L.D. Dhinesh Babu

Honey bee behavior inspired load balancing of tasks in cloud computing environments

Scheduling of tasks in cloud computing is an NP-hard optimization problem. Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing (HBB-LB), which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue.

Versatile time-cost algorithm VTCA for scheduling non-preemptive tasks of time critical workflows in cloud computing systems

In cloud computing environments, resources and infrastructure are provided as a service over internet on demand. The users are interested in reducing the service cost provided by the cloud service providers. Scheduling tasks of workflows play a vital role in determining performance of cloud computing systems. Workflows have many tasks in it and are interdependent on each other. Time critical workflows comprise of a collection of tasks which should be completed as early as possible so that other workflows get its turn. The budget involved in executing the time critical tasks is very high. The execution cost increases whenever we try to reduce the execution time. In this paper, we propose a method called versatile time-cost algorithm VTCA to schedule time critical workflows with minimum cost. VTCA will schedule the tasks to complete in earliest possible time as well as optimise the cost involved in resource provisioning. The results of experiments conducted using CloudSim simulator show that our scheduling policy minimises the completion time of workflows than other existing algorithms like min-min and fair max-min by 5% to 30% and it also reduces the costs by 5% to 35%.

A decision-based pre-emptive fair scheduling strategy to process cloud computing work-flows for sustainable enterprise management

In cloud computing, clients comply a policy of pay-as-you go, i.e., they only pay for the resources they use. So, the processing power of the clouds has to be optimised to reduce the cost at clients side. Using the resources optimally ensures enterprise sustainability of cloud service providers. Workflow is a set of tasks that are interdependent on each other. Scheduling these workflows is one of the most important challenges to optimally utilise the cloud resources and ensure better quality of service (QoS) to clients. Existing works on scheduling in cloud computing mainly focus on scheduling independent tasks rather than (inter)dependent tasks. In this paper, we propose a strategy to schedule dependent tasks called pre-emptive fair scheduling algorithm (PFSA). This is fair scheduling strategy also aims to ensure higher utilisation of virtual machines (VMs) by reducing the idle time and to minimise the number of times a pre-empted task is submitted to the virtual machine. In both cases, this algorithm tries to effectively reduce the overall processing time of dependent tasks at virtual machine, thus minimising the cost involved in processing of tasks. This economically viable decision-based strategy will be helpful for cloud service providers in ensuring sustainability.

An Analysis of Security Related Issues in Cloud Computing

Over the past two decades, the scenario in the computing world has evolved from client-server to distributed systems and then to central virtualization called as cloud computing. Computing world is moving towards Cloud Computing and it remains as buzzword of the current era. Earlier, users had complete control over their processes and data stored in personal computer where as in cloud, cloud vendor provides services and data storage in remote location over which the client has no control or information. As application and data processing takes place in public domain outside the designated firewall, several security concerns and issues arise. The main objective of the paper is to provide an overall security perspective in cloud Computing and highlight the security concerns and other issues. The paper also highlights few technical security issues in cloud computing.

Applying operations management models for facility location problem in cloud computing environments

Cloud computing has emerged as a buzzword of IT-enabled business services. It makes business sense to choose an optimal location for setting up the cloud service facilities such as data centres. The distribution of services between data centre facilities and clients of a cloud can be devised mathematically as the well-known facility location problem (FLP). The decisions of location of data centres are a vital element in strategic planning for a wide range of cloud service providers. In this paper, we have applied facility location techniques in operations management for evaluating the available potential locations and zero down to the best optimal facility location, which can be used to minimise the cost associated with setting up of data centre facilities. A case study is also presented applying FLP in Indian cloud computing scenario. By using these techniques, one can ensure desired QoS and optimum use of data centre facilities.

A firefly swarm approach for establishing new connections in social networks based on big data analytics

Social networking generates a huge amount of data which can throw useful insights if processed at the right time. Social computing is another emerging technique where social networking is made use for computational purpose. Cloud storage plays an important role in social computing. Privacy in social computing is still a debated issue. The privacy concerns and possible loopholes are discussed in detail. In this paper we are proposing some mathematical models to compute the probability of staying in social network and proposing an algorithm named firefly inspired algorithm for establishing connections FIAEC.

An execution environment oriented approach for scheduling dependent tasks of cloud computing workflows

Cloud computing workflows are collections of interdependent tasks. Workflow scheduling is mainly concerned with cost reduction and overall completion time reduction. Our algorithm devised for workflow scheduling aims to address these two QoS factors. Workflows have many tasks which may require different kinds of execution environment for each task. Supporting all these environment dependencies and task dependencies is really difficult for a cloud scheduling system. Workflows are represented using directed acyclic graph (DAG) which shows the dependencies among tasks. Introducing execution environment dependencies to a DAG will lead to a several combinatorial DAGs. In this paper, our system provides a modified DAG which supports task and execution environment dependency. This paper deals with this time-cost tradeoff and tries to balance both these QoS in compromised way. It compromises the cost when the completion time of tasks is higher and vice versa. Hence, the cost as well as makespan will be as minimum as possible. Experimental results show that our algorithm provides a better time-cost balanced solution in imposing execution environment into task DAG.

A fuzzy adaptive resonance theory inspired overlapping community detection method for online social networks

There has been a surge in the research of complex network analysis in the recent years. This paper engages with online social network, which is the most popular complex network in the modern world. Network communities help to understand the organization of real world networks. Accordingly, this paper proposes and validates a novel algorithm for overlapping community detection in online social networks. We focus on the stability-plasticity problem in complex networks and attempt to solve it using a Fuzzy Adaptive resonance theory inspired algorithm. The algorithm consists of two stages namely prediction stage and comparison stage. The proposed algorithms make use of network measures such as Edge betweenness, Betweenness centrality, and pair betweenness. The algorithm has been tested and compared with other algorithms using benchmark datasets, artificial datasets and real network datasets. The experimental results obtained were better than other overlapping community detection algorithms. The entropy of the proposed model has been evaluated using Overlapping normalized information, omega index, F-score and the cumulative performance value is 2.42 out of 3, which is better than other community detection algorithm.

A scalable cloud computing deployment framework for efficient MapReduce operations using Apache YARN

Cloud computing has become the buzz word since many years. The importance of data storage and processing in cloud has increased since the past two years. The digital data growth has contributed significantly for cloud storage. Hadoop is being used for data intensive computations and the latest Hadoop version YARN is more efficient than its predecessors. In this paper we are proposing a framework which will help in deploying cloud over YARN architecture. The paper also deals with the bulk creation of virtual machines in cloud.

An enhanced data security and trust management enabled framework for cloud computing systems

Cloud computing is an emerging and advanced technology in IT enterprise which provides services on demand. Cloud computing includes many advantages such as flexibility, improved performance and low cost. Besides its advantages, cloud has many security issues and challenges. In this paper, we propose an enhanced frame work for data security in cloud which follows the security polices such as integrity, confidentiality and availability. The data is stored in cloud by using 128 bit encryption and RSA algorithm, then we use the trust management i.e., Trusted Party Auditor (TPA) which audits the data instead of client. Thus, we show how efficiently the data can be secured related to performance analysis.