Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

Enhanced non-parametric sequence learning scheme for internet of things sensory data in cloud infrastructure

The Internet of Things (IoT) Cloud is an emerging technology that enables machine-to-machine, human-to-machine and human-to-human interaction through the Internet. IoT sensor devices tend to generate sensory data known for their dynamic and heterogeneous nature. Hence, it makes it elusive to be managed by the sensor devices due to their limited computation power and storage space. However, the Cloud Infrastructure as a Service (IaaS) leverages the limitations of the IoT devices by making its computation power and storage resources available to execute IoT sensory data. In IoT-Cloud IaaS, resource allocation is the process of distributing optimal resources to execute data request tasks that comprise data filtering operations. Recently, machine learning, non-heuristics, multi-objective and hybrid algorithms have been applied for efficient resource allocation to execute IoT sensory data filtering request tasks in IoT-enabled Cloud IaaS. However, the filtering task is still prone to some challenges. These challenges include global search entrapment of event and error outlier detection as the dimension of the dataset increases in size, the inability of missing data recovery for effective redundant data elimination and local search entrapment that leads to unbalanced workloads on available resources required for task execution. In this thesis, the enhancement of Non-Parametric Sequence Learning (NPSL), Perceptually Important Point (PIP) and Efficient Energy Resource Ranking- Virtual Machine Selection (ERVS) algorithms were proposed. The Non-Parametric Sequence-based Agglomerative Gaussian Mixture Model (NPSAGMM) technique was initially utilized to improve the detection of event and error outliers in the global space as the dimension of the dataset increases in size. Then, Perceptually Important Points K-means-enabled Cosine and Manhattan (PIP-KCM) technique was employed to recover missing data to improve the elimination of duplicate sensed data records. Finally, an Efficient Resource Balance Ranking- based Glow-warm Swarm Optimization (ERBV-GSO) technique was used to resolve the local search entrapment for near-optimal solutions and to reduce workload imbalance on available resources for task execution in the IoT-Cloud IaaS platform. Experiments were carried out using the NetworkX simulator and the results of N-PSAGMM, PIP-KCM and ERBV-GSO techniques with N-PSL, PIP, ERVS and Resource Fragmentation Aware (RF-Aware) algorithms were compared. The experimental results showed that the proposed NPSAGMM, PIP-KCM, and ERBV-GSO techniques produced a tremendous performance improvement rate based on 3.602%/6.74% Precision, 9.724%/8.77% Recall, 5.350%/4.42% Area under Curve for the detection of event and error outliers. Furthermore, the results indicated an improvement rate of 94.273% F1-score, 0.143 Reduction Ratio, and with minimum 0.149% Root Mean Squared Error for redundant data elimination as well as the minimum number of 608 Virtual Machine migrations, 47.62% Resource Utilization and 41.13% load balancing degree for the allocation of desired resources deployed to execute sensory data filtering tasks respectively. Therefore, the proposed techniques have proven to be effective for improving the load balancing of allocating the desired resources to execute efficient outlier (Event and Error) detection and eliminate redundant data records in the IoT-based Cloud IaaS Infrastructure

Energy and Performance: Management of Virtual Machines: Provisioning, Placement, and Consolidation

Cloud computing is a new computing paradigm that offers scalable storage and compute resources to users on demand through Internet. Public cloud providers operate large-scale data centers around the world to handle a large number of users request. However, data centers consume an immense amount of electrical energy that can lead to high operating costs and carbon emissions. One of the most common and effective method in order to reduce energy consumption is Dynamic Virtual Machines Consolidation (DVMC) enabled by the virtualization technology. DVMC dynamically consolidates Virtual Machines (VMs) into the minimum number of active servers and then switches the idle servers into a power-saving mode to save energy. However, maintaining the desired level of Quality-of-Service (QoS) between data centers and their users is critical for satisfying users’ expectations concerning performance. Therefore, the main challenge is to minimize the data center energy consumption while maintaining the required QoS. This thesis address this challenge by presenting novel DVMC approaches to reduce the energy consumption of data centers and improve resource utilization under workload independent quality of service constraints. These approaches can be divided into three main categories: heuristic, meta-heuristic and machine learning. Our first contribution is a heuristic algorithm for solving the DVMC problem. The algorithm uses a linear regression-based prediction model to detect over-loaded servers based on the historical utilization data. Then it migrates some VMs from the over-loaded servers to avoid further performance degradations. Moreover, our algorithm consolidates VMs on fewer number of server for energy saving. The second and third contributions are two novel DVMC algorithms based on the Reinforcement Learning (RL) approach. RL is interesting for highly adaptive and autonomous management in dynamic environments. For this reason, we use RL to solve two main sub-problems in VM consolidation. The first sub-problem is the server power mode detection (sleep or active). The second sub-problem is to find an effective solution for server status detection (overloaded or non-overloaded). The fourth contribution of this thesis is an online optimization meta-heuristic algorithm called Ant Colony System-based Placement Optimization (ACS-PO). ACS is a suitable approach for VM consolidation due to the ease of parallelization, that it is close to the optimal solution, and its polynomial worst-case time complexity. The simulation results show that ACS-PO provides substantial improvement over other heuristic algorithms in reducing energy consumption, the number of VM migrations, and performance degradations. Our fifth contribution is a Hierarchical VM management (HiVM) architecture based on a three-tier data center topology which is very common use in data centers. HiVM has the ability to scale across many thousands of servers with energy efficiency. Our sixth contribution is a Utilization Prediction-aware Best Fit Decreasing (UP-BFD) algorithm. UP-BFD can avoid SLA violations and needless migrations by taking into consideration the current and predicted future resource requirements for allocation, consolidation, and placement of VMs. Finally, the seventh and the last contribution is a novel Self-Adaptive Resource Management System (SARMS) in data centers. To achieve scalability, SARMS uses a hierarchical architecture that is partially inspired from HiVM. Moreover, SARMS provides self-adaptive ability for resource management by dynamically adjusting the utilization thresholds for each server in data centers.Siirretty Doriast

Resource management in the cloud: An end-to-end Approach

Philosophiae Doctor - PhDCloud Computing enables users achieve ubiquitous on-demand , and convenient access to a variety of shared computing resources, such as serves network, storage ,applications and more. As a business model, Cloud Computing has been openly welcomed by users and has become one of the research hotspots in the field of information and communication technology. This is because it provides users with on-demand customization and pay-per-use resource acquisition methods

Survivable Cloud Networking Services

Cloud computing paradigms are seeing very strong traction today and are being propelled by advances in multi-core processor, storage, and high-bandwidth networking technologies. Now as this growth unfolds, there is a growing need to distribute cloud services over multiple data-center sites in order to improve speed, responsiveness, as well as reliability. Overall, this trend is pushing the need for virtual network (VN) embedding support at the underlying network layer. Moreover, as more and more mission-critical end-user applications move to the cloud, associated VN survivability concerns are also becoming a key requirement in order to guarantee user service level agreements. Overall, several different types of survivable VN embedding schemes have been developed in recent years. Broadly, these schemes offer resiliency guarantees by pre-provisioning backup resources at service setup time. However, most of these solutions are only geared towards handling isolated single link or single node failures. As such, these designs are largely ineffective against larger regional stressors that can result in multiple system failures. In particular, many cloud service providers are very concerned about catastrophic disaster events such as earthquakes, floods, hurricanes, cascading power outages, and even malicious weapons of mass destruction attacks. Hence there is a pressing need to develop more robust cloud recovery schemes for disaster recovery that leverage underlying distributed networking capabilities. In light of the above, this dissertation proposes a range of solutions to address cloud networking services recovery under multi-failure stressors. First, a novel failure region-disjoint VN protection scheme is proposed to achieve improved efficiency for pre-provisioned protection. Next, enhanced VN mapping schemes are studied with probabilistic considerations to minimize risk for VN requests under stochastic failure scenarios. Finally, novel post-fault VN restoration schemes are also developed to provide viable last-gap recovery mechanisms using partial and full VN remapping strategies. The performance of these various solutions is evaluated using discrete event simulation and is also compared to existing strategies

BALANCING NON-FUNCTIONAL REQUIREMENTS IN CLOUD-BASED SOFTWARE: AN APPROACH BASED ON SECURITY-AWARE DESIGN AND MULTI-OBJECTIVE SOFTWARE DYNAMIC MANAGEMENT

Beyond its functional requirements, architectural design, the quality of a software system is also defined by the degree to which it meets its non-functional requirements. The complexity of managing these non-functional requirements is exacerbated by the fact that they are potentially conflicting with one another. For cloud-based software, i.e., software whose service is delivered through a cloud infrastructure, other constraints related to the features of the hosting data center, such as cost, security and performance, have to be considered by system and software designers. For instance, the evaluation of requests to access sensitive resources results in performance overhead introduced by policy rules evaluation and message exchange between the different geographically distributed components of the authorization system. Duplicating policy rule evaluation engines traditionally solves such performance issues, however such a decision has an impact on security since it introduces additional potential private data leakage points. Taking into account all the aforementioned features is a key factor to enhance the perceived quality of service (QoS) of the cloud as a whole. Maximizing users and software developers satisfaction with cloud-based software is a challenging task since trade-off decisions have to be dynamically taken between these conflicting quality attributes to adapt to system requirements evolution. In this thesis, we tackle the challenges of building a decision support method to optimize software deployment in a cloud environment. Our proposed holistic method operates both at the level of 1) Platform as a service (PaaS) by handling software components deployment to achieve an efficient runtime optimization to satisfy cloud providers and customers objectives 2) Guest applications by making inroads into the design of applications to enable the design of secure systems that also meet flexibility, performance and cost requirements. To thoroughly investigate these challenges, we identify three main objectives that we address as follows: The first objective is to achieve a runtime optimization of cloud-based software deployment at the Platform as a service (PaaS) layer, by considering both cloud customers and providers constraints. To fulfill this objective, we leverage the [email protected] paradigm to build an abstraction layer to model a cloud infrastructure. In a second step, we model the software placement problem as a multi-objective optimization problem and we use multi-objective evolutionary algorithms (MOEAs) to identify a set of possible cloud optimal configurations that exhibit best trade-offs between conflicting objectives. The approach is validated through a case study that we defined with EBRC1, a cloud provider in Luxembourg, as a representative of a software component placement problem in heterogeneous distributed cloud nodes. The second objective is to ameliorate the convergence speed of MOEAs that we have used to achieve a run-time optimization of cloud-based software. To cope with elasticity requirements of cloud-based applications, we improve the way the search strategy operates by proposing a hyper-heuristic that operates on top of MOEAs. Our hyper-heuristic uses the history of mutation effect on fitness functions to select the most relevant mutation operators. Our evaluation shows that MOEAs in conjunction with our hyper-heuristic has a significant performance improvement in terms of resolution time over the original MOEAs. The third objective aims at optimizing cloud-based software trade-offs by exploring applications design as a complementary step to the optimization at the level of the cloud infrastructure, tackled in the first and second objectives. We aimed at achieving security trade-offs at the level of guest applications by revisiting current practices in software methods. We focus on access control as a main security concern and we opt for guest applications that manage resources regulated by access control policies specified in XACML2. This focus is mainly motivated by two key factors: 1) Access control is the pillar of computer security as it allows to protect sensitive resources in a given system from unauthorized accesses 2) XACML is the de facto standard language to specify access control policies and proposes an access control architectural model that supports several advanced access requirements such as interoperability and portability. To attain this objective, we advocate the design of applications based on XACML architectural model to achieve a trade-off between security and flexibility and we adopt a three-step approach: First, we identify a lack in the literature in XACML with obligation handling support. Obligations enable to specify user actions that have to be performed before/during/after the access to resources. We propose an extension of the XACML reference model and language to use the history of obligations states at the decision making time. In this step, we extend XACML access control architecture to support a wider range of usage control scenarios. Second, in order to avoid degrading performance while using a secure architecture based on XACML, we propose a refactoring technique applied on access control policies to enhance request evaluation time. Our approach, evaluated on three Java policy-based systems, enables to substantially reduce request evaluation time. Finally, to achieve a trade-off between a safe security policy evolution and regression testing costs, we develop a regression-test-selection approach for selecting test cases that reveal faults caused by policy changes. To sum up, in all aforementioned objectives, we pursue the goal of analysing and improving the current landscape in the development of cloud-based software. Our focus on security quality attributes is driven by its crucial role in widening the adoption of cloud computing. Our approach brings to light a security-aware design of guest applications that is based on XACML architecture. We provide useful guidelines, methods with underlying algorithms and tools for developers and cloud solution designers to enhance tomorrow’s cloud-based software design. Keywords: XACML-policy based systems, Cloud Computing, Trade-offs, Multi-Objective Optimizatio

An Energy-Efficient Multi-Cloud Service Broker for Green Cloud Computing Environment

The heavy demands on cloud computing resources have led to a substantial growth in energy consumption of the data transferred between cloud computing parties (i.e., providers, datacentres, users, and services) and in datacentre’s services due to the increasing loads on these services. From one hand, routing and transferring large amounts of data into a datacentre located far from the user’s geographical location consume more energy than just processing and storing the same data on the cloud datacentre. On the other hand, when a cloud user submits a job (in the form of a set of functional and non-functional requirements) to a cloud service provider (aka, datacentre) via a cloud services broker; the broker becomes responsible to find the best-fit service to the user request based mainly on the user’s requirements and Quality of Service (QoS) (i.e., response time, latency). Hence, it becomes a high necessity to locate the lowest energy consumption route between the user and the designated datacentre; and the minimum possible number of most energy efficient services that satisfy the user request. In fact, finding the most energy-efficient route to the datacentre, and most energy efficient service(s) to the user are the biggest challenges of multi-cloud broker’s environment. This thesis presents and evaluates a novel multi-cloud broker solution that contains three innovative models and their associated algorithms. The first one is aimed at finding the most energy efficient route, among multiple possible routes, between the user and cloud datacentre. The second model is to find and provide the lowest possible number of most energy efficient services in order to minimise data exchange based on a bin-packing approach. The third model creates an energy-aware composition plan by integrating the most energy efficient services, in order to fulfil user requirements. The results demonstrated a favourable performance of these models in terms of selecting the most energy efficient route and reaching the least possible number of services for an optimum and energy efficient composition

Personal mobile grids with a honeybee inspired resource scheduler

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The overall aim of the thesis has been to introduce Personal Mobile Grids (PMGrids) as a novel paradigm in grid computing that scales grid infrastructures to mobile devices and extends grid entities to individual personal users. In this thesis, architectural designs as well as simulation models for PM-Grids are developed. The core of any grid system is its resource scheduler. However, virtually all current conventional grid schedulers do not address the non-clairvoyant scheduling problem, where job information is not available before the end of execution. Therefore, this thesis proposes a honeybee inspired resource scheduling heuristic for PM-Grids (HoPe) incorporating a radical approach to grid resource scheduling to tackle this problem. A detailed design and implementation of HoPe with a decentralised self-management and adaptive policy are initiated. Among the other main contributions are a comprehensive taxonomy of grid systems as well as a detailed analysis of the honeybee colony and its nectar acquisition process (NAP), from the resource scheduling perspective, which have not been presented in any previous work, to the best of our knowledge. PM-Grid designs and HoPe implementation were evaluated thoroughly through a strictly controlled empirical evaluation framework with a well-established heuristic in high throughput computing, the opportunistic scheduling heuristic (OSH), as a benchmark algorithm. Comparisons with optimal values and worst bounds are conducted to gain a clear insight into HoPe behaviour, in terms of stability, throughput, turnaround time and speedup, under different running conditions of number of jobs and grid scales. Experimental results demonstrate the superiority of HoPe performance where it has successfully maintained optimum stability and throughput in more than 95% of the experiments, with HoPe achieving three times better than the OSH under extremely heavy loads. Regarding the turnaround time and speedup, HoPe has effectively achieved less than 50% of the turnaround time incurred by the OSH, while doubling its speedup in more than 60% of the experiments. These results indicate the potential of both PM-Grids and HoPe in realising futuristic grid visions. Therefore considering the deployment of PM-Grids in real life scenarios and the utilisation of HoPe in other parallel processing and high throughput computing systems are recommended

Γ (Gamma): cloud-based analog circuit design system

Includes bibliographical references.2016 Summer.With ever increasing demand for lower power consumption, lower cost, and higher performance, designing analog circuits to meet design specifications has become an increasing challenging task, On one hand, analog circuit designers must have intimate knowledge about the underlining silicon process technology's capability to achieve the desired specifications. On the other hand, they must understand the impact of tweaking circuits to satisfy a given specification on all circuit performance parameters. Analog designers have traditionally learned to tackle design problems with numerous circuit simulations using accurate circuit simulators such as SPICE, and have increasingly relied on trial-and-error approaches to reach a converging point. However, the increased complexity with each generation of silicon technology and high dimensionality of searching for solutions, even for some simple analog circuits, have made trial-and-error approaches extremely inefficient, causing long design cycles and often missed market opportunities. Novel rapid and accurate circuit evaluation methods that are tightly integrated with circuit search and optimization methods are needed to aid design productivity. Furthermore, the current design environment with fully distributed licensing and supporting structures is cumbersome at best to allow efficient and up-to-date support for design engineers. With increasing support and licensing costs, fewer and fewer design centers can afford it. Cloud-based software as a service (SaaS) model provides new opportunities for CAD applications. It enables immediate software delivery and update to customers at very low cost. SaaS tools benefit from fast feedback and sharing channels between users and developers and run on hardware resources tailored and provided for them by software vendors. However, web-based tools must perform in a very short turn-around schedule and be always responsive. A new class of analog design tools is presented in this dissertation. The tools provide effective design aid to analog circuit designers with a dash-board control of many important circuit parameters. Fast and accurate circuit evaluations are achieved using a novel lookup-table transistor models (LUT) with novel built-in features tightly integrated with the search engine to achieve desired speed and accuracy. This enables circuit evaluation time several orders faster than SPICE simulations. The proposed architecture for analog design attempts to break the traditional analog design flow using SPICE based trial-and-error methods by providing designers with useful information about the effects of prior design decisions they have made and potential next steps they can take to meet specifications. Benefiting from the advantages offered by web-hosted architectures, the proposed architecture incorporates SaaS as its operating model. The application of the proposed architecture is illustrated by an analog circuit sizer and optimizer. The Γ (Gamma) sizer and optimizer show how web-based design-decision supporting tool can help analog circuit designers to reduce design time and achieve high quality circuit