Energy aware DAG scheduling on heterogeneous systems

We address the problem of scheduling directed a-cyclic task graph (DAG) on a heterogeneous distributed processor system with the twin objectives of minimizing finish time and energy consumption. Previous scheduling heuristics have assigned DAGs to processors to minimize overall run-time of the application. But applications on embedded systems, such as high performance DSP in image processing, multimedia, and wireless security, need schedules which use low energy too.     

Energy-aware task scheduling in heterogeneous computing environments

Efficient application scheduling is critical for achieving high performance in heterogeneous computing (HC) environments. Because of such importance, there are many researches on this problem and various algorithms have been proposed. Duplication-based algorithms are one kind of well known algorithms to solve scheduling problems, which achieve high performance on minimizing the overall completion time (makespan) of applications. However, they pursuit of the shortest makespan overly by duplicating some tasks redundantly, which leads to a large amount of energy consumption and resource waste. With the growing advocacy for green computing systems, energy conservation has been an important issue and gained a particular interest. An existing technique to reduce energy consumption of an application is dynamic voltage/frequency scaling (DVFS), whose efficiency is affected by the overhead of time and energy caused by voltage scaling. In this paper, we propose a new energy-aware scheduling algorithm with reduced task duplication called Energy-Aware Scheduling by Minimizing Duplication (EAMD), which takes the energy consumption as well as the makespan of an application into consideration. It adopts a subtle energy-aware method to search and delete redundant task copies in the schedules generated by duplication-based algorithms, and it is easier to operate than DVFS, and produces no extra time and energy consumption. This algorithm not only consumes less energy but also maintains good performance in terms of makespan compared with duplication-based algorithms. Two kinds of DAGs, i.e., randomly generated graphs and two real-world application graphs, are tested in our experiments. Experimental results show that EAMD can save up to 15.59 % energy consumption for HLD and HCPFD, two classic duplication-based algorithms. Several factors affecting the performance are also analyzed in the paper.     

Task scheduling for MapReduce in heterogeneous networks

In this paper, the task scheduling in MapReduce is considered for geo-distributed data centers on heterogeneous networks. Adaptive heartbeats, job deadlines and data locality are concerned. Job deadlines are divided according to the maximum data volume of tasks. With the considered constraints, the task scheduling is formulated as an assignment problem in each heartbeat, in which adaptive heartbeats are calculated by the processing times of tasks, jobs are sequencing in terms of the divided deadlines and tasks are scheduled by the Hungarian algorithm. Taking into account both the data transfer and processing times, the most suitable data center for all mapped jobs are determined in the reduce phase. Experimental results show that the proposed algorithms outperform the current existing ones. The proposals with sorted task-sequences have better performance than those with random task-sequences.     

Execution cost minimization scheduling algorithms for deadline-constrained parallel applications on heterogeneous clouds

Cluster Computing - The problem of minimizing the execution monetary cost of applications on cloud computing platforms has been studied recently, and satisfying the deadline constraint of an...     

Parallel morphological/neural processing of hyperspectral images using heterogeneous and homogeneous platforms

The wealth spatial and spectral information available from last-generation Earth observation instruments has introduced extremely high computational requirements in many applications. Most currently available parallel techniques treat remotely sensed data not as images, but as unordered listings of spectral measurements with no spatial arrangement. In thematic classification applications, however, the integration of spatial and spectral information can be greatly beneficial. Although such integrated approaches can be efficiently mapped in homogeneous commodity clusters, low-cost heterogeneous networks of computers (HNOCs) have soon become a standard tool of choice for dealing with the massive amount of image data produced by Earth observation missions. In this paper, we develop a new morphological/neural algorithm for parallel classification of high-dimensional (hyperspectral) remotely sensed image data sets. The algorithm’s accuracy and parallel performance is tested in a variety of homogeneous and heterogeneous computing platforms, using two networks of workstations distributed among different locations, and also a massively parallel Beowulf cluster at NASA’s Goddard Space Flight Center in Maryland.

Ant colony based constrained workflow scheduling for heterogeneous computing systems

The problem of constrained workflow scheduling on heterogeneous computing systems has been of major interest in the recent years. The user requirements are described by defining constraints on the workflow makespan and/or its execution cost. The uncertainty in the activity execution path and the dynamicity in the resource workload may cause some run-time changes of the makespan or cost. To prohibit run-time constraint violation, the system needs robust schedules. In this paper, probability of violation (POV) of constraints is proposed as a criterion for the schedule robustness. An ant colony system is then used to minimize an aggregation of violation of constraints and the POV. Simulation results on real world workflows show the effectiveness of the proposed method in finding feasible schedules. The results also indicate that the proposed method decreases the POV, as well as the expected penalty at run-time.     

Stochastic scheduling for multiclass applications with availability requirements in heterogeneous clusters

High availability plays an important role in heterogeneous clusters, where processors operate at different speeds and are not continuously available for processing. Existing scheduling algorithms designed for heterogeneous clusters do not factor in availability. We address in this paper the stochastic scheduling problem for heterogeneous clusters with availability constraints. Each node in a heterogeneous cluster is modeled by its speed and availability, and different classes of tasks submitted to the cluster are characterized by their execution times and availability requirements. To incorporate availability and heterogeneity into stochastic scheduling, we introduce metrics to quantify availability and heterogeneity in the context of multiclass tasks. A stochastic scheduling algorithm SSAC (stochastic scheduling with availability constraints) is then proposed to improve availability of heterogeneous clusters while reducing average response time of tasks. Experimental results show that our algorithm achieves a good trade-off between availability and responsiveness.

A stochastic approach to estimating earliest start times of nodes for scheduling DAGs on heterogeneous distributed computing systems

Previously, DAG scheduling schemes used the mean (average) of computation or communication time in dealing with temporal heterogeneity. However, it is not optimal to consider only the means of computation and communication times in DAG scheduling on a temporally (and spatially) heterogeneous distributed computing system. In this paper, it is proposed that the second order moments of computation and communication times, such as the standard deviations, be taken into account in addition to their means, in scheduling “stochastic” DAGs. An effective scheduling approach which accurately estimates the earliest start time of each node and derives a schedule leading to a shorter average parallel execution time has been developed. Through an extensive computer simulation, it has been shown that a significant improvement (reduction) in the average parallel execution times of stochastic DAGs can be achieved by the proposed approach.     

Link contention-constrained scheduling and mapping of tasks and messages to a network of heterogeneous processors

In this paper, we consider the problem of scheduling and mapping precedence-constrained tasks to a network of heterogeneous processors. In such systems, processors are usually physically distributed, implying that the communication cost is considerably higher than in tightly coupled multiprocessors. Therefore, scheduling and mapping algorithms for such systems must schedule the tasks as well as the communication traffic by treating both the processors and communication links as equally important resources. We propose an algorithm that achieves these objectives and adapts its task scheduling and mapping decisions according to the given network topology. Just like tasks, messages are also scheduled and mapped to suitable links during the minimization of the finish times of tasks. Heterogeneity of processors is exploited by scheduling critical tasks to the fastest processors. Our experimental study has demonstrated that the proposed algorithm is efficient and robust, and yields consistent performance over a wide range of scheduling parameters. This revised version was published online in July 2006 with corrections to the Cover Date.     

SaMW: a probabilistic meta-heuristic algorithm for job scheduling in heterogeneous distributed systems powered by microservices

Cluster Computing - Applications are evolving in ways that demand geographically distributed resources to co-operate in order to give users better Quality of Service (QoS). There is a plethora of...     

Gene delivery platforms

One of the key challenges in the experimental and therapeutic use of gene delivery agents is the development of methods that can efficiently deliver nucleic acids into living systems. During the past decade, the development of effective and safe gene delivery systems has been intensively investigated. This review summarizes the current state of gene delivery methods based on viral and non-viral agents.     

Drosophila microarray platforms.

The advent of high throughput microarrays and the complete sequencing of the Drosophila melanogaster genome have enabled global gene expression analysis in this powerful genetic model organism. Currently, researchers are using three main Drosophila array platform types, with elements composed of cDNA amplicons, oligonucleotides (short and long) or genomic amplicons. This paper provides a broad overview of these platforms.     

Of pubs and platforms

This article argues for a more explicit concern with sociality in anthropology, illustrated through a study of one of the oldest English platforms for sociality: the English pub. Using an approach derived from the study of social media, the pub is analysed in terms of the balance between structural (mainly commercial) forces and agency (mainly the desire for particular kinds of sociality). Following an introduction to the English pub, the article considers how pubs exert control over which population they serve. The next section, shows how groups of people colonize pubs, regardless of the pub's intentions. This is followed by a discussion of the various responses by pubs to this colonization. The final section, ‘Scalable sociality’, demonstrates how these processes combine to produce a phenomenon called scalable sociality, which is also a definition of social media: a series of platforms that can be sited along various scales and parameters of sociality. This is important because a similar tension between, on the one hand, commercial or state forces and, on the other hand, the development of new forms of sociality is increasingly common within many topics studied by anthropologists.     

Assays for aptamer-based platforms

Aptamers are single stranded DNA or RNA oligonucleotides that have high affinity and specificity towards a wide range of target molecules. Aptamers have low molecular weight, amenable to chemical modifications and exhibit stability undeterred by repetitive denaturation and renaturation. Owing to these indispensable advantages, aptamers have been implemented as molecular recognition element as alternative to antibodies in various assays for diagnostics. By amalgamating with a number of methods that can provide information on the aptamer-target complex formation, aptamers have become the elemental tool for numerous biosensor developments. In this review, administration of aptamers in applications involving assays of fluorescence, electrochemistry, nano-label and nano-constructs are discussed. Although detection strategies are different for various aptamer-based assays, the core of the design strategies is similar towards reporting the presence of specific target binding to the corresponding aptamers. It is prognosticated that aptamers will find even broader applications with the development of new methods of transducing aptamer target binding.     

Electrochemical biosensing based on universal affinity biocomposite platforms

Rigid conducting biocomposites are versatile and effective transducing materials for the construction of a wide range of amperometric biosensors such as immunosensors, genosensors and enzymosensors, particularly if the transducer is bulk-modified with universal affinity biomolecules. The strept(avidin)-graphite-epoxy biocomposite could be considered as an universal immobilization platform whereon biotinylated DNAs, oligonucleotides, enzymes or antibodies can be captured by means of the highly affinity (strept)avidin-biotin reaction. Universal affinity biocomposite-based biosensors offer many potential advantages compared to more traditional electrochemical biosensors commonly based on a biologically surface-modified transducer. The integration of many materials into one matrix is their main advantage. As biological bulk-modified materials, the conducting biocomposites act not only as transducers, but also as reservoir for the biomaterial. After its use, the electrode surface can be renewed by a simple polishing procedure, establishing a clear advantage of these approaches relative to classical biosensors and other common biological assays. Moreover, the same material is useful for the analysis of many molecules whose determinations are based on genetic, enzymatic or immunological reactions. The different strategies for electrochemical genosensing, immunosensing and enzymosensing, all of them being dependent on the presence of a redox enzyme marker for the generation of the electrochemical signal, based on this universal affinity biocomposite platform are all presented and discussed.