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Cloud computing is an innovative technology that mainly relies on sharing of computing resources, data as well as information over the internet. Cloud computing has been evolved from technological growth in Grid systems, Virtualization and Web2.0. Efficient Load Balancing (LB) and scheduling techniques are required for optimal utilization of computing resources and to reduce turnaround time. Load...
Cloud computing is a sophisticated technology which provides services to the end users within a fraction of time based on a pay-as-you-go model. However, energy consumption is one of the important concerns in cloud environment as large amount of energy is wasted by the data centers, hosting cloud applications and the carbon dioxide gas is released into the atmosphere polluting the environment. So...
In cloud computing resource allocation should be elastic, within the sense that it must have modification accurate and quickly based on the demand. In cloud computing, Virtual machine allocates the resource for user's needs. Some time workload of service, increase rapidly, the existing approaches solve aggressive resource provisioning tasks using SPRNT, but still some challenges occur in VM allocation...
Scheduling is a vital issue in cloud computing in order to facilitate optimized usage of resources as in today's global and competitive environment the use of scarce resource in the best possible way is very important. Heuristic based algorithms are used in optimization techniques as they are class of heuristic approaches that have their inspirations from metaheuristics which are a general framework...
Scheduling is a vital issue in cloud computing in order to facilitate optimized usage of resources. Genetic algorithms are used in optimization techniques as they are class of heuristic approaches that have their inspirations from evolutionary ideas of natural evolution. The cloud scheduling optimization problem is modelled as a population of candidate solutions and the genetic algorithm is applied...
Mission planning plays an important role in satellite control systems, especially with increase of number of satellites and more complex missions to be planned. In a general setting, the satellite mission scheduling consists in allocating tasks such as observation, communication, etc. to resources (spacecrafts (SCs), satellites, ground stations). For instance, in ground station scheduling the aim...
Shortest path finding has been a challenging task in most of the complex multipath scenarios. The complexity rises with the introduction of constraints to the scenarios. Healthcare service to the patient is one of the real world problems where travelling path has significant impact on the service time. The purpose of this research is to develop new approach to solve multiple travelling salesman problem...
Cloud computing is a distributed computing model which enables developers to automatically deploy applications during task allocation and storage distribution. Cloud computing intends to share a pool of virtualized computer resources and equipment's of computation, storage and information. Scheduling is the most important complex part in cloud computing. The ultimate aim of global scheduler is to...
Cloud computing is becoming an increasingly admired paradigm that delivers high-performance computing resources over the Internet to solve the large-scale scientific problems, but still it has various challenges that need to be addressed to execute scientific workflows. The existing research mainly focused on minimizing finishing time (makespan) or minimization of cost while meeting the quality of...
Optimization of public transport operation is an integral component of an intelligent transport system. This paper shows that Genetic Algorithm can be used in developing the optimum schedule for buses plying EDSA. In this paper, the fitness function measure covered maximizing the ratio of the total additional revenue, over the total additional costs, both on the bus operators' and passengers' sides...
Train operation adjustment is a kind of real-time multi-objective large scale combination optimization problem with multi-restriction. A novel immune genetic algorithm based on artificial immune algorithm and genetic algorithm is put forward. Then scheduling optimization model for two-way marshalling train is proposed, which is solved by the proposed immune genetic algorithm. Finally, an instance...
This paper proposed a spreadsheet based genetic algorithm (SGA) model as a practical approach to solve a complicated hybrid flowshop (HFS) with multiple unrelated batch and discrete processors, stage skipping behavior, setup times, and machine eligibility restrictions. The proposed model is capable to handle three crucial and inter-dependent decisions which include batching, loading and sequencing...
There are well-known approaches which have been exercised to generate automated timing itinerary which is limited to secluded environment. The number of entity having individual characteristics increases, thus increases the complexity of the solution. As scheduling timing itinerary is a well-known NP-completeness problem, so there is no existing particular algorithm which can solve the time scheduling...
One reason that causes world crisis upon our living environments is the effects of global warming caused by substantive carbon dioxide emissions produced by electrical diffuses of power plants industries and family energy uses of electricity consumers. Consequently, for the purpose of reducing this hazardous aftermath, our aim in this paper was not only to propose an intelligent scheduling system...
The Virtual Power Plant (VPP) is eyed as a solution to the complexities and drawbacks caused by the accelerating penetration of DGs. To actualize the concept of the VPP coordinating the smaller and diverse energy resources, sophisticated planning and scheduling is required. With a large amount of information to take into account, the possible number of solutions and the complex strategies for the...
In this paper we present meta-heuristics to solve the energy aware reward based scheduling of real-time tasks with mandatory and optional parts in homogeneous multi-core processors. The problem is NP-Hard. The meta-heuristics are the bioinspired methods Particle Swarm Optimization and Genetic Algorithm. Results are compared using synthetic systems of tasks, generated following the guidelines proposed...
One of the critical concerns in wireless sensor networks (WSNs) is the continuous maintenance of sensing coverage. Many particular applications, such as battlefield intrusion detection and object tracking, require a full-coverage at any time, which is typically resolved by adding redundant sensor nodes. With abundant energy, previous studies suggested that the network lifetime can be maximized while...
Although multicore designs seem to be a viable replacement of single core designs, the new architecture comes with its share of problems. A main problem is that we do not have the necessary software tools and techniques to utilize the power of multiple processors. This added a burden on the shoulders of software and tool developers to devise techniques to harvest the powerful architecture. One very...
This paper addresses p-processes single processor scheduling problem with a common deadline, to minimize the total execution time and reduce the penalty costs. Process scheduling is one of the most essential factor on which the efficiency and the performance of the work done by the CPU depends. Earliness and tardiness of the processes degrades the efficiency of the processor as they carry penalty...
As more and more fossil fuels are burned in order to keep up with the overgrowing demand for energy it is becoming increasingly necessary to look for alternative energy sources. Reducing peak-time energy demand is important to make the best use of renewable energies. In this paper we present an Ant Colony Optimization (ACO) based approach for the problem of scheduling tasks so as to minimize peak-times...
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