On Solving Some Issues in Cloud Computing

In past few years, cloud computing has emerged as one of the fastest growing segment in IT industry. It delivers infrastructure, platform, and software as a service on demand basis. Cloud provides several data centers at different geographical locations for service reliability and availability. Users can deploy applications and subscribe services from any location at competitive cost. However, this system doesn’t support mechanism and policies for dynamically coordinating load distribution among different cloud-based data centers. Further, cloud providers are unable to predict geographical distribution of users availing this services. There exist many challenging issues but few of them such as load balancing, event matching, and real-time data analysis have been addressed in the thesis. First three contributions in this thesis are dedicated to load balancing using evolutionary techniques. In the first contribution, a genetic algorithm based load balancing (LBGA) has been proposed with real value coded GA with a new encoding mechanism. Similarly, a particle swarm optimization based load balancing (LBPSO) is suggested. Both the schemes are simulated in cloud analyst, and performance comparisons are made with the competitive schemes.Consequently, both the schemes are grouped together to form a hybrid load balancing algorithm (HLBA). HLBA based central load balancer balances the load among virtual machines in cloud data center. HLBA utilizes the benefits of both genetic algorithm and particle swarm optimization. Different measures such as average response time, data center request service time, virtual machine cost, and data transfer cost are considered to evaluate the performance of the proposed algorithm. Suggested approach achieves better load balancing in large scale cloud computing environment as compared to other competitive approaches. In another contribution, an event matching algorithm has been developed for content-based event dissemination in publish/subscribe system. Proposed modified rapid match (MRM) algorithm has been compared with existing heuristics in the cloud system. Finally, a framework for the sensor-cloud environment for patient monitoring has been suggested. A prototype model has been developed for the purpose to validate the framework. This integrated system helps in monitoring, analyzing, and delivering real-time information on the fly

Offload decision models and the price of anarchy in mobile cloud application ecosystems

With the maturity of technologies, such as HTML5 and JavaScript, and with the increasing popularity of cross-platform frameworks, such as Apache Cordova, mobile cloud computing as a new design paradigm of mobile application developments is becoming increasingly more accessible to developers. Following this trend, future on-device mobile application ecosystems will not only comprise a mixture of native and remote applications, but also include multiple hybrid mobile cloud applications. The resource competition in such ecosystems and its impact over the performance of mobile cloud applications has not yet been studied. In this paper, we study this competition from a game theoretical perspective and examine how it affects the behavior of mobile cloud applications. Three offload decision models of cooperative and non-cooperative nature are constructed and their efficiency compared. We present an extension to the classic load balancing game to model the offload behaviors within a non-cooperative environment. Mixed-strategy Nash equilibria are derived for the non-cooperative offload game with complete information, which further quantifies the price of anarchy in such ecosystems. We present simulation results that demonstrate the differences between each decision model’s efficiency. Our modeling approach facilitates further research in the design of the offload decision engines of mobile cloud applications. Our extension to the classic load balancing game broadens its applicability to real-life applications

A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing

The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure and be charged on pay-per-use basis. However, Cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements (SLAs) violations. To achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be NP-hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to PMs in infrastructure Clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres

EFFICIENT STRATEGIES FOR SEAMLESS CLOUD MIGRATIONS USING ADVANCED DEPLOYMENT AUTOMATIONS

The increasing complexity and scale of modern computing needs have led to the development and adoption of cloud computing as a ubiquitous paradigm for data storage and processing. The hybrid cloud model, which combines both public and private cloud infrastructures, has been particularly appealing to organizations that require both the scalability offered by public clouds and the security features of private clouds. Various strategies for configuring and managing resources have been developed to optimize the hybrid cloud environment. These strategies aim to balance conflicting objectives such as cost-efficiency, performance optimization, security, and compliance with regulatory standards. This exploratory research focused on evaluating the efficiency and limitations of different configuration strategies in hybrid cloud environments. Findings indicate that each approach presents distinct advantages. Improving resource utilization and automating governance processes are significant advantages of Policy-based Resource Management, which leads to costeffectiveness. Intelligent routing of traffic is a feature of Cross-cloud Load Balancing, resulting in optimized performance and higher service availability. By centralizing control, the Hybrid Cloud Service Mesh allows for secure and streamlined cross-service communication. A notable feature of Cross-cloud Container Orchestration is its ability to simplify the migration of applications across diverse cloud environments. For immediate threat detection and regulatory compliance, real-time monitoring is facilitated by Log Management and Analytics. However, Policy-based Resource Management can be complex and inflexible. Extra costs for data transfer between different cloud providers are a drawback of Crosscloud Load Balancing. Additional network hops create latency issues in Hybrid Cloud Service Mesh configurations. If configured incorrectly, Cross-cloud Container Orchestration could expose the system to security risks. Finally, Log Management and Analytics require both ample storage and advanced analytical capabilities

Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

Proactive cloud management for highly heterogeneous multi-cloud infrastructures

Various literature studies demonstrated that the cloud computing paradigm can help to improve availability and performance of applications subject to the problem of software anomalies. Indeed, the cloud resource provisioning model enables users to rapidly access new processing resources, even distributed over different geographical regions, that can be promptly used in the case of, e.g., crashes or hangs of running machines, as well as to balance the load in the case of overloaded machines. Nevertheless, managing a complex geographically-distributed cloud deploy could be a complex and time-consuming task. Autonomic Cloud Manager (ACM) Framework is an autonomic framework for supporting proactive management of applications deployed over multiple cloud regions. It uses machine learning models to predict failures of virtual machines and to proactively redirect the load to healthy machines/cloud regions. In this paper, we study different policies to perform efficient proactive load balancing across cloud regions in order to mitigate the effect of software anomalies. These policies use predictions about the mean time to failure of virtual machines. We consider the case of heterogeneous cloud regions, i.e regions with different amount of resources, and we provide an experimental assessment of these policies in the context of ACM Framework