Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing

Cloud computing is offering utility-oriented IT services to users world wide. The market for cloud computing services has continued to expand despite a general decline in economic activity in most of the world. It enables hosting of applications from consumer, scientific and business domains. However, data centres hosting cloud computing applications consume huge amounts of energy, contributing to high operational costs and carbon footprints to the environment. With energy shortages and global climate change leading our concerns these days, the power consumption of data centres has become a key issue. Therefore, we need green cloud computing solutions that cannot only save energy, but also reduce operational costs. The vision for energy efficient management of cloud computing environments is presented here. Cloud computing – either public, private or hybrid cloud – will become an increasingly important factor in green computing.

The data-driven framework is one of the most demanded managements of cloud computing (CC). This study was focused on optimization and deep learning (DL) for the CC framework. It requires different types of frameworks' data or resources for executing various services on the cloud framework. We propose a model-based data-driven framework that explores data-driven management in terms of CC. This research is an optimal selection of CC recovery when the uncertainty of cloud networks can be based on time constraints and objective functions. We consider the emergence of CC data centers such as Amazon Web Services (AWS) and Wikipedia. These data centers can bring a considerable risk to the uncertainty of the data quality in real-time demand from organizational CC. In the present pandemic situation, data centers perform various degradation of the quality of data because the allocation of resources has increased the input of data size, especially in cloud workload. So, we use an Artificial Neural Network (ANN) model to perform a time allocation and elasticity for managing the CC workload and their management. Real-time workload resilience, trace troubleshooting, probability, and availability are implemented to analyze the data quality for better performance, which leads to overhead on the cloud platform performance.

In cloud computing, customers-desired Quality of Service (QoS) expectations are quite superficial due to lack of scalable task scheduling solutions that can adjust to long-time changes. Researchers in the literature have put forward several task scheduling algorithms to account for customers' QoS expectations. Unfortunately, most of these algorithms need improvements to ensure the provisioning of better consumers' QoS expectation. In this study, a Multi-Objective QoS model to address customers' expectation based on execution time and execution cost criteria is presented. A Cloud Scalable Multi-Objective Cat Swarm Optimization (CSO) based Simulated Annealing (SA) (CSM-CSOSA) algorithm is then proposed to solve the model. In this method, the Taguchi Orthogonal approach is used to enhanced the SA and incorporated into the local search of the proposed algorithm for enhancing it exploration capability. Implementation of the algorithm is carried out on CloudSim tool and evaluated using one dataset (Normal distributed) and one Parallel Workload (High-Performance Computing Center North(HPC2N)). Quantitative analysis of the algorithm performance is taken based on metrics of execution time, execution cost, QoS and percentage improvement. Result obtained is compared with that of Multi-Objective Genetic Algorithm (MOGA), Multi-Objective Ant Colony (MOSACO) and Multi-Objective Particle Swarm Optimization (MOPSO), where proposed method is able to return substantial performance with improved QoS.

Modified Round Robin Algorithm for Resource Allocation in Cloud Computing

Resource management for Infrastructure as a Service (IaaS) in cloud computing: A survey

Cloud Computing is a relatively new technology and aims to offer “utility based IT services”. Cloud Computing is now becoming increasingly popular because of the potential number of advantages that it aims to offer. However, with the growing popularity comes the increasing power consumption. Therefore, there is an utmost need to develop solutions that aim to save energy consumption without compromising much on the performance. Such solutions would also help reducing the costs thereby benefitting the cloud service providers. In this paper, an optimization technique called Bacterial Foraging has been used in order to continuously optimize the allocation of resources thereby improving the energy efficiency of the data centre. The results obtained after simulating a cloud computing environment and implementing the proposed algorithm make it clearly evident that cloud computing has great potential and offers significant performance gains as well as cost savings even under dynamic workload conditions.

Energy Aware Resource Optimization using Unified Metaheuristic Optimization Algorithm Allocation for Cloud Computing Environment

The resource allocation in cloud computing determines the allocation of computer and network resources of service providers to service requests of users for meeting user service requirements. It is not scalable to solve the resource allocation problem as an optimization problem to obtain the optimal solution in real time. This paper presents the development and testing of heuristics for the efficient resource allocation to obtain near-optimal solutions in a scalable manner. We first define the resource allocation problem as a Mixed Integer rogramming (MIP) optimization problem and obtain the optimal solutions for various resource-service problem types. Based on the analysis of the optimal solutions, we design heuristics for the efficient resource allocation. Then we evaluate the performance of the resource allocation heuristics using various resource-service problem types and different numbers of service requests and resources. The results show the comparable performance of the heuristics to the optimal solutions. The resource allocation heuristics also demonstrate the better computational efficiency and thus scalability than solving the MIP problems to obtain the optimal solutions. Keywords: Resource allocation; Clouds computing; Heuristics; Mixed integer programming

Cloud computing and big data: Technologies and applications

In the big data era, cloud computing provides an effective usage model for providing computing services to handle diverse data-intensive workloads. Data center capacity planning and resource provisioning policies play a vital role in longterm life cycle management of datacenters. Effective design and management of data center infrastructures while ensuring good performance is critical to minimizing the carbon footprint of the datacenter. Traditional solutions have primarily focused on optimizing data center operational phase impacts including reducing energy cost during the resource management phase. In this paper, we propose a two-phase sustainability-aware resource allocation and management framework for data center life-cycle management that jointly optimizes the data center manufacturing phase and operational phase impact without impacting the performance and service quality for the jobs. Phase 1 of the proposed approach minimizes data center building phase carbon footprint through a novel manufacturing cost-aware server provisioning plan. In phase 2, the approach minimizes the operational phase carbon footprint using a server lifetime-aware resource allocation scheme and a manufacturing cost-aware replacement plan. The proposed techniques are evaluated through extensive experiments using realistic workloads generated in a data center. The evaluation results show that the proposed framework significantly reduces the carbon footprint in the data center without impacting the performance of the jobs in the workload.

Special section: software architectures and application development environments for Cloud computing

In the Internet of Things era, a big volume of data is generated/gathered every second from billions of connected devices. The current network paradigm, which relies on centralised data centres (a.k.a. Cloud computing), becomes an impractical solution for IoT data storing and processing due to the long distance between the data source (e.g., sensors) and designated data centres. It worth noting that the long distance in this context refers to the physical path and time interval of when data is generated and when it get processed. To explain more, by the time the data reaches a far data centre, the importance of the data can be depreciated. Therefore, the network topologies have evolved to permit data processing and storage at the edge of the network, introducing what so-called fog Computing. The later will obviously lead to improvements in quality of service via processing and responding quickly and efficiently to varieties of data processing requests. Although fog computing is recognized as a promising computing paradigm, it suffers from challenging issues that involve: i) concrete adoption and management of fogs for decentralized data processing. ii) resources allocation in both cloud and fog layers. iii) having a sustainable performance since fog have a limited capacity in comparison with cloud. iv) having a secure and trusted networking environment for fogs to share resources and exchange data securely and efficiently. Hence, the thesis focus is on having a stable performance for fog nodes by enhancing resources management and allocation, along with safety procedures, to aid the IoT-services delivery and cloud computing in the ever growing industry of smart things. The main aspects related to the performance stability of fog computing involves the development of cognitive fog nodes that aim at provide fast and reliable services, efficient resources managements, and trusted networking, and hence ensure the best Quality of Experience, Quality of Service and Quality of Protection to end-users. Therefore the contribution of this thesis in brief is a novel Fog Resource manAgeMEnt Scheme (FRAMES) which has been proposed to crystallise fog distribution and resource management with an appropriate service's loads distribution and allocation based on the Fog-2-Fog coordination. Also, a novel COMputIng Trust manageMENT (COMITMENT) which is a software-based approach that is responsible for providing a secure and trusted environment for fog nodes to share their resources and exchange data packets. Both FRAMES and COMITMENT are encapsulated in the proposed Cognitive Fog (CF) computing which aims at making fog able to not only act on the data but also interpret the gathered data in a way that mimics the process of cognition in the human mind. Hence, FRAMES provide CF with elastic resource managements for load balancing and resolving congestion, while the COMITMENT employ trust and recommendations models to avoid malicious fog nodes in the Fog-2-Fog coordination environment. The proposed algorithms for FRAMES and COMITMENT have outperformed the competitive benchmark algorithms, namely Random Walks Offloading (RWO) and Nearest Fog Offloading (NFO) in the experiments to verify the validity and performance. The experiments were conducted on the performance (in terms of latency), load balancing among fog nodes and fogs trustworthiness along with detecting malicious events and attacks in the Fog-2-Fog environment. The performance of the proposed FRAMES's offloading algorithms has the lowest run-time (i.e., latency) against the benchmark algorithms (RWO and NFO) for processing equal-number of packets. Also, COMITMENT's algorithms were able to detect the collaboration requests whether they are secure, malicious or anonymous. The proposed work shows potential in achieving a sustainable fog networking paradigm and highlights significant benefits of fog computing in the computing ecosystem.

Cloud computing aims to migrate IT services to distant data centers in order to reduce the dependency of the services on the limited local resources. Cloud computing provides access to distant computing resources via Web services while the end user is not aware of how the IT infrastructure is managed. Besides the novelties and advantages of cloud computing, deployment of a large number of servers and data centers introduces the challenge of high energy consumption. Additionally, transportation of IT services over the Internet backbone accumulates the energy consumption problem of the backbone infrastructure. In this chapter, the authors cover energy-efficient cloud computing studies in the data center involving various aspects such as: reduction of processing, storage, and data center network-related power consumption. They first provide a brief overview of the existing approaches on cool data centers that can be mainly grouped as studies on virtualization techniques, energy-efficient data center network design schemes, and studies that monitor the data center thermal activity by Wireless Sensor Networks (WSNs). The authors also present solutions that aim to reduce energy consumption in data centers by considering the communications aspects over the backbone of large-scale cloud systems.

The increasing violation of Service Level Agreements (SLA) cause as a result of imbalance tasks allocation across Virtual Machines (VMs) has affected consumers’ Quality of Service (QoS) expectations. Researchers in the literature have put forward several models and tried to solve the problem using Artificial Intelligence (AI) scheduling techniques. Significant improvement has been recorded with the need for an ideal solution. In this paper, a multi-objective task scheduling problem with required consumers’ QoS expectations and a scheduling model in relation to the problem is presented. A Dynamic Multi-Objective Orthogonal Taguchi Based-Cat (dMOOTC) algorithm is then proposed to solve the model. CloudSim tool is used for implementation of the proposed algorithm and evaluated with metrics of execution time, execution cost, and QoS. The performance result as compared with Standard Cat Swarm Optimization (CSO), Multi-Objective Particle Swarm Optimization (MOPSO), Enhanced Parallel CSO (EPCSO), Orthogonal Taguchi Based-Cat Algorithm (OTB-CSO) shows the proposed solution outperformed better by returning good consumers’ QoS expectation.

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.