Mateusz Guzek

A Survey of Evolutionary Computation for Resource Management of Processing in Cloud Computing [Review Article]

Cloud computing is significantly reshaping the computing industry. Individuals and small organizations can benefit from using state-of-the-art services and infrastructure, while large companies are attracted by the flexibility and the speed with which they can obtain the services. Service providers compete to offer the most attractive conditions at the lowest prices. However, the environmental impact and legal aspects of cloud solutions pose additional challenges. Indeed, the new cloud-related techniques for resource virtualization and sharing and the corresponding service level agreements call for new optimization models and solutions. It is important for computational intelligence researchers to understand the novelties introduced by cloud computing. The current survey highlights and classifies key research questions, the current state of the art, and open problems.

Multi-objective evolutionary algorithms for energy-aware scheduling on distributed computing systems

HighlightsHeterogeneous, energy-aware precedence constrained (DAG) scheduling problem.Three multi-objective algorithms schemas are adapted: MOCell, NSGAII and IBEA.A representation with corresponding mutations and grouping crossover operators.Experimentation on a diversified and large set of real and synthetic applications.MOCell schema is the most versatile and the best performing one. The ongoing increase of energy consumption by IT infrastructures forces data center managers to find innovative ways to improve energy efficiency. The latter is also a focal point for different branches of computer science due to its financial, ecological, political, and technical consequences. One of the answers is given by scheduling combined with dynamic voltage scaling technique to optimize the energy consumption. The way of reasoning is based on the link between current semiconductor technologies and energy state management of processors, where sacrificing the performance can save energy.This paper is devoted to investigate and solve the multi-objective precedence constrained application scheduling problem on a distributed computing system, and it has two main aims: the creation of general algorithms to solve the problem and the examination of the problem by means of the thorough analysis of the results returned by the algorithms.The first aim was achieved in two steps: adaptation of state-of-the-art multi-objective evolutionary algorithms by designing new operators and their validation in terms of performance and energy. The second aim was accomplished by performing an extensive number of algorithms executions on a large and diverse benchmark and the further analysis of performance among the proposed algorithms. Finally, the study proves the validity of the proposed method, points out the best-compared multi-objective algorithm schema, and the most important factors for the algorithms performance.

A Cellular Genetic Algorithm for scheduling applications and energy-aware communication optimization

In modern parallel and distributed systems, inter-processor communications are a crucial factor of performance. The time for exchanging data is usually larger than that for computing elementary operations. Consequently, these communications slow down the execution of the application scheduled on the computing platform. Accounting for these communications is essential for attaining efficient hardware and software utilization. Moreover, energy dissipation due to the transfer of data between processing elements has become a major concern. Therefore, in this paper we develop an energy-aware static algorithm, which intrinsically optimizes the energy consumption due to the transfer of data in a distributed system. This is achieved by properly allocating and scheduling the tasks that constitute the applications on the processing elements, minimizing inter-processor communications. The proposed algorithm is a new Cellular Genetic Algorithm based on task clustering techniques. That is, the genetic operators work considering groups of tasks instead of applying them directly on the tasks. Simulation results showed that this algorithm is very compelling in terms of application completion time, inter-processor communication and energy communication dissipation.

Energy-aware fast scheduling heuristics in heterogeneous computing systems

In heterogeneous computing systems it is crucial to schedule tasks in a manner that exploits the heterogeneity of the resources and applications to optimize systems performance. Moreover, the energy efficiency in these systems is of a great interest due to different concerns such as operational costs and environmental issues associated to carbon emissions. In this paper, we present a series of original low complexity energy efficient algorithms for scheduling. The main idea is to map a task to the machine that executes it fastest while the energy consumption is minimum. On the practical side, the set of experimental results showed that the proposed heuristics perform as efficiently as related approaches, demonstrating their applicability for the considered problem and its good scalability.

Cloud Brokering: Current Practices and Upcoming Challenges

The booming cloud computing industry offers a plethora of services. Navigation through these services is a long and perilous process. Cloud brokers can help in this journey, offering a more comprehensible view to the cloud service customers and service orchestration opportunities. From the service provider perspective, brokers facilitate reaching customers. In this invited article, the authors present a broad overview of cloud brokering. The article starts with a description of cloud brokers, the related taxonomy, their place in the business environment, and the legal framework. It briefly summarizes existing broker offers and cloud brokering research topics and discusses future challenges of cloud brokering.

A Holistic Model for Resource Representation in Virtualized Cloud Computing Data Centers

Management and optimization of cloud infrastructures combine multiple challenges. The optimization of data centers targets such objectives as performance, reliability, energy consumption, and security. To achieve these goals, multiple actions can be taken, for example, task and virtual machine allocation or infrastructure management. In this work we propose a model for representation of computing, memory, storage, and communication resources in cloud computing data centers. This model is relevant for the characterization of cloud applications, virtual machines, as well as physical servers. The performance evaluation and validation of the proposed model is carried out using the Green Cloud simulator. The obtained results show good agreement with the design objectives and confirm validity of the assumptions.

Scalable and Energy-Efficient Scheduling Techniques for Large-Scale Systems

The scalability of a computing system can be identified by at least three components: (a) size, (b) geographical distribution, and (c) administrative constraints. Newer paradigms, such as clouds, grids, and clusters bring in more parameters to the aforementioned list, namely heterogeneity, energy consumption, and transparency. To optimize the performance of a computing system, it is manner that exploits heterogeneity and is scalable. Moreover, newer systems also demand energy efficiency as an integral part of schedulers. In this paper, we evaluate the behavior of low complexity energy-efficient algorithms for scheduling. The set of experimental results showed that the evaluated heuristics perform as efficiently as related approaches, demonstrating their applicability and scalability for the considered problem.

HEROS: Energy-Efficient Load Balancing for Heterogeneous Data Centers

Heterogeneous architectures have become more popular and widespread in the recent years with the growing popularity of general-purpose processing on graphics processing units, low-power systems on a chip, multi- and many-core architectures, asymmetric cores, coprocessors, and solid-state drives. The design and management of cloud computing data-centers must adapt to these changes while targeting objectives of improving system performance, energy efficiency and reliability. This paper presents HEROS, a novel load balancing algorithm for energy-efficient resource allocation in heterogeneous systems. HEROS takes into account the heterogeneity of a system during the decision-making process and uses a holistic representation of the system. As a result, servers that contain resources of multiple types (computing, memory, storage and networking) and have varying internal structures of their components can be utilized more efficiently.

A Holistic Model of the Performance and the Energy-Efficiency of Hypervisors in an HPC Environment

With a growing concern on the considerable energy consumed by HPC platforms and data centers, research efforts are targeting toward green approaches with higher energy efficiency. In particular, virtualization is emerging as the prominent approach to mutualize the energy consumed by a single server running multiple Virtual Machines VMs instances. However, little understanding has been obtained about the potential overhead in energy consumption and the throughput reduction for virtualized servers and/or computing resources, nor if it simply suits an environment as high-demanding as a High Performance Computing HPC platform. In this paper, a novel holistic model for the power of HPC node and its eventual virtualization layer is proposed. More importantly, we create and validate an instance of the proposed model using concrete measures taken on the Grid5000 platform. In particular, we use three widespread virtualization frameworks, namely Xen, KVM, and VMware ESXi and compare them with a baseline environment running in native mode. The conducted experiments were performed on top of benchmarking tools that reflect an HPC usage, i.e. HPCC, IOZone and Bonnie++. To abstract from the specifics of a single architecture, the benchmarks were run using two different hardware configurations, based on Intel and AMD processors. The benchmark scores are presented for all configurations to highlight their varying performance. The measured data is used to create a statistical holistic model of power of a machine that takes into account impacts of its components utilization metrics, as well as used application, virtualization, and hardware. The purpose of the model is to enable estimation of energy consumption of HPC platforms in areas such as simulation, scheduling or accounting.

An Evaluation Model for Selecting Cloud Services from Commercially Available Cloud Providers

Selecting the appropriate cloud services and cloudproviders according to the cloud users requirements is becoming a complex task, as the number of cloud providers increases. Cloud providers offer similar kinds of cloud services, but they are different in terms of price, quality of service, customer experience, and service delivery. The most challenging issue of the current cloud computing business is that cloud providers commit a certain Service Level Agreement (SLA), with cloud users, but there is little or no verification mechanisms which ensure that cloud providers are providing cloud services according to their commitment. In the current literature, there is a lack of an evaluation model which provides the real status of cloud providers for the cloud users. In this paper, an evaluation model is proposed, which verifies the quality of cloud services delivered for each service and provides the service status of the cloud providers. Finally, evaluation results obtained from cloud auditors are visualized in an ordered performance heat map, showing the cloud providers in a decreasing ordering of overall service quality. In this way, the proposed service quality evaluation model represents a visual recommender system for cloud service brokers and cloud users.