Using recorded values for bounding the minimum completion time in multiprocessors

Fog computing (FC) is an emerging paradigm that extends computation, communication, and storage facilities towards the edge of a network. In this heterogeneous and distributed environment, resource allocation is very important. Hence, scheduling will be a challenge to increase productivity and allocate resources appropriately to the tasks. We schedule tasks in fog computing devices based on classification data mining technique. A key contribution is that a novel classification mining algorithm I‐Apriori is proposed based on the Apriori algorithm. Another contribution is that we propose a novel task scheduling model and a TSFC (Task Scheduling in Fog Computing) algorithm based on the I‐Apriori algorithm. Association rules generated by the I‐Apriori algorithm are combined with the minimum completion time of every task in the task set. Furthermore, the task with the minimum completion time is selected to be executed at the fog node with the minimum completion time. We finally evaluate the performance of I‐Apriori and TSFC algorithm through experimental simulations. The experimental results show that TSFC algorithm has better performance on reducing the total execution time of tasks and average waiting time.

In this paper we present algorithms for the problem of scheduling n independent jobs on m identical machines. As a generalization of the classical multiprocessor scheduling problem each machine is available only at a machine dependent release time. Two objective functions are considered. To minimize the makespan, we develop a dual approximation algorithm with a worst case bound of 5/4. For the problem of maximizing the minimum completion time, we develop an algorithm, such that the minimum completion time in the schedule produced by this algorithm is at least 2/3 times the minimum completion time in the optimum schedule. The paper closes with some numerical results.

Serial and within-stage independent parallel model equivalence on the minimum completion time

Evaluation of Level of Confidence and Optimization of Roll-back Recovery with Checkpointing for Real-Time Systems

Cloud computing technology is getting more advanced these days. Cloud computing is a pay-as-you-go service since it allows for variable resource allocation for services. Task scheduling and resource allocation are becoming major requirements for efficient and effective load balancing among cloud resources. For scheduling resources in cloud environment, different tasks need to be executed simultaneously by the available resources in order to minimize the completion time. To achieve this, we proposed a new technique called Enhanced Max-Min Algorithm (EMM). This algorithm takes less time to complete all the tasks with minimum completion time. In the proposed algorithm, small size tasks are concurrently executed and resource allocation is done accordingly. When compared to Min-Min algorithm the Enhanced Max-Min algorithm provides high resource utilization and reduces the makespan.

Quantum-based predictive fog scheduler for IoT applications

Fog computing has emerged as a viable framework for processing delay sensitive applications. Modern applications consist of latency‐sensitive and latency‐tolerant jobs, leading to fog architectures that are often multi‐tiered/hierarchical. FiFSA (hierarchical first fog scheduling algorithm) and EFSA (hierarchical elected fog scheduling algorithm) are capable of scheduling both online and batch jobs on hierarchical fog‐cloud architectures. We consider heterogeneity in computing capacity—both among fog devices in separate layers, and among fog devices in the same layers. In general, online jobs with modest cpu requirements are scheduled on lower tier fog devices, and batch jobs with significant cpu requirements are scheduled on higher tier‐fog nodes, or the cloud data center (cdc). FiFSA assigns jobs to the first fog device with sufficient spare capacity. EFSA employs a MinMin heuristic that assigns jobs to the fog device that results in minimum completion time, while considering fog load. The performance of the proposed algorithms has been evaluated on a real‐life workload trace, using both simulation scenarios and a prototype testbed. FiFSA and EFSA offer an improvement of 19% to 70% in completion times and an improvement of 42% to 72% in system cost over other comparable algorithms.

Workflow scheduling is one of the significant issues for scientific applications among virtual machine migration, database management, security, performance, fault tolerance, server consolidation, etc. In this paper, existing time-based scheduling algorithms, such as first come first serve (FCFS), min–min, max–min, and minimum completion time (MCT), along with dependency-based scheduling algorithm MaxChild have been considered. These time-based scheduling algorithms only compare the burst time of tasks. Based on the burst time, these schedulers, schedule the sub-tasks of the application on suitable virtual machines according to the scheduling criteria. During this process, not much attention was given to the proper utilization of the resources. A novel dependency and time-based scheduling algorithm is proposed that considers the parent to child (P2C) node dependencies, child to parent node dependencies, and the time of different tasks in the workflows. The proposed P2C algorithm emphasizes proper utilization of the resources and overcomes the limitations of these time-based schedulers. The scientific applications, such as CyberShake, Montage, Epigenomics, Inspiral, and SIPHT, are represented in terms of the workflow. The tasks can be represented as the nodes, and relationships between the tasks can be represented as the dependencies in the workflows. All the results have been validated by using the simulation-based environment created with the help of the WorkflowSim simulator for the cloud environment. It has been observed that the proposed approach outperforms the mentioned time and dependency-based scheduling algorithms in terms of the total execution time by efficiently utilizing the resources.

The problem of scheduling of tasks in distributed, heterogeneous, and multiprocessing computing environment like grid and cloud computing is considered as one of the most important issue from research perspective. As the performance of such kind of systems is highly depends upon the way, how tasks are allocated among the multiple processing units for their efficient execution. The underlying objective of any task scheduling mechanism is to minimize the overall makespan for the execution of given set of jobs/tasks and computing machines. Scheduling of tasks in cloud computing falls in the class of NP-hard optimization problem. As a result, many meta-heuristic algorithms have been applied and tested to solve this problem but still lot of scope is there for the better strategies. The characteristic of the good algorithm is that it must be adaptable to the dynamic environment. Through this paper, we are proposing task scheduling mechanism based on particle swarm optimization (PSO) in which opposition-based learning technique is used to avoid premature convergence and to accelerate the convergence of standard PSO and compared same with the well-established task scheduling strategies based on PSO, mPSO (modified PSO), genetic algorithm GA, max–min, minimum completion time and minimum execution time. The results obtained for the various class of experiments clearly establish that the proposed opposition-based learning inspired particle swarm optimization based scheduling strategy performs better in comparison to its peers which are taken into the consideration.

We consider a scheduling game, in which both the machines and the jobs are players. A job attempts to minimize its completion time by switching machines, while each machine would like to maximize its workload by choosing a scheduling policy from the given set of policies. We consider a two-stage game. In the first stage every machine simultaneously chooses a policy from some given set of policies, and in the second stage, every job simultaneously chooses a machine. In this work, we use the price of anarchy to measure the efficiency of such equilibria where each machine is allowed to use at most two policies. We provide nearly tight bounds for every combination of two deterministic scheduling policies with respect to two social objectives: minimizing the maximum job completion, and maximizing the minimum machine completion time.

The problem of optimizing the balancing of processing load originating at the various sites of an interconnected set of heterogeneous processors of a real-time system is examined. The distributed load is assumed to be homogeneous and arbitrarily divisible with no particular affinity to individual processors. The heterogeneous processors may or may not have front-end subprocessors, and are characterized by their load processing speeds and I/O speeds pertaining to the transmission of excess load or reception of additional load. The objective of load redistribution, which is carried out during runtime, is to minimize the job execution completion time consisting of the sum of processing lime and I/O time. Depending on the value of total load, the optimal load redistribution is found to be either unique or to admit a multiplicity of solutions. In the first case, all processors complete processing in the same amount of time, which is equal to the minimum job completion time. In the second case, the infinite set of optimal redistributions is represented by load allocations for the individual processors that can range over specified intervals, and the completion limes for the different processors are generally not equal. >

Achieving the minimum average coflow completion time(CCT) and the isolation guarantees for multi-tenant, is considered a challenge in a cloud environment. This is because the minimum average CCT and isolation guarantees are two conflicting targets, and they cannot be achieved simultaneously. Prior solutions have implemented a single target either minimizing the average CCT or isolation guarantees. The prior solutions are also limited to clairvoyant scheduling. They also assume the availability of the complete knowledge of coflow sizes before the communication starts. In this paper, we propose an efficient scheduling algorithm smallest-height-first DRF(SHFDRF) for near-optimal scheduling and isolation guarantees without prior knowledge of coflow size. SHFDRF achieves the long-term isolation guarantees and the minimum average CCT by the smallest height first and the monopolistic dominant resource fairness bandwidth allocation strategy. The smallest height first and the monopolistic dominant resource fairness bandwidth allocation strategy can also improve link utilization and system throughput. The trace-driven simulation shows that SHFDRF enables communication stages to 1.28 $$\times $$ , 2.27 $$\times $$ , and 6.28 $$\times $$ faster on the 95th percentile compared to DRF, NCDRF, and Per-Flow Fairness. Even compared with minimum CCT, the completion time of coflow only slowed down by 13.9% on the 95th percentile. Overall, the performance of SHFDRF is acceptable, and it can be applied to the actual datacenter without the limitation of complete prior knowledge.

To improve the efficiency of resource allocation cloud computing, as while as to improve resource utilization for the service provider, the paper has raised a polymorphic algorithm of Ant Colony Optimization, which can assure the quality of cloud service, and also dynamically change the list that contain nodes information. When the user submits the task, the algorithm will transfer it to the Cloud Control Queen by Master. And then, according to the functions, the ant colony will be divided into test ant colony, reconnaissance ant colony, cleared ant colony and workers ant colony. The algorithm can achieve the minimum average completion time gradually, and may reduce local optima, by forecasting the completion time and other pheromone.

This paper presents a hybrid approach based discrete Particle Swarm Optimization (PSO) and chaotic strategies for solving multi-objective task scheduling problem in cloud computing. The main purpose is to allocate the summited tasks to the available resources 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 main challenges faced by Particle Swarm Optimization (PSO) when used to solve scheduling problems are premature convergence and trapping into local optimum. This paper presents an enhanced Particle Swarm Optimization algorithm hybridized with Chaotic Map strategies. The proposed approach is called Enhanced Particle Swarm Optimization based Chaotic Strategies (EPSOCHO) algorithm. Our proposed approach suggests two Chaotic Map strategies: sinusoidal iterator and Lorenz attractor to enhanced PSO algorithm in order to get good convergence and diversity for optimizing the task scheduling in cloud computing. The proposed approach is simulated and implemented in Cloudsim simulator. The performance of the proposed approach is compared with the standard PSO algorithm, the improved PSO algorithm with Longest job to fastest processor (LJFP-PSO), and the improved PSO algorithm with minimum completion time (MCT-PSO) using different sizes of tasks and various benchmark datasets. The results clearly demonstrate the efficiency of the proposed approach in terms of makespan, processing cost and resources utilization.

This paper proposes a tabu search heuristic for the Quay Crane Scheduling Problem (QCSP), the problem of scheduling a fixed number of quay cranes in order to load and unload containers into and from a ship. The optimality criterion considered is the minimum completion time. Precedence and non-simultaneity constraints between tasks are taken into account. The former originate from the different kind of operations that each crane has to perform; the latter are needed in order to avoid interferences between the cranes. The QCSP is decomposed into a routing problem and a scheduling problem. The routing problem is solved by a tabu search heuristic, while a local search technique is used to generate the solution of the scheduling problem. This is done by minimizing the longest path length in a disjunctive graph. The effectiveness of our algorithm is assessed by comparing it to a branch-and-cut algorithm and to a Greedy Randomized Adaptive Search Procedure (GRASP).