Kiranmai Bellam

Energy-Efficient Scheduling for Parallel Applications Running on Heterogeneous Clusters

High performance clusters have been widely used to provide amazing computing capability for both commercial and scientific applications. However, huge power consumption has prevented the further application of large-scale clusters. Designing energy-efficient scheduling algorithms for parallel applications running on clusters, especially on the high performance heterogeneous clusters, is highly desirable. In this regard, we propose a novel scheduling strategy called energy efficient task duplication schedule (EETDS for short), which can significantly conserve power by judiciously shrinking communication energy cost when allocating parallel tasks to heterogeneous computing nodes. We present the preliminary simulation results for Gaussian and FFT parallel task models to prove the efficiency of our algorithm.

An Energy-Efficient Scheduling Algorithm Using Dynamic Voltage Scaling for Parallel Applications on Clusters

In the past decade cluster computing platforms have been widely applied to support a variety of scientific and commercial applications, many of which are parallel in nature. However, scheduling parallel applications on large scale clusters is technically challenging due to significant communication latencies and high energy consumption. As such, shortening schedule length and conserving energy consumption are two major concerns in designing economical and environmentally friendly clusters. In this paper, we propose an energy-efficient scheduling algorithm (TDVAS) using the dynamic voltage scaling technique to provide significant energy savings for clusters. The TDVAS algorithm aims at judiciously leveraging processor idle times to lower processor voltages (i.e., the dynamic voltage scaling technique or DVS), thereby reducing energy consumption experienced by parallel applications running on clusters. Reducing processor voltages, however, can inevitably lead to increased execution times of parallel task. The salient feature of the TDVAS algorithm is to tackle this problem by exploiting tasks precedence constraints. Thus, TDVAS applies the DVS technique to parallel tasks followed by idle processor times to conserve energy consumption without increasing schedule lengths of parallel applications. Experimental results clearly show that the TDVAS algorithm is conducive to reducing energy dissipation in large-scale clusters without adversely affecting system performance.

Improving Security of Real-Time Wireless Networks Through Packet Scheduling [Transactions Letters]

Modern real-time wireless networks require high security level to assure confidentiality of information stored in packages delivered through wireless links. However, most existing algorithms for scheduling independent packets in real-time wireless networks ignore various security requirements of the packets. Therefore, in this paper we remedy this problem by proposing a novel dynamic security-aware packet-scheduling algorithm, which is capable of achieving high quality of security for realtime packets while making the best effort to guarantee realtime requirements (e.g., deadlines) of those packets. We conduct extensive simulation experiments to evaluate the performance of our algorithm. Experimental results show that compared with two baseline algorithms, the proposed algorithm can substantially improve both quality of security and real-time packet guarantee ratio under a wide range of workload characteristics.

A prefetching scheme for energy conservation in parallel disk systems

Large-scale parallel disk systems are frequently used to meet the demands of information systems requiring high storage capacities. A critical problem with these large-scale parallel disk systems is the fact that disks consume a significant amount of energy. To design economically attractive and environmentally friendly parallel disk systems, we developed two energy-aware prefetching strategies for parallel disk systems with disk buffers. First, we introduce a new buffer disk architecture that can provide significant energy savings for parallel disk systems while achieving high performance. Second, we design a prefetching approach to utilize an extra disk to accommodate prefetched data sets that are frequently accessed. Third, we develop a second prefetching strategy that makes use of an existing disk in the parallel disk system as a buffer disk. Compared with the first prefetching scheme, the second approach lowers the capacity of the parallel disk system. However, the second approach is more cost-effective and energy-efficient than the first prefetching technique. Finally, we quantitatively compare both of our prefetching approaches against two conventional strategies including a dynamic power management technique and a non-energy-aware scheme. Using empirical results we show that our novel prefetching approaches are able to reduce energy dissipation in parallel disk systems by 44% and 50% when compared against a non-energy aware approach. Similarly, our strategies are capable of conserving 22% and 30% of the energy when compared to the dynamic power management technique.

DARAW: a new write buffer to improve parallel I/O energy-efficiency

In the past decades, parallel I/O systems have been used widely to support scientific and commercial applications. New data centers today employ huge quantities of I/O systems, which consume a large amount of energy. Most large-scale I/O systems have an array of hard disks working in parallel to meet performance requirements. Traditional energy conservation techniques attempt to place disks into low-power states when possible. In this paper we propose a novel strategy, which aims to significantly conserve energy while reducing average I/O response times. This goal is achieved by making use of buffer disks in parallel I/O systems to accumulate small writes to form a log, which can be transferred to data disks in a batch way. We develop an algorithm - dynamic request allocation algorithm for writes or DARAW - to energy efficiently allocate and schedule write requests in a parallel I/O system. DARAW is able to improve parallel I/O energy efficiency by the virtue of leveraging buffer disks to serve a majority of incoming write requests, thereby keeping data disks in low-power state for longer period times. Buffered requests are then written to data disks at a predetermined time. Experimental results show that DARAW can significantly reduce energy dissipation in parallel I/O systems without adverse impacts on I/O performance.

Improving reliability and energy efficiency of disk systems via utilization control

As disk drives become increasingly sophisticated and processing power increases, one of the most critical issues of designing modern disk systems is data reliability. Although numerous energy saving techniques are available for disk systems, most of energy conservation techniques are not effective in reliability critical environments due to their limitation of ignoring the reliability issue. A wide range of factors affect the reliability of disk systems; the most important factors - disk utilization and ages - are the focus of this study. We build a model to quantify the relationship among the disk age, utilization, and failure probabilities. Observing that the reliability of a disk heavily relies on both disk utilization and age, we propose a novel concept of safe utilization zone, where energy of the disk can be conserved without degrading reliability. We investigate an approach to improving both reliability and energy efficiency of disk systems via utilization control, where disk drives are operated in safe utilization zones to minimize the probability of disk failure. In this study, we integrate an existing energy consumption technique that operates the disks at different power modes with our proposed reliability approach. Experimental results show that our approach can significantly improve reliable while achieving high energy efficiency for disk systems.

Reliability-Driven Scheduling of Periodic Tasks in Heterogeneous Real-Time Systems

In this paper we comprehensively investigated the issue of reliability-driven real-time scheduling for periodic tasks in heterogeneous systems. First, we built a reliability model in which the concept of reliability cost is introduced in the context of heterogeneous realtime systems. Next, we proposed a novel reliability- driven scheduling algorithm (referred to as Repars) for periodic tasks in heterogeneous systems. Third, after extending the reliability model to meet the needs of our fault-tolerant scheme, we developed a fault-tolerant scheduling algorithm or Refine. Refine aims to enhance system reliability while being able to tolerate one-processor failures in heterogeneous real-time systems. Experimental results showed that Repars is superior to RMFF in terms of both schedulability and reliability. When compared with Repars, Refine significantly reduced the reliability cost by up to 34% with graceful degradation in schedulability.

Scheduling of Periodic Packets in Energy-Aware Wireless Networks

Existing packets scheduling algorithms designed for energy-efficient wireless networks ignore important features of periodic packets, thereby being inadequate for periodic packets with energy constraints. To remedy this problem, we present in this paper an approach to scheduling periodic packets in wireless networks subject to both timing and energy constraints. We propose a necessary and sufficient feasibility check for a set of periodic packets to be transmitted over a wireless link. Next, we develop an algorithm to schedule periodic packets (or ESPP for short) over a wireless link. The ESPP algorithm aims at minimizing energy dissipation of periodic packets without missing deadlines of periodic packets. We show through simulation studies that ESPP can significantly reduce energy consumption of wireless networks by an average of 46.4% while guaranteeing timing constraints of periodic packets.

An Energy-Efficient Reliability Model for Parallel Disk Systems

In the last decade, parallel disk systems have increasingly become popular for data-intensive applications running on high-performance computing platforms. Conservation of energy in parallel disk systems has a strong impact on the cost of cooling equipment and backup power-generation. This is because a significant amount of energy is consumed by parallel disks in high-performance computing centers. Although a wide range of energy conservation techniques have been developed for disk systems, most energy saving schemes have adverse impacts on the reliability of parallel disk systems. To address this deficiency, we must focus on reliability analysis for energy-efficient parallel disk systems. In this paper, we make use of a Markov process to develop a quantitative reliability model for energy-efficient parallel disk systems using data mirroring. With the new model in place, a reliability analysis tool is developed to efficiently evaluate reliability of fault-tolerant parallel disk systems with two power modes.

A simulation framework for energy efficient data grids

High performance data grids are increasingly becoming popular platforms to support data-intensive applications. Reducing high energy consumption caused by data grids is a challenging issue. Most previous studies in grid computing focused on performance and reliability without taking energy conservation into account. As such, designing energy-efficient data grid systems became highly desirable. In this paper, we proposed a framework to simulate energy-efficient data grids. We presented an approach to integrate energy-aware allocation strategies into energy-efficient data grids. Our framework aims at simulating a data grid that can conserve energy for data-intensive applications running on data grids.