Resource scheduling and load balancing in distributed robotic control systems

This paper describes the latest advances made to a software architecture designed to control multiple miniature robots. As the robots themselves have very limited computational capabilities, a distributed control system is needed to coordinate tasks among a large number of robots. Two of the major challenges facing such a system are the scheduling of access to system resources and the distribution of work across multiple workstations. This paper discusses solutions to these problems in the context of a distributed surveillance task.     

Scalable network file systems with load balancing and fault tolerance for web services

Because of the rapid growth of the World Wide Web and the popularization of smart phones, tablets and personal computers, the number of web service users is increasing rapidly. As a result, large web services require additional disk space, and the required disk space increases with the number of web service users. Therefore, it is important to design and implement a powerful network file system for large web service providers. In this paper, we present three design issues for scalable network file systems. We use a variable number of objects within a bucket to decrease internal fragmentation in small files. We also propose a free space and access load-balancing mechanism to balance overall loading on the bucket servers. Finally, we propose a mechanism for caching frequently accessed data to lower the total disk I/O. These proposed mechanisms can effectively improve scalable network file system performance for large web services.     

Dual time-scale distributed capacity allocation and load redirect algorithms for cloud systems

Resource management remains one of the main issues of cloud computing providers because system resources have to be continuously allocated to handle workload fluctuations while guaranteeing Service Level Agreements (SLA) to the end users. In this paper, we propose novel capacity allocation algorithms able to coordinate multiple distributed resource controllers operating in geographically distributed cloud sites. Capacity allocation solutions are integrated with a load redirection mechanism which, when necessary, distributes incoming requests among different sites. The overall goal is to minimize the costs of allocated resources in terms of virtual machines, while guaranteeing SLA constraints expressed as a threshold on the average response time. We propose a distributed solution which integrates workload prediction and distributed non-linear optimization techniques. Experiments show how the proposed solutions improve other heuristics proposed in literature without penalizing SLAs, and our results are close to the global optimum which can be obtained by an oracle with a perfect knowledge about the future offered load.     

Probabilistic resource allocation in heterogeneous distributed systems with random failures

The problem of finding efficient workload distribution techniques is becoming increasingly important today for heterogeneous distributed systems where the availability of compute nodes may change spontaneously over time. Resource-allocation policies designed for such systems should maximize the performance and, at the same time, be robust against failure and recovery of compute nodes. Such a policy, based on the concepts of the Derman–Lieberman–Ross theorem, is proposed in this work, and is applied to a simulated model of a dedicated system composed of a set of heterogeneous image processing servers. Assuming that each image results in a “reward” if its processing is completed before a certain deadline, the goal for the resource allocation policy is to maximize the expected cumulative reward. An extensive analysis was done to study the performance of the proposed policy and compare it with the performance of some existing policies adapted to this environment. Our experiments conducted for various types of task-machine heterogeneity illustrate the potential of our method for solving resource allocation problems in a broad spectrum of distributed systems that experience high failure rates.     

Towards a hybrid load balancing policy in grid computing system

Grid computing has become conventional in distributed systems due to technological advancements and network popularity. Grid computing facilitates distributed applications by integrating available idle network computing resources into formidable computing power. As a result, by using efficient integration and sharing of resources, this enables abundant computing resources to solve complicated problems that a single machine cannot manage. However, grid computing mines resources from accessible idle nodes and node accessibility varies with time. A node that is currently idle, may become occupied within a second of time and then be unavailable to provide resources. Accordingly, node selection must provide effective and sufficient resources over a long period to allow load assignment. This study proposes a hybrid load balancing policy to integrate static and dynamic load balancing technologies. Essentially, a static load balancing policy is applied to select effective and suitable node sets. This will lower the unbalanced load probability caused by assigning tasks to ineffective nodes. When a node reveals the possible inability to continue providing resources, the dynamic load balancing policy will determine whether the node in question is ineffective to provide load assignment. The system will then obtain a new replacement node within a short time, to maintain system execution performance.     

Game-theoretic static load balancing for distributed systems

In this paper, we present a game theoretic approach to solve the static load balancing problem for single-class and multi-class (multi-user) jobs in a distributed system where the computers are connected by a communication network. The objective of our approach is to provide fairness to all the jobs (in a single-class system) and the users of the jobs (in a multi-user system). To provide fairness to all the jobs in the system, we use a cooperative game to model the load balancing problem. Our solution is based on the Nash Bargaining Solution (NBS) which provides a Pareto optimal solution for the distributed system and is also a fair solution. An algorithm for computing the NBS is derived for the proposed cooperative load balancing game. To provide fairness to all the users in the system, the load balancing problem is formulated as a non-cooperative game among the users who try to minimize the expected response time of their own jobs. We use the concept of Nash equilibrium as the solution of our non-cooperative game and derive a distributed algorithm for computing it. Our schemes are compared with other existing schemes using simulations with various system loads and configurations. We show that our schemes perform near the system optimal schemes and are superior to the other schemes in terms of fairness.     

Task distribution using factoring load balancing in Master-Worker applications

Load imbalance among workers is one of the main causes of performance shortcomings in Master-Worker applications. We have observed that this problem is very similar to the one of scheduling distributed parallel loops, which has been widely in the literature. Thus, we have adapted one of the most successful algorithms, known as Factoring, to be used for Master-Worker applications. This has leads to a simple an elegant strategy that can be used to obtain an excellent automatic and dynamic load balancing strategy for the workers. Finally, we have assessed the resulting strategy through extensive experimentation and simulation.     

On index load balancing in scalable P2P media distribution

Peer-Peer (P2P) technologies have recently been in the limelight for their disruptive power in particular they have emerged as a powerful multimedia content distribution mechanism. However, the widespread deployment of P2P networks are hindered by several issues, especially the ones that influence end-user satisfaction, including reliability. In this paper, we propose a solution for an efficient and user-oriented keyword lookup service on P2P networks. The proposed mechanism has been designed to achieve reliability via index load balancing and address the scalability issues of extremely popular keywords in the index. The system performance have been analytically derived as well implemented using the OpenDHT framework on PlanetLab.

Social-welfare based task allocation for multi-robot systems with resource constraints

This paper aims to propose a distributed task allocation algorithm for a team of robots that have constraints on energy resources and operate in an unknown dynamic environment. The objective of the allocation is to maximize task completion ratio while minimizing resource usage. The approach we propose is inspired by the social welfare in economics that helps extend the combined operational lifetime of the team by balancing resource consumptions among robots. This social welfare based task allocation method positions a robot team appropriately in preparedness for dynamic future events and enables to achieve the objectives of the system flexibly depending on the application context. Our simulation-based experiments show that the proposed algorithm outperforms a typical market-based approach in various scenarios.     

Opportunities for multiagent systems and multiagent reinforcement learning in traffic control

The increasing demand for mobility in our society poses various challenges to traffic engineering, computer science in general, and artificial intelligence and multiagent systems in particular. As it is often the case, it is not possible to provide additional capacity, so that a more efficient use of the available transportation infrastructure is necessary. This relates closely to multiagent systems as many problems in traffic management and control are inherently distributed. Also, many actors in a transportation system fit very well the concept of autonomous agents: the driver, the pedestrian, the traffic expert; in some cases, also the intersection and the traffic signal controller can be regarded as an autonomous agent. However, the “agentification” of a transportation system is associated with some challenging issues: the number of agents is high, typically agents are highly adaptive, they react to changes in the environment at individual level but cause an unpredictable collective pattern, and act in a highly coupled environment. Therefore, this domain poses many challenges for standard techniques from multiagent systems such as coordination and learning. This paper has two main objectives: (i) to present problems, methods, approaches and practices in traffic engineering (especially regarding traffic signal control); and (ii) to highlight open problems and challenges so that future research in multiagent systems can address them.     

A case for on-machine load balancing

This paper diverges from the traditional load balancing, and introduces a new principle called the on-machine load balance rule. The on-machine load balance rule leads to resource allocations that are better in tolerating uncertainties in the processing times of the tasks allocated to the resources when compared to other resource allocations that are derived using the conventional “across-the-machines” load balancing rule. The on-machine load balance rule calls for the resource allocation algorithms to allocate similarly sized tasks on a machine (in addition to optimizing some primary performance measures such as estimated makespan and average response time). The on-machine load balance rule is very different from the usual across-the-machines load balance rule that strives to balance load across resources so that all resources have similar finishing times.We give a mathematical justification for the on-machine load balance rule requiring only liberal assumptions about task processing times. Then we validate with extensive simulations that the resource allocations derived using on-machine load balance rule are indeed more tolerant of uncertain task processing times.     

Data partitioning and load balancing in parallel disk systems

Parallel disk systems provide opportunities for exploiting I/O parallelism in two possible ways, namely via inter-request and intra-request parallelism. In this paper, we discuss the main issues in performance tuning of such systems, namely striping and load balancing, and show their relationship to response time and throughput. We outline the main components of an intelligent, self-reliant file system that aims to optimize striping by taking into account the requirements of the applications, and performs load balancing by judicious file allocation and dynamic redistributions of the data when access patterns change. Our system uses simple but effective heuristics that incur only little overhead. We present performance experiments based on synthetic workloads and real-life traces. Received May 17, 1994 / Accepted June 9, 1997     

Asymmetry-aware load balancing for parallel applications in single-ISA multi-core systems

Contemporary operating systems for single-ISA (instruction set architecture) multi-core systems attempt to distribute tasks equally among all the CPUs. This approach works relatively well when there is no difference in CPU capability. However, there are cases in which CPU capability differs from one another. For instance, static capability asymmetry results from the advent of new asymmetric hardware, and dynamic capability asymmetry comes from the operating system (OS) outside noise caused from networking or I/O handling. These asymmetries can make it hard for the OS scheduler to evenly distribute the tasks, resulting in less efficient load balancing. In this paper, we propose a user-level load balancer for parallel applications, called the ’capability balancer’, which recognizes the difference of CPU capability and makes subtasks share the entire CPU capability fairly. The balancer can coexist with the existing kernel-level load balancer without detrimenting the behavior of the kernel balancer. The capability balancer can fairly distribute CPU capability to tasks with very little overhead. For real workloads like the NAS Parallel Benchmark (NPB), we have accomplished speedups of up to 9.8% and 8.5% in dynamic and static asymmetries, respectively. We have also experienced speedups of 13.3% for dynamic asymmetry and 24.1% for static asymmetry in a competitive environment. The impacts of our task selection policies, FIFO (first in, first out) and cache, were compared. The use of the cache policy led to a speedup of 5.3% in overall execution time and a decrease of 4.7% in the overall cache miss count, compared with the FIFO policy, which is used by default.     

A low-level resource allocation in an agent-based Cloud Computing platform

The distribution of computational resources in a Cloud Computing platform is a complex process with several parameters to consider such as the demand for services, available computational resources and service level agreements with end users. Currently, the state-of-the-art presents centralized approaches derived from previous technologies related to cluster of servers. These approaches allocate computational resources by means of the addition/removal of (physical/virtual) computational nodes. However, virtualization technology currently allows for research into new techniques, which makes it possible to allocate at a lower level. In other words, not only is it possible to add/remove nodes, but also to modify the resources of each virtual machine (low level resource allocation). Thus, agent theory is a key technology in this field, allowing decentralized resource allocation. This innovative approach has undeniable improvements such us computational load distribution and reduced computation time. The evaluation was carried out through experiments in a real Cloud environment, thus proving the validity of the proposed approach.     

Cooperative load balancing in distributed systems

A serious difficulty in concurrent programming of a distributed system is how to deal with scheduling and load balancing of such a system which may consist of heterogeneous computers. In this paper, we formulate the static load‐balancing problem in single class job distributed systems as a cooperative game among computers. The computers comprising the distributed system are modeled as M/M/1 queueing systems. It is shown that the Nash bargaining solution (NBS) provides an optimal solution (operation point) for the distributed system and it is also a fair solution. We propose a cooperative load‐balancing game and present the structure of NBS. For this game an algorithm for computing NBS is derived. We show that the fairness index is always equal to 1 using NBS, which means that the solution is fair to all jobs. Finally, the performance of our cooperative load‐balancing scheme is compared with that of other existing schemes. Copyright © 2008 John Wiley & Sons, Ltd.     

多机器人系统任务分配的研究进展

多机器人系统任务分配是机器人研究领域一个关键的研究课题。从多机器人任务分配分类及问题描述、多机器人任务分配的研究动态等方面对多机器人任务分配进行了综述,并根据近期文献探讨了多机器人系统任务分配需要解决的若干重要问题。     

Adaptive task allocation for multi-UAV systems based on bacteria foraging behaviour

The foraging behaviour of bacteria in colonies exhibits motility patterns that are simple and reasoned by stimuli. Notwithstanding its simplicity, bacteria behaviour demonstrates a level of intelligence that can feasibly inspire the creation of solutions to address numerous optimisation problems. One such challenge is the optimal allocation of tasks across multiple unmanned aerial vehicles (multi-UAVs) to perform cooperative tasks for future autonomous systems. In light of this, this paper proposes a bacteria-inspired heuristic for the efficient distribution of tasks amongst deployed UAVs. The usage of multi-UAVs is a promising concept to combat the spread of the red palm weevil (RPW) in palm plantations. For that purpose, the proposed bacteria-inspired heuristic was utilised to resolve the multi-UAV task allocation problem when combating RPW infestation. The performance of the proposed algorithm was benchmarked in simulated detect-and-treat missions against three long-standing multi-UAV task allocation strategies, namely opportunistic task allocation, auction-based scheme, and the max-sum algorithm, and a recently introduced locust-inspired algorithm for the allocation of multi-UAVs. The experimental results demonstrated the superior performance of the proposed algorithm, as it substantially improved the net throughput and maintained a steady runtime performance under different scales of fleet sizes and number of infestations, thereby expressing the high flexibility, scalability, and sustainability of the proposed bacteria-inspired approach.     

Task allocation and the balancing of task demands in the multi-man-machine system--some case studies

Various forms of team organisation are described. These are based on the concepts of vertical and horizontal structure. Task factors of complexity and organisation are introduced and their relationships with various forms of multiman-machine system are discussed. Experimental work is briefly described and then two case study analyses of operational systems are presented. The first, an examination of airport air traffic contol, illustrates how a multiman system can be reorganised to yield a more balanced distribution of task demands. The second, a study of an ambulance control room, shows the implucations for team organisation of a shared computer data-base. These case studies demonstrate that the concepts developed in the laboratory context can be applied to operational multiman-machine systems.     

Strategy and Simulation of Adaptive RID for Distributed Dynamic Load Balancing in Parallel Systems

Dynamic load balancing schemes are significant for efficiently executing nonuniform problems in highly parallel multicomputer systems.The objective is to minimize the total exectuion time of single applications.This paper has proposed an ARID strategy for distributed dynamic load balancing.Its principle and control protocol are described,and te communication overhead,the effect on system stability and the performance efficiency are analyzed.Finally,simulation experiments are carried out to compare the adaptive strategy with other dynamic load balancing schemes.