Metaheuristic Approaches to Task Consolidation Problem in the Cloud

The service (task) allocation problem in the distributed computing is one form of multidimensional knapsack problem which is one of the best examples of the combinatorial optimization problem. Nature-inspired techniques represent powerful mechanisms for addressing a large number of combinatorial optimization problems. Computation of getting an optimal solution for various industrial and scientific problems is usually intractable. The service request allocation problem in distributed computing belongs to a particular group of problems, i.e., NP-hard problem. The major portion of this chapter constitutes a survey of various mechanisms for service allocation problem with the availability of different cloud computing architecture. Here, there is a brief discussion towards the implementation issues of various metaheuristic techniques like Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), BAT algorithm, etc. with various environments for the service allocation problem in the cloud.

Cyber-Security Concerns with Cloud Computing

Information is modeled into virtual objects to create value for its owner. The value chain involves stakeholders with varied responsibilities in the cyber-market. Cloud computing emerged out of virtualization, distributed and grid computing, and has altered the value creation landscape, through strategic and sensitive information management. It offers services that use resources in a utility fashion. The flexible, cost-effective service models are opportunities for SMEs. Whilst using these tools for value-creation is imperative, a myriad of security concerns confront both providers and end-users. Vulnerabilities and threats are key concerns, so that value created is strategically aligned with corporate vision, appropriated and sustained. What is the extent of impact? Expert opinions were elicited of 4 C-level officers and 10 security operatives. Shared technology issues, malicious insiders and service hijacking are considered major threats. Also, an intuitive strategic model for Value-Creation Cloud-based Cyber-security is proposed as guidance in fostering IT-enabled initiatives.

Using Value-Based Approach for Managing Cloud-Based Services

Software-as-a-Service (SaaS) providers are influenced by a variety of characteristics and capabilities of the available cloud infrastructure resources (IaaS). As a result, the decision made by business service owners to lease and use certain resources is an important one in order to achieve the planned outcome. This chapter uses value based approach to manage the SaaS service provided to the customers. Based on our approach, customer satisfaction is modeled not only based on the response time, but also based on the allotted budget. Using our model, the application owner is able to direct and control the decision of renting cloud resources as per the current strategy. This strategy is led by a set of defined key performance indicators. In addition, we present a scheduling algorithm that can bid for different types of virtual machines to achieve the target value. Furthermore, we proposed the required Ontology to semantically discover the needed IaaS resources. We conduct extensive simulations using different types of Amazon EC2 instances with dynamic prices.

Software Development Methodology for Cloud Computing and Its Impact

This chapter emphasizes mainly on the software development methodology basically agile methods of software development in cloud computing platforms and its impact on software development processes. This chapter also covers the benefits of agile development methodology in cloud computing platform. Along with this all traditional software development phases are analyzed to discuss the differences between the traditional software development processes and software development in cloud computing environment. This chapter also includes a brief description of programming models such as MapReduce, BSPCloud, and Dryad etc. available in the literature to handle big data in SaaS cloud. Finally, we highlight the challenges and future scope of software development process in cloud computing environment.

Resource and Energy Efficient Virtual Machine Migration in Cloud Data Centers

Cloud computing service has been on the rise over the past few decades, which has led to an increase in the number of data centers, thus consuming more amount of energy for their operation. Moreover, the energy consumption in the cloud is proportional to the resource utilization. Thus consolidation schemes for the cloud model need to be devised to minimize energy by decreasing the operating costs. The consolidation problem is NP-complete, which requires heuristic techniques to get a sub-optimal solution. The authors have proposed a new consolidation scheme for the virtual machines (VMs) by improving the host overload detection phase. The resulting scheme is effective in reducing the energy and the level of Service Level Agreement (SLA) violations both, to a considerable extent. For testing the performance of implementation, a simulation environment is needed that can provide an environment of the actual cloud computing components. The authors have used CloudSim 3.0.3 simulation toolkit that allows testing and analyzing Allocation and Selection algorithms.

Real Time Task Execution in Cloud Using MapReduce Framework

Cloud Computing era comes with the advancement of technologies in the fields of processing, storage, bandwidth network access, security of internet etc. The development of automatic applications, smart devices and applications, sensor based applications need huge data storage and computing resources and need output within a particular time limit. Now users are becoming more sensitive towards, delay in applications they are using. So, a scalable platform like Cloud Computing is required that can provide huge computing resource, and data storage required for processing such applications. MapReduce framework is used to process huge amounts of data. Data processing on a cloud based on MapReduce would provide added benefits such as fault tolerant, heterogeneous, ease of use, free and open, efficient. This chapter discusses about cloud system model, real-time MapReduce framework, Cloud based MapReduce framework examples, quality attributes of MapReduce scheduling and various MapReduce scheduling algorithm based on quality attributes.

Dynamic Virtual Machine Placement in Cloud Computing

The aim of cloud computing is to enable users to access resources on demand. The number of users is continuously increasing. In order to fulfil their needs, we need more number of physical machines and data centers. The increase in the number of physical machines is directly proportional to the consumption of energy. This gives us one of the major challenges; minimization of energy consumption. One of the most effective ways to minimize the consumption of energy is the optimal virtual machine placement on physical machines. This chapter focuses on finding the solution to the problem of dynamic virtual machine placement for the optimized consumption of energy. An energy consumption model is built which takes into account the states of physical machines and live migration of virtual machines. On top of this, the cloud computing model is built. Unlike centralized approaches towards virtual machine placement which result in many unreachable solutions, a decentralized approach is used in this chapter which provides a list of virtual machine migrations for their optimal placement.

Cloud Service Footprint (CSF)

Cloud Computing (CC) services have made substantive advances in the past few years. It is rapidly changing the landscape of technology, and energizing the long-held promise of utility computing. Successful jump into CC is a considerable task, since the surroundings are not yet mature and the accompanied risk and governance frameworks are still evolving. This effort aims to portray an identity for CC services by employing risk and governance directions among other elements and techniques. Cloud Service Footprint (CSF) is considering practical aspects surrounding the CC paradigm and prescribing the associated directions. CSF will help Cloud Service Providers (CSPs) to characterize their service and benchmark themselves. The Client Enterprises (CEs) can utilize CSF dimensions to find a better way to navigate through CC service arena and to understand its parameters. Along with cost and functional capabilities, the Cloud Service Footprint (CSF) can provide enough information for business executives to evaluate CC services and make informed decisions.

Software as a Service, Semantic Web, and Big Data

This chapter explains the overview of software as a service (SaaS); SaaS and application service provision (ASP); the security concern of SaaS; the perspectives on SaaS adoption; the challenges of SaaS in the digital age; the overview of the Semantic Web; the current trends in the Semantic Web services; the overview of Big Data; the concept of Big Data analytics; and the prospects of Big Data in the digital age. SaaS offers a wide range of business applications through the cloud computing service providers toward enhancing organizational performance. The Semantic Web extends beyond the capabilities of the current Web 2.0, thus enabling more effective collaborations and smarter decision making in modern operations. Big Data from the cloud computing platforms provides the significant advantage, if the essential data sources are hosted by the same SaaS and enhanced by the Semantic Web technologies.

Network Virtualization

Cloud computing is a novel paradigm which relies on the vision of resource sharing over the Internet. The concept of resource virtualization, i.e. hiding the detail specification of the resources from the end users is the key idea of cloud computing. But the tenants have limited visibility over the network resources. The Network-as-a-Service (NaaS) framework integrates the cloud computing services with direct tenant access to the network infrastructure. The Network virtualization (NV) is such a platform that acts as a mediation layer to provide NaaS to tenants. NV supports the coexistence of multiple virtual networks, which is the collection of virtual nodes and virtual links on the same underlying physical infrastructure. Prior to set up a virtual network in an NV Environment, resource discovery and resource allocation are the primary job. In this chapter, we have discussed on basic NV architecture, surveyed the previous work on the resource allocation along with ongoing research projects on network virtualization.