HMDS: A Makespan Minimizing DAG Scheduler for Heterogeneous Distributed Systems

The problem of scheduling Directed Acyclic Graphs in order to minimize makespan ( schedule length ), is known to be a challenging and computationally hard problem. Therefore, researchers have endeavored towards the design of various heuristic solution generation techniques both for homogeneous as well as heterogeneous computing platforms. This work first presents HMDS-Bl , a list-based heuristic makespan minimization algorithm for task graphs on fully connected heterogeneous platforms. Subsequently, HMDS-Bl has been enhanced by empowering it with a low-overhead depth-first branch and bound based search approach, resulting in a new algorithm called HMDS . HMDS has been equipped with a set of novel tunable pruning mechanisms, which allow the designer to obtain a judicious balance between performance ( makespan ) and solution generation times, depending on the specific scenario at hand. Experimental analyses using randomly generated DAGs as well as benchmark task graphs, have shown that HMDS is able to comprehensively outperform state-of-the-art algorithms such as HEFT , PEFT , PPTS , etc., in terms of archived makespans while incurring bounded additional computation time overhead.

Adaptive Threshold Based Scheduler for Batch of Independent Jobs for Cloud Computing System

Distributed systems are efficient means of realizing high-performance computing (HPC). They are used in meeting the demand of executing large-scale high-performance computational jobs. Scheduling the tasks on such computational resources is one of the prime concerns in the heterogeneous distributed systems. Scheduling jobs on distributed systems are NP-complete in nature. Scheduling requires either heuristic or metaheuristic approach for sub-optimal but acceptable solutions. An adaptive threshold-based scheduler is one such heuristic approach. This work proposes adaptive threshold-based scheduler for batch of independent jobs (ATSBIJ) with the objective of optimizing the makespan of the jobs submitted for execution on cloud computing systems. ATSBIJ exploits the features of interval estimation for calculating the threshold values for generation of efficient schedule of the batch. Simulation studies on CloudSim ensures that the ATSBIJ approach works effectively for real life scenario.

Patterns Related to Microservice Architecture: a Multivocal Literature Review

A Microservice Architecture enables the development of distributed systems using a set of highly cohesive, independent, and collaborative services, ready for current cloud computing demands. Each microservice can be implemented in different technologies, sharing common communication channels, which results in heterogeneous distributed systems that exhibit high scalability, maintainability, performance, and interoperability. Currently, there are many options to build microservices; some of them led by patterns that establish common structures to solve recurrent problems. Nevertheless, as microservices are an emerging trend, the relationship between quality attributes, metrics, and patterns is not clearly defined, which is a concern from a software engineering point of view, since such understanding is fundamental to correctly design systems using this architecture. This paper aims to extend the knowledge on the design of microservices-based systems by presenting a multivocal systematic literature review for microservices related patterns, tying them together with quality attributes and metrics, as can be found in academic and industry research.

Energy-Efficient Scheduling Optimization for Parallel Applications on Heterogeneous Distributed Systems

Designing energy-efficient scheduling algorithms on heterogeneous distributed systems is increasingly becoming the focus of research. State-of-the-art works have studied scheduling by combining dynamic voltage and frequency scaling (DVFS) technology and turning off the appropriate processors to reduce dynamic and static energy consumptions. However, the methods for turning off processors are ineffective. In this study, we propose a novel method to assign priorities to processors for facilitating effective selection of turned-on processors to decrease static energy consumption. An energy-efficient scheduling algorithm based on bisection (ESAB) is proposed on this basis, and this algorithm directly turns on the most energy-efficient processors depending on the idea of bisection to reduce static energy consumption while dynamic energy consumption is decreased by using DVFS technology. Experiments are performed on fast Fourier transform, Gaussian elimination, and randomly generated parallel applications. Results show that our ESAB algorithm makes a better trade-off between reducing energy consumption and low computation time of task assignment (CTTA) than existing algorithms under different scale conditions, deadline constraints, and degrees of parallelism and heterogeneity.