Bharadwaj Veeravalli

On the Design of Adaptive and Decentralized Load Balancing Algorithms with Load Estimation for Computational Grid Environments

In this paper, we address several issues that are imperative to grid environments such as handling resource heterogeneity and sharing, communication latency, job migration from one site to other, and load balancing. We address these issues by proposing two job migration algorithms, which are MELISA (modified ELISA) and LBA (load balancing on arrival). The algorithms differ in the way load balancing is carried out and is shown to be efficient in minimizing the response time on large and small-scale heterogeneous grid environments, respectively. MELISA, which is applicable to large-scale systems (that is, interGrid), is a modified version of ELISA in which we consider the job migration cost, resource heterogeneity, and network heterogeneity when load balancing is considered. The LBA algorithm, which is applicable to small-scale systems (that is, intraGrid), performs load balancing by estimating the expected finish time of a job on buddy processors on each job arrival. Both algorithms estimate system parameters such as the job arrival rate, CPU processing rate, and load on the processor and balance the load by migrating jobs to buddy processors by taking into account the job transfer cost, resource heterogeneity, and network heterogeneity. We quantify the performance of our algorithms using several influencing parameters such as the job size, data transfer rate, status exchange period, and migration limit, and we discuss the implications of the performance and choice of our approaches.

CDRM: A Cost-Effective Dynamic Replication Management Scheme for Cloud Storage Cluster

Data replication has been widely used as a mean of increasing the data availability of large-scale cloud storage systems where failures are normal. Aiming to provide cost-effective availability, and improve performance and load-balancing of cloud storage, this paper presents a cost-effective dynamic replication management scheme referred to as CDRM. A novel model is proposed to capture the relationship between availability and replica number. CDRM leverages this model to calculate and maintain minimal replica number for a given availability requirement. Replica placement is based on capacity and blocking probability of data nodes. By adjusting replica number and location according to workload changing and node capacity, CDRM can dynamically redistribute workloads among data nodes in the heterogeneous cloud. We implemented CDRM in Hadoop Distributed File System (HDFS) and experiment results conclusively demonstrate that our CDRM is cost effective and outperforms default replication management of HDFS in terms of performance and load balancing for large-scale cloud storage.

On the influence of start-up costs in scheduling divisible loads on bus networks

Optimal distribution of divisible loads in bus networks is considered in this paper. The problem of minimizing the processing time is investigated by including all the overhead components that could penalize the performance of the system, in addition to the inherent communication and computation delays. These overheads are considered to be constant additive factors to the respective communication and computation components. Closed-form solution for the processing time is derived and the influence of overheads on the optimal processing time is analyzed. We derive a necessary and sufficient condition for the existence of the optimal processing time. We then study the effect of changing the load distribution sequence on the time performance. Through rigorous analysis, an optimal sequence to distribute the load among the processors is identified, whenever it exists. In case such an optimal sequence fails to exist, we present a greedy algorithm to obtain a suboptimal sequence based on some important properties of the overhead factors. Then, the effect of granularity of the data that is divisible is considered in the analysis for the case of homogeneous networks. An integer approximation algorithm capable of generating integer values of the load fractions in time O(m), where m is the number of processors in the network, is proposed. We then show that the upper bound on the suboptimal solution generated by our algorithm lies within a radius given by the sum of the computation and communication delays. Several numerical examples are presented to illustrate the concepts.

Scheduling Precedence Constrained Stochastic Tasks on Heterogeneous Cluster Systems

Generally, a parallel application consists of precedence constrained stochastic tasks, where task processing times and intertask communication times are random variables following certain probability distributions. Scheduling such precedence constrained stochastic tasks with communication times on a heterogeneous cluster system with processors of different computing capabilities to minimize a parallel applications expected completion time is an important but very difficult problem in parallel and distributed computing. In this paper, we present a model of scheduling stochastic parallel applications on heterogeneous cluster systems. We discuss stochastic scheduling attributes and methods to deal with various random variables in scheduling stochastic tasks. We prove that the expected makespan of scheduling stochastic tasks is greater than or equal to the makespan of scheduling deterministic tasks, where all processing times and communication times are replaced by their expected values. To solve the problem of scheduling precedence constrained stochastic tasks efficiently and effectively, we propose a stochastic dynamic level scheduling (SDLS) algorithm, which is based on stochastic bottom levels and stochastic dynamic levels. Our rigorous performance evaluation results clearly demonstrate that the proposed stochastic task scheduling algorithm significantly outperforms existing algorithms in terms of makespan, speedup, and makespan standard deviation.

Design and implementation of parallel video encoding strategies using divisible load analysis

The processing time needed for motion estimation usually accounts for a significant part of the overall processing time of the video encoder. To improve the video encoding speed, reducing the execution time for motion estimation process is essential. Parallel implementation of video encoding systems using either the software or the hardware approach has attracted much attention in the area of real time video coding. In this paper, we attempt to implement a video encoder on a bus network. Usually, for such a parallel system, the key concern is associated with partitioning and balancing of the computational load among the processors such that the overall processing time of the video encoder is minimized. With the use of the divisible load theory (DLT) paradigm, a strip-wise load partitioning/balancing scheme, a load distribution strategy, two implementation strategies are developed to exploit the data parallelism inherent in the video encoding process. The striking feature of our design is that,both the granularity of the load partitions and all the associated overheads caused during parallel video encoding process can be explicitly considered. This significantly contributes to the minimization of the overall processing time of the video encoder. Extensive experimental studies are carried out to test the effectiveness of the proposed strategies. The performance of the parallel video encoder is quantified using the metrics speedup and performance gain, respectively. The experimental results show that our strategies are effective for exploiting the available parallelism inherent in the video encoding process and provide a theoretical insight on how to analytically quantify and minimize the overall processing time of a parallel system. The proposed strategies can be easily extended and applied to improve other existing parallel systems.

Reliability-aware scheduling strategy for heterogeneous distributed computing systems

Heterogeneous computing systems are promising computing platforms, since single parallel architecture based systems may not be sufficient to exploit the available parallelism with the running applications. In some cases, heterogeneous distributed computing (HDC) systems can achieve higher performance with lower cost than single-machine supersystems. However, in HDC systems, processors and networks are not failure free and any kind of failure may be critical to the running applications. One way of dealing with such failures is to employ a reliable scheduling algorithm. Unfortunately, most existing scheduling algorithms for precedence constrained tasks in HDC systems do not adequately consider reliability requirements of inter-dependent tasks. In this paper, we design a reliability-driven scheduling architecture that can effectively measure system reliability, based on an optimal reliability communication path search algorithm, and then we introduce reliability priority rank (RRank) to estimate the tasks priority by considering reliability overheads. Furthermore, based on directed acyclic graph (DAG) we propose a reliability-aware scheduling algorithm for precedence constrained tasks, which can achieve high quality of reliability for applications. The comparison studies, based on both randomly generated graphs and the graphs of some real applications, show that our scheduling algorithm outperforms the existing scheduling algorithms in terms of makespan, scheduling length ratio, and reliability. At the same time, the improvement gained by our algorithm increases as the data communication among tasks increases.

Design of Fast and Efficient Energy-Aware Gradient-Based Scheduling Algorithms Heterogeneous Embedded Multiprocessor Systems

In this paper, we present two heuristic energy-aware scheduling algorithms (EGMS and EGMSIV) for scheduling task precedence graphs in an embedded multiprocessor system having processing elements with dynamic voltage scaling capabilities. Unlike most energy-aware scheduling algorithms that consider task ordering and voltage scaling separately from task mapping, our algorithms consider them in an integrated way. EGMS uses the concept of energy gradient to select tasks to be mapped onto new processors and voltage levels. EGM-SIV extends EGMS by introducing intra-task voltage scaling using a Linear Programming (LP) formulation to further reduce the energy consumption. Through rigorous simulations, we compare the performance of our proposed algorithms with a few approaches presented in the literature. The results demonstrate that our algorithms are capable of obtaining energy-efficient schedules using less optimization time. On the average, our algorithms produce schedules which consume 10% less energy with more than 47% reduction in optimization time when compared to a few approaches presented in the literature. In particular, our algorithms perform better in generating energy-efficient schedules for larger task graphs. Our results show a reduction of up to 57% in energy consumption for larger task graphs compared to other approaches.

A Robust Spanning Tree Topology for Data Collection and Dissemination in Distributed Environments

Large-scale distributed applications are subject to frequent disruptions due to resource contention and failure. Such disruptions are inherently unpredictable and, therefore, robustness is a desirable property for the distributed operating environment. In this work, we describe and evaluate a robust topology for applications that operate on a spanning tree overlay network. Unlike previous work that is adaptive or reactive in nature, we take a proactive approach to robustness. The topology itself is able to simultaneously withstand disturbances and exhibit good performance. We present both centralized and distributed algorithms to construct the topology, and then demonstrate its effectiveness through analysis and simulation of two classes of distributed applications: Data collection in sensor networks and data dissemination in divisible load scheduling. The results show that our robust spanning trees achieve a desirable trade-off for two opposing metrics where traditional forms of spanning trees do not. In particular, the trees generated by our algorithms exhibit both resilience to data loss and low power consumption for sensor networks. When used as the overlay network for divisible load scheduling, they display both robustness to link congestion and low values for the makespan of the schedule

A Novel Security-Driven Scheduling Algorithm for Precedence-Constrained Tasks in Heterogeneous Distributed Systems

In the recent past, security-sensitive applications, such as electronic transaction processing systems, stock quote update systems, which require high quality of security to guarantee authentication, integrity, and confidentiality of information, have adopted heterogeneous distributed system (HDS) as their platforms. This is primarily due to the fact that single parallel-architecture-based systems may not be sufficient to exploit the available parallelism with the running applications. Most security-aware applications end up in handling dependence tasks, also referred to as Directed Acyclic Graph (DAG), on these HDSs. Unfortunately, most existing algorithms for scheduling such DAGs in HDS fail to fully consider security requirements. In this paper, we systematically design a security-driven scheduling architecture that can dynamically measure the trust level of each node in the system by using differential equations. To do so, we introduce task priority rank to estimate security overhead of such security-critical tasks. Furthermore, we propose a security-driven scheduling algorithm for DAGs which can achieve high quality of security for applications. Our rigorous performance evaluation study results clearly demonstrate that our proposed algorithm outperforms the existing scheduling algorithms in terms of minimizing the makespan, risk probability, and speedup. We also observe that the improvement obtained by our algorithm increases as the security-sensitive data of applications increases.

On the Design of Fault-Tolerant Scheduling Strategies Using Primary-Backup Approach for Computational Grids with Low Replication Costs

Fault-tolerant scheduling is an imperative step for large-scale computational grid systems, as often geographically distributed nodes co-operate to execute a task. By and large, primary-backup approach is a common methodology used for fault tolerance wherein each task has a primary copy and a backup copy on two different processors. In this paper, we identify two cases that may happen when scheduling dependent tasks with primary-backup approach. We derive two important constraints that must be satisfied. Further, we show that these two constraints play a crucial role in limiting the schedulability and overloading efficiency of backups of dependent tasks. We then propose two strategies to improve schedulability and overloading efficiency, respectively. We propose two algorithms (MRC-ECT and MCT-LRC), to schedule backups of independent jobs and dependent jobs, respectively. MRC-ECT is shown to guarantee an optimal backup schedule in terms of replication cost for an independent task, while MCT-LRC can schedule a backup of a dependent task with minimum completion time and less replication cost. We conduct extensive simulation experiments to quantify the performance of the proposed algorithms.