Task scheduling considering fault probability for distributed computing applications over an optical network

Optical Grid that provides a way to integrate the geographical distributed resources for large- scale grid applications. This paper mainly focuses on the task scheduling issue in Optical Grid and proposes two task scheduling algorithms to mini-mize the total time or scheduling length required for a given task. A simulated optical grid has been addressed to demon-strate the effectiveness of the schedulin g approach. The performance tests and anal ysis of the two algorithms are investi-gated to show the effect of the routing policy on the schedulin g length. A more flexible policy is able to further improve scheduling length. Keywords: Optical Grid, Task Scheduling Algorithm, Communication Contention I INTRODUCTION Distributed computing, also known as computational Grid, is a technology that makes it possible to utilize the geographi-cal distributed computational resources to solve the intensive computation of large-scale data. Most Grid systems were built over the IP packet switched networks in the past. In the situation when some data-intensive Grid applications re-quire moving Gigabytes or even larger of data, the low speed of the network becomes the bottleneck of Grid system, which could not allow the remote resources to be used efficiently. Nowadays the optical network with Synchronous Digital Hierarchy (SDH), Wavelength-D ivision Multiplexing (WDM), and Automa tic Switched Optical Network (ASON) infrastructure can provide a much larger amount of bandwidth than the electronic network, such as IP packet switched network. Therefore, it is significant to use the optical network as the network infrastructure of the computational Grid. We refer to this infrastructure as Optical Grid, which employs optical network to interconnect grid computational re-sources (e.g. supercomputers, computer clusters, storage and display devices) with dynamically provisioned lightpaths [1]. It has been realized that task scheduling is a fundamental issue in achieving high performance in grid environments [6]. Task scheduling aims to optimize the overall performance of a system, e.g., minimizing the average job response time and maximizing the number of jobs exec uted in certain period of time. Optical grid task scheduling is quite different from classic grid task scheduling model. In Optical Grid system, we can treat the optical network as a kind of resource, the scheduling algorithm in Optical Grid system should schedule the tasks to appropriate resources as we ll as scheduling the communications between tasks to appropriate optical links. The objec-tive of task scheduling is to minimize the completion time (we called it as scheduling length or make span ) of an appli-cation. Usually, this problem is modeled and solved under several assumptions. Unless the assumptions are simplified enough, the problem has been proven to be NP-complete [8]. Therefore, many heuristic algorithms have been proposed. But these heuristics either are based on the assumption that the processors are fully connected, or just don’t take into ac-count the optical network’s characters, es pecially the characters of WDM optical network. While actually, in the Optical Grid system, the computational resources are more likely not fully connected, and the communication contention is an important factor that affects the completion time of a Grid application. Thus, a new heuris tic algorithm that can schedule tasks and also the communications are needed. In this paper, we mainly focus on the static scheduling problem, which means the character of the Grid applica tion, including the task execution times, task dependencies, task communications and synchronization are known a priori.

As part of the DRAGON SLAYER project, the adaptive and reliable distributed file system MELODY has emerged for supporting hard real-time applications in unpredictable environments. In MELODY, the time criticality of tasks and their sensitivity with respect to the latest file information are explicitly used for novel, flexible task scheduling algorithms and file replication management policies, featuring dynamic file replication and relocation of file copies as well as offering file versions of varying recency. The achieved adaptability far outweighs the additional overhead in comparison to simpler models, and enhances both reliability and real-time responsiveness for file access. Both the development of the model and the experimental analysis at the various stages were done in an incremental manner. This was necessary in order to cope with the complexity of the problems resulting from conflicting requirements and dynamic trade-offs (e.g. real-time responsiveness vs. reliability). As the next incremental model extension, and as the major contribution of this paper, a series of integration policies are developed for task and resource scheduling, after redefining the role and order of task and resource scheduling: the periodic and dynamic models (and combinations thereof) are used to invoke the task scheduler for a fixed interval of time. All of these policies compare very convincingly against the "classical" model where the task scheduler only schedules tasks after their resources have been allocated. The results are discussed.

Wireless Cloud computing delivers the data and computing resources through the internet, on a pay for usage basis. By using this, we can automatically update our software. We can use only the space required for the server, which reduces the carbon footprint. Task scheduling is the main problem in cloud computing which reduces the system performance. To improve system performance, there is need of an efficient task-scheduling algorithm. Existing task-scheduling algorithms focus on task-resource requirements, CPU memory, execution time and execution cost. However, these do not consider network bandwidth. In this paper, we introduce an efficient task-scheduling algorithm, which presents divisible task scheduling by considering network bandwidth. By this, we can allocate the workflow based on the availability of network bandwidth. Our proposed task-scheduling algorithm uses a nonlinear programming model for divisible task scheduling, which assigns the correct number of tasks to each virtual machine. Based on the allocation, we design an algorithm for divisible load scheduling by considering the network bandwidth.

In this paper, the power consumption of a transparent circuit-switched wavelength-division multiplexing (WDM) optical network is evaluated, considering client flow protection requirements and the daily traffic variability. Moreover, a simple heuristic algorithm is applied to the considered network scenario to reduce the power consumption of optical links. When traffic load decreases, the algorithm tries to switch off optical links according to several heuristic criteria that take into account the power consumption parameter of links, some topological consideration and the congestion of each fiber. By performing such an optimization, applying the best link-ordering criterion, it is possible to save an amount of power from 28% up to 86% of the power consumed by optical links on the basis of the traffic load. This leads to an average energy saving of 35% with respect to the energy consumed by the whole optical network. Moreover, we evaluate the impact of network design parameters on the proposed algorithm and the network power consumption. Results show that, for a 18-node network supporting an average traffic of 75 Gbits/s between each node pair, a number of wavelengths equal to 80 can drastically reduce the power consumption of optical links.

In distributed computing, the schedule by which tasks are assigned to processors is critical to minimizing the finish time of the application. However, the problem of discovering the schedule that gives the minimum finish time is NP-Complete. By combining several innovative techniques, such as insertion-based scheduling and multiple task duplication, this paper presents a new heuristic called the Heterogeneous N-predecessor Decisive Path (HNDP), for statically scheduling directed a-cyclic weighted task graphs (DAGs) on a set of heterogeneous processors to minimize makespan. We compare the performance of HNDP, under a range of input conditions, with two of the best existing heuristics namely HEFT and STDS. We show that HNDP outperforms the two heuristics in finish time and the number of processors employed over a wide range of parameters.

To support the 6G vision of seamless “space–air–ground-integrated” global coverage, optical satellite networks must enable high-speed, low-latency, and intelligent data transmission. However, conventional inter-satellite laser link-based optical transport networks suffer from inefficient bandwidth utilization and nonlinear latency accumulation caused by multi-hop routing, which severely limits their ability to support ultra-low-latency and real-time applications. To address the critical challenges of high topological complexity and stringent real-time requirements in satellite elastic optical networks, we propose an asynchronous advantage actor–critic-based quality of service-aware routing optimization mechanism for the optical inter-satellite link (OISL-AQROM). By establishing a quantitative model that correlates the optical service unit (OSU) C value with node hop count, the algorithm enhances the performance of latency-sensitive services in dynamic satellite environments. Simulation results conducted on a Walker-type low Earth orbit (LEO) constellation comprising 1152 satellites demonstrate that OISL-AQROM reduces end-to-end latency by 76.3% to 37.6% compared to the traditional heuristic multi-constrained shortest path first (MCSPF) algorithm, while supporting fine-grained dynamic bandwidth adjustment down to a minimum granularity of 2.6 Mbps. Furthermore, OISL-AQROM exhibits strong convergence and robust stability across diverse traffic loads, consistently outperforming MCSPF and deep deterministic policy gradient (DDPG) algorithm in overall efficiency, load adaptability, and operational reliability. The proposed algorithm significantly improves service quality and transmission efficiency in commercial mega-constellation optical satellite networks, demonstrating engineering applicability and potential for practical deployment in future 6G infrastructure.

We consider a sensing task scheduling problem in two-level hierarchical sensor networks. To minimize the execution time of a given task, we propose efficient scheduling strategies following the divisible load scheduling paradigm. The proposed scheduling strategies minimize the finish time by eliminating transmission collisions and idle gaps between two successive data transmissions. In-network data aggregation for sensor data is further considered at data fusion nodes. Fused data are produced by some fusion functions on original data from local clusters. The scheduling strategies consist of two phases: intra-cluster scheduling and inter-cluster scheduling. Intra-cluster scheduling deals with assigning different fractions of a sensing workload among source nodes in each cluster; inter-cluster scheduling involves the distribution of fused data among all fusion nodes. Closed-form solutions to the problem of task scheduling are derived. Energy model is described for each kind of sensor nodes, considering data acquisition, communication, and processing. Finally, simulation results are presented to demonstrate the impacts of different system parameters such as the number of sensor nodes, measurement, communication, and processing speed, on the finish time and energy consumption.

Task scheduling is an essential component of any distributed system because it routes tasks to appropriate resources for execution, such as grids, clouds, and peer-to-peer networks. Common scheduling algorithms include downsides, such as high temporal complexity, non-simultaneous processing of input tasks, and longer program execution times. Exploration-based scheduling algorithms prioritize tasks using a variety of methods, resulting in long execution times on heterogeneous distributed computing systems. As a result, task prioritization becomes a bottleneck in such systems. It is appropriate to prioritize tasks with the shortest execution time using faster algorithms. The genetic algorithm (GA) is one of the evolutionary approaches used to solve complex problems quickly. This paper proposes a parallel GA with a MapReduce architecture for scheduling jobs on cloud computing with various priority queues. The fundamental aim of this study is to employ a MapReduce architecture to minimize the total execution time of the task scheduling process in the cloud computing environment. The proposed method accomplishes task scheduling in two stages: first, the GA was used in conjunction with heuristic techniques to assign tasks to processors, and then the GA was used in conjunction with the MapReduce framework to assign jobs to processors. In our experiments, we consider heterogeneous resources that differ in their ability to execute various tasks, as well as running a job on different resources with varying execution durations. The results show that the proposed method outperforms other algorithms such as particle swarm optimization, whale optimization algorithm, moth-flame optimization, and intelligent water drops.

This article examines the requirements for the design of optical fiber communication networks used in telecommunication, data center communication, and analog applications. Figures of merit for different applications are discussed, including signal‐to‐noise ratio, bit error rate, modulation error ratio, and spurious‐free dynamic range. Optical link budgets are reviewed in detail, including transmission loss, wavelength‐dependent attenuation, splice loss, and connector loss. Factors affecting optical link performance are presented, including modal and chromatic dispersion, mode partition noise, relative intensity noise, and other link design parameters. Examples of a simple approach to optical ink design are also discussed.

DOI: 10.1049/ic:20070402 ISBN: 978-3-8007-3042-1 Location: Berlin, Germany Conference date: 16-20 Sept. 2007 Format: PDF We demonstrate theoretically and experimentally that the differential group delay and differential attenuation slope of an optical fiber with polarization-dependent loss can be explicitly obtained by reflectometric measurements in optical frequency and fiber length domains. (2 pages) Inspec keywords: polarisation; optical links; optical dispersion; reflectometry Subjects: Optical variables measurement; Optical fibre networks

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.

Experiments were conducted to evaluate the viability of commercial-off-the-shelf (COTS) technology in the design of high-performance optical intersatellite communications links. The optical links were operated at 155, 622 and 2488 Megabits per second (Mbps) and at 1550 nm wavelength. Communications components were evaluated against performance parameters for on-off keyed (OOK) systems. Transmitters, receivers, optical amplifiers, and filters, used to minimize the impact of amplified spontaneous emission (ASE), were characterized individually and within system configurations. Bit error rate (BER) as a function of photons per bit was characterized for several system configurations. Extinction ratio analysis experiments were conducted to determine the limiting factors on the systems performance. Widely varying optical powers in space (for low earth orbit) imply a robust receiver dynamic range requirement. Dynamic range of COTS receivers was examined and resulted in sufficient performance. In addition, gamma radiation tests on fiber amplifiers were also evaluated in a system context.

Cloud computing is an information technology paradigm that enables ubiquitous access to shared pools of configurable system resources and higher level services required by modern technology. Task scheduling is an important part in cloud computing for limited number of heterogeneous resources and increasing number of user tasks. Task scheduling is to allocate tasks (cloudlets) to the best suitable resources to increase performance in terms of some parameters, such as makespan and resource utilization. Allocating cloudlets with good load balancing and minimum makespan is an NP-hard optimization problem. Many meta-heuristic and heuristic algorithms have been proposed to solve the said problem, but they lack in considering the completion time of virtual machine and total length of its allocated cloudlets instead of only considering completion time of a cloudlet. This lack leads to decrease the performance of a cloud system in some cases, such as large cloudlets. To address the said problem, in this paper, we propose an optimal heuristic cloudlet allocation algorithm for resource allocation and task scheduling, referred as HCA, to cope with the increasing large number of user cloudlets under minimum resource capacity. So, we devise a new mechanism to combine optimal completion time and earliest finish time to minimize both degree of imbalance and overall completion time. The experimental results show that the proposed HCA can achieve effectively and efficiently good performance, best load balancing, and improve the resource utilization in comparison with the other existing cloudlet allocation methods.

SAAS parallel task scheduling based on cloud service flow load algorithm

Elastic optical networking is attracting much attention as a promising solution to achieve spectrum-efficient transport of higher data rates at 100 Gbits/s and beyond. If we draw an analogy to virtualization in cloud computing, it can be seen as network level resource virtualization of optical networks where spectrum resources in optical links are segmented as shareable resources and adaptively aggregated to create a wide variety of optical channels (OChs). In this paper, we discuss the benefits of introducing virtualization into the optical domain from the viewpoints of the network level and the hardware level. In elastic optical networks, a frequency slot through which an OCh is transported and the OCh itself are explicitly decoupled. While the adaptability in the frequency slot is brought about by bandwidth variable wavelength-selective switches, the adaptability in an OCh is yielded by digital coherent technology that is employed in transponders and regenerators. It is emphasized that in order to achieve transponders and regenerators that accommodate heterogeneous traffic demands in an economical manner, simply being adaptive is not enough, and being shareable is essential. We refer to this concept as hardware level virtualization. As examples, we describe a multiflow transponder and an elastic regenerator with results that show proof of concept. Based on the hardware virtualization concept, we propose an elastic optical transport system (EOTS) architecture that enables cost- and energy-efficient IP traffic offloading to the optical domain and improves programmability and automation of optical networks.