With the rapid expansion of cloud services, the demand for processing large-scale workflows on cloud platforms has grown significantly. Inefficient resource allocation not only leads to excessive energy consumption in cloud data centers but also contributes to higher carbon dioxide emissions and power shortages in smart cities. Traditional workflow scheduling methods often leave idle time slots on assigned resources, even though these slots could be utilized for additional task execution, improving resource utilization. To address these challenges, we propose energy-aware workflow scheduling algorithms based on DVFS strategies, designed to operate within deadline constraints. The proposed method leverages idle time slots on assigned resources to schedule additional tasks, while keeping energy consumption in control by selecting appropriate resources with reasonable energy demands. We conducted extensive experiments using real scientific workflows, and the simulation results show that our proposed system outperforms state-of-the-art approaches in both energy optimization and resource utilization.

Cloud computing provides a scalable and flexible platform for executing scientific workflows. Workflow ensembles, which consist of workflows with similar structures but varying input data and sizes, require efficient scheduling to optimize resource utilization, reduce costs, and improve workflow completion rates. Traditional scheduling methods often leave idle time slots between tasks, leading to resource wastage and decreased system efficiency. This paper presents Workflow Ensemble Scheduling with Gap analysis (WESG), a novel approach designed to overcome these challenges while adhering to budget and deadline constraints. WESG employs a five-step algorithm to analyze and reduce resource idleness, optimizing task allocation and resource provisioning. By effectively utilizing these gaps, WESG enhances resource efficiency, prioritizes high-priority workflows, and increases the overall number of completed workflows. Experimental results show that WESG outperforms traditional scheduling techniques, achieving better resource utilization, reducing idle gaps, and significantly improving workflow completion rates.

The rapid growth in cloud services has led to the accessibility of Workflow-as-a-Service (WaaS) as the compatible platform for executing multiple workflows. Users desire to execute independent workflows with separate defined constraints in a shorter time and in a cost-efficient manner, while cloud service providers want to increase resource utilization during scheduling phases. Controlling gap spaces can help to achieve these aims, as gaps occur as idle time slots between prior scheduled tasks and the current task on paid resources, creating opportunities for executing more tasks. In this paper, we propose a WaaS broker with a gap analysis perspective as a third-party entity to mediate between users and service providers. This broker utilizes a hybrid scheduling method with static planning and dynamic scheduling for task assignments on reserved and on-demand resources under specified deadlines. It aims to reduce the execution cost by maximizing the utilization of reserved instances, which have lower costs. The static planner creates a cost-efficient primary scheduled map with minimum gap spaces, using a defined gap threshold parameter to control task assignment and estimate the number of on-demand resources required. The dynamic scheduler then uses the static scheduled map and considers uncertainties in task execution to perform real-time resource allocation and acquire new on-demand instances as needed. To evaluate the performance, we simulate numerous scenarios of scientific multi-workflows with different deadline constraints and workflow structures. The results show that the proposed broker can execute the maximum number of multi-workflows at minimum cost compared to state-of-the-art approaches, while maintaining high resource utilization.

The manufacturing sector is recognized as the largest energy consumer within the smart grid. Excessive energy usage in production lines poses significant challenges, such as increased peak demand, high energy costs, strained grid resources, and power outages. Implementing demand response programs can address these issues and provide reliable and stable power to customers. Additionally, the integration of renewable energy sources can notably reduce carbon emissions and support sustainability objectives. To enhance efficiency, scheduling and intelligent manufacturing techniques can shift the execution time of production processes to off-peak periods and adjust consumption patterns on the production line. In this study, we propose a method called Scheduling for Industrial Processes to modify Energy consumption behavior (SIPE) under specified deadlines. SIPE offers economic benefits through an energy storage system for industrial customers participating in demand response programs. Moreover, it modulates energy consumption based on a maximum negotiated energy cost, which is determined as the highest allowable energy consumption cost within a defined scheduling period between the power provider and industrial customers. The proposed approach coordinates processes based on their durations and defined constraints. To evaluate the effectiveness of this approach, we selected Additive Manufacturing, as it is one of the most energy-intensive industries and is used across various manufacturing fields. We conducted numerous experiments by varying production parameters in the manufacturing line and compared the results with state-of-the-art approaches. The performance evaluation results demonstrate a significant reduction in both energy costs and power demand specially during peak periods.

Nowadays, cloud computing with pay-per-use infrastructures provides an ideal environment for processing large-scale scientific workflows. Workflows that are interrelated and have specific tasks in scientific applications are referred to as workflows ensemble. Mapping scientific workflow tasks based on their priorities onto computing resources while adhering to deadline and budget constraints is one of the most challenging problems in cloud computing. In this paper, we present an intelligent and adaptive algorithm for scheduling workflow ensemble with optimized resource provision under given deadline and budget constraints. The proposed method makes decisions based on workflow priorities and attempts to execute the many high-priority workflows as possible. To observe budget, deadline, and resource utilization as Quality of Service (QoS) parameters, we introduce three approaches: FastestScheduling, SchedulingWithMinCost, and GapRate analysis. By using different strategies for the main problem, an optimal scheduling map is created. To evaluate the proposed algorithm, we conduct simulations with a set of scientific workflows ensemble and present the related results. The experimental outcomes demonstrate that resource utilization is increased by using gap analysis in the public cloud while executing the best possible number of workflows with high priority under deadline and budget constraints, in comparison to the state-of-the-art approach.

A workflow is an effective way for modeling complex applications and serves as a means for scientists and researchers to better understand the details of applications. Cloud computing enables the running of workflow applications on many types of computational resources which become available on-demand. As one of the most important aspects of cloud computing, workflow scheduling needs to be performed efficiently to optimize resources. Due to the existence of various resource types at different prices, workflow scheduling has evolved into an even more challenging problem on cloud computing. The present paper proposes a workflow scheduling algorithm in the cloud to minimize the execution cost of the deadline-constrained workflow. The proposed method, EDQWS, extends the current authors’ previous study (DQWS) and is a two-step scheduler based on divide and conquer. In the first step, the workflow is divided into sub-workflows by defining, scheduling, and removing a critical path from the workflow, similar to DQWS. The process continues until only chain-structured sub-workflows, called linear graphs, remain. In the second step which is linear graph scheduling, a new merging algorithm is proposed that combines the resulting linear graphs so as to reduce the number of used instances and minimize the overall execution cost. In addition, the current work introduces a scoring function to select the most efficient instances for scheduling the linear graphs. Experiments show that EDQWS outperforms its competitors, both in terms of minimizing the monetary costs of executing scheduled workflows and meeting user-defined deadlines. Furthermore, in more than 50% of the examined workflow samples, EDQWS succeeds in reducing the number of resource instances compared to the previously introduced DQWS method.

scheduling scientific workflows of communicating tasks is the process of mapping tasks of the workflow to the cloud resources and directly or indirectly determining when to execute each task. Different objectives such as minimizing the makespan or price to be paid to the cloud provider, and respecting privacy are considered by different schedulers. Heterogeneous Earliest Finish Time (HEFT) was first designed to schedule communicating dependent set of tasks represented as a directed acyclic graph, on heterogeneous distributed systems. Because of its good performance in reducing makespan of running workflows, it is now widely used on the Grid and especially on the cloud. Based on HEFT, in this study, a static scheduling algorithm is designed to schedule scientific workflows on the cloud with the objective of improving HEFT's performance with respect to the makespan criterion. With HEFT, when a task is ready to be scheduled, the resource that can complete it the earliest is selected. With the proposed method, when a task is ready for execution we assume that it is assigned to all leased resources to be executed. Then k more tasks from the priority queue of ready tasks are hypothetically scheduled with HEFT and whichever resource finishes the job the earliest is selected as the resource to run the task which we started with. The time complexity of the developed algorithm ant its impact on the reduction of makespan of a number of real workflows are reported.

In hybrid cloud model, organizations can keep their sensitive information and critical applications in the private cloud and move other data and applications to a public cloud, if necessary. To maintain data privacy in workflow applications, we present a budget constrained hybrid cloud scheduler (BCHCS) which is a static heuristic scheduling algorithm. It is able to make decisions about scheduling sensitive tasks on private cloud and uses public cloud's resources for non-sensitive tasks, such that the makespan is minimized, while the budget limitation imposed by the user is satisfied. Experimental results show that the proposed method guarantees the execution of sensitive tasks on private cloud while achieving at least 7 percent lower makespan and higher success rate in comparison to similar existing techniques.

Scientific workflows can be considered a useful modeling method to model different scientific applications. Service-oriented computing is an attractive platform for most users to execute these applications in a pay-as-you-go manner. Therefore, scheduling workflows on the cloud as the latest trend in service-oriented computing and meeting the required users' Quality of Service requirements is an important problem to be tackled. Furthermore, the scheduling algorithms must consider the available multicore processing resources on the commercial Infrastructure as a Service cloud. Hence, considering multicore resources in addition to Quality of Service constraints makes the workflow scheduling problem more challenging to be solved. In this research, a static workflow scheduling algorithm is proposed which considers the available multicore resources on the cloud and attempts to minimize the leasing costs of the processing resources while considering not violating a user-defined deadline. The proposed algorithm uses a clustering technique to divide the workflow into a number of clusters and attempts to combine the clusters in such a way to achieve the algorithms' main goals. A flexible and extendable scoring approach chooses the best combination available in each step. Extensive simulations reveal a great reduction in the leasing costs of the workflow execution while meeting the user-defined deadline.

Cloud computing is always facing privacy and security challenges and this makes the enterprises and the research organizations hesitate to utilize the Cloud technology. Therefore without appropriate solutions to maintain security and privacy of sensitive information in the cloud environment, the potential ability of cloud, which might change the computation pattern, can lead to a vast breakdown in the information technology business. In this paper, a new scheduling model is proposed for workflow applications, which uses the hybrid cloud architecture in order to maintain privacy of the users' sensitive and private tasks and information. The proposed method schedules the sensitive tasks on the private cloud, which is the property and under control of the organization. If a user defined deadline is required to be met, the scheduler is able to schedule other tasks of the workflow on pay-per-use resources of the public cloud. In addition, the scheduler tries to find the schedule map with minimum cost. Experimental results on both scientific and randomly generated workflows show higher performance in the cost and success rate than the comparing methods.

The development of cloud computing technology has been continuously growing since its invention and has attracted the attention of many researchers in the academia and the industry, particularly during the recent years. The majority of organizations, whether large corporate businesses or typical small companies, are moving towards employing this cutting edge technology. Using private cloud provides low cost and privacy for workflow applications execution. However, an organization???s requirements to high performance resources and high capacity storage devices lead them to utilize public clouds. Public cloud leases information technology services in the form of small units and in larger scale compared to private cloud, but this model is potentially exposed to the risk of data breach and is less secure in comparison to a pure private cloud environment. The combination of public and private clouds is known as hybrid cloud, where workflow tasks can be executed on resources residing on either public or private clouds. The objective of this paper is to present a scheduling algorithm for maintaining data privacy in workflow applications, such that the budget is minimized, while the makespan limitation imposed by the user is satisfied.

Detecting and tracking people in real-time in complicated and crowded scenes is a challenging problem. This paper presents a multi-cue methodology to detect and track pedestrians in real-time in the entrance gates using stationary CCD cameras. The proposed approach is the combination of two main algorithms, the detecting and tracking for solitude situations and an estimation process for overcrowded scenes. In the former method, the detection component includes finding local maximums in foreground mask of Gaussian Mixture and Omega-shaped objects in the edge map by trained PCA. And the tracking engine employs a Dynamic VCM with automated criteria based on the shape and size of detected human shaped entities. This new approach has several advantages. First, it uses a well-defined and robust feature space which includes polar and angular data. Furthermore due to its fast method to find human shaped objects in the scene, it's intrinsically suitable for real-time purposes. In addition, this approach verifies human formed objects based on PCA algorithm, which makes it robust in decreasing false positive cases. This novel approach has been implemented in a sacred place and the experimental results demonstrated the system's robustness under many difficult situations such as partial or full occlusions of pedestrians.

Detecting and tracking people in real time in complicated and crowded scenes is a challenging problem. This paper presents a multi-cue methodology to detect and track pedestrians in real-time in the entrance gates using stationary CCD cameras. In the proposed method, the detection component includes finding local maximums in foreground mask of Gaussian mixture and ??-shaped objects in the edge map by trained PCA. And the tracking engine employs a dynamic VCM with automated criteria based on the shape and size of detected human shaped entities. This new approach has several advantages. First, it uses a well-defined and robust feature space which includes polar and angular data. Furthermore due to its fast method to find human shaped objects in the scene, it's intrinsically suitable for real-time purposes. In addition, this approach verifies human formed objects based on PCA algorithm, which makes it robust in decreasing false positive cases. This novel approach has been implemented in a sacred place and the experimental results demonstrated the system's robustness under many difficult situations such as partial or full occlusions of pedestrians.

We experimentally demonstrate that tuning the decision threshold in an O-CDMA receiver to an optimal point based on the active-user-count improves receiver sensitivity by >3-dB, and demonstrate an active-user-count monitoring technique, allowing dynamic threshold adjustment.

We experimentally demonstrate that tuning the decision threshold in an O-CDMA receiver to an optimal point based on the active-user-count improves receiver sensitivity by > 3-dB, and demonstrate an active-user-count monitoring technique, allowing dynamic threshold adjustment. (c) 2005 Optical Society of America.

We experimentally demonstrate the existence of an optimal decision threshold in an optical code-division-multiple-access (O-CDMA) system and show its relation to the number of active users. Our results show that tuning the decision threshold in an O-CDMA receiver to an optimal point based on the number of active users improves receiver sensitivity by > 3 dB. We also demonstrate a monitoring scheme to estimate the number of active users in an O-CDMA system using harmonics of RF clock tones.