Energy and performance-optimized scheduling of tasks in distributed cloud and edge computing systems

Infrastructure resources in distributed cloud data centers (CDCs) are shared by heterogeneous applications in a high-performance and cost-effective way. Edge computing has emerged as a new paradigm to provide access to computing capacities in end devices. Yet it suffers from such problems as load imbalance, long scheduling time, and limited power of its edge nodes. Therefore, intelligent task scheduling in CDCs and edge nodes is critically important to construct energy-efficient cloud and edge computing systems. Current approaches cannot smartly minimize the total cost of CDCs, maximize their profit and improve quality of service (QoS) of tasks because of aperiodic arrival and heterogeneity of tasks. This dissertation proposes a class of energy and performance-optimized scheduling algorithms built on top of several intelligent optimization algorithms. This dissertation includes two parts, including background work, i.e., Chapters 3–6, and new contributions, i.e., Chapters 7–11. 1) Background work of this dissertation. Chapter 3 proposes a spatial task scheduling and resource optimization method to minimize the total cost of CDCs where bandwidth prices of Internet service providers, power grid prices, and renewable energy all vary with locations. Chapter 4 presents a geography-aware task scheduling approach by considering spatial variations in CDCs to maximize the profit of their providers by intelligently scheduling tasks. Chapter 5 presents a spatio-temporal task scheduling algorithm to minimize energy cost by scheduling heterogeneous tasks among CDCs while meeting their delay constraints. Chapter 6 gives a temporal scheduling algorithm considering temporal variations of revenue, electricity prices, green energy and prices of public clouds. 2) Contributions of this dissertation. Chapter 7 proposes a multi-objective optimization method for CDCs to maximize their profit, and minimize the average loss possibility of tasks by determining task allocation among Internet service providers, and task service rates of each CDC. A simulated annealing-based bi-objective differential evolution algorithm is proposed to obtain an approximate Pareto optimal set. A knee solution is selected to schedule tasks in a high-profit and high-quality-of-service way. Chapter 8 formulates a bi-objective constrained optimization problem, and designs a novel optimization method to cope with energy cost reduction and QoS improvement. It jointly minimizes both energy cost of CDCs, and average response time of all tasks by intelligently allocating tasks among CDCs and changing task service rate of each CDC. Chapter 9 formulates a constrained bi-objective optimization problem for joint optimization of revenue and energy cost of CDCs. It is solved with an improved multi-objective evolutionary algorithm based on decomposition. It determines a high-quality trade-off between revenue maximization and energy cost minimization by considering CDCs’ spatial differences in energy cost while meeting tasks’ delay constraints. Chapter 10 proposes a simulated annealing-based bees algorithm to find a close-to-optimal solution. Then, a fine-grained spatial task scheduling algorithm is designed to minimize energy cost of CDCs by allocating tasks among multiple green clouds, and specifies running speeds of their servers. Chapter 11 proposes a profit-maximized collaborative computation offloading and resource allocation algorithm to maximize the profit of systems and guarantee that response time limits of tasks are met in cloud-edge computing systems. A single-objective constrained optimization problem is solved by a proposed simulated annealing-based migrating birds optimization. This dissertation evaluates these algorithms, models and software with real-life data and proves that they improve scheduling precision and cost-effectiveness of distributed cloud and edge computing systems

Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

Deadline Constrained Cloud Computing Resources Scheduling through an Ant Colony System Approach

Cloud computing resources scheduling is essential for executing workflows in the cloud platform because it relates to both execution time and execution cost. In this paper, we adopt a model that optimizes the execution cost while meeting deadline constraints. In solving this problem, we propose an Improved Ant Colony System (IACS) approach featuring two novel strategies. Firstly, a dynamic heuristic strategy is used to calculate a heuristic value during an evolutionary process by taking the workflow topological structure into consideration. Secondly, a double search strategy is used to initialize the pheromone and calculate the heuristic value according to the execution time at the beginning and to initialize the pheromone and calculate heuristic value according to the execution cost after a feasible solution is found. Therefore, the proposed IACS is adaptive to the search environment and to different objectives. We have conducted extensive experiments based on workflows with different scales and different cloud resources. We compare the result with a particle swarm optimization (PSO) approach and a dynamic objective genetic algorithm (DOGA) approach. Experimental results show that IACS is able to find better solutions with a lower cost than both PSO and DOGA do on various scheduling scales and deadline conditions

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Parallel Differential Evolution approach for Cloud workflow placements under simultaneous optimization of multiple objectives

International audienceThe recent rapid expansion of Cloud computing facilities triggers an attendant challenge to facility providers and users for methods for optimal placement of workflows on distributed resources, under the often-contradictory impulses of minimizing makespan, energy consumption, and other metrics. Evolutionary Optimization techniques that from theoretical principles are guaranteed to provide globally optimum solutions, are among the most powerful tools to achieve such optimal placements. Multi-Objective Evolutionary algorithms by design work upon contradictory objectives, gradually evolving across generations towards a converged Pareto front representing optimal decision variables – in this case the mapping of tasks to resources on clusters. However the computation time taken by such algorithms for convergence makes them prohibitive for real time placements because of the adverse impact on makespan. This work describes parallelization, on the same cluster, of a Multi-Objective Differential Evolution method (NSDE-2) for optimization of workflow placement, and the attendant speedups that bring the implicit accuracy of the method into the realm of practical utility. Experimental validation is performed on a real-life testbed using diverse Cloud traces. The solutions under different scheduling policies demonstrate significant reduction in energy consumption with some improvement in makespan

Task Scheduling Approach in Cloud Computing Environment Using Hybrid Differential Evolution

Task scheduling is one of the most significant challenges in the cloud computing environment and has attracted the attention of various researchers over the last decades, in order to achieve cost-effective execution and improve resource utilization. The challenge of task scheduling is categorized as a nondeterministic polynomial time (NP)-hard problem, which cannot be tackled with the classical methods, due to their inability to find a near-optimal solution within a reasonable time. Therefore, metaheuristic algorithms have recently been employed to overcome this problem, but these algorithms still suffer from falling into a local minima and from a low convergence speed. Therefore, in this study, a new task scheduler, known as hybrid differential evolution (HDE), is presented as a solution to the challenge of task scheduling in the cloud computing environment. This scheduler is based on two proposed enhancements to the traditional differential evolution. The first improvement is based on improving the scaling factor, to include numerical values generated dynamically and based on the current iteration, in order to improve both the exploration and exploitation operators; the second improvement is intended to improve the exploitation operator of the classical DE, in order to achieve better results in fewer iterations. Multiple tests utilizing randomly generated datasets and the CloudSim simulator were conducted, to demonstrate the efficacy of HDE. In addition, HDE was compared to a variety of heuristic and metaheuristic algorithms, including the slime mold algorithm (SMA), equilibrium optimizer (EO), sine cosine algorithm (SCA), whale optimization algorithm (WOA), grey wolf optimizer (GWO), classical DE, first come first served (FCFS), round robin (RR) algorithm, and shortest job first (SJF) scheduler. During trials, makespan and total execution time values were acquired for various task sizes, ranging from 100 to 3000. Compared to the other metaheuristic and heuristic algorithms considered, the results of the studies indicated that HDE generated superior outcomes. Consequently, HDE was found to be the most efficient metaheuristic scheduling algorithm among the numerous methods researched

Load Balancer using Whale-Earthworm Optimization for Efficient Resource Scheduling in the IoT-Fog-Cloud Framework

Cloud-Fog environment is useful in offering optimized services to customers in their daily routine tasks. With the exponential usage of IoT devices, a huge scale of data is generated. Different service providers use optimization scheduling approaches to optimally allocate the scarce resources in the Fog computing environment to meet job deadlines. This study introduces the Whale-EarthWorm Optimization method (WEOA), a powerful hybrid optimization method for improving resource management in the Cloud-Fog environment. Striking a balance between exploration and exploitation of these approaches is difficult, if only Earthworm or Whale optimization methods are used. Earthworm technique can result in inefficiency due to its investigations and additional overhead, whereas Whale algorithm, may leave scope for improvement in finding the optimal solutions using its exploitation.  This research introduces an efficient task allocation method as a novel load balancer. It leverages an enhanced exploration phase inspired by the Earthworm algorithm and an improved exploitation phase inspired by the Whale algorithm to manage the optimization process. It shows a notable performance enhancement, with a 6% reduction in response time, a 2% decrease in cost, and a 2% improvement in makespan over EEOA. Furthermore, when compared to other approaches like h-DEWOA, CSDEO, CSPSO, and BLEMO, the proposed method achieves remarkable results, with response time reductions of up to 82%, cost reductions of up to 75%, and makespan improvements of up to 80%

Hybrid scheduling algorithms in cloud computing: a review

Cloud computing is one of the emerging fields in computer science due to its several advancements like on-demand processing, resource sharing, and pay per use. There are several cloud computing issues like security, quality of service (QoS) management, data center energy consumption, and scaling. Scheduling is one of the several challenging problems in cloud computing, where several tasks need to be assigned to resources to optimize the quality of service parameters. Scheduling is a well-known NP-hard problem in cloud computing. This will require a suitable scheduling algorithm. Several heuristics and meta-heuristics algorithms were proposed for scheduling the user's task to the resources available in cloud computing in an optimal way. Hybrid scheduling algorithms have become popular in cloud computing. In this paper, we reviewed the hybrid algorithms, which are the combinations of two or more algorithms, used for scheduling in cloud computing. The basic idea behind the hybridization of the algorithm is to take useful features of the used algorithms. This article also classifies the hybrid algorithms and analyzes their objectives, quality of service (QoS) parameters, and future directions for hybrid scheduling algorithms