Cloud Computing for Optimization: Foundations, Applications, and Challenges

Cloud computing is an emerging area of research and is useful for all level of users from end users to top business companies. There are several research areas of cloud computing including load balancing, cost management, workflow scheduling etc., which has been the current research interest of researchers. To deal with such problems, some conventional methods are developed, which are not so effective. Since, last decade the use of nature inspired optimization in cloud computing is a major area of concern. In this chapter, a detailed (yet brief) survey report on the applicability of nature inspired algorithms in various cloud computing problems is highlighted. The chapter aims at providing a detailed knowledge about nature inspired optimization algorithms and their use in the above mentioned problems of cloud computing. Some future research directions of cloud computing and other application areas are also discussed.

The aim of cloud computing is to provide utility based IT services by interconnecting a huge number of computers through a real-time communication network such as the Internet. Since many organizations are using cloud computing which are working in various fields, its popularity is growing. So, because of this popularity, there has been a significant increase in the consumption of resources by different data centres which are using cloud applications (Kennedy, Encyclopedia of Machine Learning, Springer, US, 2010 [1], Shi and Eberhart, IEEE International Conference on Evolutionary Computation Proceedings of World Congress on Computational Intelligence, 1998 [2], An-Ping and Chun-Xiang, Math. Probl. Eng. 8–15, 2014 [3], Dashti and Rahmani, J. Exp. Theor. Artif. Intell., 1–16, 2015 [4]). Hence, there is a need to discuss optimization techniques and solutions which will save resource consumption but there will not be much compromise on the performance. These solutions would not only help in reducing the excessive resource allocation, but would also reduce the costs without much compromise on SLA violations, thereby benefitting the Cloud service providers. In this chapter, we discuss on the optimization of resource allocation so as to provide cost benefits to the Cloud service users and Cloud service providers.

This chapter illustrates the resource allocation in cloud IaaS. We detail how to optimize the VM instances allocation strategy using the novel ANN model. This chapter narrates the functionality and workflow of the system using the NFRLP and EARA algorithms. Further, several issues in implementing the resource allocation are also detailed. This chapter illustrates how the artificial neural network and genetic algorithm techniques are used in IaaS frame work to efficiently allocate the resources for VMs.

Cloud federation has become a consolidated paradigm in which set of cooperative service providers share their unused computing resources with other members of the federation to gain some extra revenue. Due to increase in consciousness about cloud computing, demand for cloud services among cloud users have increased, thus making it hard for any single cloud service provider to cope up with cloud users demands and satisfying the promised quality of service. Hence, the cloud federation overcomes the limitation of each cloud service provider for maintaining their individual cloud resources. This chapter emphasizes on different approaches for cloud federation formation based on game theory and also highlights the importance of trust (soft security) in federated cloud environment. Different models for cloud federation formation using coalition game and the role of a cloud service broker in cloud federation are presented in this chapter.

Cloud computing has emerged as a computing paradigm to solve large-scale problems. The main intent of Cloud computing is to provide inexpensive computing resources on a pay-as-you-go basis, which is promptly gaining momentum as a substitute for traditional information technology (IT)-based organizations. Therefore, the increased utilization of Clouds makes successful execution of scientific applications a vital research area. As more and more users have started to store and process their real-time data in Cloud environments, resource provisioning and scheduling of huge Data processing jobs becomes a key element of consideration for efficient execution of scientific applications. The base of any real-time system is a resource, and to manage the resources to handle workflow applications in Cloud computing environment is a very tedious task. An inefficient resource management system can have a direct negative effect on performance and cost and indirect effect on functionality of the system. Indeed, some functions provided by the system may become too expensive or may be avoided due to poor performance. Thus, Cloud computing faces the challenge of resource management, especially with respect to choosing resource provisioning strategies and suitable algorithms for particular applications. The major components of resource management systems are resource provisioning and scheduling. If any system is able to fulfill the requirements of these two components, the execution of scientific workflow applications will become much easier. This chapter discusses the fundamental concepts supporting Cloud computing and resource management system terms and the relationship between them. It reflects the essential perceptions behind the Cloud resource provisioning strategies. The chapter also identifies requirements based on user’s applications associated with handling real-time data. A model for resource provisioning based on user’s requirements to maximize efficiency and analysis of scientific workflows is also proposed. QoS parameter (s) based resource provisioning strategy has been proposed for workflow applications in cloud computing environment. Validation of resource provisioning strategies is presented in this book chapter.

The services offered by cloud computing and its usage are increasing day-by-day. Due to the elasticity characteristic of cloud computing, many organizations are now moving their services on cloud data centers. A cloud disaster recovery requires migration of a VM from one data center to another without disconnecting the user. Live VM migration is a key concept to transfer VM without disrupting services. Server consolidation and scheduled maintenance are added advantages of it. In cloud computing, moving large size of VM from one data center to other data center over a wide area network is a challenging task.

This chapter tries to broaden the reader’s perspective on Virtualization and how it works at the heart of Cloud Computing. Advantageous features of virtualization such as cost effectiveness, portability, security etc. can be manipulated to effectively provide cloud services to users. Virtualization can create an image of personal servers while in reality storing, processing and manipulation of data is done on a few physical servers present at the data centres of cloud service providers. We further focus on need for virtualization in the following topics: migrate workloads as needs change, protect apps from server failure, maximising uptime, consolidation and resource optimization. That done, we want the reader to learn about the architectural design of working and storage structures of a key virtualization technology, VMWare) elaborating on their functionalities, how performance goals are met, reduction of complexity and increasing reliability, security.

Cloud computing, the growing technology which most of the small as well as large organizations adopt to maintain IT as it is a very cost effective organization should consider the business risk associated with cloud computing all of which are still not resolved. The risk can be categorized in several issues like privacy, security, legal risks. To solve these types of severe risks, organization might make and develop SLA for the establishment of an agreement between the customer and the cloud providers. This chapter provides a survey on the various frameworks to develop SLA based security metrics. Various security attributes and possible threats are having also been discussed in this chapter.

This chapter is focused to provide security mechanism for complete cloud system by implementing encryption and intrusion detection system. Hybrid encryption is applied on data at cloud client level so that data in medium will be safe as well as data will be stored in cloud server in safe mode. Data in server will be accessible only to the authorized users which have the decryption key. Computation for decryption becomes challenging and difficult in case of hybrid encryption. The second phase of security will be applied in cloud server by implementing intrusion detection system which will detect the anomaly traffic towards server and block the unauthorized and unauthenticated traffic. Dimension reduction techniques are also focused in this chapter to make the efficient intrusion detection system.

The advent of cloud computing has created a paradigm shift in how people around the world communicate and do business. Its inbuilt characteristics have empowered companies to build cutting edge solutions that bring us all together than we ever were before. Cloud computing provides avenues to use storage and computing resources in metered basis to provide optimized virtual infrastructure for service providers to prosper. Service providers can concentrate on building technology rather than worrying about the infrastructure and the platform for service hosting or server maintenance. Amount of information being shared and exchanged by users is growing exponentially by the passing of each hour. People around the globe have openly embraced the era of information technology, and almost unknowingly, it has become an essential part of everyday life. In this context, securing our digital life by enabling cloud applications to perform at its fullest is of prime importance. Security engineering community is continuously optimizing security standards, tools and practices to achieve this. This chapter throws light into such methods and technologies that form the Digital Guardians of our Connected World! In any discussion related to optimization of Cloud Security, it is important to recognize the current market and research trends. This chapter adopts a case study based approach to understand the current scenario and best practices with respect to Cloud Security. We discuss the overall security objectives and challenges that developers and cloud service vendors face during life cycle of Cloud software applications. Topics related to Cloud software quality assurance including cloud penetration testing are dealt with religiously in the chapter. We then propose certain tools and techniques which would help any developer or cloud security enthusiast to understand how to secure any application to make it cloud ready. This is very important; especially, with the growing complexity of web application threats. Hence, we have dedicated a section of this chapter to identify and mitigate security loopholes in the applications in a smart and focused manner.

The synergy between Cloud and IoT has emerged largely due to the Cloud having attributes which directly benefit IoT and enable its continued growth. IoT adopting Cloud services has brought new security challenges. In this book chapter, we pursue two main goals: (1) to analyse the different components of Cloud computing and IoT and (2) to present security and privacy problems that these systems face. We thoroughly investigate current security and privacy preservation solutions that exist in this area, with an eye on the Industrial Internet of Things, discuss open issues and propose future directions.

The Internet of Things(IoT) is the future Internet evolution towards a network of interconnected smart objects such as computers, smart phones, smart watches, smart televisions, smart cars and many more. It is a matter of concern that our current infrastructure may not be able to handle large amount of data efficiently involving the growing number of smart IoT devices. As a solution, in this paper we came up with proposing a hybrid model of IoT infrastructure, as compared to the existing infrastructure to overcome its challenges. Our proposed infrastructure will be a collaboration of Fog computing combined with intelligent use of Service Oriented Architecture(SOA) which will be serving as a machine to machine communication protocol. This model will be able to transfer data reliably and systematically with low latency, less bandwidth, heterogeneity and maintaining the Quality of Service(QoS) befittingly.

Over the past few years cloud computing has skyrocketed in popularity within the IT industry. Shifting towards cloud computing is attracting not only industry but also government and academia. However, given their stringent privacy and security policies, this shift is still hindered by many security concerns related to the cloud computing features, namely shared resources, virtualization and multi-tenancy. These security concerns vary from privacy threats and lack of transparency to intrusions from within and outside the cloud infrastructure. Therefore, to overcome these concerns and establish a strong trust in cloud computing, there is a need to develop adequate security mechanisms for effectively handling the threats faced in the cloud. Intrusion Detection Systems (IDSs) represent an important part of such mechanisms. Developing cloud based IDS that can capture suspicious activity or threats, and prevent attacks and data leakage from both inside and outside the cloud environment is paramount. One of the most significant hurdles for developing such cloud IDS is the lack of publicly available datasets collected from a real cloud computing environment. In this chapter, we discuss specific requirements and characteristics of cloud IDS in the light of traditional IDS. We then introduce the first public dataset of its kind for cloud intrusion detection. The dataset consists of several terabytes of data, involving normal activities and multiple attack scenarios, collected over multiple periods of time in a real cloud environment. This is an important step for the industry and academia towards developing and evaluating realistic intrusion models for cloud computing.

This book chapter discusses the concept of edge-assisted cloud computing and its relation to the emerging domain of “Fog-of-things (FoT)”. Such systems employ low-power embedded computers to provide local computation close to clients or cloud. The discussed architectures cover applications in medical, healthcare, wellness and fitness monitoring, geo-information processing, mineral resource management, etc. Cloud computing can get assistance by transferring some of the processing and decision making to the edge either close to client layer or cloud backend. Fog of Things refers to an amalgamation of multiple fog nodes that could communicate with each other with the Internet of Things. The clouds act as the final destination for heavy-weight processing, long-term storage and analysis. We propose application-specific architectures GeoFog and Fog2Fog that are flexible and user-orientated. The fog devices act as intermediate intelligent nodes in such systems where these could decide if further processing is required or not. The preliminary data analysis, signal filtering, data cleaning, feature extraction could be implemented on edge computer leading to a reduction of computational load in the cloud. In several practical cases, such as tele healthcare of patients with Parkinson’s disease, edge computing may decide not to proceed for data transmission to cloud (Barik et al., in 5th IEEE Global Conference on Signal and Information Processing 2017, IEEE, 2017) [4]. Towards the end of this research paper, we cover the idea of translating machine learning such as clustering, decoding deep neural network models etc. on fog devices that could lead to scalable inferences. Fog2Fog communication is discussed with respect to analytical models for power savings. The book chapter concludes by interesting case studies on real world situations and practical data. Future pointers to research directions, challenges and strategies to manage these are discussed as well. We summarize case studies employing proposed architectures in various application areas. The use of edge devices for processing offloads the cloud leading to an enhanced efficiency and performance.

We acquire or provide most of the services with the help of the Internet in the fast-growing world. We should deploy various kinds of systems globally so that other users can practice the same effortlessly, and use storage framework to provide conveniences world-wide effectively. Nowadays, we practice vehicular communication technology to exchange diversified data for varied intentions, which helps vehicle operators in diversified manners such as traffic awareness, weather conditions, road assistance, automatic toll payment system, etc. In this chapter, we converse about data transmission through vehicular ad-hoc networks, cloud computing in the vehicular technology. After that, we survey of different schemes related to secure routing and geo-location details of vehicles. We illustrate concerning security demands, possible attacks, and challenges in the vehicular cloud computing (VCC) architecture. Conclusively, we suggest a new identification scheme to get access of the VCC system from the user end, which can be secured against varied attacks. Moreover, we do analysis of the suggested system to determine security worthiness and measure total required time to execute the phases.

Nowadays, online video consumption is an outstanding source of infotainment. Current social media era allows people to communicate with others around the world via Facebook, LinkedIn, YouTube and other platforms by sharing/sending photos, videos over the Internet. The proliferation of viewing platforms, file formats, and streaming technologies generate the need for video transcoding. The transcoding process ensures that video content can be consumed from any networks and devices, but it is a time-consuming, computation-intensive method and requires high storage capacity. The rise of video distribution and consumption makes the video service providers face unpredictable CAPEX and OPEX, for delivering more videos across multi-screens and networks. A cloud-based transcoding is used to overcome the limitations with on-premise video transcoding. The virtually unlimited resources of the cloud transcoding solution allow video service providers to pay as they use today, with the assurance of providing online support to handle unpredictable needs with lower cost. This chapter is designed to discuss various techniques related to cloud-based transcoding system. Various sections in this chapter also present the cloud-based video transcoding architecture, and performance metrics used to quantify cloud transcoding system.

Vehicular clouds have become an active area of research with tens of papers written and a large number of documented applications. We feel this is a good moment to summarize the main research trends in the area of vehicular clouds and to map out various research challenges and possible applications. With this in mind, the primary objective of this chapter is to present a survey of the state of the art in vehicular clouds, of the current research topics and future directions. We will take a critical look at the various vehicular cloud models proposed in the literature and their applications.