Resource Monitoring

Balancing the load in a large-scale distributed computing paradigm is challenging for efficient operation and providing clients with more efficient services. In a cloud-distributed environment, clients and organizations face challenges in maintaining the performance of the applications adjacent to the Quality of Service (QoS) and Service Level Agreement (SLA). The clients and the cloud providers grapple to allocate equal workload among the servers. To handle all these challenges, we have designed a cloud computing framework that includes all popular and current load-balancing techniques together to enhance the cloud services, better computing resource usage, and better guide system workloads through distribution. In this work, we accumulated all the requirements and a comfortable load balancer for cloud environments. Additionally, we consider the explosive growth of IP networks and wireless communications leading to massive data traffic. To manage this and maintain better traffic management, the implementation of artificial technology (AI) is needed. Compared to AWS ELB, Azure Load Balancer, and GCLB default configurations, our method shows significant gains in throughput efficiency, security, and latency management. The proposed artificial intelligence-based Reinforcement Learning (RL) was designed to reallocate and utilize resources to minimize latency and balancing among servers. The proposed work outperforms better compared to traditional load-balancing algorithms with a response time of 200 ms, resource utilization of 85%, and task completion rate of 98% in high workload conditions enhancing the efficiency and scalability of the cloud environment.

In this paper we present two solutions of monitoring automation for distributed systems. We develop this system to automate monitoring of distributes systems. Both solutions are aimed to monitor data storage and web services like web page servers. The first solution implemented in a system called Saude-Net, is an rule-based top level monitoring tool. In this system there are implemented rules which provide conditions which refer to one or more measured values. This system is able to choose the best action for an observed situation, e.g. a failure. It is possible to define more than one rule which relate to the same monitoring resource. The second concept presented in this paper refers to a fuzzy logic agent based approach to network monitoring. It is called SAMM compliant Agent. It is an extension to the Semantic-based Autonomous Monitoring and Management system (SAMM). On the one hand, it uses rules to define simple actions, based on a simple condition and an action description. On the other hand the main knowledge of this solution is defined by fuzzy logic. This system is able to manage and modify its knowledge to better fit to monitored resources. The knowledge in this concept is distributed among all the agents. The agents residing on a different hosts handle their parts of the knowledge and are capable to share/exchange them.

In this paper we present two solutions of monitoring automation for distributed systems. We develop this system to automate monitoring of distributes systems. Both solutions are aimed to monitor data storage and web services like web page servers. The first solution implemented in a system called Saude-Net, is an rule-based top level monitoring tool. In this system there are implemented rules which provide conditions which refer to one or more measured values. This system is able to choose the best action for an observed situation, e.g. a failure. It is possible to define more than one rule which relate to the same monitoring resource. The second concept presented in this paper refers to a fuzzy logic agent based approach to network monitoring. It is called SAMM compliant Agent. It is an extension to the Semantic-based Autonomous Monitoring and Management system (SAMM). On the one hand, it uses rules to define simple actions, based on a simple condition and an action description. On the other hand the main knowledge of this solution is defined by fuzzy logic. This system is able to manage and modify its knowledge to better fit to monitored resources. The knowledge in this concept is distributed among all the agents. The agents residing on a different hosts handle their parts of the knowledge and are capable to share/exchange them.

Grids allow large scale resource-sharing across different administrative domains. Those diverse resources are likely to join or quit the Grid at any moment or possibly to break down. In such environments there is a wide rage of types of resources to be monitored, with different nature, characteristics and so on. These issues make the task of monitoring complex to treat, and it is difficult to provide a generals ways for accessing to all this information. In this paper we describe the Palantir meta-information system that has been designed for uniforming the access to different monitoring and information systems and abstracting the complexity of the underlying systems. We present the API that Palantir provides to the end users, a general architectural description of its components, and the description of how GRMS providing job monitoring information can be integrated as a part of this meta-Information System.

With the increasing number of Internet of Things (IoT) devices, the volume and varieties of data being generated by these devices are increasing rapidly. Cloud computing cannot process this data due to its limitations such as latency and scalability. In order to process this data in less time, Fog computing has evolved as an extension to Cloud computing. In a Fog computing environment, a resource monitoring service plays a vital role in providing advanced services such as scheduling, scaling, and migration. Most of the works in Fog computing have assumed that a resource monitoring service is already available. However, there are not any studies, which investigate resource monitoring service in detail for Fog computing environments. Conventional methods proposed for other distributed systems such as Cloud and Grid may not be suitable due to the unique features of a Fog environment such as the limited capacity and heterogeneity of Fog devices. To improve the overall performance of Fog computing and optimise resource usage, effective resource monitoring techniques are required. Hence, we propose a Support and Confidence (SCB) based technique, which optimises the resource usage in the resource monitoring service. The performance of our proposed system is evaluated by examining a real-time traffic use case in a Fog emulator and the results are compared with traditional distributed computing techniques. The experimental results obtained from the Fog Emulator show that the proposed technique consumes fewer resources compared to conventional resource monitoring approaches.

With the increasing number of Internet of Things (IoT) devices, the volume and varieties of data being generated by these devices are increasing rapidly. Cloud computing cannot process this data due to its limitations such as latency and scalability. In order to process this data in less time, Fog computing has evolved as an extension to Cloud computing. In a Fog computing environment, a resource monitoring service plays a vital role in providing advanced services such as scheduling, scaling, and migration. Most of the works in Fog computing have assumed that a resource monitoring service is already available. However, there are not any studies, which investigate resource monitoring service in detail for Fog computing environments. Conventional methods proposed for other distributed systems such as Cloud and Grid may not be suitable due to the unique features of a Fog environment such as the limited capacity and heterogeneity of Fog devices. To improve the overall performance of Fog computing and optimise resource usage, effective resource monitoring techniques are required. Hence, we propose a Support and Confidence (SCB) based technique, which optimises the resource usage in the resource monitoring service. The performance of our proposed system is evaluated by examining a real-time traffic use case in a Fog emulator and the results are compared with traditional distributed computing techniques. The experimental results obtained from the Fog Emulator show that the proposed technique consumes fewer resources compared to conventional resource monitoring approaches.

Gossip protocols have proven to be effective means by which failures can be detected in large, distributed systems in an asynchronous manner without the limitations associated with reliable multicasting for group communications. In this paper, we discuss the development and features of a Gossip-Enabled Monitoring Service (GEMS), a highly responsive and scalable resource monitoring service, to monitor health and performance information in heterogeneous distributed systems. GEMS has many novel and essential features such as detection of network partitions and dynamic insertion of new nodes into the service. Easily extensible, GEMS also incorporates facilities for distributing arbitrary system and application-specific data. We present experiments and analytical projections demonstrating scalability, fast response times and low resource utilization requirements, making GEMS a potent solution for resource monitoring in distributed computing.

Grids allow large scale resource-sharing across different administrative domains. Those diverse resources are likely to join or quit the Grid at any moment or possibly to break down. In such environments there is a wide rage of types of resources to be monitored, with different nature, characteristics and so on. These issues make the task of monitoring complex to treat, and it is difficult to provide a generals ways for accessing to all this information. In this paper we describe the Palantir meta-information system that has been designed for uniforming the access to different monitoring and information systems and abstracting the complexity of the underlying systems. We present the API that Palantir provides to the end users, a general architectural description of its components, and the description of how GRMS providing job monitoring information can be integrated as a part of this meta-Information System.

We describe a platform for context-aware computing which enables applications to follow mobile users as they move around a building. The platform is particularly suitable for richly equipped, networked environments. The only item a user is required to carry is a small sensor tag, which identifies them to the system and locates them accurately in three dimensions. The platform builds a dynamic model of the environment using these location sensors and resource information gathered by telemetry software, and presents it in a form suitable for application programmers. Use of the platform is illustrated through a practical example, which allows a user's current working desktop to follow a user as they move around the environment.

Cloud computing represents a novel on-demand computing approach where resources are provided in compliance to a set of predefined non-functional properties specified and negotiated by means of Service Level Agreements (SLAs). In order to avoid costly SLA violations and to timely react to failures and environmental changes, advanced SLA enactment strategies are necessary, which include appropriate resource-monitoring concepts. Currently, Cloud providers tend to adopt existing monitoring tools, as for example those from Grid environments. However, those tools are usually restricted to locality and homogeneity of monitored objects, are not scalable, and do not support mapping of low-level resource metrics e.g., system up and down time to high-level application specific SLA parameters e.g., system availability. In this paper we present a novel framework for managing the mappings of the Low-level resource Metrics to High-level SLAs (LoM2HiS framework). The LoM2HiS framework is embedded into FoSII infrastructure, which facilitates autonomic SLA management and enforcement. Thus, the LoM2HiS framework detects future SLA violation threats and can notify the enactor component to act so as to avert the threats. We discuss the conceptual model of the LoM2HiS framework, followed by the implementation details. Finally, we present the first experimental results and a proof of concept of the LoM2HiS framework.

Today the cost of consumed energy from computer systems occupies a considerable part of the total operating cost of a corporation. Consequently there is a need for finding and implementing techniques for saving consumed energy by these systems as much as possible. In this paper will be shown the efficiency of Dynamic Voltage and Frequency Scaling (DVFS) and VM migration techniques for energy saving in a cloud data center.

Runtime profiling of Web-based applications and services is an effective method to aid in the provisioning of required resources, for monitoring service-level objectives, and for detecting implementation defects. Unfortunately, it is difficult to obtain accurate profile data on live client workloads due to the high overhead of instrumentation. This paper describes a cloud-based profiling service for managing the tradeoffs between: (i) profiling accuracy, (ii) performance overhead, and (iii) costs incurred for cloud computing platform usage. We validate our cloud-based profiling service by applying it to an open-source e-commerce Web application.

There are sustainable approaches on visualization in Grid environments, but there are no generic approaches for the visualization of a Grid environment itself. This work introduces a generic model and a set of services with the target of resource visualization and respect to the needs of Grid monitoring. The specified services are orchestrated as a visualization pipeline which is able to transform raw data about resources in consistent graphical views of the resources, generated according to their interconnections and relations. To achieve this, information and presentation models are integrated in the pipeline in a way that allows an easy adaptation of graphical representations to local requirements. The necessary functionality of services to conduct the transformations is also outlined. Finally, deployment scenarios are mentioned and a prototypical implementation is presented.

Collaborative e-learning based on several means of electronic communication mechanism specifically internet and web2.0 (such as forum, virtual classroom, videoconference, etc.) has become the current trend in e-learning systems. The cloud computing environment presents itself an important infrastructure for supporting these collaborative e-learning systems and for optimizing these service-based systems in a multi-tenant way. In this paper, we discuss the problem of discovering multi-tenant e-learning processes. For this, we propose an architecture for a configurable learning process discovery environment and the allocation of resources (Saas, Paas, Iaas) necessary for the execution of the corresponding learning activities.

Cloud computing has become a very important computing model to process data and execute computationally concentrated applications in pay-per-use method. Resource allocation is a process in which the resources are allocated to consumers by cloud providers based on their flexible requirements. As the data is expanding every day, allocating resources efficiently according to the consumer demand has also become very important, keeping Service Level Agreement (SLA) between service providers and consumers in prospect. This task of resource allocation becomes more challenging due to finite available resources and increasing consumer demands. Therefore, many unique models and techniques have been proposed to allocate resources efficiently. In the light of the uniqueness of the models and techniques, the main aim of the resource allocation is to limit the overhead/expenses associated with it. This research aims to present a comprehensive, structured literature review on different aspects of ...

Cloud computing represents a new trend to provide software services. In order to deliver these services there are certain quality levels that should be considered. The provided services need to comply with a set of contract terms and non-functional requirements specified by a service level agreement (SLA). In addition, to support the fulfillment of the SLA a monitoring process should be defined. This allows service providers to determine the actual quality level of services in the cloud. In this paper, we define a monitoring process for the usage of models at runtime, specifying low-and high-level nonfunctional requirements contained in a SLA. Models at runtime provide flexibility to the monitoring infrastructure due to their reflection mechanisms; the modification of non-functional requirements may dynamically change the monitoring computation, avoiding the need to adjust the monitoring infrastructure. In our approach, models at runtime are part of a monitoring middleware that interacts with cloud services; it retrieves data in the model at runtime, analyzes the information, and provides a report detailing the issues of non-compliance of non-functional requirements.

The battle being fought to survive in today’s world is that one must be able to expect the unexpected and a sudden miss of calculations on your part can leave you behind as there are always new and unique challenges facing the technology industry.
The only way to be consistent in terms to overcome these challenges is to create a very resilient initial set of ground rules and stick to them. The plan for exceptions should be focused from the start.

In cloud computing applications are provided and managed by the cloud server and data is also stored remotely in cloud configuration. As Cloud Computing is growing rapidly and clients are demanding more services and better results, load balancing for the Cloud has become a very interesting and important research area. Load balancing ensures that all the processor in the system or every node in the network does approximately the equal amount of work at any instant of time. In this paper, a fully distributed load rebalancing algorithm is presented to cope with the load balance problem. Our algorithm is compared against a centralized approach in a production system and a competing distributed solution presented in the literature.

The cloud provider plays a major role especially providing resources such as computing power for the cloud subscriber to deploy their applications on multiple platforms anywhere; anytime. Hence the cloud users still having problem for resource management in receiving the guaranteed computing resources on time. This will impact the service time and the service level agreements for various users in multiple applications. Therefore there is a need for a new resolution to resolve this problem. This survey paper conducts a study in resource allocation and monitoring in the cloud computing environment. We describe cloud computing and its properties, research issues in resource management mainly in resource allocation and monitoring and finally solutions approach for resource allocation and monitoring. It is believed that this paper would benefit both cloud users and researchers for further knowledge on resource management in cloud computing.

The cloud provider plays a major role especially providing resources such as computing power for the cloud subscriber to deploy their applications on multiple platforms anywhere; anytime. Hence the cloud users still having problem for resource management in receiving the guaranteed computing resources on time. This will impact the service time and the service level agreements for various users in multiple applications. Therefore there is a need for a new resolution to resolve this problem. This survey paper conducts a study in resource allocation and monitoring in the cloud computing environment. We describe cloud computing and its properties, research issues in resource management mainly in resource allocation and monitoring and finally solutions approach for resource allocation and monitoring. It is believed that this paper would benefit both cloud users and researchers for further knowledge on resource management in cloud computing.

Despite its importance for operating Cloud systems, Cloud monitoring has received limited attention from the research community. In this position paper, we provide an analysis of Cloud monitoring. More precisely, we discuss the main motivations, basic concepts and definitions, and point out open research issues and future directions for Cloud monitoring.

As energy-related costs have become a major economical factor for IT infrastructures and data-centers, companies and the research community are being challenged to find better and more efficient power-aware resource management strategies. There is a growing interest in "Green" IT and there is still a big gap in this area to be covered.

The cloud provider plays a major role especially providing resources such as computing power for the cloud subscriber to deploy their applications on multiple platforms anywhere; anytime. Hence the cloud users still having problem for resource management in receiving the guaranteed computing resources on time. This will impact the service time and the service level agreements for various users in multiple applications. Therefore there is a need for a new resolution to resolve this problem. This survey paper conducts a study in resource allocation and monitoring in the cloud computing environment. We describe cloud computing and its properties, research issues in resource management mainly in resource allocation and monitoring and finally solutions approach for resource allocation and monitoring. It is believed that this paper would benefit both cloud users and researchers for further knowledge on resource management in cloud computing.