Association rules learning technique for knowledge mining about scheduling algorithm performance

As processors on-chip gain in numbers and complexity, task scheduling has become an important concern in system design, and the related research has produced substantial and diversified knowledge. As a result, the efficient taping and management of this knowledge has become a concern in itself. In particular, it can bring new ways to improve scheduling algorithms. This paper describes a new algorithm class based on association rules mining. It serves to both increase the knowledge about a particular scheduling algorithm and show how to improve its performance. Two examples show how this new methodology can be used to improve makespan and processor use globally by optimizing the scheduling method locally.

Motivated by the special characteristics of multimedia tasks, we consider non-preemptive scheduling of tasks where there exists no (or very limited) information concerning the tasks before they are released. We present impossibility results and analyze algorithms for non-preemptive scheduling in single processor and multiprocessor systems. In particular, one set of our results considers the competitive ratio of the scheduling algorithm when the length of the tasks is not greater than C/sub max/ (and not smaller than C/sub min/). We show that the performance of a scheduling algorithm is improved dramatically when the release time of the tasks is O(C/sub max/) prior to their deadline; achieving a competitive ratio that is close to one.

Task scheduling is a well-known NP-complete problem. It is one of the research areas in parallel computing. Task scheduling consists of an application program and a set of processors for allocating the various tasks. An application program is represented using a directed acyclic graph(DAG) and a set of processors that are homogenous and fully connected. In this paper, we study different task duplication-based (TDB) scheduling algorithms and their priority attributes and time complexity. Also, we will discuss different types of performance metrics in task scheduling algorithms. These performance metrics help to analyze different task scheduling algorithms.

Task scheduling on multiprocessor computers with dynamically variable voltage and speed is investigated as combinatorial optimization problems, namely, the problem of minimizing schedule length with energy consumption constraint and the problem of minimizing energy consumption with schedule length constraint. The first problem has applications in general multiprocessor computing systems where energy consumption is an important concern and in mobile computers where energy conservation is a main concern. The second problem has applications in real-time multiprocessing systems where timing constraint is a major requirement. These problems emphasize the tradeoff between power and performance and are defined such that the power-performance product is optimized by fixing one factor and minimizing the other. It is found that both problems are equivalent to the sum of powers problem and can be decomposed into two subproblems, namely, scheduling tasks and determining power supplies. Such decomposition makes design and analysis of heuristic algorithms tractable. We analyze the performance of list scheduling algorithms and equal-speed algorithms and prove that these algorithms are asymptotically optimal. Our extensive simulation data validate our analytical results and provide deeper insight into the performance of our heuristic algorithms.

In the field of cloud computing, efficient task scheduling plays a vital role. Cloud computing is an on-demand service where resources are shared virtually by the users to complete their tasks. The main concept of task scheduling is to execute the tasks in such an order that can enhance the performance with full resource utilization and minimum loss. Already a lot of scheduling algorithm exists targeting to various goals like reducing execution time, cost, makespan and increase resource utilization, load balancing, etc. In this work, we have mainly carried out the performance analysis and comparison among various well-known task scheduling algorithms. The algorithms are examined and implemented in a well-known cloud simulator (CloudSim) to observe their performances by varying different parameters. Finally, the performance metrics like makespan, waiting time, turnaround time, throughput, and load balancing are observed by plotting the results graphically. Along with the comparative analysis, this work also assists one to identify a better scheduling algorithm based on different scheduling constraints and criteria.

This paper proposed an efficient real time scheduling algorithm using global scheduling paradigm running in multicore environment known as Global Preemptive Utility Accrual Scheduling (GPUAS) algorithm. The existing TUF/UA multiprocessor scheduling algorithms known as Greedy-Global Utility Accrual (G-GUA) and Non Greedy-Global Utility Accrual (NG-GUA) algorithms is seen to overlook the efficiency on its task scheduling algorithm. These algorithms have adapted the task migration attribute considering the load balancing problem in multi core platform. The existing PUAS uniprocessor scheduling algorithm is mapped into the multicore scheduling environment that consists of the global scheduling schemes considering the migration attribute of the executed tasks. The main principal of global scheduling is that it allows the executed tasks to migrate from one processor to the other processors whenever a scheduling event occurs in the system. The proposed GPUAS algorithm inherits the characteristics of PUAS in uniprocessor where it can preempt the highest PUD task at any event that occurs in the system. In this research, the proposed GPUAS algorithm enhanced the existing NG-GUA and G-GUA algorithms. The developed simulator has derived the set of parameter, events and performance metrics according to a detailed analysis of the base model. The proposed GPUAS algorithm achieved the highest accrued utility for the entire load range. The proposed GPUAS algorithm is more efficient than the existing algorithms, producing the highest accrued utility ratio and less abortion ratio making it more suitable and efficient for real time application domain.

This paper proposes a novel class of wavelength scheduling algorithms in optical burst switching networks. The proposed wavelength scheduling algorithms process a batch of data bursts together instead of processing them one by one. When a control burst with a reservation request arrives to a batch scheduler, the scheduler waits for a small amount of time, called the acceptance delay, before deciding to accept or reject the reservation request. After the acceptance delay has passed, the scheduler processes all the reservation requests that have arrived during the acceptance delay, then it accepts the requests that will maximize the utilization of the wavelength channels. We describe an optimal batch scheduler that serves as an upper bound on the performance of batch scheduling algorithms. Furthermore, we introduce four novel heuristic batch scheduling algorithms. The bursts of the proposed algorithms is evaluated using a discrete-event simulation model. Simulation results suggest that batch schedulers could decrease the blocking probability by 25% compared the best previously known wavelength scheduling algorithm.

Previously proposed wavelength scheduling algorithms in optical burst switching networks process each reservation request individually and in a greedy manner. In this paper we propose a new family of wavelength scheduling algorithms that process a batch of reservation requests together instead of processing them one by one. When a control burst with a reservation request arrives to a free batch scheduler, the scheduler waits for a small amount of time, called the acceptance delay, before deciding to accept or reject the reservation request. After the acceptance delay has passed, the scheduler processes all the reservation requests that have arrived during the acceptance delay, then it accepts the requests that will maximize the utilization of the wavelength channels. We describe an optimal batch scheduler that serves as an upper bound on the performance of batch scheduling algorithms. Furthermore, we introduce two heuristic batch scheduling algorithms. The performance of the proposed algorithms is evaluated using a discrete-event simulation model. Simulation results suggest that batch schedulers could decrease the blocking probability by 25% compared to the best previously known wavelength scheduling algorithm.

Cloud Computing provides a Computing environment where different resources, infrastructures, development platforms and software are delivered as a service to customers virtually on pay per time basis. Low cost, scalability, reliability, utility-based computing are important aspects of cloud computing. Job scheduling is an essential and most important part in any cloud environment. With increasing number of users, Job scheduling becomes a strenuous task. Ordering the jobs by scheduler while maintaining the balance between quality of services (QoS), efficiency and fairness of jobs is quite challenging. Scheduling algorithms are implemented considering parameters such as throughput, resource utilization, latency, cost, priority, computational time, physical distances, performance, bandwidth, resource availability. Though there are different scheduling algorithms available in cloud computing, a very less comparative study has been done on performance of various scheduling algorithms with respect to above mentioned parameters. This paper aims at a comparative study of various types of job scheduling algorithms that provide efficient cloud services.

Bluetooth enables wireless communication via ad hoc networks. The basic topology (piconet) is a collection of slaves controlled by a master. A scatternet is a multihop network of piconets. We anticipate that most scatternets will be composed of only a few piconets. However, even in small scatternets, efficient data flow requires the design of inter-piconet scheduling algorithms. Thus, this article presents and evaluates a load adaptive scheduling algorithm tailored for small-scale scatternets. The main advantage of this algorithm is the use of the Bluetooth low-power hold mode, which allows greater flexibility than other low-power modes. A simulation model has been developed in order to evaluate the performance of the algorithm. We show that the results obtained by the model are very close to the analytic results. Then we evaluate the performance of various intra-piconet scheduling algorithms. Finally, we present simulation results regarding inter-piconet scheduling, and compare the proposed algorithm to algorithms using the sniff mode.

Priority in task scheduling and resource allocation for cloud computing has attracted significant attention from the research community. However, traditional scheduling algorithms often lack the ability to differentiate between tasks with varying levels of importance. This limitation presents a challenge when cloud servers must handle diverse tasks with distinct priority classes and strict quality of service requirements. To address these challenges in cloud computing environments, particularly within the infrastructure of service models, we propose a novel, self-adaptive, multiclass priority algorithm with VM clustering for resource allocation. This algorithm implements a four-tiered prioritization system to optimize key objectives, including makespan and energy consumption, while simultaneously optimizing resource utilization, degree of imbalance, and waiting time. Additionally, we propose a resource prioritization and load-balancing model based on the clustering technique. The proposed work was validated through multiple simulations using the CloudSim simulator, comparing its performance against well-known task scheduling algorithms. The simulation results and analysis demonstrate that the proposed algorithm effectively optimizes makespan and energy consumption. Specifically, our work achieved percentage improvements ranging from +0.97% to +26.80% in makespan and +3.68% to +49.49% in energy consumption while also improving other performance metrics, including throughput, resource utilization, and load balancing. This novel model demonstrably enhances task scheduling and resource allocation efficiency, particularly in complex scenarios with tight deadlines and multiclass priorities.

With the changes in the era of science and technology, the emergence of 5G technology makes a communication speed leap, and the delay is also getting shorter and shorter. At present, the computing power of computing equipment is also steadily improving. As one of its key technologies, mobile edge computing (MEC) is becoming more and more important. Here, we introduce MEC task scheduling and device collaboration algorithms based on deep learning. Aiming at the multi-task joint optimization scheduling strategy microtearing mode to minimize the total service cost of multiple tasks, we establish a theoretical optimization model of multi-task joint scheduling and use the model to design a multi-task cloud collaborative optimization scheduling algorithm based on Livepeer Token rules. We propose a deep-learning device collaboration algorithm based on alternating direction multiplier. The sub differential algorithm and the improved conjugate gradient method are designed to solve the sub problem respectively. This paper simulates the proposed deep-learning task scheduling algorithm and device collaboration algorithm. The simulation results show that, in the task scheduling algorithm, the proposed algorithm reduces the cost of completing the task, but in the total task time, this algorithm has a certain gap compared with other algorithms. In the equipment collaboration algorithm, with the increase of the n value, the algorithm converges in a certain iteration increment; in terms of algorithm convergence, the algorithm needs 142 iterations at most to achieve convergence, and the fastest speed only needs 17 iterations. In the experiment based on real data sets, the proposed algorithm is also better than the other two algorithms. From these results, we can see that the proposed algorithm has good convergence, stability, and scalability, and it has excellent performance in task scheduling and device collaboration of MEC.

Objectives: Current state-of-the-art task scheduling algorithms were mainly focused on deadline, load and energy factors in centralized cloud context. So, the proposed research objective focuses on dynamic and decentralized context. Methods/Statistical Analysis: Multi-objective task scheduling has become an important criterion for the dynamic and decentralized nature of cloud environment. Moreover, existing research works assumes that the resource load, energy and task execution time are known due its homogeneous nature. In order to improve the cloud consumer’s satisfaction, a novel Locality-Load-Prediction Aware Multi-objective Task Scheduling (LLPAMTS) algorithm is proposed to eventually distribute the tasks according to dynamic nature of cloud virtual machines. Findings: Proposed LLPAMTS algorithm will effectively schedule the tasks in an optimized manner by VM Scheduler component. This scheduling algorithm exploits the various monitoring parameters like locality, load and prediction parameters. It outperforms the existing deadline, load and energy aware scheduling algorithms in terms of task transfer time, task waiting time, task execution time, and task completion time. Applications/Improvements: The proposed LLPAMTS algorithm provides an average of 5 to 10% less task completion time compared to the existing deadline, load and energy aware scheduling algorithms. Keywords: Cloud Environment, Heterogeneous Cloud, Locality-Load-Prediction Aware Scheduling, Multi-Objective, Task Scheduling

To provide uninterrupted service to all subscribers in a wireless network, we need to incorporate a low cost, flexible Heterogeneous network which will be able to link with any kind of network for efficient spectrum utilization, hence improved system capacity.In this connection, Wi-Fi/ Wi MAX integrated network seems to be an ideal solution as it is able to provide easy deployment, high speed data rate and wide range coverage with high throughput, low end to end delay, flat and low jitter.Wi-Fi/ WiMAX integrated network provides Quality of Service (QoS) that can support all kinds of real-time application in wireless networks that includes priority scheduling and queuing for bandwidth allocation that is based on traffic scheduling algorithms within wireless networks.In this paper, we have designed a Wi-Fi/ WiMAX integrated network and analyze the performance of different scheduling algorithms for that integrated network and highlight our findings on the scheduling algorithm which will give the best performance for a heterogeneous network.

This study focuses on managing equity-conscious radio resources in OFDMA-based WIMAX networks with comparison of three algorithms, Max Rate, RR and PFS. Hence WiMAX Forum has been formed, which promotes the technology and provides compatibility and teroperability for 802.16-based products. The problem in heterogeneous WiMAX, users may possess different transmit power capabilities and experience independent channel realizations. As a result, those with lower power Capabilities or poor channel conditions will starve throughput share. It is observed that users with better channel conditions always dominate usage of the spectrum, causing low throughput for users with poor channel conditions. This paper studied the performance of downlink scheduling algorithms that perform radio resource allocation in order to improve the performance of the WiMAX system. These algorithms are Max Rate, Round Robin, and Proportional Fair. In addition, scheduling algorithm was implementing in order to explain the fairness concept. The algorithms were comparing to each other in terms of system throughput and fairness index. The performance evaluations are carrying out using MATLAB software. Simultaneous results show that the most suitable scheduling algorithm in terms of fairness index and system throughput for the WiMAX network is proportional fair. Meanwhile, Max Rate provides the highest throughput, even though the number of users is increased.