A comprehensive survey for scheduling techniques in cloud computing

Many companies are increasingly using MapReduce for efficient large scale data processing such as personalized advertising, spam detection, and different data mining tasks. Cloud computing offers an attractive option for businesses to rent a suitable size Hadoop cluster, consume resources as a service, and pay only for resources that were utilized. One of the open questions in such environments is the amount of resources that a user should lease from the service provider. Often, a user targets specific performance goals and the application needs to complete data processing by a certain time deadline. However, currently, the task of estimating required resources to meet application performance goals is solely the users' responsibility. In this work, we introduce a novel framework and technique to address this problem and to offer a new resource sizing and provisioning service in MapReduce environments. For a MapReduce job that needs to be completed within a certain time, the job profile is built from the job past executions or by executing the application on a smaller data set using an automated profiling tool. Then, by applying scaling rules combined with a fast and efficient capacity planning model, we generate a set of resource provisioning options. Moreover, we design a model for estimating the impact of node failures on a job completion time to evaluate worst case scenarios. We validate the accuracy of our models using a set of realistic applications. The predicted completion times of generated resource provisioning options are within 10% of the measured times in our 66-node Hadoop cluster.

Resource provisioning framework for mapreduce jobs with performance goals

Cloud computing is a recently looming-evoked paradigm, the aim of which is to provide on-demand, pay-as-you-go, internet-based access to shared computing resources (hardware and software) in a metered, self-service, dynamically scalable fashion. A related hot topic at the moment is task scheduling, which is well known for delivering critical cloud service performance. However, the dilemmas of resources being underutilized (underloaded) and overutilized (overloaded) may arise as a result of improper scheduling, which in turn leads to either wastage of cloud resources or degradation in service performance, respectively. Thus, the idea of incorporating meta-heuristic algorithms into task scheduling emerged in order to efficiently distribute complex and diverse incoming tasks (cloudlets) across available limited resources, within a reasonable time. Meta-heuristic techniques have proven very capable of solving scheduling problems, which is fulfilled herein from a cloud perspective by first providing a brief on traditional and heuristic scheduling methods before diving deeply into the most popular meta-heuristics for cloud task scheduling followed by a detailed systematic review featuring a novel taxonomy of those techniques, along with their advantages and limitations. More specifically, in this study, the basic concepts of cloud task scheduling are addressed smoothly, as well as diverse swarm, evolutionary, physical, emerging, and hybrid meta-heuristic scheduling techniques are categorized as per the nature of the scheduling problem (i.e., single- or multi-objective), the primary objective of scheduling, task-resource mapping scheme, and scheduling constraint. Armed with these methods, some of the most recent relevant literature are surveyed, and insights into the identification of existing challenges are presented, along with a trail to potential solutions. Furthermore, guidelines to future research directions drawn from recently emerging trends are outlined, which should definitely contribute to assisting current researchers and practitioners as well as pave the way for newbies excited about cloud task scheduling to pursue their own glory in the field.

Task Scheduling in Cloud Computing based on Meta-heuristics: Review, Taxonomy, Open Challenges, and Future Trends

Distributed Grey Wolf Optimizer for scheduling of workflow applications in cloud environments

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The Cloud Computing is a most widely spreading platform for executing tasks using virtual machines (VMs) as processing elements. Therefore, implementing HPC using Cloud Computing is considered a powerful approach by isolating tasks, reducing execution time, as well as, price, and satisfying load balance. In this paper, an enhancement task scheduling algorithm on the Cloud Computing environment has been introduced to reduce the make-span, as well as, decrease the price of executing the independent tasks on the cloud resources. The principles of the algorithm is based on calculating the total processing power of the available resources (i.e., VMs) and the total requested processing power by the users' tasks, then allocating a group of users' tasks to each VM based on the ratio of its needed power relative to the total processing power of all VMs. The power of VMs has been defined based on Amazon EC2 and Google pricing models. To evaluate the performance of the enhancement algorithm, a comparative study has been done among this enhancement algorithm, the default FCFS algorithm, and the existed GA, and PSO algorithms. The experimental results show that the enhancement algorithm outperforms other algorithms by reducing make-span and the price of the running tasks.

Task Scheduling Algorithm in Cloud Computing Environment Based on Cloud Pricing Models

Cloud computing is a popular model that allows users to store, access, process, and retrieve data remotely. It provides a high-performance computing with large scale of resources. However, this model requires an efficient scheduling strategy for resources management. Recently, several algorithms are presented to solve the resource scheduling problem. Nevertheless, still the problem exists with complex applications such as workflows, which need an efficient algorithm to be scheduled on the available resources. This paper presents a novel hybrid algorithm, called CR-AC, combining both the chemical reaction optimization (CRO) and ant colony optimization (ACO) algorithms to solve the workflow-scheduling problem. The proposed CR-AC algorithm is implemented in the CloudSim toolkit and evaluated by using real applications and Amazon EC2 pricing model. Moreover, the results are compared with the most recent algorithms: modified particle swarm optimization (PSO) and cost-effective genetic algorithm (CEGA). The experimental results indicate that the CR-AC algorithm achieves better results than the traditional CRO, the ACO, the modified PSO and CEGA algorithms, in terms of total cost, time complexity, and schedule length.

Cost-Effective Algorithm for Workflow Scheduling in Cloud Computing Under Deadline Constraint

This paper presents a secure IoT-based health monitoring system that shortens the distance between a patient and the relevant medical organization. Vital signals captured from sensors are processed and encrypted using AES (Advanced Encryption Standard) algorithm before sending to the cloud for storage. A Node MCU microcontroller is utilized to carry out the processing and encryption functions, and for providing connectivity to the cloud over WiFi. In addition, a medical specialist can visualize the private health data in real-time only after providing decryption credentials. Moreover, the proposed system provides an alert by sending an email to some patient relatives or coordinating specialist if vital signs are outside the normal rates. The proposed system provides privacy, security, and real-time connectivity for private health data records.

Smart Health Monitoring System based on IoT and Cloud Computing

One of the known problems in security systems is to identify persons based on certain signatures. Biometrics have been adopted in security systems to identify persons based on some physiological or behavioral characteristics that they own. Photoplethysmography (PPG) is a physiological signal that is used to describe the volumetric change of blood flow in peripherals with heartbeats. The PPG signals gained some interest of researchers in the last few years, because they are used non-invasively, and they are easily captured by the emerging IoT sensors from fingertips. This paper presents a PPG-based approach to identify persons using a neural network classifier. Firstly, PPG signals are captured from a number of persons using IoT sensors. Then, unique features are extracted from captured PPG signals by estimating the Mel-Frequency Cepstral Coefficients (MFCCs). These features are fed into an Artificial Neural Network (ANN) to be trained first and used for identification of persons. A dataset of PPG signals for 35 healthy persons was collected to test the performance of the proposed approach. Experimental results demonstrate 100% and 98.07% accuracy levels using the hold-out method and the 10-fold cross-validation method, respectively.

PPG-based human identification using Mel-frequency cepstral coefficients and neural networks

The likelihood of failures rises in cloud computing systems as a result of their unstable nature. Additionally, the size of a cloud computing system varies with time and thus failures become a common incident. Failures have a high impact on cloud performance and the expected benefits for both customers and providers. Fault tolerance is an essential challenge facing cloud providers in order to mitigate the effects of failures and maintaining the Service Level Agreement (SLA) satisfied. Checkpointing is one of the most known reactive fault tolerance techniques used in distributed computing. However, it can incur considerable overheads that depend on the interval of the checkpoint applied and these overheads put down the performance of the cloud. In this paper, a reactive fault tolerance approach in the context of checkpointing is proposed and evaluated with the aim of getting better performance. The approach depends on applying a flexible interval of the checkpoint to reduce overheads. Simulation experiments indicate superior performance of the approach in terms of power consumption, response time, monetary cost and cloud capacity.

Nirmeen A. El-Bahnasawy

Papers

Task Scheduling Algorithm in Cloud Computing Environment Based on Cloud Pricing Models

Cost-Effective Algorithm for Workflow Scheduling in Cloud Computing Under Deadline Constraint

Smart Health Monitoring System based on IoT and Cloud Computing

PPG-based human identification using Mel-frequency cepstral coefficients and neural networks

On the design of reactive approach with flexible checkpoint interval to tolerate faults in cloud computing systems