An incentive-based distributed mechanism for scheduling divisible loads in tree networks

The underlying assumption of Divisible Load Scheduling (DLS) theory is that the processors composing the network are obedient, i.e., they do not “cheat” the scheduling algorithm. This assumption is unrealistic if the processors are owned by autonomous, self-interested organizations that have no a priori motivation for cooperation and they will manipulate the algorithm if it is beneficial to do so. In this paper, we address this issue by designing a distributed mechanism for scheduling divisible loads in tree networks, called DLS-T, which provides incentives to processors for reporting their true processing capacity and executing their assigned load at full processing capacity. We prove that the DLS-T mechanism computes the optimal allocation in an ex post Nash equilibrium. Finally, we simulate and study the mechanism under various network structures and processor parameters.     

A distributed switch scheduling algorithm

The maximum weight matching algorithm is a high-performance scheduling algorithm for cross-bar switches. It is known that it performs optimally under heavy loads. However, its centralized nature and high computational complexity limit the algorithm’s applicability. This paper presents a randomized algorithm for distributed switch scheduling that is capable of delivering high throughput.     

Solving distributed FMS scheduling problems subject to maintenance: Genetic algorithms approach

In general, distributed scheduling problem focuses on simultaneously solving two issues: (i) allocation of jobs to suitable factories and (ii) determination of the corresponding production scheduling in each factory. The objective of this approach is to maximize the system efficiency by finding an optimal planning for a better collaboration among various processes. This makes distributed scheduling problems more complicated than classical production scheduling ones. With the addition of alternative production routing, the problems are even more complicated. Conventionally, machines are usually assumed to be available without interruption during the production scheduling. Maintenance is not considered. However, every machine requires maintenance, and the maintenance policy directly affects the machine's availability. Consequently, it influences the production scheduling. In this connection, maintenance should be considered in distributed scheduling. The objective of this paper is to propose a genetic algorithm with dominant genes (GADG) approach to deal with distributed flexible manufacturing system (FMS) scheduling problems subject to machine maintenance constraint. The optimization performance of the proposed GADG will be compared with other existing approaches, such as simple genetic algorithms to demonstrate its reliability. The significance and benefits of considering maintenance in distributed scheduling will also be demonstrated by simulation runs on a sample problem.     

Fair scheduling of bag-of-tasks applications using distributed Lagrangian optimization

Large scale distributed systems typically comprise hundreds to millions of entities (applications, users, companies, universities) that have only a partial view of resources (computers, communication links). How to fairly and efficiently share such resources between entities in a distributed way has thus become a critical question.     

A modified genetic algorithm approach for scheduling of perfect maintenance in distributed production scheduling

Distributed Scheduling (DS) problems have attracted attention by researchers in recent years. DS problems in multi-factory production are much more complicated than classical scheduling problems because they involve not only the scheduling problems in a single factory, but also the problems in the higher level, which is: how to allocate the jobs to suitable factories. It mainly focuses on solving two issues simultaneously: (i) allocation of jobs to suitable factories and (ii) determination of the corresponding production schedules in each factory. Its objective is to maximize system efficiency by finding an optimal plan for a better collaboration among various processes. However, in many papers, machine maintenance has usually been ignored during the production scheduling. In reality, every machine requires maintenance, which will directly influence the machine's availability, and consequently the planned production schedule. The objective of this paper is to propose a modified genetic algorithm approach to deal with those DS models with maintenance consideration, aiming to minimize the makespan of the jobs. Its optimization performance has been compared with other existing approaches to demonstrate its reliability. This paper also tests the influence of the relationship between the maintenance repairing time and the machine age to the performance of scheduling of maintenance during DS in the studied models.     

Multi-objective list scheduling of workflow applications in distributed computing infrastructures

Executing large-scale applications in distributed computing infrastructures (DCI), for example modern Cloud environments, involves optimization of several conflicting objectives such as makespan, reliability, energy, or economic cost. Despite this trend, scheduling in heterogeneous DCIs has been traditionally approached as a single or bi-criteria optimization problem. In this paper, we propose a generic multi-objective optimization framework supported by a list scheduling heuristic for scientific workflows in heterogeneous DCIs. The algorithm approximates the optimal solution by considering user-specified constraints on objectives in a dual strategy: maximizing the distance to the user’s constraints for dominant solutions and minimizing it otherwise. We instantiate the framework and algorithm for a four-objective case study comprising makespan, economic cost, energy consumption, and reliability as optimization goals. We implemented our method as part of the ASKALON environment (Fahringer et al., 2007) for Grid and Cloud computing and demonstrate through extensive real and synthetic simulation experiments that our algorithm outperforms related bi-criteria heuristics while meeting the user constraints most of the time.     

Multiple query scheduling for distributed semantic caches

In distributed query processing systems, load balancing plays an important role in maximizing system throughput. When queries can leverage cached intermediate results, improving the cache hit ratio becomes as important as load balancing in query scheduling, especially when dealing with computationally expensive queries. The scheduling policies must be designed to take into consideration the dynamic contents of the distributed caching infrastructure. In this paper, we propose and discuss several distributed query scheduling policies that directly consider the available cache contents by employing distributed multidimensional indexing structures and an exponential moving average approach to predicting cache contents. These approaches are shown to produce better query plans and faster query response times than traditional scheduling policies that do not predict dynamic contents in distributed caches. We experimentally demonstrate the utility of the scheduling policies using MQO, which is a distributed, Grid-enabled, multiple query processing middleware system we developed to optimize query processing for data analysis and visualization applications.     

Remote database access in the distributed computing environment

This paper describes the design, implementation and testing of a set of software modules that are used for remote database access in a distributed computing environment. The goal of our research and development is to implement a client server model using Structured Query Language (SQL) functions in the Open Software Foundation's (OSF) distributed computing environment (DCE). This design is compared with another which simply uses the sockets application programming interface (API), running over the transmission control protocol/internet protocol (TCP/IP), and is implemented in subroutines that act similar to the remote procedure call (RPC) generated stub code. The prototypes for the remote SQL access project are implemented using an IBM RISC System/6000 (the client) running the AIX operating system and an IBM AS/400 (the server) running the OS/400 operating system. We selected the AS/400 for its database abilities, and the RISC System/6000 since the DCE software is available for it.     

An environment for prototyping distributed applications

Designing a distributed application is an extremely complex task. Proper facilities for prototyping distributed applications can be useful in evaluating a design, and also in understanding the effect of different parameters on the performance of an application. We describe a language for prototyping distributed applications, that supports different communication primitives with specified delay, and provides primitives to aid debugging and evaluation. Our environment for executing distributed programs supports heterogeneous computation in which processes can execute on different hardware. Different source languages can be used for coding different modules of the processes. The system has a centralized control and monitoring facility which is based on the Suntools window system.     

The distributed permutation flowshop scheduling problem

This paper studies a new generalization of the regular permutation flowshop scheduling problem (PFSP) referred to as the distributed permutation flowshop scheduling problem or DPFSP. Under this generalization, we assume that there are a total of F identical factories or shops, each one with m machines disposed in series. A set of n available jobs have to be distributed among the F factories and then a processing sequence has to be derived for the jobs assigned to each factory. The optimization criterion is the minimization of the maximum completion time or makespan among the factories. This production setting is necessary in today's decentralized and globalized economy where several production centers might be available for a firm. We characterize the DPFSP and propose six different alternative mixed integer linear programming (MILP) models that are carefully and statistically analyzed for performance. We also propose two simple factory assignment rules together with 14 heuristics based on dispatching rules, effective constructive heuristics and variable neighborhood descent methods. A comprehensive computational and statistical analysis is conducted in order to analyze the performance of the proposed methods.     

Modeling and heuristics for scheduling of distributed job shops

This paper deals with the problem of distributed job shop scheduling in which the classical single-facility job shop is extended to the multi-facility one. The mathematical formulation of the problem is comprehensively discussed. Two different mixed integer linear programming models in form of sequence and position based variables are proposed. Using commercial software of CPLEX, the small sized problems are optimally solved. To solve large sized problems, besides adapting three well-known heuristics, three greedy heuristics are developed. The basic idea behind the developed heuristics is to iteratively insert operations (one at each iteration) into a sequence to build up a complete permutation of operations. The permutation scheme, although having several advantages, suffers from redundancy which is having many different permutations representing the same schedule. The issue is analyzed to recognize the redundant permutation. That improves efficiency of heuristics. Comprehensive experiments are conducted to evaluate the performance of the two models and the six heuristics. The results show sequence based model and greedy heuristics equipped with redundancy exclusion are effective for the problem.     

分布式多媒体系统中的多点播送与组通信机制

描述多点播送和组通信机制的基本概念，讨论了通信子网中的多点播送通信机制机和端系统中的且通信机制，最后介绍应用实例。     

A complex event routing infrastructure for distributed systems

With the growing number of mega services and cloud computing platforms, industrial organizations are utilizing distributed data centers at increasing rates. Rather than the request/reply model, these centers use an event-based communication model. Traditionally, the event-based middleware and the Complex Event Processing (CEP) engine are viewed as two distinct components within a distributed system’s architecture. This division adds additional system complexity and reduces the ability for consuming applications to fully utilize the CEP toolset. This article will address these issues by proposing a novel event-based middleware solution. We introduce Complex Event Routing Infrastructure (CERI), a single event-based infrastructure that serves as an event bus and provides first class integration of CEP. An unstructured peer-to-peer network is exploited to allow for efficient event transmission. To reduce network flooding, superpeers and overlay network partitioning are introduced. Additionally, CERI provides each client node the capability of local complex query evaluation. As a result, applications can offload internal logic to the query evaluation engine in an efficient manner. Finally, as more client nodes and event types are added to the system, the CERI can scale up. Because of these favorable scaling properties, CERI serves as a foundational step in bringing event-based middleware and CEP closer together into a single unified infrastructure component.     

Comparison of a centralised and distributed approach for a generic scheduling system

PEGS (Production and Environmental Generic Scheduler) is a generic production scheduler that produces good schedules over a wide range of problems. It is centralised, using search strategies with the Shifting Bottleneck algorithm. We have also developed an alternative distributed approach using software agents. In some cases this reduces run times by a factor of 10 or more. In most cases, the agent-based program also produces good solutions for published benchmark data, and the short run times make our program useful for a large range of problems. Test results show that the agents can produce schedules comparable to the best found so far for some benchmark datasets and actually better schedules than PEGS on our own random datasets. The flexibility that agents can provide for today’s dynamic scheduling is also appealing. We suggest that in this sort of generic or commercial system, the agent-based approach is a good alternative.     

Simulation study of multitasking in distributed server systems with variable workload

The multitasking performance of a distributed server system is considered where the workload is variable. Time varying distributions are proposed for job arrival, job parallelism, and task service demand. A simulation model is used to address performance issues associated with task scheduling. The objective is to identify conditions that produce good overall system performance, while maintaining fairness of individual job execution times. Simulated results show that all task scheduling methods have merit. In all cases, the best policy depends on performance goals. Furthermore, although the paper studies distributed multiprocessor system performance, it also addresses the performance issues of other systems where multitasking and scheduling is used, since no restrictions are imposed on the server/job/task characteristics.     

分布式调度算法在VOQ交换机中的应用

为提高高速通信网络的通信效率,针对VOQ交换机,提出在交换机的各个输出端口中进行分布式通信调度(DSA)的策略。DSA算法可直接支持变长数据包通信调度,克服了传统信元交换只能调度定长数据包的缺点,降低了交换机的实现复杂度。仿真结果表明：在各种流量下,DSA算法都比信元调度算法具有更好的调度性能。     

Proactive scheduling in distributed computing—A reinforcement learning approach

In distributed computing such as grid computing, online users submit their tasks anytime and anywhere to dynamic resources. Task arrival and execution processes are stochastic. How to adapt to the consequent uncertainties, as well as scheduling overhead and response time, are the main concern in dynamic scheduling. Based on the decision theory, scheduling is formulated as a Markov decision process (MDP). To address this problem, an approach from machine learning is used to learn task arrival and execution patterns online. The proposed algorithm can automatically acquire such knowledge without any aforehand modeling, and proactively allocate tasks on account of the forthcoming tasks and their execution dynamics. Under comparison with four classic algorithms such as Min–Min, Min–Max, Suffrage, and ECT, the proposed algorithm has much less scheduling overhead. The experiments over both synthetic and practical environments reveal that the proposed algorithm outperforms other algorithms in terms of the average response time. The smaller variance of average response time further validates the robustness of our algorithm.     

A distributed perception infrastructure for robot assisted living

This paper presents an ambient intelligence system designed for assisted living. The system processes the audio and video data acquired from multiple sensors spread in the environment to automatically detect dangerous events and generate automatic warning messages. The paper presents the distributed perception infrastructure that has been implemented by means of an open-source software middleware called NMM. Different processing nodes have been developed which can cooperate to extract high level information about the environment. Examples of implemented nodes running algorithms for people detection or face recognition are presented. Experiments on novel algorithms for people fall detection and sound classification and localization are discussed. Eventually, we present successful experiments in two test bed scenarios.     

Mirage+: A kernel implementation of distributed shared memory on a network of personal computers

We describe the evolution of a distributed shared memory (DSM) system, Mirage, and the difficulties encountered when moving the system from a Unix-based* kernel on the VAX to a Unix-based kernel on personal computers. Mirage provides a network transparent form of shared memory for a loosely coupled environment. The system hides network boundaries for processes that are accessing shared memory and is upward compatible with the Unix System V Interface Definition. This paper addresses the architectural dependencies in the design of the system and evaluates performance of the implementation. The new version, MIRAGE +, performs well compared to Mirage even though eight times the amount of data is sent on each page fault because of the larger page size used in the implementation. We show that performance of systems with a large page size to network packet size can be dramatically improved on conventional hardware by applying three well-known techniques: packet blasting, compression, and running at interrupt level. The measured time for a page fault in MIRAGE + has been reduced 37 per cent by sending a page using packet blasting instead of using a handshake for each portion of the page. When compression was added to MIRAGE +, the time to fault a page across the network was further improved by 47 per cent when the page was compressed into one network packet. Our measured performance compares favorably with the amount of time it takes to fault a page from disk. Lastly, running at interrupt level may improve performance 16 per cent when faulting pages without compression.