The task allocation problem with constant communication

This dissertation focuses on the problem of assigning the modules of a program to the processors in a distributed system with the goal of minimizing the overall cost of running the program. The cost depends on the execution times of the modules on the processors and on the cost of communication between modules. This module allocation problem arises in a variety of situations where one is interested in making optimum use of available computer resources. The general module allocation problem is intractable; however it becomes polynomially-solvable when the communication graph is restricted. In this dissertation, we restrict our attention to k-trees. As the first problem, we study parametric module allocation on partial k-trees. We allow the costs, both execution and communication, to vary linearly as functions of a real parameter t. We show that if the number of processors is fixed, the sequence of optimum assignments that are obtained, as t varies from zero to infinity, can be constructed in polynomial time. As an auxiliary result, we develop a linear-time algorithm to find a separator in a k-tree. We discuss the implications of our results for parametric versions of the weighted vertex cover, independent set, and 0-1 quadratic programming problems on partial k-trees. Next, we consider two variants of the assignment problem. The first problem is to find a minimum-cost assignment when one of the processors has a limited memory. The second is to find an assignment that minimizes the maximum processor load. We present exact dynamic programming algorithms for both problems, which lead to approximation schemes for the case where the communication graph is a partial k-tree. Faster algorithms are presented for trees with uniform costs. In contrast to these results, we show that, for arbitrary graphs, no fully polynomial time approximation schemes exist unless P = NP. Both dynamic programming algorithms have been implemented. The implementation details and our experimental results are presented.

The author studies the complexity of the problem of allocating modules to processes in a distributed system to minimize total communication and execution costs. He shows that unless P=NP, there can be no polynomial-time epsilon -approximate algorithm for the problem, nor can there exist a local search algorithm that requires polynomial time per iteration and yields an optimum assignment. Both results hold even if the communication graph is planar and bipartite. On the positive side, it is shown that if the communication graph is a partial k-tree or an almost-tree with parameter k, the module allocation problem can be solved in polynomial time.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Distributed computing systems are of current interest due to the advancement of microprocessor technology and computer network. The prime function of effective utilization of distributed system is accurately mapping of task and tasks their scheduling on different processors for reducing their total finish time. Total runtime is taken time for all module with the runtime of tasks and their communication cost among tasks. In Distributed processing system, partitioning of a task into modules and proper allocation of module among processors are more important factor for efficient utilization of resources. In this paper, we discuss allocation of a set of 'm' module of a task to a set of 'n' processors (where m > n) to get an optimal utilization of available processors. We partition the task in different size of module and design an algorithm for allocation of these modules in distributed processing environment. While designing the model Per Bit Processing Rate, Inter Task Communication Cost, Task Size has been taken into consideration. We consider the constraints of minimizing execution cost and their Inter task communication and maximize the overall throughput of the system to be taking as constraints in such a way that allocated load on all the processor is balances. Keyword: Distributed computing, Task Allocation, Module distribution, Execution Cost, Communication Cost, Task Size.

Client-Agent-Server model (CAS model) which can decrease the work load of the client by adding agent processors to the Client-Server model (CS model) is proposed and an approach to parallel test generation for logic circuits on the CAS model is presented. Two problems are considered: optimal granularity problem and optimal scheme problem. First, the problem of parallel test generation on the CAS model is formulated to analyze the effect of the granularity (grain size of target faults allocated to processors) in both cases of static and dynamic task allocation (optimal granularity problem). Then the relationship between the number of processors and the total processing time is analyzed (optimal scheme problem). From the analysis, it is shown that the CAS model can reduce the total processing time over the CS model and that there exists an optimal scheme (an optimal pair of numbers of agent processors and server processors) for the CAS model which minimizes the total processing time for a given number of processors. To corroborate the analysis, the proposed parallel test generation algorithm is implemented on a network of more than 100 workstations and experimental results for the ISCAS benchmark circuits are presented. It is shown that the experimental results are very close to the theoretical results which confirms the existence of optimal granularity and optimal scheme which minimizes the total processing time for the CAS model. >

This paper considers the problem of task allocation for embedded, bus-based multicomputers. The problem is shown to be isomorphic to a generalization of vector packing, and heuristic solution techniques are investigated. A total of 256 packing algorithms are considered, using a divide-and-conquer experimentation strategy on a set of sixteen real and synthetic test cases. Performance is compared based on the number of processors, the utilization level of the broadcast bus and run time. This research differs from other approaches in that task allocation is formulated as a multi-dimensional problem, and general purpose solution techniques are developed that can accommodate arbitrary models for the schedulable resources.

In the field of parallel computing, there is an essential problem which is called Task Allocation Problem(TAP). The task allocation problem (TAP) is a problem where many of tasks require to be allocated to a set of processors.The number of m tasks that is needed to be allocated with number of n processors where (m>n) so that the time needed to process all the tasks is minimized. This paper presents an efficient algorithm (TAP_ACO_GA) to solve the task allocation problem. The proposed algorithm is based on the idea of Ant Colony Optimization Algorithm(ACO) and the idea of Genetic Algorithm (GA) .The proposed algorithm is tested in different dataset and its results are compared with the results in[2] and the results show that TAP_ACO_GA is better than the other algorithm.

In this paper, we consider the task allocation problem for autonomous agents where the distances between any two agents should always be less than a predetermined value. To easily tackle the problem, we introduce a novel concept referred to as a virtual task that helps to decouple the task allocation from the path planning in the joint task allocation and path planning problem. An overall task allocation procedure with two steps is proposed. After the virtual tasks are generated with preliminary clustering in the first step, tasks are clustered into groups and allocated to agents in the second step. To reduce the computational complexity of optimal clustering and task allocation, we propose a suboptimal method called the cluster-level traveling salesman problem (TSP). Numerical results show that the cluster-level TSP suffers only little performance degradation compared to the optimal method, even though its computational costs are dramatically reduced.

Nowadays humanoid robots have generally made dramatic progress, which can form a coalition replacing human work in dangerous environments such as rescue, defense, exploration, etc. In contrast to other types of robots, humanoid robots’ similarity to human make them more suitable for performing such a wide range of tasks. Rescue applications for robots, especially for humanoid robots are exciting. In rescue operating conditions, tasks’ dependencies, tasks’ repetitive accomplishment requirements, robots’ energy consumption, and total tasks’ accomplishment time are key factors. This paper investigates a practical variant of the Multi-Robots Task Allocation (MRTA) problem for humanoid robots as multi-humanoid robots’ task allocation (MHTA) problem. In order to evaluate relevant aspects of the MHTA problem, we proposed a robust Multi-Objective Multi-Humanoid Robots Task allocation (MO-MHTA) algorithm with four objectives, namely energy consumption, total tasks’ accomplishment time, robot’s idle time and fairness were optimized simultaneously in an evolutionary framework in MO-MHTA, which address for the first time. MO-MHTA exhibits multi-objective properties in real-world applications for humanoid robots in two phases. In the first phase, the tasks are partitioned in a fair manner with a proposed constraint k-medoid (CKM) algorithm. In the second phase, a new non-dominated sorting genetic algorithm with special genetic operators is applied. Evaluations based on extensive experiments on the newly proposed benchmark instances with three robust multi-objective evolutionary algorithms (MOEAs) are applied. The proposed algorithm achieves favorable results in comparison to six other algorithms. Besides, the proposed algorithm can be seen as benchmark algorithms for real-world MO-MHTA instances.

The problem of allocating modules to processors in a distributed system to minimize total costs when the underlying communication graph is a partial k-tree and all costs are linear functions of a real parameter t is considered. It is shown that if the number of processors is fixed, the sequence of optimum assignments that are obtained as t varies from zero to infinity can be constructed in polynomial time. As an auxiliary result, a linear time separator algorithm for k-trees is developed. The implications of the results for parametric versions of the weighted vertex cover, independent set, and 0-1 quadratic programming problems on partial k-trees are discussed. >

We consider the problem of scheduling tasks on multiprocessor architectures in the presence of communication delays. Given a set of dependent tasks, the scheduling problem is to allocate the tasks to processors such that the pre-specified precedence constraints among the tasks are obeyed and certain cost-measures (such as the computation time) are minimized. Several cases of the scheduling problem have

Edge computing (EC) extends the ability of cloud computing to the network edge to support diverse resource-sensitive and performance-sensitive IoT applications. However, due to the limited capacity of edge servers (ESs) and the dynamic computing requirements, the system needs to dynamically update the task allocation policy according to real-time system states. Service migration is essential to ensure service continuity when implementing dynamic task allocation. Therefore, this article investigates the long-term dynamic task allocation and service migration (DTASM) problem in edge-cloud IoT systems where users’ computing requirements and mobility change over time. The DTASM problem is formulated to achieve the long-term performance of minimizing the load forwarded to the cloud while fulfilling the seamless migration constraint and the latency constraint at each time of implementing the DTASM decision. First, the DTASM problem is divided into two subproblems: 1) the user selection problem on each ES and 2) the system task allocation problem. Then, the DTASM problem is formulated as a Markov decision process (MDP) and an approach based on deep reinforcement learning (DRL) is proposed. To tackle the challenge of vast discrete action spaces for DTASM task allocation in the system with a mass of IoT users, a training architecture based on the twin-delayed deep deterministic policy gradient (DDPG) is employed. Meanwhile, each action is divided into a differentiable action for policy training and one mapped action for implementation in the IoT system. Simulation results demonstrate that the proposed DRL-based approach obtains the long-term optimal system performance compared to other benchmarks while satisfying seamless service migration.

Numerical simulations running on very large High Performance Computer clusters still suffer from the I/O bottleneck. The cost of communication can overwhelm the cost of computation, and scales inversely with the number of processors used in the cluster. In previous work we have developed a multiresolution data model to help improve performance for visualizations of very large multi dimensional scientific data sets. In our approach, the data is represented as a multi level hierarchy. Reconstructive error analysis is used to identify regions in the data where the data loss is greatest. We have incorporated this data model into the OpenGGCM solar wind simulation environment. In this paper, we demonstrate that this approach can reduce the I/O and improve the overall performance of a large numerical simulation environment.

Mobile cloud computing is an emerging field of research which aims to provide a platform on which intelligent and feature-rich applications are delivered to the user at any time and at anywhere. When such a cloud-assisted mobile application workflow requires the cooperation of many devices, solving the task allocation problem becomes a critical step in ensuring the energy efficiency of the mobile cloud platform. In this paper, we construct a quadratic binary program to model the task allocation problem in such scenarios. In order to overcome the poor scalability of generic quadratic program solvers, we present an implementation of the simulated annealing algorithm and a greedy autonomous offload algorithm to approximate the optimal solution. Both heuristics are tailored to solve our task allocation problem efficiently. We verify and compare our algorithms against a commercial quadratic program solver in a series of simulations. Results show that both heuristics produce good solutions to the task allocation problem. Solutions provided by our greedy algorithms is consistently close to optimal and can be obtained in a more time efficient manor than our implementation of the simulated annealing algorithm.

To manage the huge data, cloud computing necessitates a significant number of disc I/O, network bandwidth and CPU cycles. To handle the massive volumes of data, the programming paradigm known as data flow integrates the tasks to a graph structure. Task scheduling is one of the main areas of current study that primarily aims to allocate the tasks to resources. It is essential to organise the tasks in a way which saves time and makes the most use of available resources. In recent years, scholars have offered different job scheduling techniques. This work intends to introduce a novel cost minimisation for big data processing with respect to task allocation or scheduling. Initially, the big data is processed under Map Reduce framework. This allocation processes is taken as the optimisation issue, which is solved under the consideration of minimisation factors like execution cost, makespan, communication cost, and energy consumption and risk. For solving this issue, a new hybrid optimisation algorithm known as Hybrid bald eagle and Archimedes Optimisation algorithm (HBE-ArCA) is developed. The betterment of HBE-ArCA is proven regarding cost, makespan and so on.

A central problem in the study of autonomous cooperating systems is that of how to establish mechanisms for controlling the interactions between different parts (which are called agents) of the system. One way to integrate such mechanisms into a multi-agent system is to exploit the technique of cooperation or negotiation protocols. In a protocol we distinguish to essential layers: the communication layer specifying the possible flow of messages between different agents, and the decision layer, which controls the selection of a message (speech-act) that the agent sends in a specific situation. In this report we first give a short introduction of our agent model InteRRap which provides the basis for the modeling of the different scenarios considered in the AKA-Mod project at the DFKI. The techniques we will discuss in the following are located in the plan based component and in the cooperation component of this model. The domain of application is the MARS scenario (Modeling a Multi-Agent Scenario for Shipping Companies) which implements a group of shipping companies whose goal it is to deliver a set of dynamically given orders, satisfying a set of given time and/or cost constraints. The complexity of the orders may exceed the capacities of a single company. Therefore, cooperation between companies is required in order to achieve the goal in a satisfactory way. This domain is of considerable interest for studies with economical background as well as for research projects. We give a short summary of results from economical studies that are concerned with the real-world situation in Germany in the transportation domain. They show the need for the development of new techniques from the field of computer science to tackle the problems therein. Then, an overview on related research is presented. Two approaches are discussed in more detail: the first one being based on OR-techniques and a second one being based on the concept of partial intelligent agents attempting to integrate techniques from OR and DAI. Both approaches are concerned with the situation in a single company. However, our purpose to handle the case of distributed shipping companies requires additional mechanisms, e.g. to cope with the problems of task allocation and task decomposition in multi-agent systems. Mechanisms for distributed task decomposition and task allocation processes in multi-agent systems belong to the core of our studies. Therefore, we will first discuss techniques for these problems in a general setting and then describe their implementations in the MARS system. In this description, particular emphasis is placed on the cooperation within a shipping company. Here, one company agent has to allocate a set of orders its truck agents. The truck agents support the company agents by giving cost estimations based on their route planning facility. Thus, this procedure provides the basis for the decisions of the company agents and is discussed in very detail. Finally, we present results from a series of benchmark tests. The test sets have also been run with OR-implementations and thus, give us the opportunity to compare our implementation against these approaches.