MTC: Minimizing Time and Cost of Cloud Task Scheduling based on Customers and Providers Needs using Genetic Algorithm

Cloud computing and big data: Technologies and applications

Cloud computing is the emerging domain in academia and IT Industry. It is a business framework for delivering the services and computing power on-demand basis. Cloud users have to pay the service providers based on their usage. For enterprises, cloud computing is the worthy of consideration and they try to build business systems with lower costs, higher profits and quality-of-service. Considering cost optmization, service provider may initially try to use less number of CPU cores and data centers. For that reason, this paper deals with CloudSim simulation tool which has been utilized for evaluating the number of CPU cores and execution time. Minimization of waiting time is also a considerable issue. When a large number of jobs are requested, they have to wait for getting allocated to the servers which in turn may increase the queue length and also waiting time. This paper also deals with queuing model with multi-server and finite capacity to reduce the waiting time and queue length.

Load Balancing is an important factor handling resource during running and execution time in real time applications. Virtual machines are used for dynamically access and share the resources. As per current scenario cloud computing is played major for storage, resource accessing, resource pooling and internet based service offering. Usage of cloud computing services is dynamically increased such as online shopping, education, ticketing, etc. Many users can use the cloud resources and load balancing is used for adjusting the virtual machine and balance the node. Our proposed virtualized genetic algorithms are to provide balanced virtual machine services in Hybrid cloud. The proposed algorithm and experiments are implemented by using Cloud simulator. In this paper the experiments are done with cloud computing models, Virtual Machine allocation, load balancing and simulations. Also compare the results using response time, throughput and turnaround time using cloud sim. The accuracy can be compared with existing load balancing techniques.

The problem of scheduling of tasks in distributed, heterogeneous, and multiprocessing computing environment like grid and cloud computing is considered as one of the most important issue from research perspective. As the performance of such kind of systems is highly depends upon the way, how tasks are allocated among the multiple processing units for their efficient execution. The underlying objective of any task scheduling mechanism is to minimize the overall makespan for the execution of given set of jobs/tasks and computing machines. Scheduling of tasks in cloud computing falls in the class of NP-hard optimization problem. As a result, many meta-heuristic algorithms have been applied and tested to solve this problem but still lot of scope is there for the better strategies. The characteristic of the good algorithm is that it must be adaptable to the dynamic environment. Through this paper, we are proposing task scheduling mechanism based on particle swarm optimization (PSO) in which opposition-based learning technique is used to avoid premature convergence and to accelerate the convergence of standard PSO and compared same with the well-established task scheduling strategies based on PSO, mPSO (modified PSO), genetic algorithm GA, max–min, minimum completion time and minimum execution time. The results obtained for the various class of experiments clearly establish that the proposed opposition-based learning inspired particle swarm optimization based scheduling strategy performs better in comparison to its peers which are taken into the consideration.

Response time is affected by interprocessor communications, precedence relationships among the modules, module assignments, and processor scheduling policies. Furthermore, due to sharing of resources and data among the processors, contention delays are incurred. A task response time model that considers all these factors is proposed. A Petri net is used to represent resource contention, and the task control flow graph represents module precedence and logical relationships. A queuing network with resource contention is used to estimate the response time of each module. Module response time consists of delays at the processors and resource queues and is estimated by approximating the extended queuing network as independent finite capacity queuing systems. The module response time is mapped onto a control flow graph, and task response time is obtained by aggregating the module response times in accordance with their precedence relationship in the control flow graph. The task response time derived from the analytical model compares well with that from the simulation. >

Cloud computing has become a crucial paradigm for large-scale data-intensive applications, but it also brings challenges like energy consumption, execution time, heat, and operational costs. Improving workflow scheduling in cloud environments can address these issues and optimize resource utilization, leading to significant ecological and financial benefits. As data centres and networks continue to expand globally, efficient scheduling becomes even more critical for achieving better performance and sustainability in cloud computing. Schedulers mindful of energy and deadlines will assign resources to jobs in a way that consumes the least energy while upholding the task’s quality standards. Because this scheduling involves a Non-deterministic Polynomial (NP)-hard problem, the schedulers are able to minimize complexity by utilizing metaheuristic techniques. This work has developed methods like Artificial Bee Colony (ABC), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) for optimizing the scheduler. Local search and exploration are respectably supported by heuristic algorithms. The algorithm’s exploration and exploitation features must also be balanced. The primary objective is to optimize computation-intensive workflows in a way that minimizes both energy consumption and execution time while maximizing throughput. This optimization should be achieved without compromising the Quality of Service (QoS) guarantee provided to users. The focus is on striking a balance between energy efficiency and performance to enhance the overall efficiency and cost-effectiveness of cloud computing environments. According to the simulation findings, the suggested ABC has a higher guarantee ratio for 5000 jobs when compared to the GA, PSO, GA with the longest processing time, and GA with the lowest processing time, by 7.14 percent, 4.7 percent, 3.5 percent, and 2.3 percent, respectively. It is observed that the proposed ABC possesses qualities like high flexibility, great robustness, and quick convergence leading to good performance.

We investigated attention allocation in a dual-task paradigm using behavioral and pupillary measures. We used an auditory digit span (DS) and a simple visual response time (RT) task. Participants were administered four conditions in which they performed neither task (no-task), a single task (DS or RT only), or both tasks (dual). Dependent variables were DS accuracy, RT, and task-evoked pupillary responses (TEPRs) to digits as estimates of mental effort. Participants maintained almost the same level of DS accuracy on dual as on DS only and sacrificed speed on the RT task. As expected, TEPRs increased linearly with memory load in both DS only and dual. Although TEPRs were initially higher in dual than in DS only, the slope of the increase was shallower in dual. Results suggest that TEPRs can elucidate mechanisms of attention allocation by distinguishing between effectiveness (level of behavioral performance) and efficiency (the costs of that performance in mental effort).

Online dispatching and parallel processing algorithms for saving money in systems with heterogeneous, single-buffered, speed-scalable processors

Task scheduling is an essential component of any distributed system because it routes tasks to appropriate resources for execution, such as grids, clouds, and peer-to-peer networks. Common scheduling algorithms include downsides, such as high temporal complexity, non-simultaneous processing of input tasks, and longer program execution times. Exploration-based scheduling algorithms prioritize tasks using a variety of methods, resulting in long execution times on heterogeneous distributed computing systems. As a result, task prioritization becomes a bottleneck in such systems. It is appropriate to prioritize tasks with the shortest execution time using faster algorithms. The genetic algorithm (GA) is one of the evolutionary approaches used to solve complex problems quickly. This paper proposes a parallel GA with a MapReduce architecture for scheduling jobs on cloud computing with various priority queues. The fundamental aim of this study is to employ a MapReduce architecture to minimize the total execution time of the task scheduling process in the cloud computing environment. The proposed method accomplishes task scheduling in two stages: first, the GA was used in conjunction with heuristic techniques to assign tasks to processors, and then the GA was used in conjunction with the MapReduce framework to assign jobs to processors. In our experiments, we consider heterogeneous resources that differ in their ability to execute various tasks, as well as running a job on different resources with varying execution durations. The results show that the proposed method outperforms other algorithms such as particle swarm optimization, whale optimization algorithm, moth-flame optimization, and intelligent water drops.

Objectives: To enlighten the issues and challenges of Load Balancing Algorithm (LBA) for cloud computing environment. In this study the authors proposed a LBA for cloud computing server. Methods/Statistical Analysis: The numerous LBAs are compared with objective, merit, demerit and challenges of the LBA in terms of parameters such as performance, response time, scalability, overhead and many more. This study facilitates identification of efficient LBA for optimizes resource use, minimum response time, maximum throughput and avoidance of overload. Findings: The load balancer is permitted in most instances to provide support continuity as well as dealing with extra traffic. Consequently the useful load balancing algorithms required to find inexpensive resource usage by provisioning of resources to cloud users. The use of proposed algorithm is to improve the overhead and load imbalance factor of the system. Application: The Load Balancing (LB) is a vital feature of cloud computing environment. The economical LB algorithm assures cost effective resource usage by provisioning of resources to cloud users on demand schedule in pay-as-you-say-manner. The LB may actually provide assistance prioritizing users by using suitable scheduling requirements and also it is crucial part in cloud computing for maintaining the work load across various systems over the network and gives minimum processing time, minimum response time and ignores the workload.

Cloud computing is network consisting of configurable multifarious resources and predominant-level services which can be rapidly exerted with minimal management effort over the Internet. Cloud computing endows lot of services and consumers avail services from cloud agilely bestowed through internet. Some consumers without any knowledge about quality peculiarities, consumers may merely avail cloud service and feel unhappy, disenchant by using cloud service. As it is extremely imperative for consumers to recognize about cloud service quality before availing the service, this paper bestows about contemplation of quality for the services existing in cloud. This paper induces Yajat method for cogitation about cloud services quality to forthcoming patrons. In Yajat method quality ranking fixin based on different quality peculiarities is calculated for every service in cloud. Security quandary will occur while congregating quality assessment testimony by deeming multifarious quality peculiarities from consumers for Yajat method. To solve security quandary coadunate cryptography and Coalesce cryptography method is induced in this paper. Firstly by exerting Coadunate and coalesce cryptography, quality assessment testimony about cloud is collected securely and bestowed to Yajat method as input. Secondly by availing proctors in Yajat method, quality ranking fixin are calculated and bestowed to forthcoming consumers. The output of yajat method is bestowed to forthcoming consumers by proctors of cloud. Thus this paper intends forthcoming consumers by scrutinizing the output of yajat method, effortlessly choose to avail cloud service with the quality they aspire and feel elated, blissful by using cloud service.

This paper presents a significant extension of the CSMA network access protocol. The protocol is based on the facts that, at high data rates, networks can contain multiple messages simultaneously over their span, and that in a ring, nodes needs only to detect the presence of a message arriving from the immediate up stream neighbor. When an incoming signal is detected, the node truncates the message it is presently sending instead of aborting it. The system has been named Carrier Sensed Multiple Access Ring Network, CSMA/RN.Analytical studies using three performance factors, wait or access time, service time and response or end-to-end travel time are presented. The service time is basically a function of the network rate; it changes by a factor of 4 between no load and full load. Wait time, which is zero for no load, remains small for load factors up to 70% of full load. Response time, which adds travel time while on the network to wait and service time, is mainly a function of network length, especially for longer distance networks.Simulation results are shown for CSMA/RN where messages are removed at the destination. Destination removal on an average increases network load capacity by a factor of 2, i.e., a 1 Gbps network can handle a 2 Gbps load. A wide range of local and metropolitan area network parameters including variations in message size, network length and node count are studied. In all cases performance is excellent, and message fracture usually remains less than a factor of 4. Throughput, even at overload conditions, remains high for the protocol. The nominal network rate is 1 Gbps; however, performance remains good for data rates as low as 200 Mbps. Finally, a scaling factor based upon the ratio of message to network length demonstrates that the results of this paper, and hence, the CSMA/RN protocol, are applicable to wide area networks.

The purpose of this study is to develop predictive and optimization models to reduce patients' waiting time for diagnostic tests and medical treatments. Long wait times for receiving medical care is a pressing issue in the Canadian healthcare system. These wait times are spread over different phases of treatment, such as diagnostic tests, physicians' treatments, and appointments with specialists and surgeons. An integrated system to monitor all the phases of treatment along with efficient workload distribution in each phase can reduce patients' waiting time at each of these phases. This study investigates the impact of efficient resource allocation and workload distribution in the medical system. This research also explores the effect of optimized resource allocation on the patients' waiting time in Medicare settings. Resource allocation planning is directly related to the number of patient-arrival, and it is hard to predict such uncertain parameters in the future time frame. The number of patient-arrival also varies across different medical departments and different timeframes which makes the patient-arrival prediction challenging. The goal of this study is to investigate the forecasting effect on patients' waiting time and physicians' workload. To achieve this goal, advanced machine learning technique is integrated with the optimization model. The machine learning technique is used to predict the uncertain parameters of the optimization model for a shorter time span. To predict time-dependent uncertain parameters, such as patient arrival is a major issue, as the prediction may suffer from the concept drift problem. Besides, real-time data are commonly prone to errors due to irregular fluctuations, seasonal biases, and missing values in the data. On the other hand, predicting for shorter intervals requires lower execution time combined with higher accuracy. The developed predictive ensemble model in this research has addressed these issues legitimately with four research contributions. In the first contribution (Chapter 2), we have investigated methodologies for predicting Radiologists’ workload in a short time interval by adopting a machine learning technique. An ensemble model is proposed with the fixed batch training method in this part. To excel in the execution time, a fixed batch training method is used. Secondly, in Chapter 3, an Adaptive Batched-Ranked Ensemble (ABRE) model that reduces the effect of fluctuation using the time-variant windowing technique. Besides, a data aggregation technique is developed and integrated with the offline training phase of the proposed model to tackle the concept drift problem. In the third contribution (Chapter 4), a novel Ensemble of Pruned Regressor Chain (EPRC) method is developed and trained offline to predict uncertain parameters, such as patients’ arrival. Finally, the fourth contribution in Chapter 4, the EPRC method is integrated with a novel multi-objective optimization model to reduce patients’ waiting time, and to determine workload allocation for future timespan. This research enables enhanced decision-making with effective resource allocation and workload scheduling, as well as assists in reducing healthcare expenditure.

A novel segmented random search based batch scheduling algorithm in fog computing

SummaryCloud computing has the advantage of providing flexibility, high‐performance, pay‐as‐you‐use, and on‐demand service. One of the important research issues in cloud computing is task scheduling. The purpose of scheduling is to assign tasks to available resources while providing optimization on some objectives. Tasks have diversified characteristics, and resources are heterogeneous. These properties make task scheduling an NP‐complete problem. In this study, metaheuristic and hybrid metaheuristic algorithms are developed for task scheduling problems in cloud computing environments. We have developed genetic algorithm (GA), differential evolution (DE), and simulated annealing (SA) based metaheuristic algorithms, which are also combined with a greedy approach (GR). In addition to this, we have developed hybrid metaheuristics algorithms, called DE‐SA and GA‐SA, which are also combined with a greedy approach. The proposed approaches are evaluated in terms of completion time and load balancing of virtual machines. In terms of average completion time, as the number of tasks increases, it has been observed that the DESA algorithm outperforms the solely used DE and SA algorithms. In addition, experiments show that hybrid algorithms improve both the average completion time and the average standard deviation of virtual machine loads for some task groups.