Cloud Service Execution Research Articles

Harnessing idle edge resources for execution of cloud services: A comprehensive review

AbstractThis article presents a comprehensive investigation into voluntary resource sharing at the edge of the internet for cloud service execution. Through a systematic review, 460 scientific articles published between 2019 and 2023 were analyzed, compiling the primary recent contributions in the field. The study presents a taxonomy of the results and highlights the state of the art. Despite the growing interest in the topic in recent years due to the expansion of Edge and Fog Computing, numerous challenges and open research gaps were identified for the effective utilization of idle resources for cloud service execution at the edge. This paper delves into these challenges, emphasizing the need for extensive research in areas such as resource management, interoperability, incentive model establishment, and security and privacy concerns.

Service Cost Effective and Reliability Aware Job Scheduling Algorithm on Cloud Computing Systems

Nowadays, the cloud computing services encounter several cloud Quality of Service challenges, such as reliability, cost, and response time. The most common mechanism to improve cloud service reliability is fault-tolerant technique. However, this reliability enhancement technique inevitably results in multiple replications, which lead to high service cost. In recognition of these challenges, we build a cloud computing systems resources management architecture. Then, we analyze the cloud service execution reliability on the physical resources of a VM and used a CUDA-enabled parallel two-dimensional LSTM to predict the software faults of a cloud VM. Thirdly, we propose an effective primary/backup cloud service cost calculation approach. To overcome the cloud service response time constraint, we integrate a response time slack factor into this method. Fourthly, we formulate the cloud service reliability and cost aware job scheduling problem, which aims at minimizing the total cloud service cost and rejection rate, and improving the system reliability. Fifthly, a heuristic greedy reliability and cost aware job scheduling (RCJS) algorithm is proposed. Finally, a performance evaluation is conducted and the experimental results demonstrate that our proposed RCJS algorithm significantly outperforms optimal redundant VM placement (OPVMP), MIN-MIN algorithms in terms of average service cost and rejection rate.

Thermal-benchmarking for cloud hosting green data centers

Thermal efficient usage of cloud hosting data center servers saves cooling energy and helps establish green cloud data centers. To achieve this goal, the data centers must be stress-tested to avail thermal data related to server utilization. The inherent limitations of cloud computing limit the control of cloud data center owner over the workload execution of cloud services except for the infrastructure and therefore thermal-aware workload modeling or thermal benchmarking of cloud infrastructure can fill this gap. Thermal-benchmarking techniques, through manipulation of server utilization, reveal the thermal profiles and thermal statistics of the servers that can be useful for thermal efficient data center management. This paper presents a generic approach to thermal-benchmarking and profiling of cloud hosting data center servers. We propose workload models to generate customizable thermal benchmarks for stress testing of data center servers. Additionally, we use workload traces from Alibaba cloud to generate thermal statistics to show that the proposed thermal benchmarking approach is applicable to any data center workload trace for any data center server.

A comparative review of meta-heuristic approaches to optimize the SLA violation costs for dynamic execution of cloud services

This paper presents comparative analysis results of research work done using the five most popular meta-heuristic techniques to optimize the service-level agreement (SLA) violation cost in cloud computing. The meta-heuristic algorithms have the ability to handle multifarious types of constraints and offer better results. The Quality of Service criteria, SLA penalty cost and the cloud-domain-specific constraints have been mathematically formulated in this paper. The sole motivation of this paper is that the constraints of feasible domain must be satisfied and the profit of cloud service provider should be maximized. An effort has been made to experimentally demonstrate the comparative performance of five meta-heuristic algorithms, namely Ant Colony Optimization, Particle Swarm Optimization, Genetic Algorithm, Gray Wolf Optimizer and Harmony Search. Eleven test benchmark functions have been applied to demonstrate the efficiency and performance. The best solutions of each meta-heuristic technique have been reported in four performance metric cases: worst, best, average and standard deviation.

Enhancing Service Composition by Discovering Cloud Services Community

In the cloud service community, cloud service providers typically limit component cloud services to perform a specific and limited function to maximize the versatility of cloud services. However, the business needs of consumers are often very complex, requiring multiple component cloud services to be combined to complete the business. In the process of cloud service composition, the question of how to find the right combination of partner cloud services is very challenging. This paper starts with a brief history of cloud service execution and it then analyzes the historical information of the combined cloud service execution. Based on the invocation relationship of the cloud service, the community discovery algorithm is used to divide the component cloud service into multiple cooperative communities. This paper will provide appropriate guidance for subsequent combinations of cloud services to improve the efficiency of cloud service portfolios and improve user satisfaction for combined cloud service execution. The experimental results show that the proposed method can divide the combined cloud service cooperation community in effective time and can effectively improve the success rate of the combined cloud service execution.

Rescheduling strategy of cloud service based on shuffled frog leading algorithm and Nash equilibrium

When a conflict or plan change occurs in the process of cloud service execution, if the initial scheme is not rescheduled in time, it can lead to a delivery delay and a series of nonlinear losses. The cloud service rescheduling (CSRS) problem is considered as a particularly hard combinatorial optimization problem with multi-participants, which is significantly different from traditional flow shop rescheduling problem. It is difficult to coordinate the relationship of service suppliers and construct a reward mechanism to encourage them to participate in the rescheduling. According to the various strategies of service suppliers that do or do not participate in the rescheduling, a game theory model was constructed. The main aim of this paper is to explore how to minimize the reward of suppliers and ensure that the game model develops in a desired direction. Hence, a novel hybrid approach using shuffled frog leading algorithm (SFLA) and Nash equilibrium (NE) theory was proposed to solve the CSRS problem. In addition, we carried out a case study to demonstrate that the SFLA and NE approaches can be implemented for realistically sized problem sets and that the cost savings are significant.

Fault tolerance and QoS scheduling using CAN in mobile social cloud computing

The performance of mobile devices including smart phones and laptops is steadily rising as prices plummet sharply. So, mobile devices are changing from being a mere interface for requesting services to becoming computing resources for providing and sharing services due to immeasurably improved performance. With the increasing number of mobile device users, the utilization rate of SNS (Social Networking Service) is also soaring. Applying SNS to the existing computing environment enables members of social network to share computing services without further authentication. To use mobile device as a computing resource, temporary network disconnection caused by user mobility and various HW/SW faults causing service disruption should be considered. Also these issues must be resolved to support mobile users and to provide user requirements for services. Accordingly, we propose fault tolerance and QoS (Quality of Services) scheduling using CAN (Content Addressable Network) in Mobile Social Cloud Computing (MSCC). MSCC is a computing environment that integrates social network-based cloud computing and mobile devices. In the computing environment, a mobile user can, through mobile devices, become a member of a social network through real world relationships. Essentially, members of a social network share cloud service or data with other members without further authentication by using their mobile device. We use CAN as the underlying MSCC to logically manage the locations of mobile devices. Fault tolerance and QoS scheduling consists of four sub-scheduling algorithms: malicious-user filtering, cloud service delivery, QoS provisioning, and replication and load-balancing. Under the proposed scheduling, a mobile device is used as a resource for providing cloud services, faults caused from user mobility or other reasons are tolerated and user requirements for QoS are considered. We simulate scheduling both with and without CAN. The simulation results show that our proposed scheduling algorithm enhances cloud service execution time, finish time and reliability and reduces the cloud service error rate.