Adaptive cloud resource management through workload prediction

Real-time and embedded systems have traditionally been designed for closed environments where operating conditions, input workloads, and resource availability are known a priori and are subject to little or no change at runtime. There is an increasing demand, however, for autonomous capabilities in open distributed real-time and embedded (DRE) systems that execute in environments where input workload and resource availability cannot be accurately characterized a priori. These systems can benefit from autonomic computing capabilities, such as self-(re)configuration and self-optimization, that enable autonomous adaptation under varying-even unpredictable-operational conditions. A challenging problem faced by researchers and developers in enabling autonomic computing capabilities to open DRE systems involves devising adaptive planning and resource management strategies that can meet mission objectives and end-to-end quality of service (QoS) requirements of applications. To address this challenge, this paper presents the integrated planning, allocation, and control (IPAC) framework, which provides decision-theoretic planning, dynamic resource allocation, and runtime system control to provide coordinated system adaptation and enable the autonomous operation of open DRE systems. This paper presents two contributions to research on autonomic computing for open DRE systems. First, we describe the design of IPAC and show how IPAC resolves the challenges associated with the autonomous operation of a representative open DRE system case study. Second, we empirically evaluate the planning and adaptive resource management capabilities of IPAC in the context of our case study. Our experimental results demonstrate that IPAC enables the autonomous operation of open DRE systems by performing adaptive planning and management of system resources.

Efficient on-chip resource management is crucial for Chip Multiprocessors (CMP) to achieve high resource utilization and enforce system-level performance objectives. Existing multiple resource management schemes either focus on intra-core resources or inter-core resources, missing the opportunity for exploiting the interaction between these two level resources. Moreover, these resource management schemes either rely on trial runs or complex on-line machine learning model to search for the appropriate resource allocation, which makes resource management inefficient and expensive. To address these limitations, this paper presents a predictive yet cost effective mechanism for multiple resource management in CMP. It uses a set of hardware-efficient online profilers and an analytical performance model to predict the application's performance with different intra-core and/or inter-core resource allocations. Based on the predicted performance, the resource allocator identifies and enforces near optimum resource partitions for each epoch without any trial runs. The experimental results show that the proposed predictive resource management framework could improve the weighted speedup of the CMP system by an average of 11.6% compared with the equal partition scheme, and 9.3% compared with existing reactive resource management scheme.

<p>The purpose of this paper is to provision the on demand resources to the end users as per their need using prediction method in cloud computing environment. The provisioning of virtualized resources to cloud consumers according to their need is a crucial step in the deployment of applications on the cloud. However, the dynamical management of resources for variable workloads remains a challenging problem for cloud providers. This problem can be solved by using a prediction based adaptive resource provisioning mechanism, which can estimate the upcoming resource demands of applications. The present research introduces a prediction based resource provisioning model for the allocation of resources in advance. The proposed approach facilitates the release of unused resources in the pool with quality of service (QoS), which is defined based on prediction model to perform the allocation of resources in advance. In this work, the model is used to determine the future workload prediction for user requests on web servers, and its impact toward achieving efficient resource provisioning in terms of resource exploitation and QoS. The main contribution of this paper is to develop the prediction model for efficient and dynamic resource provisioning to meet the requirements of end users.</p>

Machine learning (ML)-centric resource management in cloud computing: A review and future directions

Support for intelligent, autonomous, adaptive and distributed resource management is a key to the success of scalable and dynamic wireless sensor network applications. Distributed independent reinforcement learning (DIRL) is a micro-learning framework that enables distributed, adaptive resource management using only local information at individual sensor nodes. In this paper we propose COllective INtelligence (COIN) a macro-learning paradigm that aims to specifically address the problem of designing utility functions for individual agents in order to achieve higher system wide utility. We extend DIRL, by combining it with COIN based macro-learning paradigm to steer the system towards global optimization, improving performance with minimal communication overheads. We present results of simulation to compare our approach with other existing approaches such as team game and DIRL in an example object tracking application. Simulation results demonstrate that a combination of micro- and macro-learners is two times more energy-efficient than micro-learners (DIRL) alone and four times more energy-efficient than macro-learners (TEAM game) alone.

We introduce a variety of mechanisms to enable scalable, adaptive, and reliable quality of service (QoS) guarantees across heterogeneous networking environments. The resultant scalable resource reservation scheme (SRRS) is the first resource management scheme that provides efficient fault protection based on the virtual reservation mechanism and a penalty-based adaptable reservation mechanism, so that QoS can continue to be guaranteed in the presence of faults without wasting expensive network resources unnecessarily. Based on the proposed geographical reservation mechanism and virtual IP addressing, SRRS can enable QoS guarantees in ad hoc mobile wireless networks. Also, resource reservation and allocation in SRRS are adaptive to the networking environments so that wireless QoS-guarantee issues can be resolved in the presence of mobility, making SRRS suitable for wireless mobile networks. We evaluate the performance of SRRS based on simulation and analysis, and show that SRRS can achieve considerably lower blocking rates as compared to previous reservation protocols.

Cloud computing has transformed the IT field by offering adaptable and versatile resources to cater to the increasing demands of businesses and organizations. Virtualization technologies are instrumental in facilitating the efficient deployment and management of resources within cloud environments. However, there are notable concerns regarding the security implications of virtualization in the cloud. This research paper thoroughly examines the security aspects of virtualization technologies in cloud computing, primarily focusing on identifying potential weaknesses, risks, and strategies to mitigate security threats. Additionally, the study investigates the security features and mechanisms provided by leading virtualization platforms and management tools. It scrutinizes access controls, isolation methods, network security, data protection, and integrity mechanisms offered by virtualization technologies to safeguard the cloud infrastructure and customer data. Furthermore, the paper addresses emerging security concerns associated with containerization technologies, encompassing vulnerabilities related to container escape, risks stemming from shared kernels, and issues concerning image integrity. It explores the effectiveness of container security measures, such asisolation, sandboxing, and access controls, in reducing these risks. Lastly, the paper summarizes the main findings and provides recommendations to enhance the security of virtualization technologies in cloud computing. It emphasizes the importance of continuous monitoring, regular security updates, robust access controls, and threat intelligence integration to mitigate security risks and uphold a secure cloud infrastructure.

Recently, there has been an increased reliance on computing systems supported by a multi-tier architecture. In multi-tier computing systems, it is important to appropriately manage resource allocation to ensure fairness of a QoS (quality of service) level avoiding overload conditions in tiers. This paper proposes an adaptive resource management algorithm for multi-tier computing systems in order that all clients have the same QoS level. We introduce a computing architecture which consists of multiple tiers, a group of resource managers, and an arbiter. Each tier is specialized to execute each subtask of clients and hosts virtual machines on its server pool. Each resource manager handles resource allocation of each client and updates the resources by locally exchanging a QoS level of its client with some other resource managers. Then, the resource managers request the resources to the arbiter. The arbiter compensates the requested resources to avoid overload conditions in tiers. Based on the compensation by the arbiter, each resource manager reallocates the resources to the subtasks of its client. We show sufficient conditions for the proposed resource management algorithm to achieve a fair QoS level avoiding overload conditions in all tiers at each time.

Cronus [1-7] is an object oriented distributed system which operates in a heterogeneous computer environment of interconnected local area networks. As a distributed system architecture. Cronus faces a number of resource management issues not present in non-distributed architectures. Strategies for effectively controlling the redundancy and reconfigurability inherent in Cronus are needed to take advantage of the distributed system environment. These strategies for resource management are often conveniently separated into policies and mechanisms. A policy is a goal or guideline set by a system administrator or component designer constraining the decisions made by a resource allocator. A mechanism is an internal system structure designed to implement a class of policies.In the Cronus distributed system model, there are currently two general aspects of resource allocation mechanisms which are particular to the network environment and must be effectively managed. One of these is the binding of a client request to a particular resource manager for those resources which are available redundantly. Redundancy comes in two forms: replicated objects, (e.g., a multi-copy file) and replicated managers (any manager for a type can create a new instance of that object type). For both forms the selection of an object manager to provide the given service is an important resource management policy decision. The other important aspect of resource management mechanism is the ability to reconfigure parts of the system by dynamically migrating objects. Object migration is a powerful tool for matching system resources to tasks in a manner that attempts to maximize some measure of system performance, reliability, or survivability. Both static reconfiguration (e.g., choosing or amending the placement of object managers and their associated objects), and dynamic reconfiguration (e.g., moving an individual object in direct response to demand for its use possible in the Cronus architecture and design.The general approach to resource management in Cronus is to individually control the management of the classes of objects which make up the system. This approach extends Cronus resource management concepts beyond system resources to the abstract resources developed by applications. Resource management for an individual abstract resource (type) and for groups of related resource types (services) in Cronus is based on combining a number of carefully planned mechanisms which are part of the system architecture. In addition to resource management by resource type, a client specific policy which considers collections of object types used in a specific context can be constructed.In Cronus we achieve system wide and easily controllable resource management by requiring the object managers to cooperate in enforcing a resource management policy for their resource type. An object manager can redirect operations to a peer manager on another processor on the basis of current resource status. In the case for creation of a file to an alternate file manager which may have more storage available. Part of the basis for decisions to redirect requests are parameters, settable dynamically by system administrators through monitoring and control functions, which control the resource management strategy. The creation of objects and resource management in general thus becomes a responsibility that is decentralized among object managers on each processor based on a global allocation policy, both in terms of sharing current status and possible redirection of operations between managers.The Cronus resource management model is based on the integration of the following set of primitive mechanisms: the kernel routes requests for a given type to any available manager of that type, using the Locate mechanismCronus managers redirect requests to a more appropriate peer to accomplish resource management objectivesmanagers periodically collect current status of their peers, via a standard mechanism (ReportStatus), to be used as a basis for selecting a site for redirecting an operationusers and applications optionally indicate specific location preferences with requestsan operator can monitor and regulate the functioning of the resource management policyapplications can use the basic system mechanisms (e.g. broadcast or multicast, ReportStatus operations) to build special-purpose resource management strategies tailored to their needThere is a hierarchy of desirable locations for implementing resource management policies: object managers, shared libraries, and finally client programs or users themselves. Managers are the most desirable because they are readily identifiable with a limited set of object types which are likely to have similar policy goals. Also, managers are addressable as a group for administrative control to regulate the policy parameters, and for peer manager cooperation to implement the policy. Many decisions are negotiated between managers based upon information periodically obtained via the ReportStatus operation. The Cronus Monitoring and Control System uses the same ReportStatus operation to present the operator with a global view of how well the resource management policy is proceeding.In this paper we elaborate on these approaches to distributed resource management in Cronus, including appropriate background, detailed system design, and experiences with incorporating these ideas into three different resource managers.

Rainfed agriculture supports a large proportion of farming communities worldwide but remains highly vulnerable to climate variability, erratic rainfall, soil degradation, and resource constraints. Increasing frequency of droughts, unpredictable rainfall distribution, and temperature fluctuations threaten farm productivity and rural livelihoods in these regions. Climate adaptive crop and resource management strategies offer practical solutions to enhance resilience, improve resource-use efficiency, and sustain agricultural productivity under changing climatic conditions. This review examines climate-smart crop management practices, soil and water conservation measures, diversified farming systems, and institutional interventions that support sustainable rainfed agriculture. The article also discusses challenges associated with adoption and highlights future pathways for strengthening farm sustainability.Integrating climate-adaptive technologies with community participation and policy support can significantly enhance productivity,income stability,and resilience of rainfed farming systems

Cloud computing is the promising key technology to build future architecture of massive IT systems and one of key benefits of cloud computing is to provide its customers with elastic resources according to the fluctuation of request workloads. In this paper, we propose adaptive resource management policy to handle requests of deadline-bound application with elastic cloud. Adaptive resource management architecture has been proposed, and we divide resource management into two parts, resource provision and job scheduling. We design analytical provision model for adaptive provision based on queuing theory, by introducing a key metric named average interval time. Three job scheduling policies are raised to dequeue appropriate jobs to execute, First-Come-First-Service (FCFS), Shortest Job First (SJF) and Nearest Deadline First (NDF), for different preference toward execution order. Simulation evaluation has been set up with realistic grid workload, and results show that our provisioning model gives elastic resource provisioning for dynamic workload and FCFS achieves better performance compared with other scheduling policies.

Elasticity is a key feature of current cloud computing platforms. Dependent on their demand tenants can dynamically scale up and down their applications. To increase their revenue, cloud providers are used to over-provision their clusters, but they still have to reserve capacity to avoid that services get unresponsive and cause SLO violation during bursts. In this paper, we propose CLOUDFARM, a PaaS architecture with an adaptive SLO-based resource management mechanism. It introduces new flexible SLAs backed with a respective development model and management interface for end-user services. According to their SLAs and the price tenants pay, services can be selectively downgraded to overcome short-term peaks, e.g. While preparing for scale-out. Providers can deploy services optimistically and thus maximize their data center utilization and revenue.

Remote geovisualization has gained momentum to support large‐scale geospatial data analysis and complex decision‐making over the last few years. Cloud computing, due to its capabilities to deliver on‐demand computing resources, has been embraced to develop and deploy interactive and scalable remote geovisualization applications. However, current cloud computing frameworks do not offer a versatile resource management scheme that is readily applicable for online remote visualization services, which usually require maintaining a satisfactory service level over time under dynamic workloads. To address this gap, we propose an automatic cloud resource management approach based on a bi‐level scheduling and horizontal scaling scheme to exploit cloud resources efficiently. At the lower level, a dynamic task‐scheduling scheme using collaborative filtering techniques is proposed to allocate virtual cloud resources to execute sub‐tasks. The scheduling scheme considers spatio‐temporal patterns presented in visualization views. At the upper level, reinforcement learning is adopted to perform resource auto‐scaling based on a reward function that integrates three different facets, namely: time cost, resource cost, and service stability. The original reinforcement learning algorithm is improved in two main aspects: (1) considering the delay of resource provisioning that is common in cloud environments; and (2) using online Gaussian estimation to estimate Q values. Task scheduling and auto‐scaling interact with each other and are integrated to deliver a comprehensive and responsive resource management solution. Experimental results demonstrate that our approach outperforms several existing cloud resource management methods. The proposed approach is also applicable for other interactive visualization applications, which have similar workload characteristics and performance requirements as interactive remote geovisualization.

In this paper we present an adaptive radio resource management scheme for relay enhanced cells based on soft resource partitioning. Power masks varying in the time or frequency domain are assigned to each radio access point (RAP). Thus, soft resource partitioning enables frequency reuse one and at the same time each RAP has high power resources with reduced interference available to schedule user terminals. Our scheme allows a local power mask adaptation and we present a mechanism for adapting the power mask based on the traffic load situation within the REC. We show in dynamic system simulations for a metropolitan area scenario that the radio resource management scheme based on soft resource partitioning outperform a scheme based on hard resource partitioning. Further, we show that the adaptive resource management scheme improves the throughput of low throughput users in the cell.

Intelligent time-series forecasting framework for non-linear dynamic workload and resource prediction in cloud