In recent years, cloud computing has provided a Software As A Service (SaaS) platform where the software can be reused and applied to fulfill complicated user demands according to specific Quality of Services (QoS) constraints. The user requirements are formulated as a workflow consisting of a set of tasks. However, many services may satisfy the functionality of each task; thus, searching for the composition of the optimal service while maximizing the QoS is formulated as an NP-hard problem. This work will introduce a hybrid Artificial Bee Colony (ABC) with a Cuckoo Search (CS) algorithm to untangle service composition problem. The ABC is a well-known metaheuristic algorithm that can be applied when dealing with different NP-hard problems with an outstanding record of performance. However, the ABC suffers from a slow convergence problem. Therefore, the CS is used to overcome the ABC's limitations by allowing the abandoned bees to enhance their search and override the local optimum. The proposed hybrid algorithm has been tested on 19 datasets and then compared with two standard algorithms (ABC and CS) and three state-of-the-art swarm-based composition algorithms. In addition, extensive parameter study experiments were conducted to set up the proposed algorithm's parameters. The results indicate that the proposed algorithm outperforms the standard algorithms in the three comparison criteria (best fitness value, average fitness value, and average execution time) overall datasets in 30 different runs. Furthermore, the proposed algorithm also exhibits better performance than the state-of-the-art algorithms in the three comparison criteria over 30 different runs.

Cloud computing is a new paradigm that promises to move IT a step further towards utility computing, in which computing services are delivered as a utility service. Traditionally, Cloud employs dedicated resources located in one or more data centres in order to provide services to clients. Desktop Cloud computing is a new type of Cloud computing that aims at providing Cloud capabilities at low or no cost. Desktop Clouds harness non dedicated and idle resources in order to provide Cloud services. However, the nature of such resources can be problematic because they are prone to failure at any time without prior notice. This research focuses on the resource allocation mechanism in Desktop Clouds.The contributions of this chapter are threefold. Firstly, it defines and explains Desktop Clouds by comparing them with both Traditional Clouds and Desktop Grids. Secondly, the paper discusses various research issues in Desktop Clouds. Thirdly, it proposes a resource allocation model that is able to handle node failures.

Abstract. Volunteer cloud computing is a new type of clouds aiming at moving volunteer computing towards the cloud. The new cloud type is motivated by the fact that building a cloud out of nondedicated resources can be useful for scientific projects which cannot afford the cost of consumption of cloud services provided by cloud service providers such as Amazon. However, Volunteer Clouds are in its infancy level with some challenges and issues that ought to be tackled. This paper presents a new architecture which can facilitate volunteer clouds being a viable cloud solution.

Abstract:Desktop Cloud computing is the idea of benefiting from computing resources around us to build a Cloud system in order to have better usage of these resources instead of them being idle. However, such resources are prone to failure at any given time without prior knowledge. Such failure events have a can negative impact on the outcome of a Desktop Cloud system. This paper proposes metrics that can evaluate the behaviour of Virtual Machine (VM) allocation mechanisms in the presence of node failures. The metrics are throughput, power consumption and availability. Three VM allocation mechanisms (Greedy, FCFS and RoundRobin mechanisms) are evaluated using the given metrics.

The reliability and availability of cloud systems have become major concerns of service providers, brokers, and end-users. Therefore, studying faulttolerance mechanisms in cloud computing attracts intense attention in industry and academia. The task-scheduling mechanisms can improve the fault-tolerance level of cloud systems. A task-scheduling mechanism distributes tasks to a group of instances to be executed. Much work has been undertaken in this direction to improve the overall outcome of cloud computing, such as improving service quality and reducing power consumption. However, little work on task scheduling has studied the problem of lost tasks from the broker's perspective. Task loss can happen due to virtual machine failures, server crashes, connection interruption, etc. The broker-based concept means that the backup task can be allocated by the broker on the same cloud service provider (CSP) or a different CSP to reduce costs, for example. This paper proposes a novel fault-tolerant mechanism that employs the primary backup (PB) model of task scheduling to address this issue. The proposed mechanism minimizes the impact of failure events by reducing the number of lost tasks. The mechanism is further improved to shorten the makespan time of submitted tasks in cloud systems. The experiments demonstrated that the proposed mechanism decreased the number of lost tasks by about 13%-15% compared with other mechanisms in the literature.