Efficient Scheduling Algorithm for Cloud

Hybrid ant genetic algorithm for efficient task scheduling in cloud data centers

Efficient and Effective scheduling methods can result in more desired services to the consumers and also can improve the performance of a cloud computing environment. The primary job of a task scheduler is to ensure reduction in execution time of the tasks and to fulfill maximum resource utilization. This paper proposes an Enhanced version of Round Robin algorithm (ERR) for improved performance without affecting the good features of traditional RR and which can bring out more efficiency. The proposed algorithm is implemented and tested using CloudSim toolkit and the initial results prove that the average waiting time for the tasks in a given number of cloudlets is reduced in MRR than the conventional RR in the same conditions. The proposed method also outperforms the other existing algorithms such as ACO, GA, MPA, Min-Min and PSO in terms of execution time and residue energy.

Most static scheduling algorithms that schedule parallel programs represented by directed acyclic graphs (DAGs) are sequential. This paper discusses the essential issues on parallelization of static scheduling algorithms. An efficient parallel scheduling algorithm, the HPMCP algorithm, is proposed. It produces high-quality scheduling and is much faster than existing algorithms.

High efficient task scheduling algorithms can greatly influence the performance of reconfigurable systems. This paper analyzed the disadvantages of some current on-line reconfigurable system task scheduling algorithms, and proposed a new scheduling algorithm based on placement cost. This scheduling algorithm considered three types of cost, including hardware task execution time in FPGA, occupied FPGA area and fragmentation situation of FPGA, and also considered the unified scheduling of hardware and software tasks. When a hardware task was in scheduling, if the placement cost exceeded the setting threshold value, the task would be rejected to run in FPGA, but its software implementation could run in CPU. By reasonably rejecting some high cost tasks, this algorithm could achieve a higher overall successful scheduling rate. The simulation results show that this scheduling algorithm can achieve a higher deadline-guarantee ratio than others.

Power conservation is crucial in designing wireless sensor networks (WSNs) with long network lifetime. An energy and delay efficient TDMA scheduling algorithm for a cluster based Wireless Sensor Network is proposed. The proposed energy efficient dynamic scheduling (EEDS) algorithm in this paper uses the information about node traffic load, the number of member nodes and its QoS (Quality of Service) requirement. Our EEDS algorithm could reduce energy consumption and transmission delay. Through mathematical analysis we evaluate the performance of our algorithm and compare to the related works.

Efficient scheduling algorithm is one of the key factors in determining the performance of distributed memory machines. This paper presents an enhanced Search and Du plication Based Scheduling (SDBS) algorithm which car schedule Directed Acyclic Graphs (DA Gs) with variable execution time tasks. Since the DAGs are based on data dependencies of the tasks, the control dependencies are clustered within each node. If the node encapsulates a loop or an if-then-else kind of statement, then assuming the execution times to be fixed would not be appropriate. It is assumed in this paper that the execution time varies between two time estimates. The complexity of this scheduling algorithm is in 0(V 4- E), where V is the number of node,- and E is the number of edges in the task graph. This algo rithm is based on some realistic assumptions and generates an optimal time schedule. If the assumptions cannot be completely satisfied then the algorithm provides a schedule which is close to optimal. The performance in these cases has been obtained using extensive simulation work which indicate the closeness of the results.

Efficient scheduling algorithms are key for attaining high performance in heterogeneous computing systems. In this article, we propose a new list scheduling algorithm for assigning task graphs to fully connected heterogeneous processors with an aim to minimize the scheduling length. The proposed algorithm, called Improved Predict Priority Task Scheduling (IPPTS) algorithm has two phases: task prioritization phase, which gives priority to tasks, and processor selection phase, which selects a processor for a task. The IPPTS algorithm has a quadratic time complexity as the related algorithms for the same goal, that is <inline-formula><tex-math notation="LaTeX">$O(t^{2} \times p)$</tex-math></inline-formula> , for <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula> tasks and <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> processors. Our algorithm reduces the scheduling length significantly by looking ahead in both task prioritization phase and processor selection phase. In this way, the algorithm is looking ahead to schedule a task and its heaviest successor task to the optimistic processor, i.e., the processor that minimizes their computation and communication costs. The experiments based on both randomly generated graphs and graphs of real-world applications show that the IPPTS algorithm significantly outperforms previous list scheduling algorithms in terms of makespan, speedup, makespan standard deviation, efficiency, and frequency of best results.

The rapid growth of cloud computing environment with many clients ranging from personal users to big corporate or business houses has become a challenge for cloud organizations to handle the massive volume of data and various resources in the cloud. Inefficient management of resources can degrade the performance of cloud computing. Therefore, resources must be evenly allocated to different stakeholders without compromising the organization’s profit as well as users’ satisfaction. A customer’s request cannot be withheld indefinitely just because the fundamental resources are not free on the board. In this paper, a combined resource allocation security with efficient task scheduling in cloud computing using a hybrid machine learning (RATS-HM) technique is proposed to overcome those problems. The proposed RATS-HM techniques are given as follows: First, an improved cat swarm optimization algorithm-based short scheduler for task scheduling (ICS-TS) minimizes the make-span time and maximizes throughput. Second, a group optimization-based deep neural network (GO-DNN) for efficient resource allocation using different design constraints includes bandwidth and resource load. Third, a lightweight authentication scheme, i.e., NSUPREME is proposed for data encryption to provide security to data storage. Finally, the proposed RATS-HM technique is simulated with a different simulation setup, and the results are compared with state-of-art techniques to prove the effectiveness. The results regarding resource utilization, energy consumption, response time, etc., show that the proposed technique is superior to the existing one.

In order to offload base station (BS) traffic and to enhance efficiency of spectrum, operators can activate many Device-to-Device (D2D) pairs or links in LTE networks. This increases the overall spectral efficiency because the same Resource Blocks (RBs) are used across cellular UEs (CUEs) (i.e., all UEs connected to BS for both C-Plane and D-plane communication) and D2D links (i.e., where the UEs are connected to BS only for C-plane communication). However, significant interference problems can be caused by D2D communications as the same RBs are being shared. In our work, we address this problem by proposing a novel scheduling algorithm, Efficient Scheduling and Power control Algorithm for D2Ds (ESPAD), which reuses the same RBs and tries to maximize the overall network throughput without affecting the CUEs throughput. ESPAD algorithm also ensures that Signal to Noise plus Interference Ratio (SINR) for each of the D2D links is maintained above a certain predefined threshold. The aforementioned properties of ESPAD algorithm makes sure that the CUEs do not experience very high interference from the D2Ds. It is observed that even when the SINR drop (i.e., maximum permissible drop in SINR of CUEs) is as high as 10 dB, there is no drastic decrease in CUEs throughput (only 3.78%). We also compare our algorithm against other algorithms and show that D2D throughput improves drastically without undermining CUEs throughput.

Online scheduling and placement of hardware tasks with multiple variants on dynamically reconfigurable field-programmable gate arrays

With voltage frequency islanding (VFI) we are able to change the voltage and frequency of the processors to save power within a certain performance constraint. In this work we want to maximize potential power savings and minimize time penalty by means of efficient task scheduling in VFIs. A new scheduling algorithm is presented which schedules the tasks onto the processing cores in the VFI by ranking them based on the computation load and communication load among other tasks. The mapping of the tasks is relevant to the processing speed of the cores and the simulation results show improvements compared to the default task scheduler of Linux.

The performance of the multiprocessor system and time-sharing system rely upon CPU scheduling algorithm. Some of the well known efficient CPU scheduling algorithms are Round Robin scheduling (RR), Shortest job first(SJF), Preemptive version of SJF(SRTF), First Come First Serve (FCFS) etc. As of now, the Round Robin scheduling algorithm is considered as the efficient process scheduling algorithm among all the existing CPU scheduling algorithms. However, in RR the shortest one have to wait for a longer time and in SRTF longer process behaves as a suspended process as short tasks keep on executing. This paper proposes a new scheduling process algorithm ESRR (Efficient Shortest Remaining Time Round Robin) that consolidate two of the preemptive version of existing scheduling algorithms namely RR and SRTF. ESRR reduces total waiting time and turn around time in compare to RR and reduces the waiting time of the shorter process and it also provides a longer process to execute faster than SRTF.

In order to effectively utilize the broadcast bandwidth, it is necessary to have efficient on-line scheduling algorithms that can balance individual and overall performance and can scale in terms of database sizes and client populations. Moreover, the scheduling algorithm should be applicable to a heterogeneous environment where data items have different sizes. We address these issues in this paper. As stretch is widely adopted as a performance metric for variable-size data requests, we propose a broadcast scheduling algorithm to optimize the system performance in terms of stretch. We show by analysis that the proposed algorithm can indeed achieve the optimal performance in terms of stretch. Moreover, the proposed scheduling algorithm has very low decision overhead, which makes it a practical solution for on-demand broadcast scheduling. Simulation results demonstrate that our algorithm significantly outperforms existing scheduling algorithms under various scenarios.

Efficient task scheduling is essential for the success of deep space exploration missions, where communication delays, dynamic link availability, and limited on-board resources pose significant challenges. However, existing deep-space scheduling frameworks cannot effectively coordinate multi-agent decisions across interplanetary regions due to long delays, dynamic link conditions, and highly unbalanced resources, resulting in inefficient and unstable task allocation. To address these issues, we propose Fed-ASTRA, a novel scheduling framework that integrates a hierarchical deep space network architecture with federated multi-agent reinforcement learning. In the proposed architecture, base stations such as Earth and Mars ground centers act as regional control centers, orbital satellites function as edge intelligence nodes, and rovers serve as terminal execution units, forming a three-tier collaborative system. This hierarchical organization enables both local autonomy and cross-domain coordination, effectively balancing real-time responsiveness and long-term availability. On the algorithmic side, we model the scheduling process as a multi-agent Markov decision process and design environment-constrained action pruning (ECAP) to filter out infeasible actions caused by link outages, energy thresholds, and deadline violations. In addition, a prioritized experience replay (PER) mechanism improves sample efficiency by emphasizing high-cost task experiences. These enhancements ensure that agents learn feasible and efficient scheduling strategies under severe environmental constraints. We evaluate Fed-ASTRA using a high-fidelity deep space network simulator driven by real orbital data. Experimental results demonstrate that our framework outperforms traditional optimization approaches, heuristic methods, and baseline MARL algorithms in terms of rapidity, availability, fairness, and real-time capability. Overall, this work highlights the potential of combining hierarchical network architectures and federated reinforcement learning to achieve robust and efficient task scheduling in future deep space missions.

A novel fault-tolerant scheduling algorithm for precedence constrained tasks in real-time heterogeneous systems