A cautious scheduler for multistep transactions

Given a classC of serializable schedules, a cautiousC-scheduler is an on-line transaction scheduler that outputs schedules in classC and never resorts to rollbacks. Such a scheduler grants the current request if and only if the partial schedule it has granted so far, followed by the current request, can be extended to a schedule inC. A suitable extension is searched among the set of all possible sequences of the pending steps, which are predeclared by the transactions whose first requests have already arrived. If the partial schedule cannot be extended to a schedule inC, then the current request is delayed. An efficient cautiousCPSR-scheduler has been proposed by Casanova and Bernstein.This paper discusses cautiousWRW-scheduling, whereWRW is the largest polynomially recognizable subclass of serializable schedules currently known. Since cautiousWRW-scheduling is, in general, NP-complete as shown in this paper, we introduce, a subclass (namedWRW ^#) ofWRW and discuss an efficient cautiousWRW ^#-scheduler. We also show that the fixed point set of the cautiousWRW ^#-scheduler properly containsCPSR. Therefore, ourWRW ^#-scheduler allows more concurrency than anyCPSR- scheduler.This work was supported in part by the Natural Sciences and Engineering Research Council of Canada under Grant No. A5240 and in part by the Ministry of Education, Science and Culture of Japan under Scientific Research Grant-in-Aid.     

A self-adaptable scheduler for synchronizing transactions in dynamically configurable environments

In a database system, the scheduler has the goal of synchronizing operations belonging to several concurrent transactions. The scheduler implements a concurrency control protocol, which may have either conservative or aggressive behavior. Existing database systems have schedulers with static behavior. This paper presents a self-adaptable scheduler, called Intelligent Transaction Scheduler (ITS), which has the ability of dynamically changing its behavior to adapt itself to the characteristics of the computing environment, without any human interference by using an expert system based on fuzzy logic. ITS can implement different correctness criteria, such as classical (syntactic) serializability and semantic serializability.     

A scheduler ASIC for a programmable packet switch

While the Internet is successful in supporting traditional data-only traffic, an integrated services Internet is inevitable with the emergence of new applications such as voice, video, multimedia, and interactive video conferencing. Such an integrated services network should support a wide range of applications with diverse quality of service requirements and traffic characteristics. Provision for quality of service in packet networks in general, and in the Internet in particular, is the focus of most of the recent developments in switching and routing system design. We designed a generic, single-queue scheduler engine for use in a programmable packet switch/router to handle IP packets, ATM cells, or a combination of both. Comprising 275,000 gates, the 0.35-micron ASIC is incorporated into a prototype programmable packet switch     

A distributed scheduler for air traffic flow management

A system was developed to efficiently schedule aircraft into congested resources over long ranges and present that schedule as a decision support system. The scheduling system consists of a distributed network of independent schedulers, loosely coupled by sharing capacity information. This loose coupling insulates the schedules from uncertainty in long-distance estimations of arrival times, while allowing precise short-term schedules to be constructed. This ??rate profile?? mechanism allows feasible schedules to be produced over long ranges, essentially constructing precise short-range schedules that also ensure that future scheduling problems are solvable while meeting operational constraints. The system was tested operationally and demonstrated reduced airborne delay and improved coordination.     

A new rule-based power-aware job scheduler for supercomputers

The fast processing speeds of the current generation of supercomputers provide a great convenience to scientists dealing with extremely large data sets. The next generation of exascale supercomputers could provide accurate simulation results for the automobile industry, aerospace industry, and even nuclear fusion reactors for the very first time. However, the energy cost of super-computing is extremely high, with a total electricity bill of 9 million dollars per year. Thus, conserving energy and increasing the energy efficiency of supercomputers have become critical in recent years. Many researchers have studied this problem and are trying to conserve energy by incorporating the dynamic voltage frequency scaling technique into their methods. However, this approach is limited, especially when the workload is high. In this paper, we developed a power-aware job scheduler by applying a rule-based control method and taking into consideration real-world power and speedup profiles to improve power efficiency while adhering to predetermined power constraints. The intensive simulation results showed that our proposed method is able to achieve the maximum utilization of computing resources as compared to baseline scheduling algorithms while keeping the energy cost under the threshold. Moreover, by introducing a power performance factor based on the real-world power and speedup profiles, we are able to increase the power efficiency by up to 75%.     

A multi-level scheduler for batch jobs on grids

This paper proposes a two-level scheduler for dynamically scheduling a continuous stream of sequential and multi-threaded batch jobs on grids, made up of interconnected clusters of heterogeneous single-processor and/or symmetric multiprocessor machines. The scheduler aims to schedule arriving jobs respecting their computational and deadline requirements, and optimizing the hardware and software resource usage. At the top of the hierarchy a lightweight meta-scheduler (MS) classifies incoming jobs according to their requirements, and schedules them among the underlying resources balancing the workload. At cluster level a Flexible Backfilling algorithm carries out the job machine associations by exploiting dynamic information about the environment. Scheduling decisions at both levels are based on job priorities computed by using different sets of heuristics. The different proposals have been compared through simulations. Performance figures show the feasibility of our approach.     

A neural network job-shop scheduler

This paper focuses on the development of a neural network (NN) scheduler for scheduling job-shops. In this hybrid intelligent system, genetic algorithms (GA) are used to generate optimal schedules to a known benchmark problem. In each optimal solution, every individually scheduled operation of a job is treated as a decision which contains knowledge. Each decision is modeled as a function of a set of job characteristics (e.g., processing time), which are divided into classes using domain knowledge from common dispatching rules (e.g., shortest processing time). A NN is used to capture the predictive knowledge regarding the assignment of operation’s position in a sequence. The trained NN could successfully replicate the performance of the GA on the benchmark problem. The developed NN scheduler was then tested against the GA, Attribute-Oriented Induction data mining methodology and common dispatching rules on a test set of randomly generated problems. The better performance of the NN scheduler on the test problem set compared to other methods proves the feasibility of NN-based scheduling. The scalability of the NN scheduler on larger problem sizes was also found to be satisfactory in replicating the performance of the GA.     

A distributed multi-agent meeting scheduler

Electronic calendars are important tools that are used by consumers on a daily basis. However, scheduling a meeting that involves persons with different commitments and preferences remains a difficult task. Meeting scheduling is difficult because current calendaring applications cannot handle the responsibility of automatically and autonomously managing time slots. This paper presents a distributed multi-agent system architecture in which each person is represented by an agent. These agents automatically and autonomously work together to assist different users to book meetings on their behalf. Each agent has the capability to manage, negotiate and schedule tasks, meetings, events, appointments for its assigned user. In this multi-agent system, the agents coordinate their activities and negotiate on behalf of their associated users to find a solution that satisfies the users' meeting requirements and preferences. A prototype of this system is implemented to demonstrate how the agents can automatically book meetings.     

A comprehensive survey on scheduler for VoIP over WLAN

Voice over Internet Protocol (VoIP) technology has observed rapid growth in the world of telecommunications. VoIP offers high-rate voice services at low cost with good flexibility, typically in a Wireless Local Area Network (WLAN). In a voice conversation, each client works either as a sender or a receiver depending on the direction of traffic flow over the network. A VoIP technologically requires high throughput, less packet loss and a high fairness index over the network. The packets of VoIP streaming may experience drops because of competition among the different kinds of traffic flow over the network. A VoIP application is also sensitive to delays and requires voice packets to arrive on time from the sender to the receiver without any delay over a WLAN. To date, scheduling of VoIP traffic is still an unresolved problem. The objectives of this survey paper are to discuss fundamental principles of VoIP-related schedulers and identify current scheduler issues. This survey paper also identifies the importance of the scheduling techniques over WLANs. Related research work for real-time applications specifically for VoIP will also be highlighted.     

A smart intuitionistic fuzzy-based framework for round-robin short-term scheduler

The Journal of Supercomputing - A smart intuitionistic fuzzy-based framework is designed to facilitate adaptability by providing continuous changes in the size of time slice to scheduler at run...     

A unit-based,cost-efficient scheduler for heterogeneous Hadoop systems

The Journal of Supercomputing - A significant amount of research in the field of job scheduling is carried out in Hadoop. However, there is still need for research to overcome some challenges...     

A general distributed scalable grid scheduler for independent tasks

We consider non-preemptively scheduling a bag of independent mixed tasks (hard, firm and soft) in computational grids. Based upon task type, we construct a novel generalized distributed scheduler (GDS) for scheduling tasks with different priorities and deadlines. GDS is scalable and does not require knowledge of the global state of the system. It is composed of several phases: a multiple attribute ranking phase, a shuffling phase, and a task-resource matched peer to peer dispatching phase. Results of exhaustive simulation demonstrate that with respect to the number of high-priority tasks meeting deadlines, GDS outperforms existing approaches by 10%–25% without degrading schedulability of other tasks. Indeed, with respect to the total number of schedulable tasks meeting deadlines, GDS is slightly better. Thus, GDS not only maximizes the number of mission-critical tasks meeting deadlines, but it does so without degrading the overall performance. The results have been further confirmed by examining each component phase of GDS. Given that fully known global information is time intensive to obtain, the performance of GDS is significant. GDS is highly scalable both in terms of processors and number of tasks—indeed it provides superior performance over existing algorithms as the number of tasks increase. Also, GDS incorporates a shuffle phase that moves hard tasks ahead improving their temporal fault tolerance. Furthermore, since GDS can handle mixed task types, it paves the way to open the grid to make it amenable for commercialization. The complexity of GDS is O(n²m)$O\left(n^{2}m\right)$ where n$n$ is the number of tasks and m$m$ the number of machines.     

A cyclic MAC scheduler for collecting data from heterogeneous sensors

Many wireless sensor networks applications, e.g., structural health monitoring (SHM), require the sensors to construct a multihop network to collect the environmental data in real-time. These sensors generally generate sensing data in fixed rates, so their transmission schedules can be deterministically listed. Time division multiple access (TDMA) is especially appropriate for these applications because it can prevent radio interference, thereby reducing the transmission power and maximizing wireless spectrum reuse. However, to reserve sufficient bandwidths on distinct links of a heterogeneous WSN, a complex TDMA schedule is necessary, and a sensor node might need to keep a large TDMA schedule table in its tiny memory. To prevent a large size TDMA schedule table, this paper proposes a CyclicMAC scheduler that assigns each node a temporal transmission pattern which is merely parameterized by period and phase. The CyclicMAC scheduler determines the period to satisfy the bandwidth requirement of the node, and adjusts the phase to achieve collision-freeness and reduce the end-to-end latency as well. The end-to-end latency of the resulting schedule is proven to be optimal if the wireless links only interfere with their parent link and sibling links. As far as we know, CyclicMAC is the first that simultaneously addresses the three design issues of TDMA scheduling, which satisfies heterogeneous bandwidth requirements, minimizing schedule table size, and reducing end-to-end latency, for multihop wireless sensor networks.     

A minimized makespan scheduler with multiple factors for Grid computing systems

Most scheduling heuristics applied to Heterogeneous Computing (HC) focus on the search of a minimum makespan, instead of the reduction of cost. However, relevant studies presume that HC is based on high-speed bandwidth and communication time has ignored. Furthermore, in response to the appeal for a user-pay policy, when a user submits a job to a Grid environment for computation each implementation of a job would be charged. Therefore, the Apparent Tardiness Cost Setups-Minimum Completion Time (ATCS-MCT) scheduling heuristic considers both makespan and cost, and it composes of execution time, communication time, weight and deadline factors. This study simulates experiments in a dynamic environment, due to the nature of Grid computing being dynamic. The ATCS-MCT is compared to frequent solutions by five scheduling heuristics. This study indicates that the ATCS-MCT achieves a similarly smaller makespan, and lower cost than Minimum Completion Time (MCT) scheduling heuristic, which is the benchmark of on-line mapping.     

Improved scheduler for multi-core many-core systems

Over the years, presence of heterogeneous system has dominated the area of concurrent job execution. Heterogeneous system is the natural choice as it can be designed with the legacy system. Scheduling, on such systems, is an important activity as it affects the job execution characteristic. Heterogeneity introduces many challenges for the efficient job execution. Heterogeneity in core architecture introduces the possibility of heterogeneous memory architecture in many/multi core heterogeneous system. This makes it often impossible to determine for the same instruction if a high frequency core has low or high memory latency in comparison to the low frequency core and vice-versa. The work proposes an improved scheduler for such systems in which both core and memory are heterogeneous. It defines average effective time ( \(\hbox {AE}_\mathrm{t}\) ) as the base parameter for this purpose. Priorities of each thread (workload) and the core are dynamically generated using \(\hbox {AE}_\mathrm{t}\) for effective mapping. Experimental results, on the benchmark data, reveal that the proposed scheduler performs much better in terms of cores utilization, speedup and efficiency in comparison to other similar models.     

A robust/cautious model predictive controller

In process control, a significant number of problems are encountered where there are hard and soft constraints on the measured process variables and/or on the value and rate of change of the manipulated variables. On-line implementation of traditional control strategies becomes unfeasible since they cannot explicitly deal with process constraints. Model predictive control offers a viable alternative; however, a prominent issue is the behaviour of these algorithms when the prediction model does not match the actual plant. A possible solution is to formulate the problem in a linear/non-linear programming framework, using the cutting plane technique to locate the ‘worst’ plant/model mismatch at every time interval. This results in a very practical cautious predictive controller that computes the next controller action based on expected model/plant mismatch. Control of a stirred tank reactor illustrates the method, using a one-step ahead predictive controller.     

An abductive-based scheduler for air-crew assignment

This article presents the design and implementation of an air-crew assignment system, for producing and refining a solution to this problem, based on the artificial intelligence principles and techniques of abductive reasoning as captured by the framework of abductive logic programming (ALP). The system offers a high level of flexibility in addressing both the tasks of crew scheduling and rescheduling. Itcan be used to generate a valid and good quality initial solution and then help the human operators adjust and refine further this solution in order to meet extra requirements of the problem. These additional needs can arise either due to new foreseen requirements that the company wants to have or experiment with for a particular period in time, or due to unexpected events that have occurred while the solution (crew-roster) is in operation. This work shows the ability and flexibility of abduction, and, more specifically, of ALP, in tackling problems of this type with complex and changing requirements.     

A self-tuning multistep predictor application

A self-tuning multistep predictor is presented. It predicts the output of a stochastic process with unknown, possibly slowly time-varying parameters over a range of several sampling periods in the future. At each sampling instant it is tuned by using a recursive least-squares parameter estimator in real time. By doing this, the combination predictor-estimator converges fast to the optimal predictor for processes with known parameters (self-tuning property). The method seems to have powerful capabilities as an aid in controlling complex industrial processes which are until now only operated under manual control. The predictor can be used by the operator in selecting an appropriate control action (decision making). A typical application, the control of a blast furnace, is extensively dealt with in the paper.The paper opens new perspectives in the domain of self-tuning controllers, and it has practical importance as is indicated by the blast-furnace experiment.