Random Scheduling Algorithm Research Articles (Page 1)

The problem of resource allocation of scarce is a very important task especially in dynamic and non-stable environments when the importance and priority of recourse are changing while the system is already working in our work firstly we simulate the problem by mathematical model than the essential part is to use intelligent algorithms like comparing random scheduling, hill climbing, simulating annealing and genetic algorithms, by using these tactics of such kind of algorithms we designed an algorithm that can handle with every type of multi-agent learning-based resource allocation for distributed systems that act place of most promising approaches to these kinds of allocating systems problems. The approach will be authorized by achieving on different types of large scale systems than correlated with the regular algorithms to compare the result and get the efficacy of new algorithm.

Several recently proposed randomized testing tools for concurrent and distributed systems come with theoretical guarantees on their success. The key to these guarantees is a notion of bug depth—the minimum length of a sequence of events sufficient to expose the bug—and a characterization of d -hitting families of schedules—a set of schedules guaranteed to cover every bug of given depth d . Previous results show that in certain cases the size of a d -hitting family can be significantly smaller than the total number of possible schedules. However, these results either assume shared-memory multithreading, or that the underlying partial ordering of events is known statically and has special structure. These assumptions are not met by distributed message-passing applications. In this paper, we present a randomized scheduling algorithm for testing distributed systems. In contrast to previous approaches, our algorithm works for arbitrary partially ordered sets of events revealed online as the program is being executed. We show that for partial orders of width at most w and size at most n (both statically unknown), our algorithm is guaranteed to sample from at most w 2 n d −1 schedules, for every fixed bug depth d . Thus, our algorithm discovers a bug of depth d with probability at least 1 / ( w 2 n d −1 ). As a special case, our algorithm recovers a previous randomized testing algorithm for multithreaded programs. Our algorithm is simple to implement, but the correctness arguments depend on difficult combinatorial results about online dimension and online chain partitioning of partially ordered sets. We have implemented our algorithm in a randomized testing tool for distributed message-passing programs. We show that our algorithm can find bugs in distributed systems such as Zookeeper and Cassandra, and empirically outperforms naive random exploration while providing theoretical guarantees.

In this study, a crosspoint‐queued (CQ) switch that features a buffer size larger than one and employs the work‐conserving random scheduling algorithm under uniform Bernoulli i.i.d. traffic is investigated through both theoretical analysis and simulation. The effect of the crosspoint buffer state on switch performance is modelled and quantified. A novel method for calculating the throughput and delay of a CQ switch with a given switch size, input load, and buffer size is proposed. Compared with existing models, the proposed calculation model offers a simpler and more accurate result, and can serve as a theoretical guide in designing high‐speed switch fabrics.

Motivated by emerging big streaming data processing paradigms (e.g., Twitter Storm, Streaming MapReduce), we investigate the problem of scheduling graphs over a large cluster of servers. Each graph is a job, where nodes represent compute tasks and edges indicate data flows between these compute tasks. Jobs (graphs) arrive randomly over time and, upon completion, leave the system. When a job arrives, the scheduler needs to partition the graph and distribute it over the servers to satisfy load balancing and cost considerations. Specifically, neighboring compute tasks in the graph that are mapped to different servers incur load on the network; thus a mapping of the jobs among the servers incurs a cost that is proportional to the number of “broken edges.” We propose a low-complexity randomized scheduling algorithm that, without service preemptions, stabilizes the system with graph arrivals/departures; more importantly, it allows a smooth tradeoff between minimizing average partitioning cost and average queue lengths. Interestingly, to avoid service preemptions, our approach does not rely on a Gibbs sampler; instead, we show that the corresponding limiting invariant measure has an interpretation stemming from a loss system.

Nowadays, the administration of resources and application scheduling in a large-scale cloud storage environment is a formidable task, especially in dynamic changing scenario both in storage providers and consumers requirement sides. In order to meet the challenge of dynamic change characteristic in cloud storage application, this paper presents a new QoS-aware approach to match individual applications to most appropriate cloud storage nodes based on their particular attribute matrix, such as capacity, response time, I/O throughput, availability and user requirements etc. The experimental results illustrate that our QoS-aware method can achieve better performance than existing random scheduling algorithm in terms of load balance, response time and I/O throughput.

Input-Queued switching fabrics are widely used in high speed network switches for their scalability. However, a scheduling algorithm must be elaborative designed for input-queued switches to get high throughput. The randomized algorithms are attractive in kinds of scheduling algorithms for their low complexity and well performance. In this paper, a fluid model is introduced to analyze the rate stability of switching systems with randomized schedulers. By this method, we proved that the randomized scheduling algorithm can achieve 100% throughput under any admissible traffics which satisfy the strong law of large numbers (SLLN).

Random unitary beamforming is one of the schemes that can reduce the amount of feedback information in multiuser diversity techniques with multiple-antenna downlink transmission. In Multiple-Input Multiple-Output (MIMO) systems, throughput performance is greatly improved using AMC (Adaptive Modulation and Coding). Throughput performance is also improved by allocating power among streams appropriately. In random unitary beamforming, the transmitter has only partial channel state information (CSI) of each receiver. Thus, it is difficult for random unitary beamforming to use conventional power allocation methods that assumes that all receivers has full CSI. In this paper, we propose a new scheduling algorithm with power allocation for downlink random unitary beamforming that improves throughput performance without full CSI. We provide numerical results of the proposed scheduling algorithm and compare them to those of the conventional random unitary beamforming scheduling algorithm. We show that random unitary beamforming achieves the best system throughput performance with two transmit antennas. We also show that the proposed algorithm attains higher throughput with the small increase of feedback than the random unitary beamforming scheduling algorithm.

In this paper jobshop scheduling problem was considered on automated manufacturing systems with the closed loop and unidirectional material handling system. The objective of this research is to develop and evaluate heuristic scheduling procedures to improve productivity by minimizing makespan. Especially travel time of material handling system as well as processing time was considered in the proposed alogorithm. The heiristic algorithms are implemented for various cases. The results show that the proposal algorithms provide better solutions in productivity, frequency, job waiting time and the number of waiting jobs than the random scheduling algorithm.