Browsing by Author "Ananth, A."

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Cloud Services and Service Providers
(wiley, 2016) Chandrasekaran, K.; Ananth, A.
Cloud computing provisions resources to consumers in the form of different services like software, infrastructure, platform, and more. Many companies have come forward to offer cloud services. This chapter provides an overview of cloud services offered by various major providers such as Amazon, Microsoft, Google, EMC, Salesforce.com and IBM. They provide various tools and services in order to give cloud support for their customers. Each section briefly describes cloud services offered by a provider and their features, and identifies tools and technologies adopted by the company in order to provide services to the users. This chapter helps readers to distinguish among different services provided by various companies and make appropriate choices to suit their requirements. © 2016 John Wiley & Sons, Ltd.
Cooperative game theoretic approach for job scheduling in cloud computing
(2016) Ananth, A.; Chandrasekaran, K.
Cloud computing is the upcoming technology in current day scenario. It has emerged as a solution for providing computing resources as a service to the consumers in the form of infrastructure, platform and software. When multiple users request for services, cloud service provider has to schedule the requests to the available resources appropriately to satisfy each user's request to meet Service Level Agreements (SLAs) and also deadline constraints. Job scheduling in cloud environment is an important issue where the main aim is to schedule the jobs for effective resource utilization. Cloud service provider has to find the best possible scheduling in order to gain maximum profit from the service provided to the consumers. This paper aims at maximizing the resource utilization as well as profit for the service provider by cooperative game theory based approach for job scheduling in cloud environment. Additionally it also concentrates on minimizing the deadline violation and makespan for the jobs submitted by the user. Thus, new job scheduling technique is proposed using the concepts of game theory and genetic algorithm. This research work also focusses on cooperative game theoretic approach to provide Pareto optimal solution using Non-dominated Sorting Genetic Algorithm II (NSGA II). � 2015 IEEE.
Cooperative game theoretic approach for job scheduling in cloud computing
(Institute of Electrical and Electronics Engineers Inc., 2016) Ananth, A.; Chandrasekaran, K.
Cloud computing is the upcoming technology in current day scenario. It has emerged as a solution for providing computing resources as a service to the consumers in the form of infrastructure, platform and software. When multiple users request for services, cloud service provider has to schedule the requests to the available resources appropriately to satisfy each user's request to meet Service Level Agreements (SLAs) and also deadline constraints. Job scheduling in cloud environment is an important issue where the main aim is to schedule the jobs for effective resource utilization. Cloud service provider has to find the best possible scheduling in order to gain maximum profit from the service provided to the consumers. This paper aims at maximizing the resource utilization as well as profit for the service provider by cooperative game theory based approach for job scheduling in cloud environment. Additionally it also concentrates on minimizing the deadline violation and makespan for the jobs submitted by the user. Thus, new job scheduling technique is proposed using the concepts of game theory and genetic algorithm. This research work also focusses on cooperative game theoretic approach to provide Pareto optimal solution using Non-dominated Sorting Genetic Algorithm II (NSGA II). Â© 2015 IEEE.
Game theoretic approaches for job scheduling in cloud computing: A survey
(2015) Ananth, A.; Sekaran, K.C.
Cloud computing is one of the promising technology in current day scenario. Job scheduling in cloud environment is an important issue where the main aim is to schedule the jobs appropriately in order to effectively utilize the resources and also meet the user's satisfaction. Cloud provider has to consider various aspects like number of cloud users requesting for a service at the same time, availability of resources at that time, Service Level Agreements (SLAs), Quality of Service (QoS) requirements, etc. There are various approaches for scheduling the jobs. In this paper we focus on game theory based approaches for job scheduling in cloud. Survey of existing approaches and various issues in game theory based job scheduling is the main objective of this paper. We study the existing approaches for job scheduling focusing game theoretic approaches and analyze the open issues for research in this area. � 2014 IEEE.
Game theoretic approaches for job scheduling in cloud computing: A survey
(Institute of Electrical and Electronics Engineers Inc., 2015) Ananth, A.; Chandra Sekaran, K.C.
Cloud computing is one of the promising technology in current day scenario. Job scheduling in cloud environment is an important issue where the main aim is to schedule the jobs appropriately in order to effectively utilize the resources and also meet the user's satisfaction. Cloud provider has to consider various aspects like number of cloud users requesting for a service at the same time, availability of resources at that time, Service Level Agreements (SLAs), Quality of Service (QoS) requirements, etc. There are various approaches for scheduling the jobs. In this paper we focus on game theory based approaches for job scheduling in cloud. Survey of existing approaches and various issues in game theory based job scheduling is the main objective of this paper. We study the existing approaches for job scheduling focusing game theoretic approaches and analyze the open issues for research in this area. Â© 2014 IEEE.
Megawatt-scale solar variability study: An experience from a 1.2 MWp photovoltaic system in Australia over three years
(Institution of Engineering and Technology journals@theiet.org, 2016) Yan, R.; Saha, T.K.; Meredith, P.; Ananth, A.; Hossain, M.I.
With more photovoltaic (PV) systems being integrated into distribution networks, power utilities are facing many challenges in both planning and operation. Network operators are concerned with PV variability and associated necessity of voltage regulation, control coordination, reserve adequacy and dispatch constraints. While to address the obligatory connection agreement, it is vital for PV farm owners to accurately estimate PV variability and then provide the most cost-effective compensation method. In the literature, PV variability of different scales has been investigated over the last 20 years. However, little has focused on output fluctuations of PV systems with long-term and high-resolution recorded data at a low-voltage distribution feeder level where voltage regulation has become a serious issue. This is particularly true in Australia, where PV penetration is growing in many states and is expected to grow further in the near future. This study utilises the data of a distributed 1.2 MWp PV system in the University of Queensland recorded over the last three years with 1-min resolution to analyse the statistical characteristics of PV power variability. The results from this study will provide very useful information for both power utilities and solar farm owners regarding network operation and future PV system development. ©The Institution of Engineering and Technology 2016.
Service optimization in cloud using family gene technology
(2014) Ananth, A.; Sekaran, K.C.
Cloud computing is the upcoming technology in current day scenario. It has emerged as a solution for providing resources to the consumers in the form of software, infrastructure or platform as a service. Cloud Service Storage enables users to synchronize their files across devices and also allows them to backup online. The main aim of this paper is to provide service optimization. Scheduling of services is a NP hard problem. Thus exhaustive approaches are not suitable for these kinds of algorithms. This paper presents a genetic algorithm based approach for optimization of services by using family gene technology. Family gene technology is used to classify individuals to different families based on gene parameters and evaluate the fitness function for each individual in that family. Optimization is achieved by mapping the service requests to appropriate service instances which satisfy the request and then by applying family gene based genetic algorithm to those mapped service requests. � 2014 IEEE.
Service optimization in cloud using family gene technology
(Institute of Electrical and Electronics Engineers Inc., 2014) Ananth, A.; Chandra Sekaran, K.C.
Cloud computing is the upcoming technology in current day scenario. It has emerged as a solution for providing resources to the consumers in the form of software, infrastructure or platform as a service. Cloud Service Storage enables users to synchronize their files across devices and also allows them to backup online. The main aim of this paper is to provide service optimization. Scheduling of services is a NP hard problem. Thus exhaustive approaches are not suitable for these kinds of algorithms. This paper presents a genetic algorithm based approach for optimization of services by using family gene technology. Family gene technology is used to classify individuals to different families based on gene parameters and evaluate the fitness function for each individual in that family. Optimization is achieved by mapping the service requests to appropriate service instances which satisfy the request and then by applying family gene based genetic algorithm to those mapped service requests. Â© 2014 IEEE.