There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

A comprehensive survey for scheduling techniques in cloud computing

Load balancing mechanisms and techniques in the cloud environments

Over the ages, nature has constantly been a rich source of inspiration for science, with much still to discover about and learn from. Swarm Intelligence (SI), a major branch of artificial intelligence, was rendered to model the collective behavior of social swarms in nature. Ultimately, Particle Swarm Optimization algorithm (PSO) is arguably one of the most popular SI paradigms. Over the past two decades, PSO has been applied successfully, with good return as well, in a wide variety of fields of science and technology with a wider range of complex optimization problems, thereby occupying a prominent position in the optimization field. However, through in-depth studies, a number of problems with the algorithm have been detected and identified; e.g., issues regarding convergence, diversity, and stability. Consequently, since its birth in the mid-1990s, PSO has witnessed a myriad of enhancements, extensions, and variants in various aspects of the algorithm, specifically after the twentieth century, and the related research has therefore now reached an impressive state. In this paper, a rigorous yet systematic review is presented to organize and summarize the information on the PSO algorithm and the developments and trends of its most basic as well as of some of the very notable implementations that have been introduced recently, bearing in mind the coverage of paradigm, theory, hybridization, parallelization, complex optimization, and the diverse applications of the algorithm, making it more accessible. Ease for researchers to determine which PSO variant is currently best suited or to be invented for a given optimization problem or application. This up-to-date review also highlights the current pressing issues and intriguing open challenges haunting PSO, prompting scholars and researchers to conduct further research both on the theory and application of the algorithm in the forthcoming years.

Major Advances in Particle Swarm Optimization: Theory, Analysis, and Application

With the growth in computing technologies, cloud computing has added a new paradigm to user services that allows accessing Information Technology services on the basis of pay-per-use at any time and any location. Owing to flexibility in cloud services, numerous organizations are shifting their business to the cloud and service providers are establishing more data centers to provide services to users. However, it is essential to provide cost-effective execution of tasks and proper utilization of resources. Several techniques have been reported in the literature to improve performance and resource use based on load balancing, task scheduling, resource management, quality of service, and workload management. Load balancing in the cloud allows data centers to avoid overloading/underloading in virtual machines, which itself is a challenge in the field of cloud computing. Therefore, it becomes a necessity for developers and researchers to design and implement a suitable load balancer for parallel and distributed cloud environments. This survey presents a state-of-the-art review of issues and challenges associated with existing load-balancing techniques for researchers to develop more effective algorithms.

Issues and Challenges of Load Balancing Techniques in Cloud Computing: A Survey

By providing the flexible and shared computing and communication resources along with the cloud services, the fog computing became an attractive paradigm to support delay-sensitive tasks in the Internet of Things (IoT). The existing researches for offloading delay-sensitive tasks in a hierarchical fog-cloud environment mostly focused on minimizing the overall communication delay. However, a fair offloading strategy selects a suitable computing device in terms of fog node or cloud server based on the resource requirements of the task while meeting the deadline. In this article, we design a new delay-dependent priority-aware task offloading (DPTO) strategy for scheduling and processing the tasks, generated from the IoT devices to suitable computing devices. The proposed strategy assigns a priority on each task based on its deadline and assigns it to a suitable multilevel-feedback queue. This schema reduces the waiting time of the delay-sensitive tasks on the queue and minimizes the starvation problem of the low priority tasks. Moreover, the DPTO strategy selects an optimal computing device for each task based on its resource availability and transmission time from the IoT device. This strategy minimizes the overall offloading time of the tasks while meeting the deadlines. Finally, the extensive simulation results with various performance parameters show the effectiveness of the proposed strategy over the existing baseline algorithms.

DPTO: A Deadline and Priority-Aware Task Offloading in Fog Computing Framework Leveraging Multilevel Feedback Queueing

Cloud computing has emerged as a dominant and transformational paradigm in Information technology domain over the last few years. It begins to affect a multitude of industries such as government, finance, telecommunications, and education. The Quality of Service (QoS) of a cloud service provider is an important research field which encompasses different critical issues such as efficient load balancing, response time optimization, completion time improvement, makespan improvement, and reduction in wastage of bandwidth, accountability of the overall system. This paper highlights a new cloudlet allocation policy with suitable load balancing technique that helps in distributing the cloudlets to the virtual machines (VMs) equally likely to their capacity which makes the system more active, alive, and balanced. This reduces the completion time of the cloudlet(s) as well as reduces the makespan of the VM(s) and the host(s) of a data center. Eventually, this proposed work improves the QoS. The experimental results obtained using CloudSim 3.0.3 toolkit extending few base classes are compared and analyzed with several existing allocation policies.

Development and Analysis of a New Cloudlet Allocation Strategy for QoS Improvement in Cloud

Heuristic-based load-balancing algorithm for IaaS cloud

Workflow scheduling is one of the challenging issues in emerging trends of the distributed environment that focuses on satisfying various quality of service (QoS) constraints. The cloud receives the applications as a form of a workflow, consisting of a set of interdependent tasks, to solve the large-scale scientific or enterprise problems. Workflow scheduling in the cloud environment has been studied extensively over the years, and this article provides a comprehensive review of the approaches. This article analyses the characteristics of various workflow scheduling techniques and classifies them based on their objectives and execution model. In addition, the recent technological developments and paradigms such as serverless computing and Fog computing are creating new requirements/opportunities for workflow scheduling in a distributed environment. The serverless infrastructures are mainly designed for processing background tasks such as Internet-of-Things (IoT), web applications, or event-driven applications. To address the ever-increasing demands of resources and to overcome the drawbacks of the cloud-centric IoT, the Fog computing paradigm has been developed. This article also discusses workflow scheduling in the context of these emerging trends of cloud computing.

A Survey on Scheduling Strategies for Workflows in Cloud Environment and Emerging Trends

Nowadays, cloud computing leverages the capability of Internet-of-Things (IoT) applications by providing computing resources as a form of virtual machine (VM) instances. However, the Cloud data center consumes a large amount of energy while transmitting and computing the IoT applications which lead to a high carbon footprint . On the other hand, the Fog nodes provide various cloud services at the edge of the network which can run the IoT applications locally with minimum energy consumption and delay. Due to the limited resource capacity, the Fog nodes are not suitable for processing the resource-intensive IoT applications. To address these challenges, in this paper, we build sustainable infrastructure in Fog-Cloud environment for processing delay-intensive and resource-intensive applications with an optimal task offloading strategy. The proposed offloading strategy uses Firefly algorithm for finding an optimal computing device based on two Quality-of-Service (QoS) parameters such as energy consumption and computational time. The main objectives of this strategy are to minimize the computational time and the energy consumption of the IoT applications with minimum delay. The effect of the control parameters of the Firefly technique is investigated thoroughly. Through comparisons, we show that the proposed method performs better than the existing ones in terms of various performance metrics including computational time, energy consumption, CO 2 emission, and Temperature emission.

Mainak Adhikari

Papers

DPTO: A Deadline and Priority-Aware Task Offloading in Fog Computing Framework Leveraging Multilevel Feedback Queueing

Development and Analysis of a New Cloudlet Allocation Strategy for QoS Improvement in Cloud

Heuristic-based load-balancing algorithm for IaaS cloud

A Survey on Scheduling Strategies for Workflows in Cloud Environment and Emerging Trends

Energy efficient offloading strategy in fog-cloud environment for IoT applications