Inter-task Communication Cost Research Articles

A High Performance Model for Task Allocation in Distributed Computing System Using K-Means Clustering Technique

Tasks allocation is an important step for obtaining high performance in distributed computing system (DCS). This article attempts to develop a mathematical model for allocating the tasks to the processors in order to achieve optimal cost and optimal reliability of the system. The proposed model has been divided into two stages. Stage-I, makes the ‘n' clusters of set of ‘m' tasks by using k-means clustering technique. To use the k-means clustering techniques, the inter-task communication costs have been modified in such a way that highly communicated tasks are clustered together to minimize the communication costs between tasks. Stage-II, allocates the ‘n' clusters of tasks onto ‘n' processors to minimize the system cost. To design the mathematical model, executions costs and inter tasks communication costs have been taken in the form of matrices. To test the performance of the proposed model, many examples are considered from different research papers and results of examples have compared with some existing models.

Locality-aware task scheduling for homogeneous parallel computing systems

In systems with complex many-core cache hierarchy, exploiting data locality can significantly reduce execution time and energy consumption of parallel applications. Locality can be exploited at various hardware and software layers. For instance, by implementing private and shared caches in a multi-level fashion, recent hardware designs are already optimised for locality. However, this would all be useless if the software scheduling does not cast the execution in a manner that promotes locality available in the programs themselves. Since programs for parallel systems consist of tasks executed simultaneously, task scheduling becomes crucial for the performance in multi-level cache architectures. This paper presents a heuristic algorithm for homogeneous multi-core systems called locality-aware task scheduling (LeTS). The LeTS heuristic is a work-conserving algorithm that takes into account both locality and load balancing in order to reduce the execution time of target applications. The working principle of LeTS is based on two distinctive phases, namely; working task group formation phase (WTG-FP) and working task group ordering phase (WTG-OP). The WTG-FP forms groups of tasks in order to capture data reuse across tasks while the WTG-OP determines an optimal order of execution for task groups that minimizes the reuse distance of shared data between tasks. We have performed experiments using randomly generated task graphs by varying three major performance parameters, namely: (1) communication to computation ratio (CCR) between 0.1 and 1.0, (2) application size, i.e., task graphs comprising of 50-, 100-, and 300-tasks per graph, and (3) number of cores with 2-, 4-, 8-, and 16-cores execution scenarios. We have also performed experiments using selected real-world applications. The LeTS heuristic reduces overall execution time of applications by exploiting inter-task data locality. Results show that LeTS outperforms state-of-the-art algorithms in amortizing inter-task communication cost.

Adaptive dual-criteria task group allocation for clustering-based multi-workflow scheduling on parallel computing platform

Workflow scheduling has long been an important research topic in the field of parallel computing. Clustering-based methods are one of the major types of workflow scheduling approaches which have been shown superior to other kinds of methods in many cases due to their advantage of minimizing inter-task communication costs. Most previous research dealt with single workflow scheduling and focused on how to cluster the tasks within a workflow into a set of task groups. Recent research showed that utilizing idle time gaps between scheduled tasks is a promising direction for efficient multiple workflow scheduling. Since executing multiple workflows simultaneously is an inevitable need in modern shared parallel computing platforms, efficient task group allocation becomes a critical issue. In this paper, we studied such issue and proposed an innovative dual-criteria task group allocation method which considers both task group's finish time and potential resource utilization to effectively improve overall multi-workflow execution performance. In addition, an adaptive task group rearrangement mechanism was adopted to further improve performance. The proposed method has been evaluated with a series of simulation experiments and compared to previous approaches. The experimental results show that our method outperforms previous approaches across different workload conditions and workflow properties in terms of average makespan. The performance improvement ranges from 5 to 29 % for different conditions, achieving the largest performance improvement for workflows of smaller CCR.

Scheduling Workflow Applications on the Heterogeneous Cloud Resources

As cloud computing model recently become promising and enables users to obtain their required services, many users desirous to run their workflow applications on it. Scheduling workflow is one of the most important challenges in the cloud. For optimal use of the capabilities of the distributed system, an efficient scheduling algorithm is needed. Addressing the problem of scheduling workflow applications onto Cloud environment is the main contribution of this paper. Heterogeneity of resource types is one of the most important issues which significantly affect workflow scheduling in Cloud environment. On the other hand, a workflow application is usually consisting of different tasks with the need for different resource types to complete which we call it heterogeneity in workflow. The main idea in this paper is to match the heterogeneity in workflow application to the heterogeneity in Cloud environment. To obtain this objective a new scheduling algorithm is introduced, which is based upon the idea of detecting the set of tasks that could run concurrently and distribute them into different sub-workflows and then allocate each sub-workflow in resource cluster instead of allocating individual tasks. This can reduce inter-task communication cost and thus improve workflow execution performance. First we perform global scheduling and then conduct local scheduling. On the Global-scheduling to achieve high parallelism the received DAG partition into multiple sub-workflows that is realized by WPRC algorithm. On the Local-scheduling, sub-workflows were generated at the global level are dispatched to selected resource clusters. We used the simulation to evaluate the performance of the proposed algorithm in comparison with three well-known approaches. The results show that the proposed algorithm outperforms other approaches in different QoS related terms.

Task Allocation in heterogeneous Distributed Real Time System for Optimal Utilization of Processor’s Capacity

In Distributed Real Time System (DRTS), systematic allocation of the tasks among processors is one of the important parameter, which determine the optimal utilization of available resources. If this step is not performed properly, an increase in the number of processing nodes results in decreasing the total system throughput. The Inter-Task Communication (ITC) is always the most costly and the least reliable parameter in the loosely coupled DRTS. In this paper an efficient task allocation algorithm is discussed, which performs a static allocation of a set of tasks T = {t1,t2,…tm} of a program to a set of n processors P = {p1,p2,….pn}, (where, m >> n) to minimize the application program's Parallel Processing Cost(PPC) with the goal to maximize the overall throughput of the system through and allocated load on all the processors should be approximately balanced. While designing the algorithm the Execution Cost (EC) and Inter Task Communication Cost (ITCC) have been taken into consideration.

Dynamic task allocation in distributed computing systems by heuristic algorithms

Efficient task scheduling is a crucial step to achieve high performance for multiprocessor platform. In a distributed computing system (DCS) the tasks of a program must be assigned dynamically to the heterogeneous processors to utilise the computational capabilities and resources of the system efficiently. In this article, the problem of finding an optimal dynamic assignment of a modular program for multiprocessor system is analysed. This paper deals with dynamic task assignment problem for allocating m tasks of a distributed program to n heterogeneous processors (m >>> n) with effect to minimise the total cost of the program. This allocation permits each task to be migrated from one processor to another during the execution of the program. The dynamic task allocation model is developed by considering three different categories including the effect of processor relative speed. This development implies the coupling of logical modelling and statistical notions. We design the mathematical model and study the phase wise execution cost (EC), inter-task communication cost (ITCC), migration cost (MC) and residence cost (RC) of each task with respect to each processor in the form of matrices.

An Effective Load Balancing Task Allocation Algorithm using Task Clustering

In Distributed Computing Systems (DCSs), a program is split into small tasks and distributed among several computing elements to minimize the overall system cost. Several challenges have been posed by this mode of processing which can be classified mainly into two broad categories. One class belongs to the hardware oriented issues of building such systems more and more effective while the other aims at designing efficient algorithms to make the best use of the technology in hand. The task allocation problem in a DCS belongs to the later class. Intrinsically, task allocation problem is NPhard. To overcome this issue, it is necessary to introduce heuristics for generating near optimal solution to the given problem. This paper deals with the problem of task allocation in DCSs in such a way that the load on each processing node is almost balanced. Further, the development of an effective algorithm for allocating ‘m’ tasks to ‘n’ processors of a given distributed system using task clustering by taking both Inter Task Communication Cost (ITCC) and the Execution Cost (EC) is taken into consideration. General Terms Distributed Systems, Task Allocation

Performance Intensification of DRTS under Static Load Sharing Scheme

istributed Real Time System (DRTS) provides enormous platform for parallel applications. It is an alternative for highly expensive parallel machines. Task allocation for parallel applications over it, is a crucial phase where strategy for task allocation should be chosen to maximize the throughput and enhance the overall processor utilization. Task allocation is NP-hard or NP-complete problem. A new heuristic for this problem has been suggested and implemented in this paper. To achieve this goal, tasks should be clustered in such a way that it can minimize the inter-task communication cost as well as it must also be taken care that the execution cost of tasks must also be minimum over the processor where the tasks are going to get assigned. Here k- mean clustering has been used to cluster the tasks in the required number of clusters. The proposed model has been simutated in matlab. General Terms

A hybrid heuristic–genetic algorithm for task scheduling in heterogeneous processor networks

Efficient task scheduling on heterogeneous distributed computing systems (HeDCSs) requires the consideration of the heterogeneity of processors and the inter-processor communication. This paper presents a two-phase algorithm, called H2GS, for task scheduling on HeDCSs. The first phase implements a heuristic list-based algorithm, called LDCP, to generate a high quality schedule. In the second phase, the LDCP-generated schedule is injected into the initial population of a customized genetic algorithm, called GAS, which proceeds to evolve shorter schedules. GAS employs a simple genome composed of a two-dimensional chromosome. A mapping procedure is developed which maps every possible genome to a valid schedule. Moreover, GAS uses customized operators that are designed for the scheduling problem to enable an efficient stochastic search. The performance of each phase of H2GS is compared to two leading scheduling algorithms, and H2GS outperforms both algorithms. The improvement in performance obtained by H2GS increases as the inter-task communication cost increases.

Scheduling Algorithm: Tasks Scheduling Algorithm for Multiple Processors with Dynamic Reassignment

Distributed computing systems [DCSs] offer the potential for improved performance and resource sharing. To make the best use of the computational power available, it is essential to assign the tasks dynamically to that processor whose characteristics are most appropriate for the execution of the tasks in distributed processing system. We have developed a mathematical model for allocating “M” tasks of distributed program to “N” multiple processors (M>N) that minimizes the total cost of the program. Relocating the tasks from one processor to another at certain points during the course of execution of the program that contributes to the total cost of the running program has been taken into account. Phasewise execution cost [EC], intertask communication cost [ITCT], residence cost [RC] of each task on different processors, and relocation cost [REC] for each task have been considered while preparing a dynamic tasks allocation model. The present model is suitable for arbitrary number of phases and processors with random program structure.

A high performance algorithm for static task scheduling in heterogeneous distributed computing systems

Effective task scheduling is essential for obtaining high performance in heterogeneous distributed computing systems (HeDCSs). However, finding an effective task schedule in HeDCSs requires the consideration of both the heterogeneity of processors and high interprocessor communication overhead, which results from non-trivial data movement between tasks scheduled on different processors. In this paper, we present a new high-performance scheduling algorithm, called the longest dynamic critical path (LDCP) algorithm, for HeDCSs with a bounded number of processors. The LDCP algorithm is a list-based scheduling algorithm that uses a new attribute to efficiently select tasks for scheduling in HeDCSs. The efficient selection of tasks enables the LDCP algorithm to generate high-quality task schedules in a heterogeneous computing environment. The performance of the LDCP algorithm is compared to two of the best existing scheduling algorithms for HeDCSs: the HEFT and DLS algorithms. The comparison study shows that the LDCP algorithm outperforms the HEFT and DLS algorithms in terms of schedule length and speedup. Moreover, the improvement in performance obtained by the LDCP algorithm over the HEFT and DLS algorithms increases as the inter-task communication cost increases. Therefore, the LDCP algorithm provides a practical solution for scheduling parallel applications with high communication costs in HeDCSs.

On the Task Assignment Problem: Two New Efficient Heuristic Algorithms

We study the problem of task allocation in heterogeneous distributed systems. The objective is the minimization of the sum of processor execution and intertask communication costs. We transform the problem to a maximization one, where we try to determine and avoid large communication costs and inefficient allocations. After an appropriate graph transformation, we propose two fast algorithms, the Matching based and Max Edge heuristics, in order to consider tradeoffs between task clustering and task processor assignment. Their performance is evaluated through an experimental study where solution quality is compared with one of the best up-to-date heuristics for the problem. Results prove that algorithm Max Edge provides greatly improved solutions within short computational time even for large size instances.