Utilization-based techniques for statically mapping heterogeneous applications onto the HiPer-D hetergeneous computing system

Includes bibliographical references (pages 16-18).This research investigates the problem of allocating a set of heterogeneous applications to a set of heterogeneous machines connected together by a high-speed network. The proposed resource allocation heuristics were implemented on the High Performance Distributed Computing Program's (HiPer-D) Naval Surface Warfare Center testbed. The goal of this study is to design static resource allocation heuristics that balance the utilization of the computation and network resources while ensuring very low failure rates. A failure occurs if no allocation is found that allows the system to meet its resource and quality of service constraints. The broader goal is to determine an initial resource allocation that maximizes the time before run-time re-allocation is required for managing an increased workload. This study proposes two heuristics that perform well with respect to the load-balancing and failure rates. These heuristics are, therefore, very desirable for HiPer-D like systems where low failure rates can be a critical requirement

Metaheuristic Based Scheduling Meta-Tasks in Distributed Heterogeneous Computing Systems

Scheduling is a key problem in distributed heterogeneous computing systems in order to benefit from the large computing capacity of such systems and is an NP-complete problem. In this paper, we present a metaheuristic technique, namely the Particle Swarm Optimization (PSO) algorithm, for this problem. PSO is a population-based search algorithm based on the simulation of the social behavior of bird flocking and fish schooling. Particles fly in problem search space to find optimal or near-optimal solutions. The scheduler aims at minimizing makespan, which is the time when finishes the latest task. Experimental studies show that the proposed method is more efficient and surpasses those of reported PSO and GA approaches for this problem

Improved Task Scheduling for Virtual Machines in the Cloud based on the Gravitational Search Algorithm

The rapid and convenient provision of the available computing resources is a crucial requirement in modern cloud computing environments. However, if only the execution time is taken into account when the resources are scheduled, it could lead to imbalanced workloads as well as to significant under-utilisation of the involved Virtual Machines (VMs). In the present work a novel task scheduling scheme is introduced, which is based on the proper adaptation of a modern and quite effective evolutionary optimization method, the Gravitational Search Algorithm (GSA). The proposed scheme aims at optimizing the entire scheduling procedure, in terms of both the tasks execution time and the system (VMs) resource utilisation. Moreover, the fitness function was properly selected considering both the above factors in an appropriately weighted function in order to obtain better results for large inputs. Sufficient simulation experiments show the efficiency of the proposed scheme, as well as its excellence over related approaches of the bibliography, with similar objectives.Comment: 8 page

Effective Load Balance Scheduling Schemes for Heterogeneous Distributed System

Importance of distributed systems for distributing the workload on the processors is globally accepted. It is an agreed fact that divides and conquers is the effective strategy for load balancing problems. In today’s time, load balancing is the major issue for scheduling algorithm such as in Parallel and Distributed Systems including Grid and Cloud Computing and many more. Load Balancing is the phenomena of spreading or distributing the workload among the processors so that all processors keep busy for all the time, in order to prevent ideal time of processors. In this work, presents a load balancing algorithm for heterogeneous distributed system (HeDS) with aim of minimizing the load imbalance factor (LIF). The proposed algorithm is using optimization techniques such as Max-Max and Min-Max strategy and applied on Folded Crossed Cube (FCC) network. Makespan, speedup and average resource utilization are also evaluated for performance matrices. The experimental results of the proposed algorithms have showed better in comparison to with previous work under various test conditions

A Prolific Scheme for Load Balancing Relying on Task Completion Time

In networks with lot of computation, load balancing gains increasing significance. To offer various resources, services and applications, the ultimate aim is to facilitate the sharing of services and resources on the network over the Internet. A key issue to be focused and addressed in networks with large amount of computation is load balancing. Load is the number of tasks‘t’ performed by a computation system. The load can be categorized as network load and CPU load. For an efficient load balancing strategy, the process of assigning the load between the nodes should enhance the resource utilization and minimize the computation time. This can be accomplished by a uniform distribution of load of to all the nodes. A Load balancing method should guarantee that, each node in a network performs almost equal amount of work pertinent to their capacity and availability of resources. Relying on task subtraction, this work has presented a pioneering algorithm termed as E-TS (Efficient-Task Subtraction). This algorithm has selected appropriate nodes for each task. The proposed algorithm has improved the utilization of computing resources and has preserved the neutrality in assigning the load to the nodes in the network

IMMEDIATE/BATCH MODE SCHEDULING ALGORITHMS FOR GRID COMPUTING: A REVIEW

Immediate/on-line and Batch mode heuristics are two methods used for scheduling in the computational grid environment. In the former, task is mapped onto a resource as soon as it arrives at the scheduler, while the later, tasks are not mapped onto resource as they arrive, instead they are collected into a set that is examined for mapping at prescheduled times called mapping events. This paper reviews the literature concerning Minimum Execution Time (MET) along with Minimum Completion Time (MCT) algorithms of online mode heuristics and more emphasis on Min-Min along with Max-Min algorithms of batch mode heuristics, while focusing on the details of their basic concepts, approaches, techniques, and open problems

A game-theoretic and hybrid genetic meta-heuristics model for security-assured scheduling of independent jobs in computational grids

Scheduling independent tasks in Computational Grids commonly arises in many Grid-enabled large scale applications. Much of current research in this domain is focused on the improvement of the efficiency of the Grid schedulers, both at global and local levels, which is the basis for Grid systems to leverage large computing capacities. However, unlike traditional scheduling, in Grid systems security requirements are very important to scheduling tasks/applications to Grid resources. The objective is thus to achieve efficient and secure allocation of tasks to machines. In this paper we propose a new model for secure scheduling at the Grid sites by combining game-theoretic and genetic-based meta-heuristic approaches. The game-theoretic model takes into account the realistic feature that Grid users usually perform independently of each other. The scheduling problem is then formalized as a noncooperative non-zero sum game with Nash equilibria as the solutions. The game cost function is minimized, at global and user levels, by using four genetic-based hybrid meta-heuristics. We have evaluated the proposed model through a static benchmark of instances, for which we have measured two basic metrics, namely the makespan and flowtime. The obtained results suggest that it is more resilient for the Grid users (and local schedulers) to tolerate some job delays defined as additional scheduling cost due to security requirements instead of taking a risk of allocating at unreliable resources.Peer ReviewedPostprint (published version

Design and Analysis of Self-Adapted Task Scheduling Strategies in Wireless Sensor Networks

In a wireless sensor network (WSN), the usage of resources is usually highly related to the execution of tasks which consume a certain amount of computing and communication bandwidth. Parallel processing among sensors is a promising solution to provide the demanded computation capacity in WSNs. Task allocation and scheduling is a typical problem in the area of high performance computing. Although task allocation and scheduling in wired processor networks has been well studied in the past, their counterparts for WSNs remain largely unexplored. Existing traditional high performance computing solutions cannot be directly implemented in WSNs due to the limitations of WSNs such as limited resource availability and the shared communication medium. In this paper, a self-adapted task scheduling strategy for WSNs is presented. First, a multi-agent-based architecture for WSNs is proposed and a mathematical model of dynamic alliance is constructed for the task allocation problem. Then an effective discrete particle swarm optimization (PSO) algorithm for the dynamic alliance (DPSO-DA) with a well-designed particle position code and fitness function is proposed. A mutation operator which can effectively improve the algorithm’s ability of global search and population diversity is also introduced in this algorithm. Finally, the simulation results show that the proposed solution can achieve significant better performance than other algorithms

Task allocation to actors in wireless sensor actor networks: an energy and time aware technique

AbstractTask allocation is a critical issue in proper engineering of cooperative applications in embedded systems with latency and energy constraints, as in wireless sensor and actor networks (WSANs). Existing task allocation algorithms are mostly concerned with energy savings and ignore time constraints and thus increase the makespan of tasks in the network as well as the probability of malfunctioning of the network. In this paper we take both energy awareness and reduction of actor tasks’ times to completion in WSANs into account and propose a two-phase task allocation technique based on Queuing theory. In the first phase, tasks are equally assigned to actors just to measure the capability of each actor to perform the assigned tasks. Tasks are then allocated to actors according to their measured capabilities in such a way to reduce the total completion times of all tasks in the network. The results of simulations on typical scenarios shows 45% improvement in the makespan of tasks in a network compared to the wellknown opportunistic load balancing (OLB) task allocation algorithm that is generally used in distributed systems. It is shown that our algorithms provide better tradeoffs between load balancing and completion times of all tasks in a WSAN compared to OLB