analysis of the energy consumption of the software stack of two of today's most used NICs in data centres, Ethernet and Infiniband. We carefully design for this purpose a set of benchmark experiments to assess the impact of different traffic patterns and interface settings on energy consumption. Using our benchmark results, we derive an energy consumption model for network transfers. Based on this model, we propose an energy consumption model for VM migration providing accurate predictions for paravirtualised VMs running on homogeneous hosts. We present a comprehensive analysis of our model on different machine sets and compare it with other models for energy consumption of VM migration, showing an improvement of up to 24% in accuracy, according to the NRMSE error metric. © 2015 Elsevier B.V

Efficient Scheduling of Scientific Workflows with Energy Reduction Using Novel Discrete Particle Swarm Optimization and Dynamic Voltage Scaling for Computational Grids

One of the most significant and the topmost parameters in the real world computing environment is energy. Minimizing energy imposes benefits like reduction in power consumption, decrease in cooling rates of the computing processors, provision of a green environment, and so forth. In fact, computation time and energy are directly proportional to each other and the minimization of computation time may yield a cost effective energy consumption. Proficient scheduling of Bag-of-Tasks in the grid environment ravages in minimum computation time. In this paper, a novel discrete particle swarm optimization (DPSO) algorithm based on the particle’s best position (pbDPSO) and global best position (gbDPSO) is adopted to find the global optimal solution for higher dimensions. This novel DPSO yields better schedule with minimum computation time compared to Earliest Deadline First (EDF) and First Come First Serve (FCFS) algorithms which comparably reduces energy. Other scheduling parameters, such as job completion ratio and lateness, are also calculated and compared with EDF and FCFS. An energy improvement of up to 28% was obtained when Makespan Conservative Energy Reduction (MCER) and Dynamic Voltage Scaling (DVS) were used in the proposed DPSO algorithm

A Bio-Inspired Hybrid Computation for Managing and Scheduling Virtual Resources using Cloud Concepts

Resource allocation and scheduling is one of the major issues in manufacturing industries which are constrained to offer dynamic and virtualized resources to end users in-order to maximize the profit. Cloud manufacturing is a new paradigm that can satisfy the requirements of modern manufacturing industries. In this work, two variants of heuristic algorithm are used to solve resource scheduling issues in casting industries. Particle swarm optimization algorithm is used in this work, because it can solve large scale optimization problems with better search speed, and genetic algorithms can be used to provide solution for non-linear and highly intricate engineering problems. This work uses a hybrid approach which combines the advantages of genetic algorithm with particle swarm optimization in-order to provide global convergence at effective and optimal cost. Experimentation was carried out for casting of engine block in manufacturing industry and the simulation results shows that PSO with GA provides global optimal convergence and also produces effective results with respect to time, cost and resource utilizatio