Real-time scheduling of divisible loads in cluster computing environments

Cluster computing has become an important paradigm for solving large-scale problems. To enhance the quality of service (QoS) and provide performance guarantees in a cluster computing environment, various real-time scheduling algorithms and workload models have been investigated. Computational loads that can be arbitrarily divided into independent tasks represent many real-world applications. However, the problem of providing performance guarantees to divisible load applications has only recently been studied systematically. In this work, three important and necessary design decisions, (1) workload partitioning, (2) node assignment, and (3) task execution order, are identified for real-time divisible load scheduling. A scheduling framework that can configure different policies for each of the three design decisions is proposed and used to generate various algorithms. This paper systematically studies these algorithms and identifies scenarios where the choices of design parameters have significant effects.     

Requirement-aware strategies for scheduling real-time divisible loads on clusters

This paper investigates the real-time scheduling problem for handling heterogeneous divisible loads on cluster systems. Divisible load applications occur in many fields of science and engineering. Such applications can be easily parallelized in a master–worker fashion, but pose several scheduling challenges. We consider divisible loads associated with deadlines to enhance quality-of-service (QoS) and provide performance guarantees in distributed computing environments. In addition, since the divisible loads to be performed may widely vary in terms of their required hardware and software, we capture the loads’ various processing requirements in our load distribution strategies, a unique feature that is applicable for running proprietary applications only on certain eligible processing nodes. Thus in our problem formulation each load can only be processed by certain processors as both the loads and processors are heterogeneous. We propose scheduling algorithms referred to as Requirements-Aware Real-Time Scheduling (RARTS) algorithms, which consist of a novel scheduling policy, referred to as Minimum Slack Capacity First (MSCF), and two multi-round load distribution strategies, referred to as All Eligible Processors (AEP) and Least Capability First (LCF). We perform rigorous performance evaluation studies to quantify the performance of our strategies on a variety of scenarios.     

Communication infrastructure in distributed scheduling

The emergence of distributed artificial intelligent (DAI) introduced a new approach to solve scheduling problems by a set of scheduling systems that interact with each other in the problem-solving process. In this paper, we describe a communication infrastructure to handle connection and communication between distributed Internet scheduling systems for distributed applications. First, we present an agent model of distributed scheduling systems where agents can communicate and coordinate activities with each other via an agent communication language. Then, we define the syntax and semantics for the agent communication languages, and negotiation mechanism. Following that, we discuss the design and development of the prototype for the multi-agent scheduling systems. We conclude with a discussion of communication issues for heterogeneous agent-based scheduling systems to solve distributed scheduling problems.     

An efficient Nash-implementation mechanism for network resource allocation

We propose a mechanism for auctioning bundles of multiple divisible goods in a network where buyers want the same amount of bandwidth on each link in their route. Buyers can specify multiple routes (corresponding to a source-destination pair). The total flow can then be split among these multiple routes. We first propose a one-sided VCG-type mechanism. Players do not report a full valuation function but only a two-dimensional bid signal: the maximum quantity that they want and the per-unit price they are willing to pay. The proposed mechanism is a weak Nash implementation, i.e., it has a non-unique Nash equilibrium that implements the social-welfare maximizing allocation. We show the existence of an efficient Nash equilibrium in the corresponding auction game, though there may exist other Nash equilibria that are not efficient. We then generalize this to arbitrary bundles of various goods. Each buyer submits a bid separately for each good but their utility function is a general function of allocations of bundles of various divisible goods. We then present a double-sided auction mechanism for multiple divisible goods. We show that there exists a Nash equilibrium of this auction game which yields the efficient allocation with strong budget balance.     

An efficient distributed algorithm for finding all hinge vertices in networks

Let G=(V, E) be a graph with vertex set V of size n and edge set E of size m. A vertex v∈V is called a hinge vertex if there exist two vertices in V\{v} such that their distance becomes longer when v is removed. In this paper, we present a distributed algorithm that finds all hinge vertices on an arbitrary graph. The proposed algorithm works for named static asynchronous networks and achieves O(n ²) time complexity and O(m) message complexity. In particular, the total messages exchanged during the algorithm are at most 2m(log n+nlog n+1) bits.     

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.     

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.     

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.     

OLSR-aware channel access scheduling in wireless mesh networks

Wireless mesh networks (WMNs) have emerged as a key technology having various advantages, especially in providing cost-effective coverage and connectivity solutions in both rural and urban areas. WMNs are typically deployed as backbone networks, usually employing spatial TDMA (STDMA)-based access schemes which are suitable for the high traffic demands of WMNs. This paper aims to achieve higher utilization of the network capacity and thereby aims to increase the application layer throughput of STDMA-based WMNs. The central idea is to use optimized link state routing (OLSR)-specific routing layer information in link layer channel access schedule formation. This paper proposes two STDMA-based channel access scheduling schemes (one distributed, one centralized) that exploit OLSR-specific information to improve the application layer throughput without introducing any additional messaging overhead. To justify the contribution of using OLSR-specific information to the throughput, the proposed schemes are compared against one another and against their non-OLSR-aware versions via extensive ns-2 simulations. Our simulation results verify that utilizing OLSR-specific information significantly improves the overall network performance both in distributed and in centralized schemes. The simulation results further show that OLSR-aware scheduling algorithms attain higher end-to-end throughput although their non-OLSR-aware counterparts achieve higher concurrency in slot allocations.     

Selective Smooth Fictitious Play: An approach based on game theory for patrolling infrastructures with a multi-robot system

The multi-robot patrolling problem is defined as the activity of traversing a given environment. In this activity, a fleet of robots visits some places at irregular intervals of time for security purpose. To date, this problem has been solved with different approaches. However, the approaches that obtain the best results are unfeasible for security applications because they are centralized and deterministic. To overcome the disadvantages of previous work, this paper presents a new distributed and non-deterministic approach based on a model from game theory called Smooth Fictitious Play. To this end, the multi-robot patrolling problem is formulated by using concepts of graph theory to represent an environment. In this formulation, several normal-form games are defined at each node of the graph. This approach is validated by comparison with best suited literature approaches by using a patrolling simulator. The results for the proposed approach turn out to be better than previous literature approaches in as many as 88% of the cases of study. Moreover, the novel approach presented in this work has many advantages over other approaches of the literature such distribution, robustness, scalability, and dynamism. The achievements obtained in this work validate the potential of game theory to protect infrastructures.     

An experiment in language design for distributed systems

This paper describes a recent experiment in the design, implementation and application of a programming language designed specifically for distributed systems. The fundamental nature of such systems is used in the derivation of an experimental language, which is described and illustrated. Having outlined an implementation, the project is evaluated and some of the more significant findings are reported. In particular, the paper discusses (and argues for) an explicit language facility for expressing the distribution of a program over an actual system.     

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.     

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.     

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.     

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.     

Fault tolerant mechanism design

We introduce the notion of fault tolerant mechanism design, which extends the standard game theoretic framework of mechanism design to allow for uncertainty about execution. Specifically, we define the problem of task allocation in which the private information of the agents is not only their costs of attempting the tasks but also their probabilities of failure. For several different instances of this setting we present both, positive results in the form of mechanisms that are incentive compatible, individually rational, and efficient, and negative results in the form of impossibility theorems.     

AS prediction mechanism for distributed threads systems

In distributed real-time systems, if a task misses its deadline, an exception can be thrown. In this context, end-to-end deadline missing prediction mechanisms can reduce exception throwing because they define an estimated response time. With this estimated response time the system can carry out remedial actions in time to avoid the throw of an exception. In this work, we propose the Available Slack (AS) deadline missing prediction mechanism, which defines an estimated response time for distributed tasks using information such as computation time and end-to-end deadline. We show how AS behaves in simulations with different system workloads like pipelines, balanced and non-balanced loads.     

Mechanism design for the truthful elicitation of costly probabilistic estimates in distributed information systems

This paper reports on the design of a novel two-stage mechanism, based on strictly proper scoring rules, that allows a centre to acquire a costly forecast of a future event (such as a meteorological phenomenon) or a probabilistic estimate of a specific parameter (such as the quality of an expected service), with a specified minimum precision, from one or more agents. In the first stage, the centre elicits the agents' true costs and identifies the agent that can provide an estimate of the specified precision at the lowest cost. Then, in the second stage, the centre uses an appropriately scaled strictly proper scoring rule to incentivise this agent to generate the estimate with the required precision, and to truthfully report it. In particular, this is the first mechanism that can be applied to settings in which the centre has no knowledge about the actual costs involved in the generation an agents' estimates and also has no external means of evaluating the quality and accuracy of the estimates it receives. En route to this mechanism, we first consider a setting in which any single agent can provide an estimate of the required precision, and the centre can evaluate this estimate by comparing it with the outcome which is observed at a later stage. This mechanism is then extended, so that it can be applied in a setting where the agents' different capabilities are reflected in the maximum precision of the estimates that they can provide, potentially requiring the centre to select multiple agents and combine their individual results in order to obtain an estimate of the required precision. For all three mechanisms (the original and the two extensions), we prove their economic properties (i.e. incentive compatibility and individual rationality) and then perform a number of numerical simulations. For the single agent mechanism we compare the quadratic, spherical and logarithmic scoring rules with a parametric family of scoring rules. We show that although the logarithmic scoring rule minimises both the mean and variance of the centre's total payments, using this rule means that an agent may face an unbounded penalty if it provides an estimate of extremely poor quality. We show that this is not the case for the parametric family, and thus, we suggest that the parametric scoring rule is the best candidate in our setting. Furthermore, we show that the ‘multiple agent’ extension describes a family of possible approaches to select agents in the first stage of our mechanism, and we show empirically and prove analytically that there is one approach that dominates all others. Finally, we compare our mechanism to the peer prediction mechanism introduced by Miller et al. (2007) [29] and show that the centre's total expected payment is the same in both mechanisms (and is equal to total expected payment in the case that the estimates can be compared to the actual outcome), while the variance in these payments is significantly reduced within our mechanism.     

Proactive cooperative scheduling and buffer management for multimedia networks

Is it possible, and to what extent feasible, for link schedulers and buffer managers in multimedia networks with propagation delays to relieve effectively congestion by means of feedback signaling? We show that a proactive cooperative control algorithm for distributed resource scheduling and buffer management obviates the problem of propagation delays. The algorithm is based on traffic predictions of correlated input traffic streams into network nodes. Nodes projecting to be under stress signal their neighboring upstream nodes for the latter to readjust their flow rates, whileguaranteeing quality of service as negotiated at call setup. Detailed implementations of the adaptive, feedback control mechanisms of distributed asynchronous algorithms that project future overload conditions are presented. The design allows the underlying single-node link scheduling and buffer management algorithm to be run in either a cooperative or a noncooperative mode of operation.     

Game theoretical applications for multi-agent systems

We consider game-theoretic principles for design of cooperative and competitive (non-cooperative self-interested) multi-agent systems. Using economic concepts of tâtonnement and economic core, we show that cooperative multi-agent systems should be designed in games with dominant strategies that may lead to social dilemmas. Non-cooperative multi-agent systems, on the other hand, should be designed for games with no clear dominant strategies and high degree of problem complexity. Further, for non-cooperative multi-agent systems, the number of learning agents should be carefully managed so that solutions in the economic core can be obtained. We provide experimental results for the design of cooperative and non-cooperative MAS from telecommunication and manufacturing industries.