Efficient Scheduling Algorithms on Bandwidth Reservation Service of Internet Using Metaheuristics

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.

This paper describes the progress made in improving the bandwidth-on-demand (BoD) capabilities of the Japanese academic backbone network called SINET3. Since the start of layer-1 BoD services in 2008, we have expanded the service provision area nationwide, introduced functions to improve the usability in response to user requests, and flexibly managed the network resources by taking into account a hybrid accommodation of the layer-1 and layer2/3 services. We are also approaching layer-2 BoD services for applications requiring a relatively small amount of bandwidth. The current status for layer-1 BoD services and the near-future plan for layer-2 BoD services including the initial implementation image are described.

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.

Many of today's high-level parallel languages support dynamic, fine-grained parallelism. These languages allow the user to expose all the parallelism in the program, which is typically of a much higher degree than the number of processors. Hence an efficient scheduling algorithm is required to assign computations to processors at runtime. Besides having low overheads and good load balancing, it is important for the scheduling algorithm to minimize the space usage of the parallel program. This article presents an on-line scheduling algorithm that is provably space efficient and time efficient for nested-parallel languages. For a computation with depth D and serial space requirement S 1 , the algorithm generates a schedule that requires at most S 1 + O (K•D•p ) space (including scheduler space) on p processors. Here, K is a user-adjustable runtime parameter specifying the net amount of memory that a thread may allocate before it is preempted by the scheduler. Adjusting the value of K provides a trade-off between the running time and the memory requirement of a parallel computation. To allow the scheduler to scale with the number of processors we also parallelize the scheduler and analyze the space and time bounds of the computation to include scheduling costs. In addition to showing that the scheduling algorithm is space and time efficient in theory, we demonstrate that it is effective in practice. We have implemented a runtime system that uses our algorithm to schedule lightweight parallel threads. The results of executing parallel programs on this system show that our scheduling algorithm significantly reduces memory usage compared to previous techniques, without compromising performance.

We examine bandwidth on demand (BoD) services from the perspective of customers' use of BoD to most economically satisfy their bandwidth requirements and how those decisions affect the viability of carriers to offer BoD services.

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.

Scheduling algorithms are fundamental components in the process of resource management in mobile communication networks with heterogeneous QoS requirements such as delay, delay jitter, packet loss rate or throughput. The random characteristics of the propagation environment and the use of complex physical layers in order to combat this random behavior further complicates the design of simple, efficient, scalable and fair scheduling algorithms. This paper presents the main criteria used in the design of scheduling algorithms for 3G/4G mobile communications networks and provides a survey of scheduling mechanisms proposed for use in TDMA and CDMA based systems.Keywordsscheduling4GGPSutility functions

Location-awareness plays a key role in various applications in future wireless networks. In GPS-challenged environments, location-awareness can be achieved via wireless navigation networks, which call for an efficient scheduling algorithm to optimize the navigation performance under communication constraints. In this paper, we develop a general framework for the design and analysis of scheduling algorithms for navigation networks with multiple measurement pairs per time slot. In particular, we provide sufficient and necessary conditions for the stability of the error evolution, and derive bounds on the time-averaged network localization errors (NLEs) for opportunistic and random scheduling. Furthermore, we show that the two scheduling algorithms are optimal in terms of the error scaling with respect to the number of agents, and we quantify the performance gain from measurement pair selections exploiting the network states. These results provide guidelines for designing efficient scheduling algorithms for network navigation.

Job scheduling is an important issue in computational grid. In computational grid, computing resources are connected through networks and may locate at different network or regional areas. So, the computational capabilities or network status of computing resources may be different. If we want to take advantage of computational grid, an efficient scheduling algorithm is necessary to assign jobs to the appropriate computing nodes. In this paper, we propose a genetic algorithm based evolutional algorithm (G-EA) to solve the scheduling problems in computational grid. The proposed algorithm uses the optimal searching technique of genetic algorithm and takes different computing capabilities of computing nodes and dynamic network status into consideration. In order to verify the performance of G-EA, a simulation was performed, and it was then compared with four other scheduling algorithms. The results show that the proposed G-EA outperformed all other schedulers across a range of scenarios.

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 Static List Scheduling (SLS), initially the ALAP (As Late As Possible) algorithm is performed, followed by ASAP (As Soon As Possible) algorithm. The time interval for control steps and successor selection are the vital drawback of ASAP algorithm. Hence, in this paper, the optimized scheduling algorithm is proposed for High Level Synthesis (HLS), where the ALAP in SLS is followed by Force Directed Scheduling (FDS). The proposed scheduling algorithm is implemented in Icarus Verilog and targeted in ZC7020 FPGA board. The proposed optimized scheduling algorithm is evaluated in terms of control steps and resource utilization. The evaluation is carried out for various benchmarks such as, EW Filter, IIR filter, FIR filter, AR filter and Discrete Cosine Transform (DCT). The achieved results in terms of the control steps and resource utilization (i.e. LUT and Flip-flop) shows the proposed optimized scheduling algorithm outperforms the conventional Static List Scheduling (SLS) algorithm.

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

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.

Aim: Our research aims to explore a fast and efficient scheduling algorithm. The purpose is to schedule large-scale tasks on a limited number of processors reasonably while improving resource utilization. Methods: This paper proposes a clustering and duplication-based method for large-scale task scheduling on a limited amount of processors. We cluster large-scale task to reduce the scale of the task in our method at first. Second, duplication-based task scheduling is carried out. Third, we optimize the local effect more precisely by deduplication in the last stage. Results: We compare our algorithm with the state-of-the-art algorithms in the article. The results demonstrate that our scheduling scheme obtains about 30% optimization compared to existing large-scale scheduling methods and runs roughly ten times faster than existing duplication-based algorithms when scheduling large-scale tasks to a limited number of processors as compared to similar algorithms. Conclusion: In this paper, we propose a task scheduling algorithm that can decrease the scheduling time of large- scale tasks on a limited number of processors and speed up the global execution time of the task. Further, we will study large-scale task scheduling on heterogeneous processor clusters. Keywords