Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Adaptive Dynamic Scheduling <s> An adaptive particle swarm optimization (APSO) that features better search efficiency than classical particle swarm optimization (PSO) is presented. More importantly, it can perform a global search over the entire search space with faster convergence speed. The APSO consists of two main steps. First, by evaluating the population distribution and particle fitness, a real-time evolutionary state estimation procedure is performed to identify one of the following four defined evolutionary states, including exploration, exploitation, convergence, and jumping out in each generation. It enables the automatic control of inertia weight, acceleration coefficients, and other algorithmic parameters at run time to improve the search efficiency and convergence speed. Then, an elitist learning strategy is performed when the evolutionary state is classified as convergence state. The strategy will act on the globally best particle to jump out of the likely local optima. The APSO has comprehensively been evaluated on 12 unimodal and multimodal benchmark functions. The effects of parameter adaptation and elitist learning will be studied. Results show that APSO substantially enhances the performance of the PSO paradigm in terms of convergence speed, global optimality, solution accuracy, and algorithm reliability. As APSO introduces two new parameters to the PSO paradigm only, it does not introduce an additional design or implementation complexity. <s> BIB001 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Adaptive Dynamic Scheduling <s> Elastic compute clouds are best represented by the virtual clusters in Amazon EC2 or in IBM RC2. This paper proposes a simulation based approach to scheduling scientific workflows onto elastic clouds. Scheduling multitask workflows in virtual clusters is a NP-hard problem. Excessive simulations in months of time may be needed to produce the optimal schedule using Monte Carlo simulations. To reduce this scheduling overhead is necessary in real-time cloud computing. We present a new workflow scheduling method based on iterative ordinal optimization (IOO). This new method outperforms the Monte Carlo and Blind-Pick methods to yield higher performance against rapid workflow variations. For example, to execute 20,000 tasks on 128 virtual machines for gravitational wave analysis, an ordinal optimized schedule can be generated in a few minutes, which is O(103)~O(104) faster than using Monte Carlo simulations. The ordinal optimized schedule results in higher throughput with lower memory demand. The cloud experimental results being reported verified our theoretical findings on the relative performance of three workflow scheduling methods studied in this paper. <s> BIB002 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Adaptive Dynamic Scheduling <s> This work presents TIRAMOLA, a cloud-enabled, open-source framework to perform automatic resizing of NoSQL clusters according to user-defined policies. Decisions on adding or removing worker VMs from a cluster are modeled as a Markov Decision Process and taken in real-time. The system automatically decides on the most advantageous cluster size according to user-defined policies, it then proceeds on requesting/releasing VM resources from the provider and orchestrating them inside a NoSQL cluster. TIRAMOLA's modular architecture and standard API support allows interaction with most current IaaS platforms and increased customization. An extensive experimental evaluation on an HBase cluster confirms our assertions: The system resizes clusters in real-time and adapts its performance through different optimization strategies, different permissible actions, different input and training loads. Besides the automation of the process, it exhibits a learning feature which allows it to make very close to optimal decisions even with input loads 130% larger or alternating 10 times faster compared to the accumulated information. <s> BIB003 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Adaptive Dynamic Scheduling <s> In Cloud computing, resources such as CPU, RAM, and disk etc. are provided as a service via the Internet. Elasticity is a key feature of Cloud Computing. It aims to enabling a Cloud Service Provider (CSP) to negotiate the possibility to borrow external resources from other CSPs when its own facilities are not able to satisfy a client request. Reciprocally, a CSP may sell some of its unused resources to another CSP in case of under load. Today, Cloud Federation is a key approach considered for Cloud elasticity. In the context of the Easi-Clouds European ITEA 2 research project, we aim to develop Pricing-as-a-Service (PraaS) suited to the Federated Cloud environment. The aim of this paper is twofold. First, we provide a state of the art of the pricing and revenue sharing models in federated environment. We discuss the specifications of Cloud Federation as well as its different drivers and barriers. Three types of pricing strategies (on-demand, spot and reserved) are presented. In the second part, we present the problem of revenue sharing in the federation with some properties we are willing to fulfill. We propose and evaluate our revenue sharing model suited to the Federated environment with numerical analysis via simulation. We compare our approach to the proportional share and the Shapley value method. Finally, we provide and analyze the results of our simulations with our conclusion and perspectives. <s> BIB004 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Adaptive Dynamic Scheduling <s> Compared to traditional distributed computing like grid system, it is non-trivial to optimize cloud task’s execution performance due to its more constraints like user payment budget and divisible resource demand. In this paper, we analyze in-depth our proposed optimal algorithm minimizing task execution length with divisible resources and payment budget: 1) We derive the upper bound of cloud task length, by taking into account both workload prediction errors and hostload prediction errors. With such state-of-the-art bounds, the worst-case task execution performance is predictable, which can improve the quality of service in turn. 2) We design a dynamic version for the algorithm to adapt to the load dynamics over task execution progress, further improving the resource utilization. 3) We rigorously build a cloud prototype over a real cluster environment with 56 virtual machines, and evaluate our algorithm with different levels of resource contention. Cloud users in our cloud system are able to compose various tasks based on off-the-shelf web services. Experiments show that task execution lengths under our algorithm are always close to their theoretical optimal values, even in a competitive situation with limited available resources. We also observe a high level of fair treatment on the resource allocation among all tasks. <s> BIB005
As there are various cloud users with various QoS for the cloud resource utilization, a good scheduling approach should have adaptability to adjust the scheduling process according to dynamic scheduling requirements in the cloud environment. A micro EC algorithm or local learning can help in this regard. However, the dynamic requirements may come from different aspects. For example, the scheduling objective may change during the system runtime due to changing or different preferences of the cloud users and/or cloud providers. Moreover, the uncertain characteristics of cloud tasks and the addition or removal of some cloud resources often make the scheduling environment change. As such, elasticity is a key feature of cloud resources  . Elastic resource provisioning requires scheduling algorithms to work on dynamic topologies, infrastructures, and in real time. Therefore, adaptive control of the EC algorithm according to the environment is a key research issue in developing EC algorithms for complex scheduling in cloud computing. Although it has been proposed to schedule cloud resources by considering the elasticity of the cloud environment BIB004 BIB002 BIB003 , research in using EC algorithms is challenging due to the real-time requirement, but the development of targeted micro and parallel algorithms should be a promising topic. EC approaches formulated this way should be able to adjust the algorithmic parameters and operators adaptively and accommodate changing search requirements of the dynamic environments. Adaptive control of EC algorithms has brought high performance to resource scheduling BIB001 BIB005 . It should be noted that adaptive control of EC algorithms can also help with real-time scheduling.