An optimal VM Placement, Energy Efficient and SLA at Cloud Environment - A Comparative Analysis

In the cloud computing framework, computing resources can be increased or decreased in response to the users’ different application loads. The data is stored and the applications are running on the servers in the clouds. Users do not have to worry about lost or corrupt data. The clouds can distribute computing resources according to the users’ needs or preferences to provide fl exible management. Users do not have to buy expensive computing devices. They only need to pay for the computing services provided by the clouds. Cloud computing provides a platform for computational experiments with abundant computing and storage resources. The system can be considered as a whole and the control and management decisions are sent as services to agents. The challenge in the present study is to reduce energy consumption thus guarantee Service Level Agreement (SLA) at its highest level

Green and efficient synthesis of 2-(4-oxo-3,4-dihydroquinazolin-2-yl)-2,3-dihydropthalazine-1,4-dione

412-4172-Hydrazinoquinazolin-3H-4-ones 1a,b were reacts with each of the anhydrides, phthalic anhydride 2a, succinic anhydride 2b and maleic anhydride 2c independently in PEG-600 at RT to yield 2-(2-(4-oxo-3,4-dihydroquinazolin-2-yl)hydrazine-ecarbonyl)benzoic acid 3a,b, 4-oxo-4-(2-(4-oxo-3,4-dihydroquinazolin-2-yl)hydrazinyl)butanoic acid 3c,d and 4-oxo-4-(2-(4-oxo-3,4-dihydroquinazolin-2-yl)hydrazinyl)but-2-enoic acid 3e,f, respectively. 3a,b,<b style="mso-bidi-font-weight: normal"> 3c,d, 3e,f have been transformed into 2-(4-oxo-3,4-dihydroquinazolin-2-yl)-2,3-dihydrophthalazine-1,4-dione 4a,b, 1-(4-oxo-3,4-dihydroquinazolin-2-yl)piperazine-3,6-dione 4c,d and 1-(4-oxo-3,4-dihydroquinazolin-2-yl)-1,2-dihydropyridazine-3,6-dione 4e,f, respectively by heating each in PEG-600 at 100 °C for 3-3.5 hr in high yields and in high purity, involving a dehydrative ring closure. The final compounds <b style="mso-bidi-font-weight: normal">4a-f have also been prepared alternatively by reacting 1 with <b style="mso-bidi-font-weight: normal">2 in PEG-600 at 100 °C for 3.5-4 hr

SONG: A Multi-Objective Evolutionary Algorithm for Delay and Energy Aware Facility Location in Vehicular Fog Networks

With the emergence of delay- and energy-critical vehicular applications, forwarding sense-actuate data from vehicles to the cloud became practically infeasible. Therefore, a new computational model called Vehicular Fog Computing (VFC) was proposed. It offloads the computation workload from passenger devices (PDs) to transportation infrastructures such as roadside units (RSUs) and base stations (BSs), called static fog nodes. It can also exploit the underutilized computation resources of nearby vehicles that can act as vehicular fog nodes (VFNs) and provide delay- and energy-aware computing services. However, the capacity planning and dimensioning of VFC, which come under a class of facility location problems (FLPs), is a challenging issue. The complexity arises from the spatio-temporal dynamics of vehicular traffic, varying resource demand from PD applications, and the mobility of VFNs. This paper proposes a multi-objective optimization model to investigate the facility location in VFC networks. The solutions to this model generate optimal VFC topologies pertaining to an optimized trade-off (Pareto front) between the service delay and energy consumption. Thus, to solve this model, we propose a hybrid Evolutionary Multi-Objective (EMO) algorithm called Swarm Optimized Non-dominated sorting Genetic algorithm (SONG). It combines the convergence and search efficiency of two popular EMO algorithms: the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Speed-constrained Particle Swarm Optimization (SMPSO). First, we solve an example problem using the SONG algorithm to illustrate the delay–energy solution frontiers and plotted the corresponding layout topology. Subsequently, we evaluate the evolutionary performance of the SONG algorithm on real-world vehicular traces against three quality indicators: Hyper-Volume (HV), Inverted Generational Distance (IGD) and CPU delay gap. The empirical results show that SONG exhibits improved solution quality over the NSGA-II and SMPSO algorithms and hence can be utilized as a potential tool by the service providers for the planning and design of VFC networks