Some computational grid applications have very large resource requirements and need simultaneous access to resources from more than one parallel computer. Current scheduling systems do not provide mechanisms to gain such simultaneous access without the help of human administrators of the computer systems. In this work, we propose and evaluate several algorithms for supporting advanced reservation of resources in supercomputing scheduling systems. These advanced reservations allow users to request resources from scheduling systems at specific times. We find that the wait times of applications submitted to the queue increases when reservations are supported and the increase depends on how reservations are supported. Further, we find that the best performance is achieved when we assume that applications can be terminated and restarted, backfilling is performed, and relatively accurate run-time predictions are used.

Scheduling with advanced reservations

Cluster Computing addresses the latest results in these fields that support High Performance Distributed Computing (HPDC). In HPDC environments, parallel and/ or distributed computing techniques are applied to the solution of computationally intensive applications across networks of computers. The journal represents an important source of information for the growing number of researchers, developers and users of HPDC environments. Cluster Computing: the Journal of Networks, Software Tools and Applications provides a forum for presenting the latest research and technology in the fields of parallel processing, distributed computing systems and computer networks.

http://ieeexplore.ieee.org/iel5/8434/26558/01183567.pdf

Cluster computing

Toward human-centric smart manufacturing: A human-cyber-physical systems (HCPS) perspective

Many applications in the real world include optimizing specific targets, such as cost minimization, energy conservation, climate, and maximizing production, efficiency, and sustainability. The optimization problem is strongly non-linear with multifunctional landscapes under several dynamic, non-linear constraints in some instances. It is challenging to address those issues. Also, with the increasing strength of modern computers, simplistic brute force methods are still inefficient and unwanted. Practical algorithms are also vital for these implementations whenever possible. Cloud computing has become an essential and popular emerging computing environment that supports on-demand services and provides internet-based services. Cloud computing allows a range of services and tools to be easily accessed from anywhere in the world. Since cloud computing has global access to its services, there will always be threats and challenges facing its servers and services, such as; task scheduling, security, energy efficiency, network load, and other challenges. In the research area, many algorithms have been addressed to solve these problems. This paper investigates relevant analysis and surveys on the above topics, threats, and outlooks. This paper offers an overview of nature-inspired algorithms, their applications, and valuation, emphasizing cloud computing problems. Many problems in science and engineering can be viewed Review Article Yahia et al.; AJRCOS, 8(2): 1-16, 2021; Article no.AJRCOS.68322 2 as optimization problems with complex non-linear constraints. Highly nonlinear solutions typically need advanced optimization algorithms, and conventional algorithms can have difficulty addressing these issues. Because of its simplicity and usefulness, nature-inspired algorithms are currently being used. There are nevertheless some significant concerns with computing and swarming intelligence influenced by evolution.

/pdf/comprehensive-survey-for-cloud-computing-based-nature-3rk1ufa76a.pdf

Comprehensive Survey for Cloud Computing Based Nature-Inspired Algorithms Optimization Scheduling

Inter-connected cloud computing is an inherent evolution of Cloud Computing. Numerous benefits provided by connecting clouds have garnered attraction from the academic as well as the industry sector. Just as every new evolution faces challenges, inter-connected clouds have their own set of challenges such as security, monitoring, authorization and identity management, vendor lock-in, and so forth. This article considers the vendor lock-in problem, which is a direct consequence of the lack of interoperability and portability. An extensive literature review by surveying more than 120 papers has been done to analyze and categorize various solutions suggested in literature for solving the interoperability and portability issues of inter-connected clouds. After categorizing the solutions, the literature has been mapped to a specific solution and a comparative analysis of the papers under the same solution has been done. The term “inter-connected clouds” has been used generically in this article to refer to any collaboration of clouds which may be from the user side (Multi-clouds or Aggregated service by Broker) or the provider side (Federated clouds or Hybrid clouds). Lastly, two closely related issues (Brokers and Meta-scheduling) and the remaining challenges of inter-connected clouds are discussed.

Interoperability and Portability Approaches in Inter-Connected Clouds: A Review

Cloud computing, a novel and promising model of Service-oriented computing, provides a pay-per-use framework to solve large-scale scientific and business workflow applications. Workflow scheduling in cloud is challenging due to dynamic nature of the cloud, particularly, on demand provisioning, elasticity, heterogeneous resource types, static & dynamic pricing models and virtualization. An example of workflow scheduling is mapping workflow tasks to cloud computing resources. Additionally, these workflow applications have a runtime constraint—the most typical being the cost of the computation and the time that computation requires to complete. Therefore, the focus is on two criteria: makespan and cost. This paper presents an algorithm called NBWS (Normalization based Budget constraint Workflow Scheduling) which generates a workflow schedule which minimizes the schedule length while satisfying the budget constraint. The algorithm undergoes a process of min–max normalization tailed by computing expect reasonable budget 
$$ (erb) $$

 for dispatching the workflow tasks into one of the virtual machines. To minimize the execution time, NBWS algorithm maps the workflow tasks to resources which are having the earliest finish time within the allocated budget. The experimental results demonstrate that NBWS outperforms current state-of-the-art heuristics with respect to budget constraint and minimizing the makespan.

Budget aware scheduling algorithm for workflow applications in IaaS clouds

Cloud computing, a novel and promising methodology in the distributed computing domain, provides a pay-per-use framework to solve large-scale scientific and business workflow applications. These workflow applications have a constraint that each of them must completed within the limited time (deadline constraint). Therefore, scheduling a workflow with deadline constraints is increasingly becoming a crucial research issue. However, many analytical reviews on scheduling problems reveal that existing solutions fail to provide cost-effective solutions and they do not consider the parameters like CPU performance variation, delay in acquisition and termination of Virtual Machines (VMs). This paper presents a Cost-Effective Firefly based Algorithm (CEFA) to solve workflow scheduling problems that can occur in an Infrastructure as a Service (IaaS) platform. The proposed CEFA uses a novel method for problem encoding, population initialization and fitness evaluation with an objective to provide cost-effective and optimized workflow execution within the time limit. The performance of the proposed CEFA is compared with the state-of-the-art algorithms such as IaaS Cloud-Partial Critical Path (IC-PCP), Particle Swarm Optimization (PSO), Robustness-Cost-Time (RCT), Robustness-Time-Cost (RTC), and Regressive Whale Optimization (RWO). Our experimental results demonstrate that the proposed CEFA outperforms current state-of-the-art heuristics with the criteria of achieving the deadline constraint and minimizing the cost of execution.

Cost-effective workflow scheduling approach on cloud under deadline constraint using firefly algorithm

TOPSIS inspired Budget and Deadline Aware Multi-Workflow Scheduling for Cloud computing

TOPSIS inspired cost-efficient concurrent workflow scheduling algorithm in cloud

Cloud Computing can be viewed as a computing model containing a pool of resources and Internet based application services. Cloud makes on-demand delivery of these computational resources (data, software and infrastructure) among multiple services via a computer network. An infrastructure-as-a-service cloud system provides computational capacities to remote users. In present scenario, most of the Infrastructure as a Service (IaaS) Clouds use simple resource allocation policies like immediate and best effort. In private cloud, since the resources are limited, maximizing the utilization of resources and giving the guaranteed service for the user are the ultimate goal. Hence efficient scheduling is needed which is a major challenge in satisfying the user’s requirement (QoS). In this paper, we propose an advanced reservation technique with backfilling in scheduling policy that aims at serving the user requests by satisfying the required QoS, achieving the guaranteed service for the request by making an efficient provisioning of cloud resources.

Shyamala Loganathan

Papers

Budget aware scheduling algorithm for workflow applications in IaaS clouds

Cost-effective workflow scheduling approach on cloud under deadline constraint using firefly algorithm

TOPSIS inspired Budget and Deadline Aware Multi-Workflow Scheduling for Cloud computing

TOPSIS inspired cost-efficient concurrent workflow scheduling algorithm in cloud

Differentiated Policy Based Job Scheduling with Queue Model and Advanced Reservation Technique in a Private Cloud Environment