Demand-driven Gaussian window optimization for executing preferred population of jobs in cloud clusters

Scheduling is one of the essential enabling technique for Cloud computing which facilitates efficient resource utilization among the jobs scheduled for processing. However, it experiences performance overheads due to the inappropriate provisioning of resources to requesting jobs. It is very much essential that the performance of Cloud is accomplished through intelligent scheduling and allocation of resources. In this paper, we propose the application of Gaussian window where jobs of heterogeneous in nature are scheduled in the round-robin fashion on different Cloud clusters. The clusters are heterogeneous in nature having datacenters with varying sever capacity. Performance evaluation results show that the proposed algorithm has enhanced the QoS of the computing model. Allocation of Jobs to specific Clusters has improved the system throughput and has reduced the latency

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

An RMS for Non-predictably Evolving Applications

International audienceNon-predictably evolving applications are applications that change their resource requirements during execution. These applications exist, for example, as a result of using adaptive numeric methods, such as adaptive mesh refinement and adaptive particle methods. Increasing interest is being shown to have such applications acquire resources on the fly. However, current HPC Resource Management Systems (RMSs) only allow a static allocation of resources, which cannot be changed after it started. Therefore, non-predictably evolving applications cannot make efficient use of HPC resources, being forced to make an allocation based on their maximum expected requirements. This paper presents CooRMv2, an RMS which supports efficient scheduling of non-predictably evolving applications. An application can make "pre-allocations" to specify its peak resource usage. The application can then dynamically allocate resources as long as the pre-allocation is not outgrown. Resources which are pre-allocated but not used, can be filled by other applications. Results show that the approach is feasible and leads to a more efficient resource usage

SciTech News Volume 71, No. 1 (2017)

Columns and Reports From the Editor 3 Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division Aerospace Section of the Engineering Division 9 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 11 Reviews Sci-Tech Book News Reviews 12 Advertisements IEEE