Optimizing Resource allocation while handling SLA violations in Cloud Computing platforms

International audienceIn this paper we study a resource allocation problem in the context of Cloud Computing, where a set of Virtual Machines (VM) has to be placed on a set of Physical Machines (PM). Each VM has a given demand (e.g. CPU demand), and each PM has a capacity. However, each VM only uses a fraction of its demand. The aim is to exploit the difference between the demand of the VM and its real utilization of the resources, to exploit the capacities of the PMs as much as possible. Moreover, the real consumption of the VMs can change over time (while staying under its original demand), implying sometimes expensive ''SLA violations'', corresponding to some VM's consumption not satisfied because of overloaded PMs. Thus, while optimizing the global resource utilization of the PMs, it is necessary to ensure that at any moment a VM's need evolves, a few number of migrations (moving a VM from PM to PM) is sufficient to find a new configuration in which all the VMs' consumptions are satisfied. We modelize this problem using a fully dynamic bin packing approach and we present an algorithm ensuring a global utilization of the resources of 66%. Moreover, each time a PM is overloaded at most one migration is necessary to fall back in a configuration with no overloaded PM, and only 3 different PMs are concerned by required migrations that may occur to keep the global resource utilization correct. This allows the platform to be highly resilient to a great number of changes

User subscription-based resource management for Desktop-as-a-Service platforms

The Desktop-as-a-Service (DaaS) idiom consists of utilizing a cloud or other server infrastructure to host the user's desktop environment as a virtual desktop. Typical for cloud and DaaS services is the pay-as-you-go pricing model in combination with the availability of multiple subscription types to accommodate the needs of the users. However, optimal cost-efficient allocation of the virtual desktops to the infrastructure proves to be a combinatorial NP-hard problem, for which a heuristic is presented in the current article. We present a cost model for the DaaS service, from which a revenue of different configurations of virtual desktops to the servers can be derived. In this cost model, both subscription fee and penalties for degraded service are recorded, that are described in service-level agreements (SLAs) between the service provider and the users, and make realistic assumptions that different subscription types result in particular SLA contracts. The heuristic proposed states that for a given user base for which the virtual desktops (VDs) must be hosted, the VDs should be spread evenly over the infrastructure. Experiments through discrete event simulation show that this heuristic yields an approximation within 1 % of the theoretically achievable revenue

A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing

The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure and be charged on pay-per-use basis. However, Cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements (SLAs) violations. To achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be NP-hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to PMs in infrastructure Clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres

DeepScaler: Holistic Autoscaling for Microservices Based on Spatiotemporal GNN with Adaptive Graph Learning

Autoscaling functions provide the foundation for achieving elasticity in the modern cloud computing paradigm. It enables dynamic provisioning or de-provisioning resources for cloud software services and applications without human intervention to adapt to workload fluctuations. However, autoscaling microservice is challenging due to various factors. In particular, complex, time-varying service dependencies are difficult to quantify accurately and can lead to cascading effects when allocating resources. This paper presents DeepScaler, a deep learning-based holistic autoscaling approach for microservices that focus on coping with service dependencies to optimize service-level agreements (SLA) assurance and cost efficiency. DeepScaler employs (i) an expectation-maximization-based learning method to adaptively generate affinity matrices revealing service dependencies and (ii) an attention-based graph convolutional network to extract spatio-temporal features of microservices by aggregating neighbors' information of graph-structural data. Thus DeepScaler can capture more potential service dependencies and accurately estimate the resource requirements of all services under dynamic workloads. It allows DeepScaler to reconfigure the resources of the interacting services simultaneously in one resource provisioning operation, avoiding the cascading effect caused by service dependencies. Experimental results demonstrate that our method implements a more effective autoscaling mechanism for microservice that not only allocates resources accurately but also adapts to dependencies changes, significantly reducing SLA violations by an average of 41% at lower costs.Comment: To be published in the 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023

An Energy Aware Resource Utilization Framework to Control Traffic in Cloud Network and Overloads

Energy consumption in cloud computing occur due to the unreasonable way in which tasks are scheduled. So energy aware task scheduling is a major concern in cloud computing as energy consumption results into significant waste of energy, reduce the profit margin and also high carbon emissions which is not environmentally sustainable. Hence, energy efficient task scheduling solutions are required to attain variable resource management, live migration, minimal virtual machine design, overall system efficiency, reduction in operating costs, increasing system reliability, and prompting environmental protection with minimal performance overhead. This paper provides a comprehensive overview of the energy efficient techniques and approaches and proposes the energy aware resource utilization framework to control traffic in cloud networks and overloads

Enforcing CPU allocation in a heterogeneous IaaS

International audienceIn an Infrastructure as a Service (IaaS), the amount of resources allocated to a virtual machine (VM) at creation time may be expressed with relative values (relative to the hardware, i.e., a fraction of the capacity of a device) or absolute values (i.e., a performance metric which is independent from the capacity of the hardware). Surprisingly, disk or network resource allocations are expressed with absolute values (bandwidth), but CPU resource allocations are expressed with relative values (a percentage of a processor). The major problem with CPU relative value allocations is that it depends on the capacity of the CPU, which may vary due to different factors (server heterogeneity in a cluster, Dynamic Voltage Frequency Scaling (DVFS)). In this paper, we analyze the side effects and drawbacks of relative allocations. We claim that CPU allocation should be expressed with absolute values. We propose such a CPU resource management system and we demonstrate and evaluate its benefits

Adaptive Energy-Optimized Consolidation Algorithm

We have been hearing about cloud computing for quite a long time now. This type of computing is booming and emerging as a popular computing paradigm for its scalability and flexibility in nature. Cloud computing provides the provision of service on-demand, on-demand resources supply and services to end-users. However, energy consumption and energy wastage are becoming a major concern for cloud providers due to its direct impression on costs required for operations and carbon emissions. To tackle this issue, Adaptive Energy-Optimized Consolidation Algorithm has been proposed to efficiently manage energy consumption in cloud environments. This algorithm involves sharing by dividing, in this process resource allocation is done into two different phases, those are, consolidation of tasks and consolidation of resources. Compared to single-task consolidation algorithms, the proposed two-phase Adaptive energy optimized consolidation algorithm shows improved performance in terms of energy efficiency and resource utilization. The results of experiments conducted using a cloud-sim show the effectiveness of the proposed algorithm in decreasing energy consumption while maintaining the quality-of-service requirements of computing in cloud.  The need for an hour is to automate things without human intervention. Thus, using Autonomous computing refers to a type of computing system that is capable of performing tasks and making decisions without the intervention of humans. This type of system typically relies on Artificial.Intelligence, Machine.Learning, and other futuristic technologies to study the data, identify patterns, and make decisions based on that data. Cloud computing can certainly be incorporated into an autonomous computing system. The performance of an automated computing environment depends on a various factor, considering the quality of the different algorithms used, also the amount and quality of various data available to the system, the computational resources available, and the system's ability to learn and adapt over time. However, by incorporating cloud computing, an autonomous computing system can potentially access more resources and process data more quickly, which can improve its overall performance