Utility-Maximizing Task Scheduling for Partially Observable Multiagent Systems

Recently, cloud computing have been witnessing high deployment rate of large scale scientific and business applications, this is due to the on-demand provisioning of shared pool of computational resources like networks, storage, and servers, it offers. Each of these applications is made up of various tasks whose execution determine the overall performance of the application. Task scheduling problem on cloud is an NP-hard problem, and thus task scheduling constitute one of the crucial aspects of resources management system in cloud computing, which ensures the attainment of the general user Quality of Service (QoS) performance in terms of response time, total execution time(makespan), throughput among others. In addition, appropriate task scheduling is effective in reducing the operational cost of cloud service providers in terms of energy consumption and resource utilization. This paper focuses on task scheduling problem using a novel Chaotic Symbiotic Organisms Search (CSOS) algorithm to minimize makespan and cost. The main idea is to prevent the premature convergence of SOS at early stages of optimization process by implementing a chaotic map, which enlarges the search space and provides diversity. The performance of the proposed CSOS algorithm is evaluated by extensive simulation using CloudSim toolkit simulation framework and compared with SOS and PSO. Simulation results reveal significant improvement in performance by the proposed CSOS in reducing cost and makespan, in task scheduling.

Cloud computing provides computing resources like software and hardware as a service by the network for several users. Task scheduling is one of the main problems to attain cost-effective execution. The main purpose of task scheduling is to allocate tasks to resources so that it can optimize one or more criteria. Since the problem of task scheduling is one of the Nondeterministic Polynomial-time (NP)-hard problems, meta-heuristic algorithms have been widely employed for solving task scheduling problems. One of the new bio-inspired meta-algorithms is Seagull Optimization Algorithm (SOA). In this paper, we proposed an energy-aware and cost-efficient SOA-based Task Scheduling (SOATS) algorithm. The aims of proposed algorithm to make a trade-off between five objectives (i.e., energy consumption, makespan, cost, waiting time, and load balancing) using a fewer number of iterations. The experiment results by comparing with several meta-heuristic algorithms (i.e., Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Whale Optimization Algorithm (WOA)) prove that the proposed technique performs better in solving task scheduling problem. Moreover, we compared the proposed algorithm with well-known scheduling methods: Cost-based Job Scheduling (CJS), Moth Search Algorithm based Differential Evolution (MSDE), and Fuzzy-GA (FUGE). In the heavily loaded environment, the SOATS algorithm improved energy consumption and cost saving by 10 and 25%, respectively.

Task scheduling is one of the very crucial facets of cloud computing. The task scheduling method must assign jobs to virtual machines. In cloud computing, task scheduling includes a frontal influence on a system's resource utilization and operational costs. Diverse meta-heuristic algorithms, in addition to their modifications, have been developed to improve the efficiency of task executions in the cloud. In this paper, a multiobjective optimization model is applied using the metaheuristics cuckoo search optimization algorithm (MCSO) to enhance the performance of a cloud system with limited computing resources while minimizing the time and cost. Finally, we analyze the performance of the proposed MCSO with the existing methods, such as Bee Life Algorithm (BLA), A Time–Cost aware Scheduling (TCaS) algorithm, Modified Particle Swarm Optimization (MPSO), and Round Robin (RR), for the evaluation metrics makespan and cost. Based on the outcomes of the experiments, it can be inferred that the proposed MCSO provides essential schedule jobs with the shortest makespan and average cost. Index Terms— Task Scheduling, Multiobjective, Cloud Computing, Cost, Makespan.

Because the task scheduling problem is np-complete problem, it is hard to find a deterministic algorithm to solve the problem of task scheduling in the cloud computing platform. Therefore this paper presents a task scheduling mechanism based on simulated annealing algorithm. The algorithm is a modern heuristic algorithm and overcome the shortcoming of the local optimum search method. The algorithm uses a greedy algorithm to generate the initial value, and heat to a sufficiently high temperature and according to certain rules to generate a new value. If the new value is better than the original value or at a certain probability can be accepted, then replace the original value with the new value until cool. Experiments proved the feasibility and effectiveness of the algorithm. Compared with traditional algorithms, this task scheduling mechanism not only meets the needs of users and improves the performance of the system.

The number of smartphone users and mobile devices has increased significantly. The Mobile Cloud Applications based on cloud computing have also been increased. The mobile apps can be used in Augmented Reality, E-Transportation, 2D/3-D Games, E-Healthcare, and Education. The modern cloud-based frameworks provide such services on Virtual Machines. The existing frameworks worked well, but these suffered the problems such as overhead, resource utilization, lengthy boot-time, and cost of running Mobile Applications. This study addresses these problems by proposing a Dynamic Decision-Based Task Scheduling Technique for Microservice-based Mobile Cloud Computing Applications (MSCMCC). The MSCMCC runs delay-sensitive applications and mobility with less cost than existing approaches. The study focused on Task Scheduling problems on heterogeneous Mobile Cloud servers. We further propose Task Scheduling and Microservices based Computational Offloading (TSMCO) framework to solve the Task Scheduling in steps, such as Resource Matching, Task Sequencing, and Task Scheduling. Furthermore, the experimental results elaborate that the proposed MSCMCC and TSMCO enhance the Mobile Server Utilization. The proposed system effectively minimizes the cost of healthcare applications by 25%, augmented reality by 23%, E-Transport tasks by 21%, and 3-D games tasks by 19%, the average boot-time of microservices applications by 17%, resource utilization by 36%, and tasks arrival time by 16%.

In cloud computing, optimizing task scheduling is crucial for improving overall system performance and resource utilization. To minimize cloud service costs and prevent resource wastage, advanced techniques must be employed to efficiently allocate cloud resources for executing tasks. This research presents a novel multi-objective task scheduling method, BSSA, which combines the Backtracking Search Optimization Algorithm (BSA) and the Sparrow Search Algorithm (SSA). BSA enhances SSA’s convergence accuracy and global optimization ability in later iterations, improving task scheduling results. The proposed BSSA is evaluated and compared against traditional SSA and other algorithms using a set of 8 benchmark test functions. Moreover, BSSA is tested for task scheduling in cloud environments and compared with various metaheuristic scheduling algorithms. Experimental results demonstrate the superiority of the proposed BSSA, validating its effectiveness and efficiency in cloud task scheduling.

Greedy method, as an efficient computing tool, can be applied to various combinatorial or nonlinear optimization problems where finding the global optimum is difficult, if not computationally infeasible. A greedy algorithm has the nature of making the locally optimal choice at each stage and then solving the subproblems that arise later. It iteratively makes one greedy choice after another, reducing each given problem into a smaller one. In other words, a greedy algorithm never reconsiders its choices. Clearly, greedy method often fails to find the globally optimal solution. However, a greedy algorithm can be proven to yield the global optimum for a given class of problems such as Kruskal's algorithm and Prim's algorithm for finding minimum spanning tree, Dijkstra's algorithm for finding single-source shortest path, and the algorithm for finding optimum Huffman tree [5]. Even for some optimization problems proven to be NP hard, a greedy algorithm may generate near optimal solution with high probability if one exploits the problem structure properly. In this chapter, we focus on the optimization problems arising from plume detection, localization and tracking and provide convincing argument on the usefulness of greedy algorithms. Detection, identification, localization, tracking and prediction of chemical, biological or nuclear propagation is crucial to battlefield surveillance and homeland security. In addition, post-accident management for public protection relies critically on detecting and tracing dangerous gas leakages promptly. The determination of source origins and release rates is useful for the forecast of gas concentration in the atmosphere and for the management staff to prioritize off-site evacuation plans. A lot of research has been focused on detecting and localizing single or multiple plume sources with autonomous vehicles [11] or sensor networks such as [22] for a vapor-emitting source, [2] for a nuclear source, and [14, 15] for a chemical source. In [12] the plume detection and localization problem is formulated as abrupt change detection using sparse sensor measurements. The development of a large scale testbed has been reported in [8] for plume detection, identification and tracking. In [3] dense sensor coverage has been used for radioactive source detection while [26] showed that using three error-free intensity sensors, one can identify the plume origin to any desired accuracy with high probability. Although this approach offers an effective solution with linear complexity of the hypothesis space, a major limitation is that the continuous time dynamic model of plume propagation has to be in the product form. O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

In this paper, the adaptive decentralised control problem of the multi-agent system is considered. Each agent is described by a discrete-time nonlinear inverse model with the unknown parameter. The output of each agent has an influence from the history outputs of its neighbour agents. To deal with uncertainty, a projection-type parameter estimation algorithm is adopted to identify parameter. According to the certainty equivalence principle, the local control law of each agent is designed only using the history outputs of its own and neighbour agents. Under the parameter update law and the local dual control, it is shown that the output of each agent tracks the average value of history outputs from its neighbour agents, and the closed-loop system eventually achieves strong synchronisation. Finally, two simulations for an example are provided to illustrated the validity of the results.

In this work, we are interested in the problem of task scheduling on large‐scale data‐intensive computing systems. In order to achieve good performance, one must construct not only good task schedules but also good data allocation across nodes on the system, since before a task can be executed, it must have access to data distributed on the system. In this article, we present a general formulation of a static problem that combines both scheduling and replication problems in data‐intensive distributed systems. We show that this problem does not admit an approximation algorithm. However, considering a restricted version of the problem that considers some practical constraints, an approximation algorithm can be designed. From a practical perspective, we introduce a novel heuristic for the problem that is based on nodes clustering. We compare the heuristic with two adapted approaches from other works in the literature by computational simulations using an extensive set of instances based on real computer grids. We show that our heuristic often obtains the best solutions and also runs faster than other approaches.

In a mobile crowd sensing system, a smartphone undertakes many different sensing tasks that demand data from various sensors. In this paper, we consider the problem of scheduling different sensing tasks assigned to a smartphone with the objective of minimizing sensing energy consumption while ensuring Quality of SenSing (QoSS). First, we consider a simple case in which each sensing task only requests data from a single sensor. We formally define the corresponding problem as the Minimum Energy Single-sensor task Scheduling (MESS) problem and present a polynomial-time optimal algorithm to solve it. Furthermore, we address a more general case in which some sensing tasks request multiple sensors to report their measurements simultaneously. We present an Integer Linear Programming (ILP) formulation as well as an effective polynomial-time heuristic algorithm, for the corresponding Minimum Energy Multi-sensor task Scheduling (MEMS) problem. Extensive simulation results show that the proposed algorithms achieve over 79% energy savings on average compared to a widely-used baseline approach, and moreover, the proposed heuristic algorithm produces close-to-optimal solutions.

Most of the existing solutions on task scheduling and resource management in grid computing are based on the traditional client/server model, enforcing a homogeneous policy on making decisions and limiting the flexibility, unpredictable reliability and scalability of the system. Thus, we need a well organised system architecture to provide high system availability with resource scheduling scheme for grid environment. In this paper, we integrate grid with P2P on to the hypercube topology with additional neighbourhood links for task scheduling and load balancing, which gives better makespan and balances the load. We propose the SPA-based task scheduling methodology in order to enhance system performance in hypercubic P2P grid (HPGRID). The simulation results show that the SPA-based min-mean scheduling minimises the makespan along with load balancing and guarantees the high system availability in system performance. At last, the min-mean algorithm is compared with min-min and max-min algorithm using SPA, by simulate experimentation, and it shows that the new algorithm has a better quality of system load balancing and the utilisation of system resources.

This paper proposes to solve the task scheduling problem in cloud computing by using a load balance aware genetic algorithm LAGA with Min-min and Max-min methods. Task scheduling problems are of great importance in cloud computing, and become especially challenging when taking load balance into account. Our proposed LAGA algorithm has several advantages when solving this kind of problems. Firstly, by introducing the time load balance TLB model to help establish the fitness function with makespan, the algorithm benefits from the ability to find the solution that performs best on load balance among a set of solutions with the same makespan. More importantly, the interaction between makespan and TLB helps the algorithm to minimize makespan in the same time. Secondly, Min-min and Max-min methods are used to produce promising individuals at the beginning of evolution, leading to noticeable improvement of evolution efficiency. We evaluated LAGA on several task scheduling problems and compared with a Min-min, Max-min improved version of genetic algorithm MMGA, which does not use the TLB strategy. The results show that LAGA can obtain very competitive results with good load balancing properties, and outperform MMGA in both makespan and TLB objectives.

Symbiotic Organism Search optimization based task scheduling in cloud computing environment

Cloud computing suffers from various challenging issues in Internet of Things (IoT) networks like real-time response, energy-efficient execution, and cost of computation. Fog is an emerging distributed computing paradigm which is useful for delay-sensitive tasks in IoT network. An offloading strategy decides where to offload the task and a task scheduling strategy chooses an appropriate fog node based on the requirements of the task while meeting the quality of services (QoS) criteria. Although the computational offloading and task scheduling problem has been widely studied, there is very limited research on delay-energy tradeoff. We propose a fog network that follows an M/M/c queue for computational offloading and a differential scale-based Best Worst Method (BWM) for computation of optimal weights in multi-objective task scheduling. The optimization problem minimizes the execution delay while meeting QoS criteria. The numerical experiments show the efficacy for the different QoS criteria.

This paper explores integrating biomechanics data and bio-inspired models to enhance efficiency in business administration, focusing on task scheduling, resource allocation, and workflow optimization. Biomechanics, traditionally applied in fields such as healthcare and sports, is used to analyze human movement and physical strain in business processes, particularly in physically demanding environments like manufacturing and logistics. Bio-inspired models, such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), are applied to solve complex optimization problems in resource management and task scheduling. The study presents three case studies to demonstrate the practical application of these methodologies: (1) workflow optimization in a manufacturing environment using biomechanics data to reduce physical strain and improve task completion times; (2) resource allocation in Supply Chain Management (SCM) using PSO to minimize transportation and labor costs while improving warehouse utilization and delivery times; and (3) task scheduling in an office environment using GA to enhance task efficiency, workload distribution, and employee satisfaction. The case study results demonstrate the practical application of these methodologies: (1) a 21.6% reduction in shoulder joint strain and an 18.2% improvement in task completion time in a manufacturing setting; (2) a 16.1% reduction in transportation costs and an 18.6% improvement in warehouse utilization in SCM using PSO; and (3) a 17.6% decrease in makespan and a 29.8% improvement in workload distribution through GA-based task scheduling in an office environment. These findings underscore the potential of combining human-centered biomechanics data with bio-inspired optimization models to improve operational efficiency, employee well-being, and cost-effectiveness significantly.