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The Internet of Things (IoT) is constantly evolving. The variety of IoT applications has caused new demands to emerge on users' part and competition between computing service providers. On the one hand, an IoT application may exhibit several important criteria, such as deadline and runtime simultaneously, and it is confronted with resource limitations and high energy consumption on the other hand. This has turned to adopting a computing environment and scheduling as a fundamental challenge. To resolve the issue, IoT applications are considered in this paper as a workflow composed of a series of interdependent tasks. The tasks in the same workflow (at the same level) are subject to priorities and deadlines for execution, making the problem far more complex and closer to the real world. In this paper, a hybrid Particle Swarm Optimization and Simulated Annealing algorithm (PSO–SA) is used for prioritizing tasks and improving fitness function. Our proposed method managed the task allocation and optimized energy consumption and makespan at the fog-cloud environment nodes. The simulation results indicated that the PSO–SA enhanced energy and makespan by 5% and 9% respectively on average compared with the baseline algorithm (IKH-EFT). [ABSTRACT FROM AUTHOR]

Workflow applications are a popular tool used by scientists to model and run applications on cloud computing. With the rise of multi-core parallel systems and cloud computing high-performance computing is becoming more accessible to scientists, but it also presents new challenges. One pressing issue is the energy consumption of these systems, both for environmental and financial reasons. To minimize energy usage in cloud computing, methods such as energy-efficient scheduling are gaining attention. However, current scheduling solutions have limitations, or failing to address the problem as a multi-objective optimization balancing performance and energy. This problem is considered complex and NP-complete. Many researchers have attempted to resolve it using heuristic and meta-heuristic methods. Although these methods may not always provide optimal results, they are still a subject of active research. This paper proposes a new multi-objective optimization algorithm that combines the Heterogeneous Earliest Finish Time and BAT algorithm to optimize multiple conflicting objectives for energy, cost, makespan, and resource utilization. The results are verified using the Analysis of Variance statistical tool, and the proposed algorithm is shown to be superior to existing contemporary algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid approach based Binary Artificial Bee Colony (BABC) and Pareto Dominance strategy for scheduling workflow applications considering different Quality of Services (QoS) requirements in cloud computing. The main purpose is to schedule a given application onto the available machines in the cloud environment with minimum makespan (i.e. schedule length) and processing cost while maximizing resource utilization without violating Service Level Agreement (SLA) among users and cloud providers. The proposed approach is called Enhanced Binary Artificial Bee Colony based Pareto Front (EBABC-PF). Our proposed approach starts by listing the tasks according to priority defined by Heterogeneous Earliest Finish Time (HEFT) algorithm, then gets an initial solution by applying Greedy Randomized Adaptive Search Procedure (GRASP) and finally schedules tasks onto machines by applying Enhanced Binary Artificial Bee Colony (BABC). Further, several modifications are considered with BABC to improve the local searching process by applying circular shift operator then mutation operator on the food sources of the population considering the improvement rate. The proposed approach is simulated and implemented in the WorkflowSim which extends the existing CloudSim tool. The performance of the proposed approach is compared with Heterogeneous Earliest Finish Time (HEFT) algorithm, Deadline Heterogeneous Earliest Finish Time (DHEFT), Non-dominated Sort Genetic Algorithm (NSGA-II) and standard Binary Artificial Bee Colony (BABC) algorithm using different sizes of tasks and various benchmark workflows. The results clearly demonstrate the efficiency of the proposed approach in terms of makespan, processing cost and resources utilization. [ABSTRACT FROM AUTHOR]

For many years, job scheduling in cloud computing has been researched to improve and optimize the environment. Although many researchers have worked on the issue of job scheduling, however, a comprehensive approach still misses out on various fronts like consideration of multi objective functions, handling the problem of local minima, and best resource utilization. An attempt has been made in the paper to present a reliable and comprehensive scheduling approach based on the meta-heuristic for the cloud computing environment. The proposed algorithm imitates the behavior of Rock Hyrax optimization for scheduling the jobs in a dynamic and heterogeneous cloud environment by considering the quality of service parameters like makespan time and energy consumption of data centers. The result establishes the claim that the proposal presented in this paper can schedule jobs in a dynamic environment on the virtual machine by keeping energy consumption low. The proposal is implemented through an experimental setup in the CloudSim environment and considered for variable jobs. The proposed algorithm for scheduling in the cloud environment is evaluated both qualitatively and quantitatively by considering both jobs and virtual machines statically and dynamically. The proposed algorithm is also compared with the prevalent approaches proposed in the past and shows better results. Our results indicate that the proposed meta-heuristic algorithm based on Rock Hyrax has lowered the makespan time by 5–15% and reduces energy consumption by 4–12%. [ABSTRACT FROM AUTHOR]

In this paper, we proposed an effective approach for scheduling of multiprocessor unit time tasks with chain precedence on to large multiprocessor system. In this work, we considered splitable and non-splitable multiprocessor tasks, which is a new and interesting dimension to the generalized scheduling problem. The proposed longest chain maximum processor scheduling algorithm is proved to be optimal for uniform chains and monotone (non-increasing/non-decreasing) chains for both splitable and non-splitable multiprocessor unit time tasks chain. Scheduling arbitrary chains of non-splitable multiprocessor unit time tasks is proved to be NP-complete problem. But scheduling arbitrary chains of splitable multiprocessor unit time tasks is still an open problem to be proved whether it is NP-complete or can be solved in polynomial time. We have used three heuristics (a) maximum criticality first, (b) longest chain maximum criticality first and (c) longest chain maximum processor first for scheduling of arbitrary chains. We have also compared the performance of all three scheduling heuristics and found out that the proposed longest chain maximum processor first performs better in most of the cases. Also we have evaluated the performance of the mentioned heuristics by scheduling scientific work-flows on real multi-processor server platform and analyzed power and performance trade-off of the same scheduling policies. [ABSTRACT FROM AUTHOR]

An important function of many cyber-physical systems ( CPS) is to provide a close monitoring of the operation environment to be able to adapt to changing situations effectively. One of the commonly applied techniques for that is to invoke time-constrained periodic application transactions to check the status of the operation environment. The status of the environment is represented by the values of the physical entities in the operation environment which are maintained as real-time data objects in a real-time database. Unfortunately, meeting the deadlines of application transactions and maintaining the quality of real-time data objects are conflicting with each other, because they compete for the same computation resources. To address this problem of update and application transactions co-scheduling problem, in this paper, we propose a fixed priority co-scheduling algorithm called periodic co-scheduling ( PCS). PCS uses periodic update transactions to maintain the temporal validity of real-time data objects. It judiciously decides the priority orders among all the update and application transactions so that the constructed schedule can satisfy the deadline constraints of all the application transactions and at the same time maximize the qualities of the real-time data objects to ensure the correct execution of application transactions. The effectiveness of the algorithm is validated through extensive simulation experiments. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of on-line scheduling a job sequence on two uniform machines. A job can be either rejected, in which case we pay its penalty, or scheduled on machines, in which case it contributes its processing time to the makspan of the constructed schedule. The objective is to minimize the sum of the makespan of the schedule for all accepted jobs and the penalties of all rejected jobs. Both preemptive and non-preemptive versions are considered. For the preemptive version, we present an optimal on-line algorithm with a competitive ratio [InlineMediaObject not available: see fulltext.] for any s≥1, where s is the machine speed ratio. For the non-preemptive version, we present an improved lower bound. Moreover, as an optimal algorithm for s≥1.6180 is known, we present a modified version of the known algorithm, and show that it becomes optimal for any 1.3852≤ s<1.6180 and has a smaller competitive ratio than that of original version for any 1≤ s<1.3852. The maximum gap between its competitive ratio and the lower bound is 0.0534. [ABSTRACT FROM AUTHOR]

This paper considers two semi-online versions of scheduling problem P2||Cmax where one type of partial information is available and one type of additional algorithmic extension is allowed simultaneously. For the semi-online version where a buffer of length 1 is available and the total size of all jobs is known in advance, we present an optimal algorithm with competitive ratio 5/4. We also show that it does not help that the buffer length is greater than 1. For the semi-online version where two parallel processors are available and the total size of all jobs is known in advance, we present an optimal algorithm with competitive ratio 6/5. [ABSTRACT FROM AUTHOR]

In this paper we investigate the following scheduling problem: We have two sets of identical machines, the jobs have two processing times one for each set of machines. We consider two different objective functions, in the first model the goal is to minimize the maximum of the makespans on the sets, in the second model we minimize the sum of the makespans. We consider the online, semi online and offline versions of these problems. [ABSTRACT FROM AUTHOR]