Dynamic scheduling of network resources with advance reservations in optical grids

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.

We consider routing and wavelength assignment in wavelength-routed all-optical networks (WAN) with circuit switching. The conventional approaches to address this issue consider the two aspects of the problem disjointly by first finding a route from a predetermined set of candidate paths and then searching for an appropriate wavelength assignment. We adopt a more general approach in which we consider all paths between a source-destination (s-d) pair and incorporate network state information into the routing decision. This approach performs routing and wavelength assignment jointly and adaptively, and outperforms fixed routing techniques. We present adaptive routing and wavelength assignment algorithms and evaluate their blocking performance. We obtain an analytical technique to compute approximate blocking probabilities for networks employing fixed and alternate routing. The analysis can also accommodate networks with multiple fibers per link. The blocking performance of the proposed adaptive routing algorithms are compared along with their computational complexity.

WiMAX network provides effective internet communication between users with low expense and ease of deployment. It is used efficiently in small to medium enterprises. However, proficient resource allocation and scheduling is still a critical requirement in WiMAX networks due to the varying factors related to the network communication. If the network resources are not appropriately allocated, then there are possibilities for missing out critical data, or wasting more resources on transmitting less important data which in turn will have adverse affect on data transmission in the future stages. Hence, in this paper, we propose to develop a Priority Based Dynamic Resource Allocation and Scheduling for WiMAX Networks. In this technique, the incoming data are dynamically allocated, and then users are prioritized to identify the critical users. The allocation and scheduling is performed by considering user priority, robustness and also power consumption rate to ensure effective network performance.

Assigning jobs to optimum resources in a grid environment is the main aim of a grid scheduler. Communication cost has always been an important issue in grid environments. Proposing new scheduling algorithms to consider this cost accurately and allocate jobs to an optimum resource efficiently has always been of great importance. In this paper we have proposed a grid scheduling algorithm that is aware of the costs for different network paths. We have also considered soft real-time characteristics of jobs in our proposed algorithm. We have simulated and compared our algorithm with some key scheduling algorithms such as Least Load First (LLF), Random, and First Come First Served (FCFS) in Gridsim by considering soft real-time jobs. Results show the superiority of our scheduling algorithm due to its ability to predict network cost and to satisfy the demands of soft real-time tasks.

Opportunistic networks are formed by small devices that communicate over a wireless link with each other. Opportunistic network possess various heterogeneous resources. Resource management and scheduling of these heterogeneous resources are major problems in the Opportunistic network due to highly dynamic behavior of the devices in the opportunistic network. Especially challenging is the multisuser aspect in the opportunistic network environment, since with a high and changeable number of users the workload is difficult to predict, and so making a proper scheduling of tasks becomes a serious problem. The overheads imposed by the waiting queues may become a major part of the total execution time. So to efficiently manage the resources in the opportunistic network, a Grid environment is considered to address the problem of fair sharing. An auction model which is an economic based technique has also been considered in the Grid environment. In the proposed work, a second price auction mechanism is considered to employ a pricing mechanism that is incentive compatible. Incentive compatible pricing mechanisms ensure that the users' self-interests are maximized when they honestly reveal their preferences to the allocation mechanism. These mechanisms can provide powerful guarantees about optimal user behavior and can deliver highly efficient allocations. So in the proposed work, a second price auction mechanism is considered which is very efficient and also it elicits honest information from the participants thereby successfully managing the resource allocation in the Grid environment. A Gossip based mechanism is also considered in the proposed work in order to reduce the overheads caused by the waiting queues.

In optical WDM networks, transmission of information along optical lines is advantageous since it has high transmission capacity, scalability, feasibility and also high reliability. But large amount of information is being carried; any problem during transmission can lead to severe damage to the data being carried. Hence it is essential to consider the routing as well as the wavelength assignment problems and then develop a combined solution for both the problems. In this paper, we propose to develop a routing and wavelength assignment algorithm for selecting the suitable alternate path for the data packets transmission. Two stages are based on the available bandwidth and the number of wavelength used in the link as construction of alternate paths, route and wavelength selection. In proposed work, Adaptive Routing and First-Fit Wavelength Assignment (AR-FFWA) algorithm to be used. For each pair of source and destination, the path with the minimum granularity values are selected as the primary path for data transmission, allocating the sufficient wavelength and the performances will be evaluated by using ns-2 simulation models. When we compared to existing system the overall blocking probability will be reduced to too low.

Grid computing provides a high performance computing platform to solve larger scale applications by coordinating and sharing computational power, data storage and network resources across dynamic and geographically dispersed organizations. Scheduling onto the Grid is NP-complete, so there is no best scheduling algorithm for all grid computing systems. An alternative is to select an appropriate scheduling algorithm to use in a given grid environment because of the characteristics of the tasks, machines and network connectivity. Job scheduling is one of the key research area in grid computing. The goal of scheduling is to achieve highest possible system throughput and to match the application need with the available computing resources. Motivation of this study is to encourage and help the amateur researcher in the field of grid computing, so that they can understand easily the concept of scheduling and can contribute in developing more efficient and practical scheduling algorithm. This will benefit interested researchers to carry out further work in this thrust area of research.

In trust-based network computing environment, in order to establish a harmonious network environment, and enhance the network resources sharing utilization, resources scheduling research faces more problems than ever before. A resource scheduling algorithm, using cooperative particle optimization and putting task completion price and overall system trust benefits as optimized object, is proposed based on the trust model proposed by Internet-based Virtual Computing Environment (iVCE). Simulation results indicate that the algorithm has obvious performance advantage in the aspects of whole system credibility and resource utilization rate than other classic scheduling algorithms. The algorithm can realize rational scheduling of network resources, and optimize the system performance in trusted computing environment.

With more and more research carried on in the QoS of Grid, QoS-based Grid task scheduling algorithm has become a hot research aspect. In this paper, various existing QoS-based Grid scheduling algorithms are analyzed firstly. And by introducing the conception of QoS matching offset between tasks and resources in Grid scheduling, resources and tasks can be clustering upon their offset in order that the resources in scheduling are able to be allocated on demand. Meanwhile, we take into consideration some parameters in the scheduling like QoS benefit value obtained by the task and some restricted condition: deadline of the task. The simulation results show that the algorithm's performance is better than most of the proposed algorithms in the aspects of effective resource utility, resource load balance, task acceptance rate and average QoS benefit value.

Mobile cloud computing (MCC) as an emerging and prospective computing paradigm, can significantly enhance computation capability and save energy of smart mobile devices (SMDs) by offloading computation-intensive tasks from resource-constrained SMDs onto the resource-rich cloud. However, how to achieve energy-efficient computation offloading under the hard constraint for application completion time remains a challenge issue. To address such a challenge, in this paper, we provide an energy-efficient dynamic offloading and resource scheduling (eDors) policy to reduce energy consumption and shorten application completion time. We first formulate the eDors problem into the energy-efficiency cost (EEC) minimization problem while satisfying the task-dependency requirements and the completion time deadline constraint. To solve the optimization problem, we then propose a distributed eDors algorithm consisting of three subalgorithms of computation offloading selection, clock frequency control and transmission power allocation. More importantly, we find that the computation offloading selection depends on not only the computing workload of a task, but also the maximum completion time of its immediate predecessors and the clock frequency and transmission power of the mobile device. Finally, our experimental results in a real testbed demonstrate that the eDors algorithm can effectively reduce the EEC by optimally adjusting the CPU clock frequency of SMDs based on the dynamic voltage and frequency scaling (DVFS) technique in local computing, and adapting the transmission power for the wireless channel conditions in cloud computing.

Clusters, Grids, and peer‐to‐peer (P2P) networks have emerged as popular paradigms for next generation parallel and distributed computing. They enable aggregation of distributed resources for solving large‐scale problems in science, engineering, and commerce. In Grid and P2P computing environments, the resources are usually geographically distributed in multiple administrative domains, managed and owned by different organizations with different policies, and interconnected by wide‐area networks or the Internet. This introduces a number of resource management and application scheduling challenges in the domain of security, resource and policy heterogeneity, fault tolerance, continuously changing resource conditions, and politics. The resource management and scheduling systems for Grid computing need to manage resources and application execution depending on either resource consumers' or owners' requirements, and continuously adapt to changes in resource availability.The management of resources and scheduling of applications in such large‐scale distributed systems is a complex undertaking. In order to prove the effectiveness of resource brokers and associated scheduling algorithms, their performance needs to be evaluated under different scenarios such as varying number of resources and users with different requirements. In a Grid environment, it is hard and even impossible to perform scheduler performance evaluation in a repeatable and controllable manner as resources and users are distributed across multiple organizations with their own policies. To overcome this limitation, we have developed a Java‐based discrete‐event Grid simulation toolkit called GridSim. The toolkit supports modeling and simulation of heterogeneous Grid resources (both time‐ and space‐shared), users and application models. It provides primitives for creation of application tasks, mapping of tasks to resources, and their management. To demonstrate suitability of the GridSim toolkit, we have simulated a Nimrod‐G like Grid resource broker and evaluated the performance of deadline and budget constrained cost‐ and time‐minimization scheduling algorithms. Copyright © 2002 John Wiley & Sons, Ltd.

The significant requirement in cloud computing is detecting the deterministic model for performing resource scheduling. Resource scheduling is crucial because of the presence of several anomalies and high dimensionality values shown during resource scheduling. The number of tasks envisioned in contention at resource broker of IAAS and higher dimensionality of proposed anomalies values for quality resource aspects was out of scope concerning the existing resource scheduling techniques. Therefore, the contemporary resource scheduling techniques were not prominent for optimal resource scheduling. For optimising resource scheduling in terms of earlier mentioned properties like the maximum amount of tasks and maximum dimensionality anomalies projection, an ensemble resource scheduling technique has been derived in this article under batch scheduling classification. Further, the simulation study envisioned that the projected model of this contribution is more significant and robust for delivering optimum resource scheduling when compared to other contemporary models in terms of dimensionality and volume of anomalies.

In order to obtain the lowest possible latency, routing algorithms should try to avoid a message waiting for resources (network links) blocked by other messages or multiplexing of more messages over one physical channel. This requirement becomes especially important in the case of long messages. The only type of protocols able to guarantee waiting free routing under heavy load are algorithms based on deflection (also called nonminimal adaptive or hot potato) routing. This paper deals with problems connected with the use of deflection algorithms. In contrast to the case of nonadaptive or partially (e.g., minimal) adaptive routing, it is very infrequent that an unrestricted deflection routing becomes deadlocked and, similarly, livelock is not a serious problem. On the other hand, there is another phenomenon, called a deflection jam, that limits throughput of deflection algorithms used to route long messages. It has been observed for many deflection heuristics, interconnection network topologies, and both virtual cut-through and wormhole routing. A deflection jam is a sudden and persistent saturation of a network which sometimes occur, after a very long period of undisturbed communication. This paper describes events that trigger this saturation which suggest ways to design improved and stable deflection routing algorithms.

Nowadays scheduling jobs on parallel computers or grid components in grid environment has becoming more and more important. Detailed knowledge of the individual user preference is a necessary factor to be taken into account for developing appropriate scheduling strategies. Other than minimizing makespan [1], workload and the balancing of resource utilization is also an important aspect for scheduling. In the premise of satisfying the preference of individual user, previous approaches mainly focused on minimizing makespan, however workload balancing is rarely optimized. Therefore some of resources may leave underused while other resources are overused. In this paper, a new dynamic scheduling scheme is proposed, using the packing and partial cloning, and the expected QoS can be obtained. This scheme has been further evaluated by simulations and shown to yield good results compared with the former studies.

PurposeIn recent years, it is imperative to establish the structure of manufacturing industry in the context of smart factory. Due to rising demand for exchange of information with various devices, and huge number of sensor nodes, the industrial wireless networks (IWNs) face network congestion and inefficient task scheduling. For this purpose, software-defined network (SDN) is the emerging technology for IWNs, which is integrated into cognitive industrial Internet of things for dynamic task scheduling in the context of industry 4.0.Design/methodology/approachIn this paper, the authors present SDN based dynamic resource management and scheduling (DRMS) for effective devising of the resource utilization, scheduling, and hence successful transmission in a congested medium. Moreover, the earliest deadline first (EDF) algorithm is introduced in authors’ proposed work for the following criteria’s to reduce the congestion in the network and to optimize the packet loss.FindingsThe result shows that the proposed work improves the success ratio versus resource usage probability and number of nodes versus successful joint ratio. At last, the proposed method outperforms the existing myopic algorithms in terms of query response time, energy consumption and success ratio (packet delivery) versus number of increasing nodes, respectively.Originality/valueThe authors proposed a priority based scheduling between the devices and it is done by the EDF approach. Therefore, the proposed work reduces the network delay time and minimizes the overall energy efficiency.