Cloud federation is a new computing paradigm that has paved the way for cloud service providers (CSPs) to offer their unused resources (virtual machine) to other CSPs when their resource demands are low. Federation also allows CSPs to outsource their resource requests to other CSPs when their computing resources’ demands are high. Thus, in cloud federation environment reliability and availability of services offered by service providers increase as the CSPs are able to share their resources among themselves. Moreover, to maintain the reliability and availability of cloud services offered through federation, it is important that the computational environment of member CSPs within the federation is fault tolerant. Therefore, there is a need for fault tolerant system to guarantee cloud service reliability and availability in cloud federation environment. In this article, we propose a proactive fault tolerance system that preempts faults within the federation on the basis of CPU temperature. The fault tolerance system within the federation is modeled as a multi-objective optimization problem of maximizing profit and minimizing migration cost while redistributing resources (virtual machine) from faulty CSPs to non-faulty CSPs within the federation. To address this issue, we have also proposed an algorithm called Preference Based Fault Management (PBFM) to manage the federation in the event of faults. We perform extensive experiments to evaluate the effectiveness of our proposed mechanism and compare it with two other mechanisms MCAFM (Migration Cost Assured Fault Management) and PAFM (Profit Assured Fault Management). Results show that our proposed mechanism PBFM yields an optimized solution to the general problem of profit and migration cost trade-off in presence of faulty CSPs.

The growing market of cloud computing resulted in increased demand for cloud resources and it will become difficult for individual service providers (SPs) to fulfill all resource requests. That leads to a situation where two or more SPs may form a group (federation) and share the resources in order to fulfill the cloud users’ demand and gain economic advantage. Now, due to the formation of more than one federations by different cloud providers, it may be difficult for users to select a suitable federation who can deliver cloud services at a fair price. In this context, it is necessary to have a framework that will efficiently allocate resources of cloud federations to the users at a fair price and stop market manipulation. In this article, we propose a multi-unit double auction mechanism called TARA (Truthful DoubleAuction forResourceAllocation) that can be used to efficiently choose cloud federations for users from which they can get resources. Here, we consider a multi-seller and multi-buyer double auction mechanism for heterogeneous resources, where every buyer submits their bids and every seller places their ask (the price of a resource that is offered by a federation). TARA achieves some important properties like truthfulness (also known as incentive compatibility), individual rationality and budget balance for both buyers and sellers. TARA is also computationally efficient and posses high system efficiency. The simulation results also show that total utility of buyer is more than some existing double auction mechanisms.

With more awareness and growth in the cloud market, demands for computational resources have increased in order to provide services to the cloud users. Sometimes it is difficult for an individual cloud service provider (CSP) to meet the level of promised quality of service (QoS) and to fulfill all types of resource requests dynamically. Cloud federation has become a consolidated paradigm in which group of cooperative CSPs share their unused resources with peers to gain some economic benefit. Hence, the cloud federation overcomes the limitation of each CSP for maintaining QoS during sudden spikes in resource demand. However, the presence of untrusted CSPs degrades the QoS of the services delivered through federation. Trusted CSPs are highly reputed in the federation as they can extend their resources and services to maintain the level of committed QoS by the member CSPs of the federation. Therefore, to guarantee delivery of committed QoS, it will be necessary to form a federation with trusted CSPs only. In this paper, we present a broker based cloud federation architecture. The cloud federation formation is modeled as a hedonic coalitional game. The main objective of this work is to find the most suitable and stable federation of trusted CSPs that will maximize the satisfaction level of each individual CSP on the basis of QoS and profit. The proposed coalitional game inspired cloud federation formation (CGCFF) algorithm has been extensively compared with selected existing techniques. Simulation results show that the set of federation formed by CGCFF is Nash-stable and performs better than these techniques in terms of satisfaction, quality and profit.

This paper proposes an alternate ranking system based on social network metrics and their evaluation in a composite distributed framework. The most important aspect of social network domain is voluminous data. In order to know key trends, predictive analysis of huge sets of raw data can be effective. Again these analytics are generally very compute intensive. The most efficient solution of such compute-intensive analysis is to map such problems in a distributed domain. The main contributions of this paper are twofold. First, the analysis of cricket community from the viewpoint of social network and finding ranking of players and countries based on the properties of graph centrality measures. Second, the paper proposes a comprehensive distributed framework to offer infrastructural support for the large data analysis as well as graph processing. Hadoop is an open-source framework to store and process large data sets in a cloud environment. MapReduce is a popular programming model for processing that data in a distributed manner. However, MapReduce alone is not efficient for graph processing. Giraph is an alternative programming paradigm for graph processing in Hadoop. Using a practical case study of social network analysis of cricket community, this paper captures the significance of the alternate ranking in this sport, as well as shows effectiveness of the proposed framework in the process of analyzing voluminous data.

Information propagation in online social media draw attention in different research domains due to it's influence and importance in public domain. The complexity of this research problem arises due to its huge volume and transient nature. For different user domain data processing, methodologies and derivations of information spreading are quite different in nature. In this paper, we study information propagation in online social network twitter, considering different centrality measures and simulate through random walk. Assuming that the structure of these websites is a kind of scale free network but exhibits the property of small world network, we compare information propagation to sight the most pivotal nodes in the network in aspect of information propagation.

A game theoretic approach is proposed for joint power control and route adaptations for wireless sensor networks to improve the network lifetime. A data collecting sensor network is assumed that employs asynchronous duty-cycling for energy conservation. In such networks, overhearing dominates the energy consumption, which can be controlled by adapting the transmit power levels as well as the route selections. The goal of this work is to determine assignments of transmit power levels and parent selections for all sensor nodes to maximize the lifetime of the network by controlling the overhearing in the nodes, while maintaining acceptable quality of routes. Performance results of the proposed schemes from computer simulations are presented.

Resource management is one of the focus areas of Grid which identifies Job Modeling to be a very important part of it. A proper Job Modeling can be helpful in allocating jobs to their most suitable resource providers in Grid. This paper presents a feedback-guided Automatic Job Modeling technique that describes the process required to identify the most suitable resource provider for a particular job.

In a dynamic environment like Grid, it is difficult to manage resources at application or user level. In order to support application execution in the context of Grid, a Resource Broker is essential and the task of resource brokering in such a heterogeneous, fast changing, distributed environment is non-trivial. In this paper we present the design and implementation of resource brokering strategies within a multi-agent framework. These strategies help in finding out an optimal allocation of resources for executing multiple concurrent jobs in a Grid environment. We discuss the different stages in resource brokering and their implementation within the framework. The paper also presents results of a preliminary implementation and demonstrates the effectiveness of our strategies.

In this paper we present the design and implementation of a multiagent framework for executing multiple concurrent jobs in a grid environment. Resource broker plays an imperative role in our framework. The aim of the resource broker is to find, select, reserve and allocate suitable resources to each job submitted by the client. Resource broker always tries to optimize resource utilization and ensure fairness. The multiagent framework assists in executing multiple concurrent jobs in an adaptive execution environment. Services offered by the framework include resource brokering, performance analysis, local tuning and more importantly rescheduling depending on the changing resource requirements. Part of the framework makes use of mobile agents for scheduling and rescheduling purposes. The paper discusses the implementation of the resource brokering and also describes the implementation of a part of the framework which schedules and reschedules the jobs and manages the resources on the basis of some performance monitoring data