A hyper-heuristic cost optimisation approach for Scientific Workflow Scheduling in cloud computing

Alkhanak, Ehab Nabiel; Lee, Sai Peck

doi:10.1016/j.future.2018.03.055

Cited by 65 publications

(35 citation statements)

References 42 publications

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“…Tian et al [16] developed a method to find the optimal solution to minimize the energy consumption in job migrations. Alkhanak and Lee [17] proposed a completion time driven hyper-heuristic approach for cost optimization of cloud scheduling. Ren and Zhong [18] established a mathematical model of cloud task scheduling and proposed an improved simulated annealing algorithm to shorten the completion time of tasks under a given user satisfaction.…”

Section: A Service Scheduling In Cloud Computing Environmentsmentioning

confidence: 99%

A Trust-Based Agent Learning Model for Service Composition in Mobile Cloud Computing Environments

Cao

et al. 2019

IEEE Access

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Mobile cloud computing has the features of resource constraints, openness, and uncertainty which leads to the high uncertainty on its quality of service (QoS) provision and serious security risks. Therefore, when faced with complex service requirements, an efficient and reliable service composition approach is extremely important. In addition, preference learning is also a key factor to improve user experiences. In order to address them, this paper introduces a three-layered trust-enabled service composition model for the mobile cloud computing systems. Based on the fuzzy comprehensive evaluation method, we design a novel and integrated trust management model. Service brokers are equipped with a learning module enabling them to better analyze customers' service preferences, especially in cases when the details of a service request are not totally disclosed. Because traditional methods cannot totally reflect the autonomous collaboration between the mobile cloud entities, a prototype system based on the multi-agent platform JADE is implemented to evaluate the efficiency of the proposed strategies. The experimental results show that our approach improves the transaction success rate and user satisfaction. INDEX TERMS Mobile cloud computing, service composition, trust management, user preference learning, multi-agent technology.

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Section: A Service Scheduling In Cloud Computing Environmentsmentioning

confidence: 99%

A Trust-Based Agent Learning Model for Service Composition in Mobile Cloud Computing Environments

Cao

et al. 2019

IEEE Access

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“…Alkhanak and Lee 60 have proposed a Completion Time Driven Hyper-Heuristic (CTDHH) approach for cost optimization of scientific dataflows scheduling in the cloud environment. Alkhanak and Lee 60 have proposed a Completion Time Driven Hyper-Heuristic (CTDHH) approach for cost optimization of scientific dataflows scheduling in the cloud environment.…”

Section: An Overview Of Non-deterministic Cost-based Query Plan Enumementioning

confidence: 99%

“…Scheduling dataflows in the cloud is an optimization problem, very similar to query optimization. Alkhanak and Lee 60 have proposed a Completion Time Driven Hyper-Heuristic (CTDHH) approach for cost optimization of scientific dataflows scheduling in the cloud environment.…”

Section: An Overview Of Non-deterministic Cost-based Query Plan Enumementioning

confidence: 99%

Deterministic and non‐deterministic query optimization techniques in the cloud computing

Azhir

Navimipour

Hosseinzadeh

et al. 2019

Concurrency and Computation

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Query optimization is considered as one of the main challenges of query processing phases in the cloud environments. The query optimizer attempts to provide the most optimal execution plan by considering the possible query plans. Therefore, the execution cost of a query can be affected by some factors, including communication costs, unavailability of resources, and access to large distributed data sets. In addition, it is known as NP-hard problem and many researchers are focused on this problem in recent years. Some techniques are proposed for solving this problem. Deterministic and non-deterministic methods are two main categories to study these techniques. The deterministic and non-deterministic query optimization methods can be further divided into three subcategories, cost-based query plan enumeration, multiple query optimization, and adaptive query optimization methods. Moreover, this paper presents the advantages and disadvantages of the algorithms for solving the query optimization problems in the cloud environments. Moreover, these techniques are compared in terms of optimization, time, cost, efficiency, and scalability. Finally, some key areas are offered to improve the cloud query optimization mechanisms in the future. KEYWORDScloud computing, database, query optimization, review INTRODUCTIONThe data transfer operation and resource sharing are facilitated by rapid progress of the distributed IT-based systems. 1,2 Cloud computing supports several computers through a network. 3 The cloud computing has a large-scale distributed architecture and virtualized services to deliver the requests to users. 4,5 Moreover, the cloud computing provides important financial advantages and long level cooperation possibilities for organizations and institutions. 6 The cloud computing is defined as a distributed IT-based technology based on service business model. 7This paradigm provides many benefits for users, such as the provision of computing capabilities, heterogeneous network access, scalability, and elasticity with measured services. 8,9 The cloud computing gives shared access to a large pool of resources, including data storage, memory, processing, and virtual machines. 10 A cloud client, such as a web browser and mobile app can be helpful in accessing these services. 11 Enormous amounts of data are retrieved from geo-distributed data sources and cross-layer data-handling requirements to make a change in business model. 12 The cloud storage as one of the main services is provided by cloud computing, 13 which allows the users to store their data in virtual pools instead of their servers. 14 In addition, subscribers can access the data from any area of cloud. 15 Therefore, the reliability and availability are necessary to recover the information and query processing.The query processing involves three main steps, as shown in Figure 1. First, the query is translated into an expression of the relational algebra.Second, an optimal evaluation plan for the query plan is generated. The query optimization is the main part o...

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“…Then the distance of each SA and its partner SA is compared with respect three constraints such as Repulsion Rate (RR), Cohesion Rate (CR), and Attraction Rate (AR). If the distance is below RR then the velocity of SA is changed according to Equation (10). When the distance is between the RR and CR then the velocity is updated based on Equation (11).…”

Section: Random Vm Selection Based On Starling Flock For Energy Efmentioning

confidence: 99%

“…These parameters greatly affect the efficiency of the cloud computing in both cloud provider and cloud consumer perspectives [9]. The efficient task scheduling mechanism must address the few performance metrics such as resource utilization, less computational cost, low energy consumption, scalability as part of the cloud provider's benefit [10]. As part of cloud user's concern, the scheduling algorithm must provider minimum makespan, reduced response time and low cost in getting the service.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Starling Social Spider Algorithm for Energy and Load Aware Task Scheduling in Cloud Computing

M¹,

S²

2019

IJITEE

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The efficiency of the cloud-based systems is greatly relying on the task scheduling algorithm which affects the performance parameters such as makespan, response time, degree of imbalance and cost. In recent years, the energy efficiency is also considered as another challenging issue which affects the efficiency of cloud computing systems. This paper proposes a Hybrid Starling Social Spider Algorithm (Starling-SSA) for Energy and Load Aware Task Scheduling in cloud computing. The Starling-SSA is designed as a hybrid algorithm inspired by the intelligent behavior of social spider and the collective response behavior of starling birds. The foraging behavior of spider is implemented to identify the best VMs for the given task with minimum makespan and degree of imbalance. In addition to this, the distance factor is incorporated inspired by starling flock distance in order identify the closeness of VM pairs and avoids the VMs that are far away, thereby VMs can be limited during the searching process. This will greatly reduce energy consumption by taking only VMs that are belongs to the distance factor. The performance metrics such as makespan, degree of imbalance and energy efficiency are evaluated against the existing algorithms such as EATS, CBAT and HC-ACO. The results presents a significant improvements when comparing to the existing algorithms

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A hyper-heuristic cost optimisation approach for Scientific Workflow Scheduling in cloud computing

Cited by 65 publications

References 42 publications

A Trust-Based Agent Learning Model for Service Composition in Mobile Cloud Computing Environments

A Trust-Based Agent Learning Model for Service Composition in Mobile Cloud Computing Environments

Deterministic and non‐deterministic query optimization techniques in the cloud computing

Hybrid Starling Social Spider Algorithm for Energy and Load Aware Task Scheduling in Cloud Computing

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