Performance Evaluation of Adaptive Scheduling Algorithm for Shared Heterogeneous Cluster Systems

Cluster computing systems have recently generated enormous interest for providing easily scalable and cost-effective parallel computing solution for processing large-scale applications. Various adaptive space-sharing scheduling algorithms have been proposed to improve the performance of dedicated and homogeneous clusters. But commodity clusters are naturally non-dedicated and tend to be heterogeneous over the time as cluster hardware is usually upgraded and new fast machines are also added to improve cluster performance. The existing adaptive policies for dedicated homogeneous and heterogeneous parallel systems are not suitable for such conditions. Most of the existing adaptive policies assume a priori knowledge of certain job characteristics to take scheduling decisions. However such information is not readily available without incurring great cost. This paper fills these gaps by designing robust and effective space-sharing scheduling algorithm for non-dedicated heterogeneous cluster systems, assuming no job characteristics to reduce mean job response time. Evaluation results show that the proposed algorithm provide substantial improvement over existing algorithms at moderate to high system utilizations

Submicron Systems Architecture Project: Semiannual Technical Report

No abstract available

Mechanisms for efficient, protected messaging

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (p. 143-149).by Whay Sing Lee.Ph.D

Decentralized load balancing in heterogeneous computational grids

With the rapid development of high-speed wide-area networks and powerful yet low-cost computational resources, grid computing has emerged as an attractive computing paradigm. The space limitations of conventional distributed systems can thus be overcome, to fully exploit the resources of under-utilised computing resources in every region around the world for distributed jobs. Workload and resource management are key grid services at the service level of grid software infrastructure, where issues of load balancing represent a common concern for most grid infrastructure developers. Although these are established research areas in parallel and distributed computing, grid computing environments present a number of new challenges, including large-scale computing resources, heterogeneous computing power, the autonomy of organisations hosting the resources, uneven job-arrival pattern among grid sites, considerable job transfer costs, and considerable communication overhead involved in capturing the load information of sites. This dissertation focuses on designing solutions for load balancing in computational grids that can cater for the unique characteristics of grid computing environments. To explore the solution space, we conducted a survey for load balancing solutions, which enabled discussion and comparison of existing approaches, and the delimiting and exploration of the apportion of solution space. A system model was developed to study the load-balancing problems in computational grid environments. In particular, we developed three decentralised algorithms for job dispatching and load balancing—using only partial information: the desirability-aware load balancing algorithm (DA), the performance-driven desirability-aware load-balancing algorithm (P-DA), and the performance-driven region-based load-balancing algorithm (P-RB). All three are scalable, dynamic, decentralised and sender-initiated. We conducted extensive simulation studies to analyse the performance of our load-balancing algorithms. Simulation results showed that the algorithms significantly outperform preexisting decentralised algorithms that are relevant to this research

Fault-Tolerant Load Management for Real-Time Distributed Computer Systems

This paper presents a fault-tolerant scheme applicable to any decentralized load balancing algorithms used in soft real-time distributed systems. Using the theory of distance-transitive graphs for representing topologies of these systems, the proposed strategy partitions these systems into independent symmetric regions (spheres) centered at some control points. These central points, called fault-control points, provide a two-level task redundancy and efficiently re-distribute the load of failed nodes within their spheres. Using the algebraic characteristics of these topologies, it is shown that the identification of spheres and fault-control points is, in general, is an NP-complete problem. An efficient solution for this problem is presented by making an exclusive use of a combinatorial structure known as the Hadamard matrix. Assuming a realistic failure-repair system environment, the performance of the proposed strategy has been evaluated and compared with no fault environment, through an extensive and detailed simulation. For our fault-tolerant strategy, we propose two measures of goodness, namely, the percentage of re-scheduled tasks which meet their deadlines and the overhead incurred for fault management. It is shown that using the proposed strategy, up to 80% of the tasks can still meet their deadlines. The proposed strategy is general enough to be applicable to many networks, belonging to a number of families of distance transitive graphs. Through simulation, we have analyzed the sensitivity of this strategy to various system parameters and have shown that the performance degradation due to failures does not depend on these parameter. Also, the probability of a task being lost altogether due to multiple failures has been shown to be extremely low

Parallelisation of EST clustering

Master of Science - ScienceThe field of bioinformatics has been developing steadily, with computational problems related to biology taking on an increased importance as further advances are sought. The large data sets involved in problems within computational biology have dictated a search for good, fast approximations to computationally complex problems. This research aims to improve a method used to discover and understand genes, which are small subsequences of DNA. A difficulty arises because genes contain parts we know to be functional and other parts we assume are non-functional as there functions have not been determined. Isolating the functional parts requires the use of natural biological processes which perform this separation. However, these processes cannot read long sequences, forcing biologists to break a long sequence into a large number of small sequences, then reading these. This creates the computational difficulty of categorizing the short fragments according to gene membership. Expressed Sequence Tag Clustering is a technique used to facilitate the identification of expressed genes by grouping together similar fragments with the assumption that they belong to the same gene. The aim of this research was to investigate the usefulness of distributed memory parallelisation for the Expressed Sequence Tag Clustering problem. This was investigated empirically, with a distributed system tested for speed against a sequential one. It was found that distributed memory parallelisation can be very effective in this domain. The results showed a super-linear speedup for up to 100 processors, with higher numbers not tested, and likely to produce further speedups. The system was able to cluster 500000 ESTs in 641 minutes using 101 processors