Existing Scheduling Algorithms Research Articles

Efficient Management of Containers for Software Defined Vehicles

Containerization technology, such as Docker, is gaining in popularity in newly established software-defined vehicle architectures (SDVA). However, executing those containers can quickly become computationally expensive in constrained environments, given the limited CPU, memory, and energy resources in the Electric Control Units (ECU) of SDVA. Consequently, the efficient management of these containers is crucial for enabling the on-demand usage of the applications in the vehicle based on the available resources while considering several constraints and priorities, including failure tolerance, security, safety, and comfort. In this paper, we propose a dynamic software container management approach for constrained environments such as embedded devices/ECUs in SDVA within smart cars. To address the conflicting objectives and constraints within the vehicle, we design a novel search-based approach based on multi-objective optimization. This approach facilitates the allocation, movement, or suspension of containers between ECUs in the cluster. Collaborating with our industry partner, Ford Motor Company, we evaluate our approach using different real-world software-defined scenarios. These scenarios involve using heterogeneous clusters of ECU devices in vehicles based on real-world software containers and use-case studies from the automotive industry. The experimental results demonstrate that our scheduler outperforms existing scheduling algorithms, including the default Docker scheduler -Spread- commonly used in automotive applications. Our proposed scheduler exhibits superior performance in terms of energy and resource cost efficiency. Specifically, it achieves a 35% reduction in energy consumption in power-saving mode compared to the scheduler employed by Ford Motor Company. Additionally, our scheduler effectively distributes workload among the ECUs in the cluster, minimizing resource usage, and dynamically adjusts to the real-time requirements and constraints of the car environment. This work will serve as a fundamental building block in the automotive industry to efficiently manage software containers in smart vehicles considering constraints and priorities in the real world.

Survey on MapReduce Scheduler Algorithms in Hadoop Framework

Hadoop serves as a robust framework tailored for the storage and processing of vast data volumes across clusters. Its foundation lies in the Hadoop Distributed File System (HDFS) for data storage, complemented by the MapReduce paradigm for data processing. MapReduce, functioning as a parallel computing framework, operates adeptly on distributed clusters to manage large-scale data processing tasks. Within this framework, scheduling emerges as a pivotal aspect, influencing the overall system's performance and efficiency. The essence of scheduling in MapReduce lies in enhancing performance, minimizing response times, and optimizing resource allocation. This paper undertakes a systematic exploration of existing scheduling algorithms, offering a fresh classification and detailed examination of each category. Furthermore, the analysis delves into the core principles, objectives, as well as the strengths and weaknesses of these scheduling algorithms.

Rock-hyrax: An energy efficient job scheduling using cluster of resources in cloud computing environment

In a cloud computing environment, job scheduling allows the service provider to schedule resources based on demand. Job scheduling must also ensure QoS, end-user satisfaction, and the efficient usage of resources. Cloud computing vendors assign virtualized computing resources to end-users based on job requirements that are dynamically scalable and pay-per-use. The assignment of jobs requires proper investigation and mapping of available resources. In this paper, we have proposed a novel job scheduling scheme based on Rock Hyrax. Our Rock Hyrax approach uses objective functions to map jobs to available resources. The objective function considers a variety of QoS parameters like makespan, response time and energy efficiency. Our method employs two key QoS parameters: makespan and energy consumption. The node behavior and characteristics, such as processing power, storage, and network connectivity to cluster similar resources, have also been considered for scheduling. An experimental setup is created for a thorough study of the proposal using CloudSim simulator. For both the jobs and virtual machines, static and dynamic scenarios for performance evaluation have been developed. To compare our work with existing scheduling algorithms like ACO, PSO, BFO, and ABC has been considered and we have found that the proposal reduces makespan by 2–9% as increased in jobs. Furthermore, the proposed method reduces total energy consumption in data centers by 7–23% as jobs request increases. The findings support the claim that the proposed method surpasses the existing methods and significantly shortens the time needed to determine the resource required for the job.

A Context-Aware Internet of Things (IoT) founded Approach to Scheming an Operative Priority-Based Scheduling Algorithms

In recent years, smart computing has emerged as a promising and rapidly expanding field of technology. It senses the environment in real time and gives powerful analytics to perform intelligent decisions. Creating a scheduling algorithm based on priorities in order to decrease IoT process latency was the primary emphasis of the study challenge. The constraints of existing scheduling algorithms were investigated in order to build a scheduling algorithm that is based on priorities. We provide a context-based priority scheduling method to get around these restrictions. In order to determine which steps of the IoT process were crucial, we developed context attributes. Once the criticality has been identified, the proposed scheduling technique is used to schedule the IoT processes. The outcomes of the algorithms were confirmed using a variety of evaluation indicators. As demonstrated by the experimental results, the suggested scheduling algorithms outperformed the state-of-the-art techniques. Smart ATM uses a Case Study technique to analyse the algorithm. We identified the sensors that are part of the ATM and the settings in which they are relevant. We determined the priority value for each sensor. The processes are subsequently categorized according to their priority values. Then, a priority-based FCFS scheduling algorithm is used, and its performance is assessed using metrics like Average TAT, Cost, Energy, and the High critical process TAT ratio.

ACO intelligent task scheduling algorithm based on Q-learning optimization in a multilayer cognitive radio platform

With the rapid development of cognitive radio technology, multilayer heterogeneous cognitive radio computing platforms with large computing, high-throughput, ultralarge bandwidth and ultralow latency have become a research hotspot. Aiming at the core scheduling problems of multilayer heterogeneous computing platforms, this paper abstracts the bidirectional interconnection topology, node computing capacity, and internode communication capability of the heterogeneous computing platform into an undirected graph model and abstracts the nodes with dependencies, nodes’ computing requirements, and internode communication requirements in streaming tasks into a directed acyclic graph (DAG) model so as to transform the task-scheduling problem into a deployment-scheduling problem from DAG to undirected graph. To efficiently solve this graph model, this paper calculates and forms a component scheduling sequence based on the dependencies of functional components in streaming domain tasks. Then, according to the scheduling sequence, ant colony optimization (ACO) algorithms, such as ant colonies and Q-learning select functional components, deploy components to different computing nodes, calculate the scheduling cost, guide the solution space search of agents, and complete the scenario migration adaptation of the scheduling algorithms to intelligent scheduling of domain tasks. So, this paper proposes the ACO field task intelligent scheduling algorithm based on Q-learning optimization (QACO). QACO uses the Q-table matrix of Q-learning as the initial pheromone of the ant colony algorithm, which not only solves the dimensional disaster of the Q-learning algorithm but also accelerates the convergence speed of the ant colony intelligent scheduling algorithm, reduces the task scheduling length, and further enhances the search ability of the existing scheduling algorithm to solve the spatial set. Based on the randomly generated DAG domain task map, three experimental test scenarios are designed to verify the algorithm performance. The experimental results show that compared with the Q-learning, ACO, and genetic algorithms (GA) algorithms, the proposed algorithm improves the convergence speed of the solution by 72.3%, 63.4%, and 64% on average, reduces the scheduling length by 2.8%, 2.2%, and 0.9% on average, and increases the parallel acceleration ratio by 2.8%, 2.1%, and 0.9% on average, respectively. The practical application value of the algorithm is analyzed through typical radar task simulation, but the load balancing of the algorithm needs to be further improved.

Scheduling Dynamic Software Updates in Mobile Robots

We present NeRTA ( Ne xt R elease T ime A nalysis), a technique to enable dynamic software updates for low-level control software of mobile robots. Dynamic software updates enable software correction and evolution during system operation. In mobile robotics, they are crucial to resolve software defects without interrupting system operation or to enable on-the-fly extensions. Low-level control software for mobile robots, however, is time sensitive and runs on resource-constrained hardware with no operating system support. To minimize the impact of the update process, NeRTA safely schedules updates during times when the computing unit would otherwise be idle. It does so by utilizing information from the existing scheduling algorithm without impacting its operation. As such, NeRTA works orthogonal to the existing scheduler, retaining the existing platform-specific optimizations and fine-tuning, and may simply operate as a plug-in component. To enable larger dynamic updates, we further conceive an additional mechanism called bounded reactive control and apply mixed-criticality concepts. The former cautiously reduces the overall control frequency, whereas the latter excludes less critical tasks from NeRTA processing. Their use increases the available idle times. We combine real-world experiments on embedded hardware with simulations to evaluate NeRTA. Our experimental evaluation shows that the difference between NeRTA’s estimated idle times and the measured idle times is less than 15% in more than three-quarters of the samples. The combined effect of bounded reactive control and mixed-criticality concepts results in a 150+% increase in available idle times. We also show that the processing overhead of NeRTA and of the additional mechanisms is essentially negligible.

Self-Organizing Neural Scheduler for the Flexible Job Shop Problem With Periodic Maintenance and Mandatory Outsourcing Constraints.

Scheduling is significant in improving the production efficiency and reducing delivery delays for manufacturing enterprises. Unlike the flexible job-shop scheduling problem, two special constraints are encountered in real-world power supply manufacturing systems: 1) periodic maintenance and 2) mandatory outsourcing. As the characteristics of these constraints are not considered in existing scheduling algorithms, schedules generated by most existing approaches are not optimal or even conflict with these constraints. In this article, a self-organizing neural scheduler (SoNS) is proposed to overcome this limitation. A long short-term memory encoder is developed to transform the variable-length structural information into fixed-length feature vectors. Moreover, the reinforcement learning model is proposed to automatically select policies for improving candidate schedules. To validate the effectiveness of the proposed algorithm, extensive experiments are conducted on over 300 problem instances. The nonparametric Kruskal-Wallis tests confirm that the proposed algorithm outperforms several state-of-the-art methods in terms of effectiveness and robustness within a limited computational budget. It demonstrates that the proposed SoNS can solve scheduling problems with the periodic maintenance and mandatory outsourcing constraints effectively.

Contention-Free Scheduling for Single Preemption Multiprocessor Platforms

The Contention-Free (CF) policy has been extensively researched in the realm of real-time multi-processor scheduling due to its wide applicability and the performance enhancement benefits it provides to existing scheduling algorithms. The CF policy improves the feasibility of executing other real-time tasks by assigning the lowest priority to a task at a moment when it is guaranteed not to miss its deadline during the remaining execution time. Despite its effectiveness, existing studies on the CF policy are largely confined to preemptive scheduling, leaving the efficiency and applicability of limited preemption scheduling unexplored. Limited preemption scheduling permits a job to execute to completion with a limited number of preemptions, setting it apart from preemptive scheduling. This type of scheduling is crucial when preemption or migration overheads are either excessively large or unpredictable. In this paper, we introduce SP-CF, a single preemption scheduling approach that incorporates the CF policy. SP-CF allows a preemption only once during each job’s execution, following a priority demotion under the CF policy. We also propose a new schedulability analysis method for SP-CF to determine whether each task is executed in a timely manner and without missing its deadline. Through simulation experiments, we demonstrate that SP-CF can significantly enhance the schedulability of the traditional rate-monotonic algorithm and the earliest deadline first algorithm.

MSU-TSCH: A Mobile Scheduling Updated Algorithm for TSCH in the Internet of Things

On top of the low-power IEEE 802.15.4 radio, IEEE created IEEE 802.15.4e time slotted channel hopping (TSCH), an exceptionally efficient, reliable, and predictable time-frequency enabled medium access control (MAC) protocol for the industrial sector. However, the IEEE 802.15.4e TSCH specification does not address how scheduling would be created, updated, or maintained. Moreover, TSCH does not support mobility. Although several scheduling techniques have been proposed in the literature to fill this gap, these techniques did not consider networking metrics such as duty cycle, network capacity, end-to-end delay, and packet delivery ratio. In this paper, we develop a new dynamic scheduling approach for TSCH that supports mobility and adapts to topology changes, called mobile scheduling updated TSCH (MSU-TSCH). attempts to schedule time slots by first picking the closest nodes to the sink. We undertake comprehensive simulations to assess the effectiveness of MSU-TSCH with PRCOS, an existing scheduling algorithm in a mobile context. This proposal reduces the received energy consumption by up to 40%, on the other hand, it decreases the average end-to-end latency by 30%.

Energy-efficient cooperative resource allocation and task scheduling for Internet of Things environments

Offloading Internet of Things (IoT) tasks to the cloud for further processing might not always lead to an optimal execution time, particularly in situations such as resource contention, under-provisioning, over-provisioning, and fragmentation. In addition, dynamically optimizing the number of Virtual Machines (VMs) for resource scheduling in order to meet application requirements remains a major research challenge. Further, existing resource scheduling algorithms focus primarily on minimizing operational costs while maximizing resource sharing and utilization. Considering energy utilization as part of the resource allocation and scheduling process as an optimization objective for maintaining load balancing has often been neglected. To address these challenges and more, we propose a cooperative energy-aware resource allocation and scheduling strategy based on a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria decision-making method. We used the Grid Workloads Archive dataset to evaluate our proposed TOPSIS-based Resource Allocation (TOPREAL) approach. Experimental results with respect to the allocation of VM resources when considering processing a large segment of tasks indicate that TOPREAL outperforms existing algorithms in terms of energy savings, with an average improvement of 40.25%, while maintaining an average improvement of 16.21% when it comes to execution time. Results also demonstrate that our method can save an average of 78.06 processing hours and 63,215 kJ of energy when compared to existing scheduling algorithms. These results demonstrate the effectiveness of our proposed model and the viability of using multi-criteria decision-making techniques such as TOPSIS to solve the resource allocation and scheduling problem in edge environments.

SLA Aware Task-Scheduling Algorithm in Cloud Computing Using Whale Optimization Algorithm

Task scheduling in Cloud Computing paradigm poses new challenges for cloud provider as heterogeneous, diversified tasks arrived on to cloud console. To schedule these type of tasks efficiently on to virtual resources in cloud paradigm, an effective scheduler is needed, which precisely maps tasks to virtual machines by considering priorities of both tasks and VMs. Existing scheduling algorithms failed to map tasks precisely to virtual resources due to high dynamic nature in cloud environment which leads to increase of makespan and SLA violations will be increased. In this paper, authors proposed a task-scheduling mechanism, which considers task priorities and VMs. To model this scheduling paradigm we have chosen whale optimization through which our scheduler will take decisions for scheduling tasks precisely onto virtual resources in cloud environment. Entire simulation was carried out on CloudSim. Initially we have chosen random generated workload to run simulation and after that, we have considered a real-time workload named as BigDataBench and ran our simulation. Finally, we compared our proposed work with classical baseline mechanisms. From simulations we observed that proposed whale scheduler improved makespan for PSO, ACO, GA, and W-schedulers by 20.07%, 17.55%, 19.9%, and 6.35%, respectively, and 17.3%, 17.86%, 17.64%, and 5.93%, respectively, for BigDataBench workloads. SLA violations improved over PSO, ACO, GA, and W-Scheduler by 56.76%, 42.17%, 35.29%, and 24.53%, respectively, and 63.42%, 23.33%, 55.51%, and 40.1%, respectively, for BigDataBench workloads. From extensive simulation results, our proposed scheduler using whale optimization approach minimizes makespan and SLA violations to a great extent.

Sub-6 GHz V2X-assisted MmWave optimal scheduling for vehicular networks

Sub-6 GHz assisted millimeter wave (mmWave) technology is a promising solution to address the ever-increasing data exchange demand in Vehicle-to-Everything (V2X) communication. The omni-directional sub-6 GHz channels exchange vehicle and control information in the vehicular network, while the directional mmWave technology is used for high-rate data transmission. Consider the huge amounts of vehicular data, the transmission constraints imposed by mmWave, and the unique vehicular ad hoc network (VANET) data features, a sub-6 GHz assisted-mmWave scheduling algorithm is needed to facilitate V2X data transmission. Existing scheduling algorithms in the literature fall short of addressing realistic V2X data features. Therefore, in this paper, we propose a novel objective to maximize the network utility that succinctly captures realistic V2X data features, including varying data sizes, data importance and their freshness decay with transmission delay. We formulate the scheduling problem into a novel mixed-integer sum-of-ratios optimization problem. To solve the challenging NP-complete problem, we first derive an equivalent parametric mixed-integer linear programming problem with existence and uniqueness proofs. Next, we provide the necessary condition for the optimality of the equivalent problem. A novel Parameterization-based Iterative Algorithm (PIA) is then developed to learn the global optimal solution with convergence analysis. Comparative simulation studies with the brute-force algorithm, the widely used branch and bound (BB) method, the greedy algorithm, and our previously developed maximum weight matching based iterative algorithm (MWMIA) are conducted to verify the correctness and effectiveness of the proposed PIA scheduling algorithm.

Offloading Elastic Transfers to Opportunistic Vehicular Networks Based on Imperfect Trajectory Prediction

Due to the high cost of cellular networks, vehicle users would like to offload elastic traffic through vehicular networks as much as possible. This demand prompts researchers to consider how to make the vehicular network system achieve better performance for requests coming online, such as maximizing throughput. The traffic in vehicular networks is transferred through opportunistic contacts between vehicles and infrastructures. When making scheduling decisions, the scheduler must be aware of vehicles’ future trajectories. Vehicles’ future trajectories are usually predicted by trajectory prediction algorithms when users are unwilling to report their future trips. Unfortunately, no trajectory prediction algorithm can be completely accurate, and these inaccurate prediction results will degrade the throughput achieved by scheduling algorithms. In this paper, we focus on reducing the negative impact of inaccurate predictions. Specifically, we measure two data-driven trajectory prediction algorithms that have been widely used for trajectory predictions and understand the characteristics of the accuracy of predicted contacts. Based on the enlightenment from the measurement, we design a system, i.e., i-Offload, to offload elastic traffic under imperfect trajectory predictions. The experimental results show that our system has good throughput and high scheduling efficiency even under imperfect trajectory predictions. Compared with existing scheduling algorithms, our method improves the throughput by about one time.

Improved Method for Enhanced Quality of Service in IoHT Task Dependency Optimization

Keeping a proper level of task dependency throughout the scheduling process is critical to achieving the aim of decreasing the make-span rate in Internet of Health Things (IoHT) projects. We provide a smart model strategy for effective task scheduling in the IoHT environment for e-healthcare systems by merging hybrid moth flame optimisation (HMFO) with cloud computing. The HMFO algorithm guarantees that all available resources are distributed evenly, resulting in improved quality of service (QoS). We study the Google cluster dataset to learn about the scheduling behaviours of cloud-based jobs in order to train our model. After training, an HMFO model may be used to plan activities in real time. To assess the success of our strategy, we run simulations in the CloudSim environment, taking into account crucial parameters such as resource utilisation, reaction time, and energy consumption. According to a comparative analysis, our hybrid HMFO system surpasses the alternatives in terms of reaction time, average run duration, and cost savings. Our method has proven to be effective due to the favourable effects it has had on response rates, prices, and run times. Combining IoT and cloud computing has the potential to improve healthcare delivery in a variety of ways. One unique strategy we offer for scheduling IOHT jobs is to combine a deep neural network (DNN) algorithm with the MFO technique. Job scheduling in electronic healthcare systems can be optimised with the help of our hybrid MFO-DNN algorithm by taking into account a variety of different objectives, the most important of which are lowering response times while improving resource utilisation and maintaining consistent load balances. The MFO approach searches the search space and provides early solutions, while the DNN algorithm refines and improves those first findings. In comprehensive simulations conducted in a real-world hospital setting, the hybrid MFO-DNN technique outperformed existing scheduling algorithms in terms of reaction time, resource utilisation, and load balancing. The simulated healthcare environments were as true to life as was feasible. The suggested technique has been demonstrated to be both dependable and scalable, making it appropriate for use in large-scale IOHT deployments. This study considerably enhances the state of the art in IOHT task scheduling in E healthcare systems by developing a hybrid optimisation technique that takes advantage of the strengths of both MFO and DNN. The findings indicate that this strategy has the potential to improve the quality and efficiency of healthcare delivery, which helps patients receive care that is both effective and timely.

Cloud Task Scheduling using the Squirrel Search Algorithm and Improved Genetic Algorithm

With cloud computing, resources can be networked globally and shared easily between users. A range of heterogeneous needs are met on demand by software, hardware, storage, and networking. Dynamic resource allocation and load distribution pose challenges for cloud servers. In this regard, task scheduling plays a significant role in enhancing the performance of cloud computing. With the increase in the number of users and the capability of cloud computing, cloud data centers are experiencing concerns regarding energy consumption. To leverage cloud resources energy efficiently and provide real-time services to users, a viable cloud task scheduling solution is required. To address these problems, this paper proposes a new hybrid task scheduling algorithm based on squirrel search and improved genetic algorithms for cloud environments. The proposed scheduling algorithm surpasses existing scheduling algorithms across multiple parameters, including makespan, energy consumption, and execution time.

A novel approach for Credit-Based Resource Aware Load Balancing algorithm (CB-RALB-SA) for scheduling jobs in cloud computing

In recent years, cloud computing has gained popularity, mainly because of its utility and relevance to current technological trends. It is an arrangement that is highly customizable and encapsulated for providing better computational services to its clients worldwide. In the cloud, computing scheduling plays a pivotal role in optimizing resources. A better scheduling algorithm should be efficient and impartial, reducing the makespan time with proper resource utilization. However, most scheduling algorithms customarily lead to less resource utilization, termed load imbalance. The analysis of the existing papers exhibits better Makespan time but cannot guarantee the load-balanced mapping of jobs with proper resource utilization. Therefore, to eliminate the shortcomings of the prevalent/existing algorithms and enhance the performance, CB-RALB-SA, Credit-based Resource Aware Load Balancing scheduling algorithm has been rendered. The proposed work ensures a balanced distribution of tasks based on the capabilities of the resources, which eventually proves sustainable improvement against the existing scheduling algorithms. Therefore, a novel Credit Based Resource Aware Load Balancing Scheduling algorithm (CB-RALB-SA) is proposed. The tasks weighted by the credit-based scheduling algorithm are then mapped to the resources considering each resource’s load and computing capability using FILL and SPILL functions of Resource Aware and Load using Honey bee optimization heuristic algorithm. With the experimental evaluations and results, it has been proved that the proposed approach provides 48.5% better in Processing Time and 16.90 % better results in makespan time than the Existing CBSA-LB algorithm. Thus, it improves the processor’s efficiency while uplifting the whole system’s performance and has saved memory allocated to tasks and RAM.

A scheduling algorithm based on reinforcement learning for heterogeneous environments

Efficient application scheduling is critical for achieving high performance in heterogeneous computing environments. For heterogeneous static scheduling problems, applications with a set of distributed tasks are modeled as directed acyclic graphs (DAG). The goal of scheduling is to properly allocate processors to DAG nodes and minimize the average completion time of all DAG applications. Most existing scheduling algorithms cannot fully utilize scheduling information, and are designed to schedule one DAG application at a time. Therefore, this study proposes a scheduling algorithm based on reinforcement learning for heterogeneous computing environments. A convolutional neural network with graph attention is first used to fully process and encode the scheduling information. Then, a fully connected neural network selects an appropriate node from the DAG applications for execution. Finally, a heuristic method is employed to allocate a processor to the selected node based on the earliest finish time and node duplication. This process is then repeated for every DAG node. The proximal policy optimization algorithm is used to tune all network hyperparameters. The training phase employs a reward function suitable for multi-DAG scheduling to ensure optimal performance when multiple DAG applications are scheduled concurrently. Experiments of various scheduling scenarios and types of applications were conducted, and the results show that the proposed algorithm reduces the average termination time of applications by 13.03% over that of existing algorithms.

Load Balancing Algorithms for Hadoop Cluster in Unbalanced Environment.

Considering that in the process of job scheduling, the cluster load should be prebalanced rather than remedied when the load is seriously unbalanced; therefore, in this paper, the task scheduling flow of the Hadoop cluster is analyzed deeply. On the Hadoop platform, a self-dividing algorithm is proposed for load balancing. An intelligent optimization algorithm is used to solve load balance. A dynamic feedback load balancing scheduling method is proposed from the point of view of task scheduling. In order to solve the shortcoming of the fair scheduling algorithm, this paper proposes two ways to improve the resource utilization and overall performance of Hadoop. When the mapping task is completed and the tasks to be reduced are assigned, the task assignment is based on the performance of the nodes to be reduced. It gives full play to the advantages of the ant colony algorithm and the hive colony algorithm so that the fusion algorithm can better deal with load balance. Then, three existing scheduling algorithms are introduced in detail: single queue scheduling, capacity scheduling, and fair scheduling. On this basis, an improved task scheduling strategy based on genetic algorithm is proposed to allocate and execute application tasks to reduce task completion time. The experiment verifies the effectiveness of the algorithm. The LBNP algorithm greatly improves the efficiency of reducing task execution and job execution. The delay capacity scheduling algorithm can ensure that most tasks can achieve localization scheduling, improve resource utilization, improve load balance, and speed up job completion time.

A survey on cloud computing scheduling algorithms

Cloud computing has emerged as one of the hottest topics in technology and has quickly become a widely used information and communication technology model. Performance is a critical component in the cloud environment concerning constraints like economic, time, and hardware issues. Various characteristics and conditions for providing solutions and designing strategies must be dealt with in different situations to perform better. For example, task scheduling and resource allocation are significant challenges in cloud management. Adopting proper techniques in such conditions leads to performance improvement. This paper surveys existing scheduling algorithms concerning the macro design idea. We classify these algorithms into four main categories: deterministic algorithms, metaheuristic algorithms, learning algorithms, and algorithms based on game theory. Each category is discussed by citing appropriate studies, and the MapReduce review is addressed as an example.

Mixed-Flow Load-Balanced Scheduling for Software-Defined Networks in Intelligent Video Surveillance Cloud Data Center

As the large amount of video surveillance data floods into cloud data center, achieving load balancing in a cloud network has become a challenging problem. Meanwhile, we hope the cloud data center maintains low latency, low consumption, and high throughput performance when transmitting massive amounts of data. OpenFlow enables a software-defined solution through programing to control the scheduling of data flow in the cloud data center. However, the existing scheduling algorithm of the data center cannot cope with the congestion of the network center effectively. Even for some dynamic scheduling algorithms, adjustments can only be made after congestion occurs. Hence, we propose a proactive and dynamically adjusted mixed-flow load-balanced scheduling (MFLBS) algorithm, which not only takes into account the different sizes of flows in the network but also maintains maximum throughput while balancing the load. In this paper, the MFLBS problem was formulated, along with a set of heuristic algorithms for real-time feedback and adjustment. Experiments with mesh and tree network models show that our MFLBS is significantly better than other dynamic scheduling algorithms, including one-hop DLBS and static scheduling algorithm FCFS. The MFLBS algorithm can effectively reduce the delay of small flows and average delay while maintaining high throughput.