Autonomic Cloud Computing: Open Challenges and Architectural Elements

As Clouds are complex, large-scale, and heterogeneous distributed systems, management of their resources is a challenging task. They need automated and integrated intelligent strategies for provisioning of resources to offer services that are secure, reliable, and cost-efficient. Hence, effective management of services becomes fundamental in software platforms that constitute the fabric of computing Clouds. In this direction, this paper identifies open issues in autonomic resource provisioning and presents innovative management techniques for supporting SaaS applications hosted on Clouds. We present a conceptual architecture and early results evidencing the benefits of autonomic management of Clouds.Comment: 8 pages, 6 figures, conference keynote pape

Workflow Scheduling Techniques and Algorithms in IaaS Cloud: A Survey

In the modern era, workflows are adopted as a powerful and attractive paradigm for expressing/solving a variety of applications like scientific, data intensive computing, and big data applications such as MapReduce and Hadoop. These complex applications are described using high-level representations in workflow methods. With the emerging model of cloud computing technology, scheduling in the cloud becomes the important research topic. Consequently, workflow scheduling problem has been studied extensively over the past few years, from homogeneous clusters, grids to the most recent paradigm, cloud computing. The challenges that need to be addressed lies in task-resource mapping, QoS requirements, resource provisioning, performance fluctuation, failure handling, resource scheduling, and data storage. This work focuses on the complete study of the resource provisioning and scheduling algorithms in cloud environment focusing on Infrastructure as a service (IaaS). We provided a comprehensive understanding of existing scheduling techniques and provided an insight into research challenges that will be a possible future direction to the researchers

Idle block based methods for cloud workflow scheduling with preemptive and non-preemptive tasks

[EN] Complex workflow applications are widely used in scientific computing and economic analysis, which commonly include both preemptive and non-preemptive tasks. Cloud computing provides a convenient way for users to access different resources based on the ¿pay-as-you-go¿ model. However, different resource renting alternatives (reserved, on-demand or spot) are usually provided by the service provider. The spot instances provide a dynamic and cheaper alternative comparing to the on-demand one. However, failures often occur due to the fluctuations of the price of the instance. It is a big challenge to determine the appropriate amount of spot and on-demand resources for workflow applications with both preemptive and non-preemptive tasks. In this paper, the workflow scheduling problem with both spot and on-demand instances is considered. The objective is to minimize the total renting cost under deadline constrains. An idle time block-based method is proposed for the considered problem. Different idle time block-based searing and improving strategies are developed to construct schedules for workflow applications. Schedules are improved by a forward and backward moving mechanism. Experimental and statistical results demonstrate the effectiveness of the proposed algorithm over a lot of tests with different sizes.This work is supported by the National Natural Science Foundation of China (No. 61572127, 61272377), the National Key Research and Development Program of China (No. 2017YFB1400800). Ruben Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness, under the project "SCHEYARD - Optimization of Scheduling Problems in Container Yards" (No. DPI2015-65895-R) financed by FEDER funds.Chen, L.; Li, X.; Ruiz García, R. (2018). Idle block based methods for cloud workflow scheduling with preemptive and non-preemptive tasks. Future Generation Computer Systems. 89:659-669. https://doi.org/10.1016/j.future.2018.07.037S6596698

A Study on Factors Contributing to Efficiency of Scheduling Algorithms in a Cloud Computing Environment; Overview of Several Algorithms

Cloud computing is the latest distributed technology providing a rich environment of dynamically shared resources through virtualization, which can fulfill the requirements of users by allocating resources to programs. Any program in a cloud environment is delivered by workflows which are a series of interlinked tasks to accomplish a goal. One of the most important tasks in cloud computing is correct mapping of tasks onto resources. It is essential to schedule processes in distributed systems such as cloud, since it leaves a tremendous impact on the system performance. This is done by scheduling algorithms. Therefore, it is crucial to present and adopt an efficient algorithm in the cloud environment. This article attempted to examine the parameters effective in the efficiency of scheduling algorithms including deadline, cost constraint, balanced loading, power consumption and fault tolerance. Additionally, the performances of several algorithms were briefly discussed

Hybridized Darts Game with Beluga Whale Optimization Strategy for Efficient Task Scheduling with Optimal Load Balancing in Cloud Computing

A cloud computing technology permits clients to use hardware and software technology virtually on a subscription basis. The task scheduling process is planned to effectively minimize implementation time and cost while simultaneously increasing resource utilization, and it is one of the most common problems in cloud computing systems. The Nondeterministic Polynomial (NP)-hard optimization problem occurs due to limitations like an insufficient make-span, excessive resource utilization, low implementation costs, and immediate response for scheduling. The task allocation is NP-hard because of the increase in the amount of combinations and computing resources. In this work, a hybrid heuristic optimization technique with load balancing is implemented for optimal task scheduling to increase the performance of service providers in the cloud infrastructure. Thus, the issues that occur in the scheduling process is greatly reduced. The load balancing problem is effectively solved with the help of the proposed task scheduling scheme. The allocation of tasks to the machines based on the workload is done with the help of the proposed Hybridized Darts Game-Based Beluga Whale Optimization Algorithm (HDG-BWOA). The objective functions like higher Cloud Data Center (CDC) resource consumption, increased task assurance ratio, minimized mean reaction time, and reduced energy utilization are considered while allocating the tasks to the virtual machines. This task scheduling approach ensures flexibility among virtual machines, preventing them from overloading or underloading. Also, using this technique, more tasks is efficiently completed within the deadline. The efficacy of the offered arrangement is ensured with the conventional heuristic-based task scheduling approaches in accordance with various evaluation measures

A review on various optimization techniques of resource provisioning in cloud computing

Cloud computing is the provision of IT resources (IaaS) on-demand using a pay as you go model over the internet.It is a broad and deep platform that helps customers builds sophisticated, scalable applications. To get the full benefits, research on a wide range of topics is needed. While resource over-provisioning can cost users more than necessary, resource under provisioning hurts the application performance. The cost effectiveness of cloud computing highly depends on how well the customer can optimize the cost of renting resources (VMs) from cloud providers. The issue of resource provisioning optimization from cloud-consumer potential is a complicated optimization issue, which includes much uncertainty parameters. There is a much research avenue available for solving this problem as it is in the real-world. Here, in this paper we provide details about various optimization techniques for resource provisioning